Characterisation of the Physiological, Biochemical and Molecular Responses of Sorghum to Drought Stress Tatenda Goche 2015276153 Thesis submitted in fulfilment of the requirements for the degree PhD in Botany in the Faculty of Natural and Agricultural Sciences, Department of Plant Sciences - Qwaqwa Campus, University of the Free State Supervisor: Dr. Rudo Ngara Co-supervisor: Dr. Stephen Chivasa November 2018 DECLARATION I, Tatenda Goche, declare that the PhD research thesis that I herewith submit for the PhD qualification in Botany at the University of the Free State is my independent work and that I have not previously submitted it for a qualification at another institution of higher education. I, Tatenda Goche, hereby declare that I am aware that the copyright is vested in the University of the Free State. I, Tatenda Goche, hereby declare that all royalties as regards intellectual property that was developed during the course of and/ or in connection with the study at the University of the Free State will accrue to the University. Tatenda Goche ii DEDICATION To my nieces, Layla and Liora. iii ACKNOWLEDGEMENTS I would like to express my sincere gratitude to my supervisor Dr. Rudo Ngara for allowing me the opportunity to pursue this degree. I would also like to thank my co-supervisor Dr. Stephen Chivasa at Durham University, United Kingdom for his support throughout my research and for the iTRAQ, osmolyte and gene expression analyses of my samples. My sincere gratitude to Colleen Turnbull, Sepideh Abedi, Adrian Brown and Mahsa Movahedi for your assistance and guidance during my 4.5 months research visit at Durham University in the United Kingdom. I am also grateful for the financial support I received from the National Research Foundation, the Royal Society and the University of the Free State tuition fee bursary. Lastly I would like to thank Sellwane Moloi and the Plant Biotechnology Research Group for being great friends and colleagues. iv TABLE OF CONTENTS DECLARATION ................................................................................................................. ii DEDICATION ................................................................................................................... iii ACKNOWLEDGEMENTS ............................................................................................... iv TABLE OF CONTENTS .................................................................................................... v LIST OF TABLES ............................................................................................................. ix LIST OF FIGURES ............................................................................................................ x LIST OF ABBREVIATIONS ........................................................................................... xii ABSTRACT...................................................................................................................... xiv CHAPTER 1 ........................................................................................................................ 1 LITERATURE REVIEW ................................................................................................... 1 1.1 The Production, Uses and Importance of Cereal Crops .................................................... 1 1.2 Sorghum and its Uses ...................................................................................................... 1 1.3 Plant Abiotic Stresses ...................................................................................................... 3 1.4 The Complexity of Abiotic Stress Responses in Plants .................................................... 3 1.5 Drought Stress ................................................................................................................. 7 1.5.1 Plant Responses to Drought Stress ............................................................................ 7 1.5.2 Osmolyte Accumulation in Stressed Plants ............................................................... 9 1.5.3 Drought Induced Reactive Oxygen Species Accumulation in Plants ........................ 10 1.5.4 Metabolic Adaptations of Plants to Drought Stress .................................................. 11 1.5.5 Sorghum Responses to Drought Stress .................................................................... 12 1.5.6 Combined Heat and Drought Stress……….……………………………….………..15 1.6 Proteomics .................................................................................................................... 15 1.6.1 Plant Proteomics ..................................................................................................... 16 1.6.1.1 Gel Based Proteomics ....................................................................................... 16 1.6.1.2 Non-gel Based Proteomics ................................................................................ 17 1.6.1.2.1 Protein Labelling for Mass Spectrometry vs Label-free Appraaches .............. 17 1.6.1.2.2 iTRAQ Analysis ............................................................................................ 19 1.6.1.2.3 iTRAQ Analysis in Plant Biotic and Abiotic Stress Studies ........................... 21 1.6.2 Plant Root Proteomics and Gene Expression Analysis ............................................. 21 1.7 The Use of Model Plant Systems in Plant Abiotic Stress Studies ................................... 22 1.8 Aim and Objectives of this Study .................................................................................. 24 CHAPTER 2 ...................................................................................................................... 25 v MATERIALS AND METHODS ...................................................................................... 25 2.1 Plant Material ................................................................................................................ 25 2.1.1 Sorghum Germplasm .............................................................................................. 25 2.1.2 Sorghum and Arabiopsis Cell Suspension Cultures ................................................. 26 2.2 Plant Growth Conditions and Drought Stress Treatment ................................................ 26 2.2.1 Determination of Field Capacity ............................................................................. 26 2.2.2 Measurement of Growth and Physiological Parameters ........................................... 27 2.2.3 Drought Stress Treatment Experiments ................................................................... 27 2.2.3.1 Seedling Growth for Physiological Measurements and Protein Extraction ........ 27 2.2.3.2 Seedling Growth for Osmolyte and Gene Expression Analysis ......................... 28 2.2.4 Osmotic Stress Treatment of Cell Suspension Cultures ........................................... 28 2.3 Measurement of Growth and Physiological Parameters ................................................. 29 2.3.1 Growth Measurements ............................................................................................ 29 2.3.2 Physiological Measurements ................................................................................... 30 2.3.2.1 Measurement of Relative Water Content .......................................................... 30 2.3.2.2 Leaf Chlorophyll Content ................................................................................. 30 2.3.2.3 Leaf Stomatal Conductance and Surface Temperature ...................................... 31 2.3.2.4 Relative Cell Death........................................................................................... 31 2.4 Osmolyte Content Analysis ........................................................................................... 32 2.4.1 Proline Content Analysis ......................................................................................... 32 2.4.2 Glycine Betaine Content Analysis ........................................................................... 33 2.5 Protein Extraction and Quantification ............................................................................ 34 2.5.1 Protein Extraction from Sorghum Root and Leaf Tissue .......................................... 34 2.5.2 Protein Quantification ............................................................................................. 34 2.6 Protein Gel Electrophoresis ........................................................................................... 35 2.6.1 One Dimensional (1D) Polyacrylamide Gel Electrophoresis (PAGE) ...................... 35 2.6.1.1 Coomassie Brilliant Blue (CBB) Staining ......................................................... 36 2.6.1.2 Protein Precipitation ......................................................................................... 36 2.6.2 Two Dimensional - Differential Gel Electrophoresis (2D-DiGE) ............................. 37 2.6.2.1 Sample Preparation and Labelling for 2D-DiGE ............................................... 37 2.6.2.2 1D Gel Electrophoresis for DiGE Labelled Samples ......................................... 38 2.6.2.3 2D Gel Electrophoresis for DiGE Labelled Samples ......................................... 38 2.6.2.3.1 Rehydration of 7 cm Immobilised pH Gradient (IPG) Strips .......................... 38 vi 2.6.2.3.2 Isoelectric Focusing (IEF) of IPG Strips ........................................................ 38 2.6.2.3.3 Equilibration of IPG Strips ............................................................................ 39 2.6.2.3.4 Second Dimension SDS-PAGE ..................................................................... 39 2.7 iTRAQ Analysis ............................................................................................................ 40 2.7.1 Sample Preparation for iTRAQ Analysis ................................................................. 40 2.7.2 Sample Labelling and iTRAQ Analysis ................................................................... 41 2.7.3 Mass Spectra Data Analysis .................................................................................... 43 2.7.4 Bioinformatic analysis ............................................................................................ 43 2.8 Gene Expression Analysis ............................................................................................. 44 2.8.1 Total RNA Extraction and Analysis ........................................................................ 44 2.8.2 Agarose Gel Electrophoresis of RNA ...................................................................... 45 2.8.3 Complementary Deoxy-ribonucleic Acid (cDNA) Synthesis ................................... 45 2.8.4 Primer Designing .................................................................................................... 46 2.8.5 Polymerase Chain Reaction for Primer Testing ....................................................... 49 2.8.6 Agarose Gel electrophoresis of PCR products ......................................................... 49 2.8.7 Quantitative Real Time-Polymerase Chain Reaction (qRT-PCR) analysis ............... 50 2.9 Statistical Analyses ....................................................................................................... 52 CHAPTER 3 ...................................................................................................................... 53 COMPARATIVE PHYSIOLOGICAL AND BIOCHEMICAL RESPONSES OF SORGHUM TO DROUGHT STRESS............................................................................. 53 3.1 Introduction ................................................................................................................... 53 3.2.1 Field Capacity and Seed Germination Experiment .................................................. 54 3.2.2 Effects of Drought Stress on Growth Parameters ..................................................... 55 3.2.3 Leaf Relative Water Content ................................................................................... 61 3.2.4 Leaf Stomatal Conductance and Surface Temperature ............................................. 62 3.2.5 Leaf Chlorophyll Content........................................................................................ 64 3.2.6 Root and Leaf Relative Cell Death .......................................................................... 65 3.2.7 Osmolyte Content Analysis ..................................................................................... 66 3.2.7.1 Proline and Glycine Betaine (GB) Content Analysis ......................................... 66 3.3 Discussion ..................................................................................................................... 68 CHAPTER 4 ...................................................................................................................... 74 IDENTIFICATION OF DROUGHT STRESS RESPONSIVE PROTEINS FROM SORGHUM ROOT TISSUE AND GENE EXPRESSION ANALYSIS OF SELECTED TARGETS ......................................................................................................................... 74 vii 4.1 Introduction ................................................................................................................... 74 4.2. One Dimensional Protein Profiles of Sorghum Root and Leaf....................................... 76 4.3 2D-DiGE Profiles of Sorghum Root and Leaf Samples .................................................. 78 4.4 iTRAQ Analysis of the Sorghum Drought Stress Responsive Root Proteins .................. 82 4.4.1 Bioinformatic Analyses on the Identified Drought Stress Responsive Sorghum Root Proteins ........................................................................................................................... 84 4.4.2 Gene ontology analysis ......................................................................................... 119 4.4.3 Functional Categories of Differentially Expressed Drought Responsive Proteins... 124 4.4.3.1 Metabolism .................................................................................................... 126 4.4.3.2 Disease/defence .............................................................................................. 127 4.4.3.3 Protein Destination and Storage ...................................................................... 129 4.4.3.4 Signal Transduction ........................................................................................ 130 4.4.3.5 Energy ............................................................................................................ 131 4.4.3.6 Protein Synthesis ............................................................................................ 132 4.4.3.7 Transporters ................................................................................................... 134 4.4.3.8 Other Functional Groups ................................................................................ 134 4.4 Drought-Induced Gene Expression Analysis in Sorghum Root and Leaf Tissue ........... 136 4.5 Discussion ................................................................................................................... 145 CHAPTER 5 .................................................................................................................... 155 DEVELOPING MOLECULAR MARKERS FOR DROUGHT TOLERANCE ......... 155 5.1 Introduction ................................................................................................................. 155 5.2 Drought Marker Gene Expression in ICSB 338 and White Sorghum ........................... 156 5.3 Heat Stress Activates Drought-Responsive Genes ....................................................... 158 5.4: Identifying Homologues of Sorghum Drought Marker Genes in a Different Species ... 159 5.4.1: Responses of Arabidopsis Thioredoxin Homologues to Sorbitol .......................... 162 5.4.2: Responses of Arabidopsis Peptidase Homologues to Sorbitol ............................... 165 5.4.3: Response of Arabidopsis Xyloglucanase Homologues to Sorbitol ........................ 167 5.5 Discussion ................................................................................................................... 169 CHAPTER 6 .................................................................................................................... 173 GENERAL DISCUSSION, CONCLUSION AND RECOMMEDATIONS ................. 173 REFERENCES……………………………………………………………...………………….…179 APPENDICES ................................................................................................................. 197 viii LIST OF TABLES Table 2.1: Sorghum varieties used in the study. .................................................................. 25 Table 2.2: Isoelectric focusing programme for 7 cm IPG strips. .......................................... 39 Table 2.3: List of target genes and primer sequences used in quantitative real time PCR analysis. .............................................................................................................................. 47 Table 2.4: List of target genes and primer sequences used in quantitative real time PCR analysis. .............................................................................................................................. 48 Table 2.5: Thermal cycling conditions for PCR. ................................................................. 49 Table 2.6: Thermal cycling conditions for qRT-PCR. ......................................................... 51 Table 4.1: List of drought stress responsive proteins identified from ICSB 338 root samples using the iTRAQ and database searches. ............................................................................. 85 Table 4.2: List of drought stress responsive proteins identified from SA 1441 root samples using the iTRAQ and database searches. ........................................................................... 104 Table 4.3: Drought stress responsive proteins selected for primer designing. .................... 137 Appendix, Table A1: Preparation of BSA standard solutions for protein quantification. .. 197 Appendix, Table A2: Preparation of resolving and stacking gels for gel electrophoresis. . 197 Appendix, Table B1: Arabidopsis homologues of sorghum genes .................................... 200 ix LIST OF FIGURES Figure 1.1: Sorghum crop under cultivation.......................................................................... 2 Figure 1.2: Gene expression regulatory network in response to drought and cold stress ........ 4 Figure 1.3: The complexity of plant responses to abiotic stresses.......................................... 6 Figure 1.4: Strategy for analysis of protein expression in rice roots by 8-plex isobaric tagging ........................................................................................................................................... 20 Figure 3.1: Germination rates of different sorghum varieties.. ............................................ 55 Figure 3.2: The effects of drought on sorghum plant morphology.. ..................................... 57 Figure 3.3: Effects of drought on sorghum shoot and root growth....................................... 59 Figure 3.4: The effects of drought on sorghum root and shoot weight. ................................ 60 Figure 3.5: The effects of drought stress on leaf relative water content of sorghum. ........... 61 Figure 3.6: The effects of drought on sorghum leaf stomatal conductance and surface temperature. ........................................................................................................................ 63 Figure 3.7: The effects of drought on sorghum leaf chlorophyll content. ............................ 64 Figure 3.8: The effects of drought on root and leaf cell death.. ........................................... 65 Figure 3.9: Effects of drought stress on sorghum leaf proline and glycine betaine (GB) content. ............................................................................................................................... 67 Figure 4.1: One dimensional SDS-PAGE analysis of sorghum root tissues. ........................ 77 Figure 4.2: One dimensional SDS-PAGE analysis of sorghum leaf tissues ......................... 78 Figure 4.3: 2D-DiGE analysis of sorghum root total soluble protein ................................... 80 Figure 4.4: 2D-DiGE analysis of sorghum leaf total soluble protein ................................... 81 Figure 4.5: The relationship between ICSB 338 and SA 1441 identified drought stress responsive root proteins....................................................................................................... 83 Figure 4.6: Cellular component predictions of the identified sorghum root drought stress responsive total soluble protein based on GO annotation ................................................... 120 Figure 4.7: Biological process predictions of the identified sorghum root drought stress responsive protein based on GO annotation. ...................................................................... 121 Figure 4.8: Molecular process predictions of the identified sorghum root drought stress responsive proteins based on GO annotation ..................................................................... 123 Figure 4.9: Functional classification of the differentially expressed sorghum root drought stress responsive proteins .................................................................................................. 125 Figure 4.10: ICSB 338 and SA 1441 sorghum plants during the drought stress period ...... 138 x Figure 4.11: Sorghum root and leaf total RNA gel ............................................................ 140 Figure 4.12: Primer analysis of sorghum root drought responsive genes. .......................... 141 Figure 4.13: Drought stress-induced gene expression in sorghum root tissue .................... 143 Figure 4.14: Drought stress-induced gene expression in sorghum leaf tissue. ICSB 338 ... 144 Figure 5.1: Gene expression in ICSB 338 and White sorghum cell cultures.. .................... 157 Figure 5.2: Drought-responsive genes are also responsive to high temperature. ................ 159 Figure 5.3: Arabidopsis RNA gel ..................................................................................... 161 Figure 5.4: PCR-amplification of fragments of Arabidopsis genes. ................................... 162 Figure 5.5: The response of Arabidopsis thioredoxin genes to sorbitol .............................. 164 Figure 5.6: The response of Arabidopsis peptidase genes to sorbitol ................................. 166 Figure 5.7: The response of Arabidopsis xyloglucanase genes to sorbitol. ........................ 168 Appendix, Figure A1: Heat-map showing the up-regulation and down-regulation of the 871 proteins common to both ICSB 338 and SA 1441 .............................................................. 198 Appendix, Figure B1: Sorbitol-induced gene expression in sorghum cell suspension cultures.. ........................................................................................................................... 199 Appendix, Figure C1-C13: Melt curves of genes used in this study………………….…..209 xi LIST OF ABBREVIATIONS 1D One-dimensional 1D-SDS-PAGE One-dimensional sodium dodecyl sulfate polyacrylamide gel electrophoresis 2D Two-dimensional 2D-DiGE Two dimensional-differential gel electrophoresis ABA Abscisic acid APS Ammonium persulfate BSA Bovine serum albumin CAM Crassulacean acid metabolism CBB Coomassie Brilliant Blue CD Cell death CHAPS 3-[(3-Cholamidopropyl) dimethylammonio]-1 propanesulfonate DNA Deoxy-ribonucleic acid DTT Dithiothreitol Cleland’s reagent EDTA Ethylenedinitrilo-tetraacetic acid EGRIN Environmental gene regulatory influence networks ESI Electronspray ionisation FC Field capacity HILIC-MS Hydrophilic interaction liquid chromatography – mass spectrometry hrs hours iTRAQ isobaric Tags for Relative and Absolute Quantitation kDa kilo Dalton LC/MS Liquid chromatography mass spectrometry LEA Late-embryogenesis abundant protein MALDI Matrix assisted laser desorption ionisation xii mins minutes MOPS 3-(N-Morpholino)-propanesulfonic acid MSMO Murashige and Skoog Basal Salt with minimum organics MW Molecular weight NAA 1-naphthaleneacetic acid NCBI National Centre for Biotechnology Information PAGE Polyacrylamide gel electrophoresis PEP Phospho-enoyl pyruvate pmol picomoles PTM Post-translational modification qRT-PCR quantitative real-time polymerase chain reaction RNA Ribonucleic acid ROS Reactive oxygen species RuBisCo Ribulose-1,5-biphosphate carboxylase/oxygenase RWC Relative water content RWL Relative water loss SDS Sodium dodecyl sulfate TAE Tris acetate-(ethylenedinitrilo)-tetraacetic acid TAIR The Arabidopsis Information Resource TCA Trichloroacetic acid TEMED N,N,N’,N’-Tetramethylethylenediamine v/v volume to volume Vhrs Volt hours w/v weight to volume xiii ABSTRACT Characterisation of the Physiological, Biochemical and Molecular Responses of Sorghum to Drought Stress Tatenda Goche PhD thesis, Department of Plant Sciences-Qwaqwa Campus, University of the Free State Drought is a major threat to global food security due to its detrimental effects on plant growth, productivity and yield quality. Many climatic models are predicting the increasing duration and severity of drought episodes. Therefore, understanding plant adaptive responses to drought stress is important in developing new biotechnological solutions to avert crop yield losses to drought. Sorghum (Sorghum bicolor) is an African indigenous crop that is well-adapted to thrive on marginal lands. This makes the crop a suitable model plant for studying adaptive responses to drought. In this study, the physiological and biochemical responses of two sorghum varieties with contrasting phenotypic traits to drought stress was analysed under drought stress. The two sorghum varieties used were the drought susceptible ICSB 338 and the drought tolerant SA 1441. The sorghum plants were grown in soil until the V3 growth stage before withholding water for 8 days, re-watering and then assessing the physiological changes following the drought stress treatment. Physiological analyses of the plants revealed striking differences between the sorghum varieties. The growth parameters of both roots and shoots exhibited more tolerance related responses in the drought-tolerant variety, while the susceptible variety was adversely affected and had poor recovery after re- watering. The leaf relative water content, stomatal conductance and chlorophyll content, supported the observed physiological adaptations. The analysis of proline and glycine betaine xiv showed that there was an increase in the accumulation of the two omolytes in response to drought stress. The drought tolerant variety showed significantly higher osmolyte accumulation earlier than the drought susceptible variety in both root and shoot tissue. The isobaric tags for relative and absolute quantitation (iTRAQ) analysis was used to identify drought-stress responsive root proteins in the two sorghum varieties. In the root proteome, 1169 and 1043 proteins were positively identified for ICSB 338 and SA 1441 sorghum varieties, respectively. Of these proteins, 237 and 184 were drought responsive for ICSB 338 and SA 1441, respectively. A large proportion of the proteins are involved in disease/defence (26% for ICSB 338 and 23% for SA 1441) followed by metabolism (25% for ICSB 338 and 21% for SA 1441). To validate gene function, eight proteins with the highest fold-change in response to drought were selected for gene expression analysis using quantitative real time- polymerase chain reaction (qRT-PCR). The results showed that all the genes evaluated were drought stress responsive. In order to develop the eight target genes as drought markers, their expression was analysed in cell suspension cultures of White sorghum and ICSB 338 with and without sorbitol treatment. The gene expression analysis showed that seven of the eight drought responsive genes could distinguish between White sorghum and ICSB 338 in the cell suspension culture system without sorbitol treatment. In addition, all the eight genes could distinguish between White sorghum and ICSB 338 in response to the sorbitol-induced osmotic stress. Following this, the responses of the genes to heat stress was analysed in the White sorghum cell suspension cultures. The results showed that seven of the genes were also heat responsive. These genes are recommended for use as drought markers in marker assisted selection for drought tolerance. As proof-of-concept and to develop a workflow for the use of the drought markers in other crops, three published genes from our research group were used. Four Arabidopsis (Arabidopsis thaliana) homologues of each sorghum gene were selected for gene expression analysis. The results showed that there is differential gene expression xv between homologues of the same gene in response to osmotic stress. In conclusion, the comparative sorghum physiological, biochemical, protein and gene expression data generated in this study forms a foundation for further sorghum molecular studies. Furthermore, the drought marker genes toolkit developed in this study can be utilised by plant breeders in marker assisted selection for the improvement of agriculturally important crops against drought. Keywords: Sorghum, Arabidopsis, drought stress, proteomics, cell suspension culture, 2D- DiGE, iTRAQ, qRT-PCR, gene expression, drought markers. xvi CHAPTER 1 LITERATURE REVIEW 1.1 The Production, Uses and Importance of Cereal Crops Cereals such as maize (Zea mays), sorghum (Sorghum bicolor), pearl millet (Pennisetum glaucum), wheat (Triticum aestivum) and rice (Oryza sativa) are the mainstay of diets in many countries, mostly in sub-Saharan Africa (Alexandratos et al., 2012). Their demand has increased with the corresponding increase in world population. The production quantity, harvested area and yield of cereal crops has subsequently increased by 2.5%, 0.5% and 1.8%, respectively from 2000 to 2013 (OECD/Food and Agriculture Organization of the United Nations, 2015). Subsequently, cereals make up the majority of crop sector production, worldwide with 106 million tons having been produced from 98.6 million ha in 2015 (Macauley, 2015). 1.2 Sorghum and its Uses Sorghum (Figure 1.1) is an herbaceous annual short day plant with a C4 photosynthetic pathway (Rosenow et al., 1983). It is known by common names such as milo, kafir and guinea corn in different parts of the world. Plants with a C4 photosynthetic pathway produce greater biomass and yield per unit of water transpired compared to C3 plants (Erickson et al., 2012). One of the major strengths of sorghum is the ability to grow in tropical, sub-tropical, temperate and semi-arid regions (Nathan, 1978; Jackson and Arthur, 1980). This highly adaptive tolerance to multiple environments makes it a crop of universal value (Kimber et al., 2013). In order of importance and production among cereal crops, sorghum is ranked fifth in the world after wheat, rice, maize and barley (Hordeum vulgare) (Pocketbook, 2015). 1 Figure 1.1: Image of sorghum crop stand (PlantVillage, 2018). The utility of sorghum is fairly diverse ranging from food, feed, fibre, biofuel to building material (Almolares et al., 1999; Kimber et al., 2013). As a food crop, sorghum has a good nutritional value consisting of 70-80% carbohydrates, 11-13% gluten free protein, 2-5% fat, 1-3% fibre and 1-2% ash (Plessis et al., 2015). It is a staple food of over 500 million people in more than 30 countries of the semi-arid tropics (Dahlberg et al., 2012). In addition, surplus sorghum is most suitable as animal feed owing to its low production cost and high nutrient quality in comparison to alternatives such as maize (Jackson and Arthur, 1980; Alexandratos and Bruinsma, 2012). Sorghum is naturally drought tolerant (Rosenow et al., 1983) with a wide genetic diversity (Motlhaodi et al., 2017). This makes the crop a potential model system in abiotic stress studies (Ngara and Ndimba, 2014). 2 1.3 Plant Abiotic Stresses An abiotic stress is any factor exerted by the environment on the optimal functioning of an organism (Cramer and Nowak, 1992; Vahdati and Leslie, 2013). Examples of abiotic stresses that significantly affect plant growth and development are drought, temperature extremes, salinity, nutrient deficiency or toxicity, soil pH (Wang et al., 2003b; Ahmad and Prasad, 2011), excess light and increased soil hardness (Verslues et al., 2006). Generally, abiotic stresses on plants cause a combination of physiological, biochemical and molecular changes in plants that adversely affect growth (Jaleel et al., 2009; Vahdati and Leslie, 2013). The resulting low biomass production as well as reduced grain yield pose a major threat to agricultural productivity and food security. 1.4 The Complexity of Abiotic Stress Responses in Plants Plant responses towards abiotic stresses are a complex phenomenon involving morphological, physiological, biochemical and molecular mechanisms at both cellular and whole plant levels (Farooq et al., 2009). Different abiotic stress response pathways can possess potential ‘sites’ for crosstalk. Crosstalk refers to a convergence in signalling or response pathways, which may include two pathways interacting to achieve a similar outcome in either an additive or negatively regulatory way (Knight and Knight, 2001; Roychoudhury et al., 2013). An example of crosstalk within drought and cold stress responses (Shinozaki et al., 2003) is illustrated in Figure 1.2 below. The diagram shows the gene expression regulatory network in response to drought and cold stresses. From the diagram, the open double-headed arrow is showing crosstalk between dehydration-responsive element binding proteins (DREB2) and abscisic acid-responsive element binding protein/ abscisic acid-responsive element binding factor (AREB/ABF). Both specific and interlinked pathways are shown in the abscisic acid (ABA)-dependent and ABA-independent drought and cold stress response networks. 3 Figure 1.2: Gene expression regulatory network in response to drought and cold stress illustrating specificity and cross-talk. Transcription factors that control stress-inducible gene expression are shown in circles or ovals. Small shaded circles indicate the modification of transcription factors in response to stress signals for their activation, such as phosphorylation. The upper part of the figure shows transcription cascades that are involved in rapid and emergency responses to drought and cold stresses, such as those involving inducer of C-repeat binding factor expression (ICE), DREB2 or 9- cis-epoxycarotenoid dioxygenase (NCED). Lower parts of the figure show transcription cascades that are involved in slow and adaptive processes in stress responses, such as those involving AREB/ABF, MYB, MYC and CBF/DREB1. The open double-headed arrow suggests crosstalk between DREB2 and AREB/ABF that is based on DRE/CRT acting as a coupling element for ABRE (Shinozaki et al., 2003). Wilkins et al. (2016), analysed environmental gene regulatory influence networks (EGRINs) in rice in response to different abiotic stresses and concluded that water deficits and high temperatures shared various pathways. In another study, more than half of the drought responsive genes were also triggered under both salinity and ABA treatments (Seki et al., 2001; Seki Motoaki et al., 2002). This suggests the possibility of similar responses to 4 different stresses. Furthermore, osmotic and oxidative stresses are secondary stresses common to drought, cold, salinity, heat and chemical pollution (Wang et al., 2003a). These secondary stresses disrupt the osmotic and ionic homeostasis of cells and also damage proteins, nucleic acids as well as membranes (Figure 1.3). In essence, it would be difficult to completely isolate unique stress response mechanisms due to this crosstalk between pathways. Nevertheless, plants respond to these stresses by synthesising genes involved in signalling perception and transduction, and the activation of stress responsive genes and proteins (Shinozaki and Yamaguchi-Shinozaki, 2000). This study focuses on drought stress and is effects on plants. 5 Figure 1.3: The complexity of plant responses to abiotic stresses (Wang, 2003a). 6 1.5 Drought Stress Drought stress, sometimes referred to as water or osmotic stress, is defined as insufficient soil moisture to meet the needs of a crop at a particular time (Blum, 2009; Salekdeh et al., 2009). It occurs as a result of drastic temperature increases, drying up of previously moist areas, and low or erratic rainfall patterns. Many climatic models are predicting increased frequency and duration of drought episodes in the immediate to long-term future (Anjum et al., 2011; Pocketbook, 2015). Apart from water scarcity, climate change is also predicted to increase the incidence of floods and elevated surface temperatures (OECD-FAO Agricultural Outlook 2015-2024, 2015), which all negatively affect plant growth and development. Consequently, drought and other abiotic stress factors are major threats to global food security due to their detrimental effects on plant growth, productivity and yield quality (Yang et al., 2015b). Therefore, crops which are better adapted to these abiotic stresses are required to counter the negative effects of climate change and maintain adequate food provision for the growing population. 1.5.1 Plant Responses to Drought Stress Plants are sessile organisms, which need to adjust to constantly changing environmental conditions. Plant responses to abiotic stresses are thus complex, involving morphological, physiological, biochemical and molecular changes at both cellular and whole plant levels (Farooq et al., 2009). In many cases, the molecular responses are synchronised across several cell layers in the same tissue or across different tissues and organs. Such coordinated responses require cell-to-cell communication, which is mediated by mobile signals transmitted through the plasmodesmata or secreted into the extracellular matrix (Isaacson et al., 2006). 7 During periods of water deficits, plants attempt to maintain essential processes at the expense of non-life threatening ones. This is because the plant functions as a system that self regulates in periods of stress. Generally, drought impairs seed germination (Harris et al., 2002), mainly because water is required for imbibition and activation of enzymes to initiate mitosis, cell expansion and elongation. The disruption of any of these processes translates to reduced plant growth, and yield (Harris et al., 2002). Furthermore, plants exhibit reduced leaf number and size, reduced stem elongation and increased root proliferation in response to drought stress (Farooq et al., 2009). The declining leaf area reduces the surface area for photosynthesis and ultimately plant growth. Conversely, an increase in root proliferation during periods of water stress is due to the plant’s ability to allocate photo-assimilates towards root growth in order to capture soil moisture from deeper levels (Blum, 2005). Plants also respond to the detrimental effects of drought stress by synthesising the stress- signalling phytohormone ABA (Ackerson and Radin, 1983; Davies et al., 1986). The accumulation of ABA results in stomatal closure, thus reducing transpirational water loss and its deleterious effects on plant growth (Ludlow and Muchow, 1990; Cornic and Massacci, 1996). Once the required response has been elicited, the ABA concentrations return to basal levels (Seiler et al., 2011). ABA dependent and ABA independent pathways operate in regulating the expression of osmotic stress responsive genes (Yamaguchi-Shinozaki and Shinozaki, 2006). These genes are responsible for the expression of stress response proteins, such as chaperones, enzymes for osmolyte biosynthesis, late embryogenesis abundant (LEA) proteins, channel and signalling proteins (Mahajan and Tuteja, 2005). Although stomatal closure leads to reduced transpirational water loss, it also reduces gaseous exchange, thus resulting in the reduction of photosynthesis as demonstrated by Baldocchi et al. (1985), in soybean (Glycine max). Reduced photosynthesis means that fewer photo-assimilates are produced, leading to an overall diminished plant growth (Anjum et al., 2011). 8 1.5.2 Osmolyte Accumulation in Stressed Plants One of the plant responses to drought stress is the synthesis and accumulation of osmolytes. Osmolytes are low molecular weight metabolites synthesised as an inherent mechanism of osmotic adjustment in stressed plants (Di Martino et al., 2003). Plants accumulate either organic or inorganic solutes in the cytosol, primarily to lower water potential and maintain turgidity (Hamilton and Heckathorn, 2001). Inorganic solutes such as K+, Na+ and Cl- are usually compartmentalised during osmotic stress because their accumulation interferes with cellular activities (Hasegawa et al., 1986). Solutes whose accumulation does not interfere with cellular function are termed compatible solutes. The most common compatible solutes synthesised by plants under abiotic stress include amino acids (proline, glycine), sugars (mannitol, sorbitol, sucrose, trehalose), polyols (glycerol, inositol, sorbitol) and their derivatives (methyl-inositol), quaternary ammonium compounds (glycine betaine) and tertiary sulphodium compounds (Di Martino et al., 2003; Valadez-Bustos et al., 2016). Under drought stress, the accumulation of osmolytes maintains turgidity in plant cells, minimising interruption of cellular metabolism whilst sustaining growth (Blum, 2005; Amrhein et al., 2013). Various studies have been conducted to illustrate the change in osmolyte content in plants under drought stress. For example, Tully et al. (1979) observed increases in proline content under drought stress in barley leaf tissue. A comparative study between the leaf tissue of chickpea (Cicer arietinum) cultivars with contrasting responses to drought stress was carried out. The results showed that the drought tolerant variety accumulated higher proline content compared to the drought sensitive variety (Mafakheri et al., 2010). Other studies in leaf tissue of crops such as peanut (Arachis hypogaea) (Quilambo and Scott, 2004), and pea (Pisum sativum) (Alexieva et al., 2001) showed an increase in proline content in response to drought stress. In sorghum leaf tissue, drought tolerant varieties 9 accumulated higher proline levels compared to drought susceptible varieties in response to drought stress (Sivaramakrishnan et al., 1988). 1.5.3 Drought Induced Reactive Oxygen Species Accumulation in Plants Plants have evolved an efficient enzymatic antioxidative system that protects cellular components against oxidative damage and maintains reactive oxygen species (ROS) at optimum levels for signal transduction (You and Chan, 2015). The enzymes responsible for ROS-scavenging and detoxification in plants include superoxide dismutase, ascorbate peroxidase, catalase, glutathione peroxidase, monodehydroascorbate reductase, dehydroascorbate reductase, glutathione reductase, glutathione S-transferase, and peroxiredoxin. All these enzymes function in detoxifying ROS in different sites where they are located (Noctor et al., 2014). Besides the enzymatic scavengers of ROS, non-enzymatic antioxidants such as glutathione, ascorbic acid, proline, carotenoids, tocopherols and flavonoids are also responsible for maintaining ROS homeostasis in plants (Gill and Tuteja, 2010). These systems are switched on following the onset of drought stress. There are many studies that illustrate increases in ROS accumulation in response to drought stress in crops such as rice (Boo and Jung, 1999), wheat (Loggini et al., 1999), pea (Moran et al., 1994) and sunflower (Helianthus annuus) (Sgherri and Navari-Izzo, 1995). Increases in non-enzymatic antioxidant systems such as ascorbic acid and glutathione in response to drought stress were reported in Diosporus spp root tissue (Wei et al., 2015), while glutathione reductase and superoxide dismutase were also shown to increase in response to salinity and drought stresses in monocots of the genus Juncus (Al Hassan et al., 2017). 10 1.5.4 Metabolic Adaptations of Plants to Drought Stress Metabolic adaptation also plays an important role in maintaining plant cellular homeostasis in periods of drought. Plants which possess a C4 photosynthetic pathway produce greater biomass and yield per unit of water transpired compared to C3 plants (Erickson et al., 2012) and are thus better adapted to hot and dry environments. Such plants possess bundle sheath cells which accumulate CO2 in the form of malate (Edwards and Walker, 1983). The C4 photosynthetic pathway is also more efficient in CO2 use because it utilises the enzyme phospho-enoyl pyruvate (PEP) carboxylase instead of the ribulose biphosphate carboxylase oxygenase (RuBisco) as the first CO2 acceptor (Lambers et al., 2008). PEP carboxylase has a higher affinity for CO2 compared to RuBisCO and does not fix O2. Therefore, the energy consuming photorespiration process does not occur in C4 plants (Stern et al., 2000). An example of a C3 plant is rice whilst C4 plants include sorghum and maize. Another photosynthetic pathway that makes plants survive drought conditions is the crassulacean acid metabolism (CAM) pathway. In this pathway, carbon fixation occurs during the night when the stomata are open to form 4-carbon acids (Monson and Fall, 1989). During the day these acids are broken down to pyruvate and CO2 in the presence of light and photosynthesis occurs (Monson and Fall, 1989). Plants with the CAM photosynthetic pathway are mainly succulent plants like cactus, which need to survive in semi-arid to arid environments. Compared to C4 plants, however, CAM plants have extremely high rates of water use efficiency but lower photosynthetic rates (Stern et al., 2000). Some of the roles of signalling pathways are outlined in Section 4.1. 11 1.5.5 Sorghum Responses to Drought Stress The responses of sorghum to drought stress are complex. Sorghum varieties in drought prone environments have more developed water saving mechanisms such as dense, extended root systems as a way of facilitating survival during periods of water scarcity (Schittenhelm and Schroetter, 2014). Drought tolerant and resistant sorghum varieties thus exhibit greater root weight, root volume, and root/shoot ratios compared to their drought susceptible counterparts (Nour and Weibel, 1978). The increase in shoot/root dry matter ratios, however, is not necessarily due to increased root mass but due to reduced shoot growth and mass (Blum, 2005). According to Merrill and Rawlins (1979), the sorghum plant directs resources to deeper soil penetration through higher root density in the deeper soil profile during drought stress. This is in agreement with Blum and Arkin (1984), where a higher root concentration was observed at the shallow top soil layers in well-watered plants. In contrast, drought stressed sorghum plants, had a more homogeneous root distribution within all the soil layers. Reverse water flow is also an important mechanism in dry soils especially where plants need to access nutrients from the dry top soil. Xu and Bland (1993), demonstrated that sorghum can extract water from deep soil levels via an elongated root system and subsequently efflux the water to the dry upper soil layers before resuming uptake again. This adaptation ensures that the sorghum plants can access mineral nutrients even in hot and dry conditions. Apart from root morphology and growth patterns, the plant’s water status, and its photosynthetic and antioxidant capacity under drought stress are all important aspects to consider when evaluating a crop’s tolerance to water stress. According to Zhang and Kirkham (1996), the chlorophyll content and relative leaf water content were higher for sorghum compared to sunflower under drought stress. Only after 7-8 days of withholding water was 12 the level of antioxidant enzymes, non-enzymatic antioxidants (ascorbate, glutathione, carotenoids) and malondialdehyde affected in sorghum. For sunflower, the responses were detected earlier at days 5-6 following drought stress. Although these changes were not consistently higher for sorghum compared to sunflower, the authors concluded that antioxidant responses differ between the two species and the onset of responses is much delayed for sorghum compared to sunflower (Zhang and Kirkham, 1996). Sorghum has been shown to allocate water to younger, more productive upper leaves compared to older leaves during water stress as a way of using the limited water more efficiently during the vegetative stage (Blum and Arkin, 1984; Blum, 2010). Under drought stress with soil moisture below 20%, the rate of transpiration for sorghum is mainly controlled by the reduction in total leaf surface area through leaf senescence (Blum and Arkin, 1984). Furthermore, leaf rolling in sorghum causes partial shading of the leaf, thus reducing both the surface area exposed to light and the rate of transpiration. The two most sensitive stages of sorghum to drought stress with respect to grain filling are the pre- and post-flowering stages (Borrell et al., 1999). Some sorghum varieties are thus pre- anthesis drought tolerant whilst others are post-anthesis drought tolerant. The ability of a sorghum plant to maintain a green leaf phenotype during grain filling is a vital drought adaptation trait (Borrell et al., 1999). Borrell et al. (1999), exposed sorghum hybrids to post- anthesis drought and concluded that hybrids possessing the stay green trait had a significant yield advantage over those which did not. However, the genetic and physiological mechanisms that form the basis for the stay green trait are yet to be fully understood. Johnson et al. (2015), investigated gene expression differences between a stay green and a senescent sorghum variety using microarray analysis. They observed increased gene expression of delta1-pyrroline-5-carboxylate synthase 2 (P5CS2), a proline biosynthetic 13 enzyme in the stay green variety compared to the senescent variety. This correlated with higher proline levels in the stay green variety. Furthermore, the P5CS2 gene was found to lie within the Stg1 QTL region (Johnson et al., 2015) and could possibly be a diagnostic marker gene for the stay green trait in sorghum. The presence of the stay green (stg) quantitative trait loci (QTL) in sorghum leads to decreased tillering and reduced upper leaf size. Decreased tillering greatly reduces the canopy size at anthesis under terminal drought (Borrell et al., 1999). This strategy reduces transpirational leaf surface area resulting in soil water conservation for use during grain filling (Burch et al., 1978). Such responses ultimately results in higher biomass post anthesis and increased grain number and yield (Borrell et al., 1999). Another drought stress adaptation exhibited by sorghum is the use of lesser soil water before anthesis (Burch et al., 1978; Borrell et al., 1999). This physiological adaptation can be attributed to low axial hydraulic conductance due to the increased deposition of lignin and suberin in the hypoderm and endodermis of stressed sorghum plants (Cruz et al., 1992). Sorghum also responds to drought stress through the accumulation of osmolytes and epicuticular wax. The most common compatible solutes found in sorghum in response to drought stress are soluble carbohydrates, amino acids, organic acids and betaines (Yang et al., 2003; Anjum et al., 2011). Sorghum varieties selected for osmotic adjustment gave high grain yield and also developed greater root length, higher soil water extraction ability and greater dry weight compared to those of low osmotic adjustment under drought stress (Santamaria et al., 1990). The epicuticular wax in sorghum accumulates on the abaxial or adaxial leaf surfaces, and lowers leaf surface temperatures due to their light reflectance ability (Johnson et al., 1983). Jordan et al. (1984), reported that an epicuticular wax load of greater than 0.067 g m−2 is an effective barrier against water loss in sorghum under any 14 condition. Furthermore, Hamissou and Weibel (2004), reported that the presence of epicuticular wax cover reduces transpirational water loss. Burow et al. (2009), successfully mapped the locus of BLOOM-CUTICLE (BLMC), which is associated with high cuticular wax production. An increase in plant death rating was also observed in the mutants with less epicuticular wax production. An increase in plant death rating was also observed in the mutants with less epicuticular wax. Plant responses to drought stress can also be analysed on a protein and/or gene level. The identification of drought responsive proteins and the functional validation of the corresponding genes could assist in the understanding the molecular mechanisms of drought response pathways in plants. 1.5.6 Combined Heat and Drought Stress High temperatures accompanying drought leads to high plant tissue temperatures. This ultimately leads to heat stress due to the unavailability of water to meet the evaporative demand (Grill and Ziegler, 1998). Although heat and drought stress episodes almost always occur combined under field conditions, the majority of studies have focused on independent heat or drought stress (FAO, 2015). Due to the biological cross-talk between the two stress responses, mostly emanating from common responses such as closure of stomata, suppression of photosynthesis, increased leaf temperature and ROS accumulation, the plant responses to these stresses are similar. In spite of this study focusing on drought stress, the combined heat and osmotic stress analysis is performed in vitro in order to further develop heat and drought marker genes. 1.6 Proteomics The proteome is defined as the entire protein complement of a cell, tissue or organism under defined conditions (Blackstock and Weir, 2003). Accordingly, proteomics refer to the 15 systematic analysis of protein populations in a tissue, cell or subcellular compartment (Van Wijk, 2001). Proteomics is a field of study which began with the introduction of two- dimensional gel electrophoresis (2DE) in 1975 (O’Farrell, 1975). There was relatively low activity in the field then until the sequencing of the human genome and the development of electrospray ionisation (ESI) and matrix assisted laser desorption ionisation (MALDI)-based mass spectrometry MS (Aebersold and Munn, 2003). The improvements in the sensitivity of 2DE together with protein detection and quantification methods, mass spectrometry, genomics and bioinformatics led to an increase in proteome analyses (Salekdeh and Komatsu, 2007). Proteomics also assists in elucidating the role of post-translational modifications (PTMs), protein interactions and novel gene identifications thus making it a vital tool for global phenotypic characterisation (Hu et al., 2016). Although early efforts were focused on human and yeast studies mainly because of the availability of genomic data (Bradshaw, 2008), plant proteomics is also now advancing. With the genomic sequence of sorghum being complete (Paterson et al., 2009), it is expected that linked proteomic studies will also gradually increase. 1.6.1 Plant Proteomics 1.6.1.1 Gel Based Proteomics The breadth of methods used to quantitatively study proteomes is increasing. However, 2DE remains the mostly used method (Klose et al., 2002). Proteomic changes during different growth and developmental stages of plants as well as the analysis of differentially expressed proteins under both biotic and abiotic stresses are some of the most widely studied. The first dimension (1D) of protein analysis involves the separation of proteins according to their net charge through isoelectric focusing. The second dimension (2D) follows, which involves the separation of proteins according to molecular weight using SDS-PAGE. The 16 immobilised pH gradient (IPG) strips with resolved proteins previously used in the first dimension are applied to the second dimension gels and electrophoresed. Sodium dodecyl sulphate coats proteins according to their mass and proteins subsequently migrate as ellipsoids with a uniform negative mass charge to mass ratio (Garfin, 1995). In the 2D-based gel electrophoretic method, thousands of tissue or subcellular proteins can be separated in one gel run. Various protein spot visualisation methods are available and these range from visible stains such as Coomassie brilliant blue and silver to fluorescent stains such as Sypro Ruby. However, the major disadvantages of gel based proteomics remains the inability to detect low abundant proteins and gel-to-gel variation (Zhou et al., 2005). In order to reduce these limitations in gel based proteomics, two dimensional-differential gel electrophoresis (2D-DiGE) was developed (Rabilloud and Lelong, 2011). 2D-DiGE allows the labelling of up to three samples with Cy dyes and thus reduces the gel-gel variation that exists in the traditional 2DE. 2D-DiGE has been performed in comparative proteome studies in sorghum (Jedmowski et al., 2014), maize (Vidal et al., 2015) and barley (Wendelboe‐Nelson and Morris, 2012) among others. This method is however laborious and time consuming. The use of non-gel based methods for proteome analysis addresses some of the challenges of gel-based proteomics. 1.6.1.2 Non-gel Based Proteomics 1.6.1.2.1 Protein Labelling for Mass Spectrometry vs Label-free Approaches Non-gel based approaches in proteomics utilise liquid chromatographic (LC) separation techniques coupled with tandem mass spectrometry (MS/MS). The samples may be labelled or unlabelled as in label free approaches. Various methods can be utilised in order to label samples by introducing isotopes at either protein or peptide level for mass spectrometry based analysis. These comprise of amino acid based labelling, N-terminal peptide labelling of the 17 epsilon-amino group of lysine residues and C-terminal peptide of glutamic or aspartic acid residue labelling (Goodlett et al., 2001). Amino acid based labelling consists of isotope coded affinity tag (ICAT), visible isotope coded affinity tag (VICAT), mass coded abundant tagging (MCAT) and quantitation using enhanced signal tags (QUEST). The N-terminal category of labelling are realised by utilising the N-hydroxysuccinimide (NHS) chemistry as well as active esters. The N-terminal labelling method consists of the isobaric tags for relative and absolute quantitation (iTRAQ) (Ross et al., 2004), tandem mass tags (TMT) (Thompson et al., 2003) and global internal standard technology (GIST). The C-terminal peptide labelling methods include esterification using deuterated alcohols (Goodlett et al., 2001). The ICAT method was the first quantitative based approach to be introduced (Gygi et al., 1999). The ICAT method is perhaps the most characterised approach and consists of three components, namely thiol functional groups, a linkage group and a biotin moiety for affinity purification of the previously cysteine-derivatised peptides. The main disadvantage of ICAT is that only two sample labels are available. Therefore the analysis of various samples at the same time is impossible. Furthermore, ICAT is unsuitable for the analysis of samples which do not contain cysteine residues such as phosphopeptides (Ross et al., 2004). These challenges led to the development of the 2- or 4-plex isotope coded affinity tag (ICAT), 4- or 8-plex iTRAQ (Ross et al., 2004) and 2- or 6-plex tandem mass tag (TMT) (Thompson et al., 2003) based techniques. Label-free based approaches of protein quantification represents a strategy which avoids the isotope labelling step. Two categories utilised in label-free based measurements are peak area and spectal counting. Peak area is a method that measures the quantity of analytes from the integrated peak area from the the extracted ion chromatogram (EIC). The method relies on the principle that the detected ion signal is positively proportional to the analyte 18 concentration by ESI within a certain range when coupling with LC. On the other hand, spectral counting represents a method based on the observation that there is typically positive correlation between protein abundance and the number of its proteolytic peptides and vice versa (Fan et al., 2010). Although MS/MS-based gel-free label-free approaches and isotopic labelling methods work equally well especially where accuracy is important, the former is generally underutilised (Leroy et al., 2012). This may be because isotopic labelling exhibits greater precision in comparison to label-free workflows. However, since MS-based label-free approaches have been adapted to support absolute quantitation (Silva et al., 2016), such approaches are expected to increase in utility. This study utilises the iTRAQ method, which is a gel-free label-based workflow. 1.6.1.2.2 iTRAQ Analysis The iTRAQ method is based on the tagging of primary amines, that is, the N-terminal based labelling (Ross et al., 2004). The iTRAQ reagents are multiplexed, amine specific and of a stable isotope nature and allow both relative and absolute identification and quantitation (Martínez-Esteso et al., 2014). The iTRAQ reagents currently in use are the 4-plex (114, 115, 116 and 117) or 8-plex which includes the 113, 118, 119 and 121 in addition to the 4-plex reagents. This allows the tracking of multiple samples, up to eight, in an LC-MS run or the following of biological systems of a time course nature. An example of the iTRAQ workflow is shown in Figure 1.4 for rice root tissue (Wang et al., 2014). Protein is extracted from the samples followed by a series of steps where the precipitated protein is reduced, alkylated and digested. Sample labelling, pooling, fractionation by LC-MS/MS ensures protein identification and quantitation. 19 Figure 1.4: A workflow for analysis of protein expression in rice roots by 8-plex isobaric tagging (Wang et al., 2014). 20 1.6.1.2.3 iTRAQ Analysis in Plant Biotic and Abiotic Stress Studies The iTRAQ method is being increasingly used in various plant biotic and abiotic stress studies. This is mainly because the technology is a more efficient way for protein identification and quantitation compared to the traditional 2DE. The 2DE is unable to identify proteins which are in low abundance, too hydrophobic, extremely small/large, as well as extremely acidic/basic (Zieske, 2006). Crops in which the iTRAQ technology has been applied include sorghum (Zhou et al., 2016), rice (Wang et al., 2014; Chen et al., 2016), tobacco (Nicotiana tabacum) (Zhong et al., 2017), soybean (Li et al., 2016a), maize (Yu et al., 2016), faba bean (Vicia faba) (Cao et al., 2017) among others. The increase in the use of this technology also lies in its accuracy at quantitation, ability to analyse from 4 up to 8 samples and its high resolution power (Bindschedler and Cramer, 2010). 1.6.2 Plant Root Proteomics and Gene Expression Analysis The first plant organ to detect a deficit in water supply to the plant is the root system (Ghatak et al., 2016). Roots send both water and minerals through the xylem sap to aerial parts of the plant. In addition, it has been shown that roots also send molecular signals using the same mechanism (Davies et al., 1986). ABA, a vital root-shoot stress signal, is transmitted through the xylem sap (Hartung et al., 2002). When the signal reaches the leaf tissue, stomatal closure is effected as a water saving mechanism. However, gaseous exchange is also hindered in the process. For these reasons, it is important to study the responses of roots to drought stress. Comparative differential root proteome expression analysis has been performed in tomato (Solanum lycopersicum), where cellular metabolic activity and protein biosynthesis was suppressed by drought stress (Zhou et al., 2013). Post-transcriptional regulation and protein translation were shown to be high in the drought resistant variety compared to the drought susceptible tomato variety. In another study, wild peanut (Arachis duranensis) was exposed 21 to drought stress and 31 root proteins unique to drought stress perception were identified (do Carmo et al., 2018). These included chitinase 2, an MLK like protein, a glycine-rich protein DOT 1-like, a muturase A and heat shock-related proteins. Gene expression analysis was also carried out for all the genes corresponding to the drought responsive proteins in root tissue using qRT-PCR. A total of 15 of the genes were up-regulated, while 14 genes were down- regulated in root tissue in response to drought stress (do Carmo et al., 2018). In sorghum, previous investigations into the proteome profiles of drought susceptible and tolerant varieties have focused on the leaf tissue (Jedmowski et al., 2014). Currently, there are no comparative proteomic studies between the root tissue of sorghum varieties with contrasting responses to drought stress. This study will therefore provide information which could be useful in understanding sorghum root proteome changes under drought stress. Model plant systems such as Arabidopsis (Arabidopsis thaliana) are also important in drought stress studies. This is because model plant systems are easier to manipulate and have well characterised genetic tools. 1.7 The Use of Model Plant Systems in Plant Abiotic Stress Studies Model plant systems have been used to obtain knowledge on the molecular and biochemical responses of plants to biotic and abiotic stresses (Ngara and Ndimba, 2014). Historically, Arabidopsis, maize and rice have been utilised as plant model systems in a range of studies. This has resulted in knowledge gains in numerous plant growth and developmental processes. Arabidopsis is currently still the leading model plant system in the plant ‘omics’ studies. Arabidopsis is a flowering plant native to Eurasia. The plant is an important model system for gene identification and function (Rensink and Buell, 2004). The low content of repeated DNA, low level of methylation, efficient chemical and radiation mutagenesis, short generation time, large number of seeds and relatively smaller genome (Koornneef and 22 Meinke, 2010), makes Arabidopsis an appropriate model for molecular biology. Since the publication of the complete genome sequence of Arabidopsis (The Arabidopsis Genome Initiative, 2000), it has been easier to manipulate the sequenced genome for gene functional studies. More importantly, there are many gene knockout mutant lines available to the scientific community. The availability of vast numbers of natural accessions adapted to varying environments also contributes to the advantages of using Arabidopsis in stress response studies (Weigel, 2012). The utilisation of a model plant system, such as Arabidopsis, is an invaluable method for validating crop gene function. Over-expression analyses have been successfully done to validate gene function. For instance, Yu et al. (2006) over-expressed the sorghum gene SbSTS1, responsible for defence responses, in transgenic Arabidopsis in order to determine the gene function in planta. Yan et al. (2013), also utilised Arabidopsis to validate the sorghum basic helix-loop-helix (bHLH) gene function. These studies demonstrate the importance of utilising a plant with well-known characteristics in validating gene expression and function. Although Arabidopsis is a model plant system widely utilised, the plant is naturally drought susceptible and agriculturally unimportant. This has led to suggestion of adopting sorghum as a model plant system in abiotic stress studies (Ngara and Ndimba, 2014). Sorghum is a good plant system for abiotic stress studies because it is naturally drought tolerant (Rosenow et al., 1983) and possesses great diversity in its gene pool (Motlhaodi et al., 2017). 23 1.8 Aim and Objectives of this Study The goal of this study is to identify sorghum proteins and genes recruited in sorghum adaptive responses to drought stress. A few of these proteins will be selected for functional validation with the possibility of developing drought markers for assisting plant breeders in the selection process during classical plant breeding programs for drought tolerance. These genes could also be used in improving crop productivity under drought stress through conventional breeding or genetic engineering. This will help increase food security in this time of uncertainty in weather patterns. The global aim of the research was: To evaluate the physiological, biochemical and molecular responses of two sorghum varieties to drought stress with the ultimate goal of working towards the development of drought markers. The specific objectives were: i. To characterise the physiological and biochemical responses of two sorghum varieties with contrasting phenotypic drought traits after exposure to drought stress, ii. To perform a comparative quantitative analysis of the root proteome in response to drought stress and validate the expression of selected gene targets, and iii. To develop molecular markers for drought tolerance. 24 CHAPTER 2 MATERIALS AND METHODS 2.1 Plant Material 2.1.1 Sorghum Germplasm Five sorghum (Sorghum bicolor L. Moench) varieties were initially used in the study in order to select two with contrasting tolerance to drought stress. The seeds were obtained from the Agricultural Research Council (ARC) - Grain Crops Institute (GCI), Potchefstroom, South Africa; Capstone Seeds, Howick, South Africa; and Agricol, Pretoria, South Africa as shown in Table 2.1. Table 2.1: Sorghum varieties used in the study. Variety Source Phenotypic trait SA 1441 ARC-GCI Drought tolerant ICSV 210 ARC-GCI Drought resistant ICSB 338 ARC-GCI Drought susceptible Ns 5511 Agricol Unknown Cap 1003 Capstone seeds Unknown 25 2.1.2 Sorghum and Arabiopsis Cell Suspension Cultures White sorghum (Ngara et al., 2008), Arabidopsis (Arabidopsis thaliana var. Landsberg erecta) (May and Leaver, 1993), and ICSB 338 sorghum cell suspension cultures (Ramulifho, 2017) were used as in vitro systems for studying the effects of osmotic stress in plants. The sorghum cell suspension cultures were maintained on sorghum cell suspension culture medium [4.4g/L Murashige and Skoog Basal Salt with minimum organics (MSMO) medium, 3% (w/v) sucrose; adjusted to pH 5.8 using 1 M NaOH. Arabidopsis cell suspension cultures were maintained in MSMO medium with an addition of 3% (w/v) sucrose and 1 mg/mL each of NAA and kinetin growth hormones; adjusted to pH 5.7. The sorghum cell suspension cultures were maintained at 30°C in dark conditions, while the Arabidopsis cell suspension cultures were maintained at 22-23°C under both dark and light conditions with agitation at 130 rpm on an orbital shaker. Arabidopsis cell cultures were maintained by weekly subculturing into fresh growth medium using 10% (v/v) ratio inoculum. Sorghum cell cultures were maintained by fortnightly subculturing into fresh medium using 10% (v/v) ratio inoculum. Early-mid log phase cells were used for the experiments, equating to 4 days after subculturing of Arabidopsis cell cultures and 8 days for sorghum cell cultures. 2.2 Plant Growth Conditions and Drought Stress Treatment 2.2.1 Determination of Field Capacity The field capacity (FC) of the soil was determined using a protocol adapted from Vineeth et al., (2016). Ten plastic pots of 10 cm diameter and depth were used in this experiment. Each pot was filled with 150 g of potting soil mix (Culterra, Muldersdrift, South Africa) and then saturated with water. The excess water was allowed to drip from the pots for three hrs. The pots were then weighed to obtain the saturated soil weight (SW). After weighing, the soil was oven-dried at 40°C and weighed daily until constant dry weight was reached (DW). The FC was estimated using the following equation: 26 FC = SW – DW Where FC = field capacity, SW = saturated soil weight, DW = dry soil weight. The field capacity of the soil was noted and used in devising a daily watering regime for the germination and drought stress experiments. 2.2.2 Measurement of Growth and Physiological Parameters Sorghum seeds were imbibed in distilled water for 30 min and ten seeds per variety were sown in plastic pots. The pots were saturated with Nitrosol® nutrient solution [Envirogreen (Pty) Ltd, Braamfontein, South Africa] with standardised macro and micro nutrient content [N (80 g/kg); P (20 g/kg); K (58 g/kg); Ca (6 g/kg); Mg (7 g/kg); S (4 g/kg); Mn (40 mg/kg); Mo (15 mg/kg); Fe (60 mg/kg); Cu (1 mg/kg); Zn (1 mg/kg); Bo (23 mg/kg)], allowed to drip for three hrs and placed in a growth chamber (Model: GC-539DH, Already Enterprises Inc., Taipei, Taiwan). The soil was kept moist by irrigating daily to field capacity with distilled water. The seeds were grown on a 27/19°C day/night temperature cycle in the growth chamber with a 16/8 hrs light/dark cycle. Germination was recorded by counting the emerging seedlings every second day until the sixth day. 2.2.3 Drought Stress Treatment Experiments 2.2.3.1 Seedling Growth for Physiological Measurements and Protein Extraction ICSB 338 (drought susceptible) and SA 1441 (drought tolerant) sorghum seeds were germinated as described above and well-watered until the V3 growth stage (three fully expanded leaves with a fourth emerging leaf). Thereafter, drought stress was imposed by withholding water for 8 days. The control plants were maintained at 100% FC throughout the experiment. Following drought stress, some plants were re-watered for 24 hrs. These plants were used as the re-watered plants in subsequent physiological and growth measurements. 27 2.2.3.2 Seedling Growth for Osmolyte and Gene Expression Analysis Seedlings for osmolyte content analysis as well as gene expression analysis were grown at Durham University, United Kingdom. The seeds were germinated on moist filter paper in petri dishes and incubated in a 30°C dark room for 72 hrs. After germination, the seedlings were transplanted into Levington F2 + sand compost and sand mix (ICL Everris Ltd, Ipswich, United Kingdom) in square plastic pots (6.5 x 6.5 x 6.5 cm3). The seedlings were well- watered until the V3 growth stage. At the V3 growth stage, water was withheld and root and leaf samples were collected at days 0, 4, 8 and 12 as follows. For osmolyte content analysis, each biological replicate was a pool of three leaf discs, each derived from an independent plant. At each sampling time-point, the roots were washed to remove soil, blotted dry, and about 100 mg placed in an Eppendorf tube and snap-frozen in liquid nitrogen prior to storage at -80°C. A total of three biological replicates were generated for the root samples, with each replicate consisting of tissue from a single plant. For gene expression analysis, the third leaf was excised from the plant at the leaf point of attachment with the plant whilst the roots were washed over running water and quickly blotted dry with filter paper. The samples were quickly wrapped in aluminium foil, flash frozen in liquid nitrogen and stored at -80°C until further use. 2.2.4 Osmotic Stress Treatment of Cell Suspension Cultures Early-mid log phase sorghum and Arabidopsis cell cultures were aliquoted into 10 mL cultures, using sterile 25 mL Erlenmeyer flasks. Both light-grown and dark-grown Arabidopsis cell cultures were used in this experiment, which gave rise to four cell types/series – white sorghum, ICSB 338 sorghum, dark-grown Arabidopsis, and light-grown Arabidopsis. Cell cultures of both sorghum lines were grown in complete darkness as they cannot withstand light (Ngara et al., 2008). Control cultures were mock-treated by addition of 28 2 mL sterilised MilliQ-water. The cells were harvested immediately, flash frozen in liquid nitrogen and stored at -80°C. The remaining flasks were treated by addition of 2 mL of 2 M sorbitol stock solution to achieve a final sorbitol concentration of 0.4 M. The cell suspension cultures were then returned to their respective incubation conditions and harvested at 6, 24, 48 or 72 hrs later. Cell cultures were filtered through 2 layers of Miracloth and the cells were immediately flash frozen in liquid nitrogen and stored at -80°C. 2.3 Measurement of Growth and Physiological Parameters 2.3.1 Growth Measurements After 8 days of drought stress (Section 2.2.3.1), the root and shoot length of ICSB 338 and SA 1441 sorghum varieties were measured in both control and drought-stressed plants and 24 hrs after re-watering. The shoot length was measured from the point of attachment of the stem with the roots to the tip of the longest leaf. The root length was measured from the point of attachment with the stem to the tip of the longest root. Five biological replicates were used per treatment for all measurements per sorghum variety. The fresh and dry weight measurements were taken after 8 days of drought stress and 24 hrs after re-watering. The same measurements were taken for the respective controls. Briefly, the intact root system of harvested sorghum plants was gently shaken to remove all the soil lumps, washed under running water and blotted dry on paper towel. The plants were excised at the point of attachment with the roots. Each shoot and root tissue sample was then weighed to obtain the fresh weight. The shoot and root tissues were separately oven-dried at 60°C for 48 hrs and weighed to obtain the dry weight. Five biological replicates were used per treatment for all measurements per sorghum variety. 29 2.3.2 Physiological Measurements 2.3.2.1 Measurement of Relative Water Content Relative water content (RWC) was estimated as previously described (Barrs and Weatherley, 1962). Briefly, the third leaf was excised from the plants and immediately weighed to determine the fresh weight (FW) for five biological replicates. The leaf samples were saturated in distilled water in 50 mL Falcon tubes in the dark for 24 hrs at 4°C, blotted dry between filter paper and weighed to determine the turgid weight (TW). The leaf samples were then wrapped with paper towel and oven dried at 60°C for 24 hrs to obtain the dry weight (DW). The RWC was be calculated using the formula Where RWC = relative water content, FW = fresh weight, DW = dry weight, TW= turgid weight. 2.3.2.2 Leaf Chlorophyll Content The chlorophyll content of sorghum plants was measured on the third leaf using a CCM 200 Plus Chlorophyll Content Meter (Opti-Science, ADC BioScientific Ltd., Hoddesdon, UK) according to the manufacturer’s instructions. The measurements were taken daily at the same time of the day for the eight days of drought stress so as to reduce technical variation and 24 hrs after re-watering. Ten biological replicates were used per treatment for each sorghum variety and the chlorophyll content was expressed as Chlorophyll Content Index (CCI). 30 2.3.2.3 Leaf Stomatal Conductance and Surface Temperature The leaf stomatal conductance and surface temperature of the sorghum plants was measured using an SC-1 Leaf Porometer (Decagon Devices, Inc., Washington, USA) according to the manufacturer’s instructions with minor modifications. The leaf abaxial conductance and surface temperature measurements were taken simultaneously on a daily basis at the same time on the third leaf for eight days and 24 hrs after re-watering. Ten biological replicates were used per treatment for each sorghum variety. 2.3.2.4 Relative Cell Death The root and leaf relative cell death was determined using the Evans blue method as previously described (Ngara, 2009). Firstly, 100 mg of the control and drought stressed sorghum root tissue or 1 cm x 1 cm leaf discs was weighed. Separate leaf discs and root samples were boiled to kill all cells and used as the 100% cell death samples. The plant samples were each submerged in 1 mL of 0.25% (w/v) Evans blue solution in 10 mL centrifuge tubes. The tubes were incubated on a horizontal shaker with gentle shaking for 20 min. The plant samples were then transferred to a 2 mL Eppendorf tube and washed at least three times with distilled water to remove all the Evans blue solution on the surface of the samples. After washing, 1.2 mL of 1% (w/v) sodium dodecyl sulphate (SDS) in 50% (v/v) methanol was added to the samples before grinding using a plastic pestle. Thereafter, the samples were vortexed and centrifuged at 7 300 × g for three min. A 0.8 mL aliquot from the supernatant was taken and its optical density was determined spectrophotometrically at 600 nm. For the calculation of the relative cell death the following equation was used: 31 CD = (y - z)/(z - x) Where CD % = cell death percentage x = optical density value for the control sample y = optical density value for the drought stressed sample z = optical density value for the boiled sample 2.4 Osmolyte Content Analysis The proline and glycine betaine contents of sorghum leaf and root tissue in control and drought stressed samples were determined using Hydrophilic Interaction Liquid Chromatography-Mass Spectrometry (HILIC-MS). For leaf tissue, three 7 mm diameter biological replicate discs per control and drought stressed treatment were used, while for roots, approximately 100 mg of ground sample was used. Three biological replicates were harvested at days 0, 4, 8 and 12 after drought stress (Section 2.2.3.2) and weighed. Thereafter, 125 µL of 0.25 N HCl was added to each sample and incubated at 60°C on a heat block for 5 min as previously described (González-Torralva et al., 2017). After the incubation period, the liquid extract was collected and stored at -80°C pending osmolyte content analysis. 2.4.1 Proline Content Analysis The chromatographic separation of leaf extract samples for proline content analysis was performed on an Acquity UPLC BEH Amide column (2.1×100 mm, 1.7 µL particle size) (Waters, Milford, USA) as previously described (Prinsen et al., 2016). A volume of 2 µL of the leaf extracts was diluted by a factor of 100, injected into the column and the column 32 temperature was maintained at 35°C. The same volume of root extract was injected into the column with no dilution. For optimal chromatographic separation, a gradient with two solvents, namely A (10 mM ammounium formate in 85% acetonitrile containing 0.15% formic acid) and B (10 mM ammounium formate in MilliQ-water containing 0.15% formic acid pH 3.0) was established at a flow rate of 200 µL/min as follows. Initially solvent A was maintained at 100% for 6 min. A gradient was then started for 0.1 min at which solvent A was decreased to 94.1% whilst solvent B was increased to 5.9%. Following this, solvent A was further decreased to 82.4% whilst solvent B was increased to 17.6% from 6.1 to 10 min. From 10 to 12 min, solvent A was set at 70.6% and solvent B at 29.4%. This was followed by the equilibration of the column for 6 min in the initial conditions (100% solvent A) giving a total run time of 18 min including the calibration process. The column was then coupled to a QTRAP 6500 mass spectrometer (AB Sciex, Redwood city, USA) for the Multiple Reaction Monitoring (MRM) analysis of proline content. The MRM transition was 11670. Peaks of interest were integrated using Analyst software (AB Sciex) and quantified with reference to external standards. 2.4.2 Glycine Betaine Content Analysis The chromatographic separation of the leaf extract samples for glycine betaine content analysis was performed on an Acquity UPLC BEH Amide column (2.1×100 mm, 1.7 µL particle size) (Waters). A volume of 2 µL of the leaf and root extracts (Section 2.4) was injected into the column and the temperature of the column was maintained at 30 °C. Only the leaf extracts were diluted by a factor of 100. For optimal chromatographic separation, a gradient with two solvents, namely A (10 mM ammounium formate in 85% acetonitrile containing 0.15% formic acid) and B (10 mM ammounium formate in MilliQ-water containing 0.15% formic acid pH 3.0) was established at a flow rate of 200 µL/min as follows. Initial conditions were 100% solvent A for 6 min. A hold step was maintained for 2 33 min. Thereafter, solvent B was ramped to 100% at 5 min. This was followed by a hold step for 5 min and equilibration at 100% solvent A for 5 min. The column was then coupled to a QTRAP 6500 hybrid triple quadruple mass spectrometer system (AB Sciex) for the MRM analysis of glycine betaine content. The MRM transitions were ES+ 11858, 11859. 2.5 Protein Extraction and Quantification 2.5.1 Protein Extraction from Sorghum Root and Leaf Tissue Total soluble protein (TSP) extraction was performed as previously described (Ngara, 2009). Approximately 1 g fresh weight of sorghum root and leaf samples (Section 2.2.3.1) were ground to a fine powder using sterile frozen pestle and mortar. Five biological replicates were used for control and drought-stressed root and leaf tissues. The ground plant tissue was transferred to 1.5 mL Eppendorf tubes and precipitated in 1 mL of 10% (w/v) trichloroacetic acid (TCA) on ice. The homogenate was then briefly vortexed and centrifuged at 15 000 × g for 10 min at room temperature. Following centrifugation, the supernatant was discarded and the pellet was washed thrice with 1.5 mL of ice-cold 80% (v/v) acetone by centrifugation at 15 000 × g for 10 min per wash. The pellet was air-dried at room temperature for 5 min and re-suspended in 1 mL and 1.4 mL of urea extraction buffer [9 M urea, 2 M thiourea and 4% 3-(3-Cholamidopropyl) dimethylammonio]-1-propanesulfonate (CHAPS)] for the root and leaf sample, respectively. The TSP was extracted in the urea extraction buffer overnight at room temperature with vigorous vortexing before centrifugation at 15 000 × g for 10 min. The supernatant containing the TSP was then carefully collected and stored at -20°C pending protein analysis. 2.5.2 Protein Quantification The concentration of all protein extracts was determined using a Bradford assay (Bradford, 1976) with minor modifications as previously described (Ngara, 2009). Bovine serum 34 albumin (BSA) standards were prepared in duplicate from a 5 mg/mL BSA stock solution in 2 mL plastic cuvettes as shown in Appendix, Table A1. Protein samples were also prepared in duplicate in 2 mL plastic cuvettes by mixing 10 µL of each respective protein extract with 10 µL of 0.1 M HCl and 80 µL distilled water. The Protein Assay Dye Reagent Concentrate (BIO-RAD, Hercules, California, USA) was diluted in the ratio 1 part Bradford reagent to 4 parts distilled water. A volume of 900 µL of the dilute Bradford reagent was then added to all the BSA standards as well as the protein samples, mixed and incubated for 5 min at room temperature before reading the absorbance at 595 nm. The 0 mg/mL BSA standard solution was used as a blank. The standards were used to plot a standard curve from which the concentrations of the unknown protein samples were estimated. 2.6 Protein Gel Electrophoresis 2.6.1 One Dimensional (1D) Polyacrylamide Gel Electrophoresis (PAGE) Protein extracts were separated on a 1D SDS-PAGE as previously described (Laemmli, 1970). The 1D gels were cast on 10.1 cm (width) x 8.3 cm (height) spacer glass plates mounted with 1 mm thick spacers using the Mini – PROTEAN® Tetra cell (BIO-RAD) gel casting system following manufacturer’s instructions. Stacking and resolving gels of 5% (v/v) and 12% (v/v), respectively were prepared as shown in Appendix, Table A2. The protein samples were prepared by combining each sample with 2X SDS sample loading buffer [100 mM Tris HCl, pH 6.8, 4% (w/v) SDS, 20% (v/v) glycerol, 200 mM DTT, and a tint of bromophenol blue] in equal amounts in a 1.5 mL Eppendorf tube, pulse vortexed and pulse centrifuged. The protein samples were heated on a heat block at 100°C for 5 min and pulse centrifuged prior to loading 2.5 µg for root and 10 µg for leaf samples and onto the 1D SDS-PAGE gels. A volume of 6 µL of an unstained protein marker (New England Biolabs, Hertfordshire, UK) was loaded in the first well. Gel electrophoresis was carried out in 35 electrode running buffer [25 mM Tris, 192 mM glycine and 0.1% (w/v) SDS] on a Mini- PROTEAN® Tetra System (BIO-RAD) using a Power Pac™ Basic (BIO-RAD). Initially the gels were run at 100 V for 30 min before increasing the voltage to 150 V until the bromophenol blue dyes reached the bottom of the gels. 2.6.1.1 Coomassie Brilliant Blue (CBB) Staining The proteins separated by 1D SDS-PAGE were detected and visualised by staining using a modified CBB R-250 staining procedure in three stages. Three staining solutions, namely, Coomassie I [0.025% (w/v) CBB R-250, 10% (v/v) glacial acetic acid and 25% (v/v) propan- 2-ol], Coomassie II [0.003% (w/v) CBB R-250, 10% (v/v) glacial acetic acid and 10% (v/v) propan-2-ol] and Coomassie III [0.003% (w/v) CBB R-250 and 10% (v/v) glacial acetic acid], were prepared from a 1.25% (w/v) CBB stock solution and used as follows: The gels were immersed in warm Coomassie I for 30 mins with gentle shaking. This was followed by Coomassie II and Coomassie III. The gels were then immersed in a de-staining solution [10% (v/v) acetic acid and 1% (v/v) glycerol] until the bands were clearly distinct against a clear background. The gels were scanned and imaged on a Gel Doc™ XR+ Molecular imager with Image Lab ™ software version 5.2.1 (BIO-RAD). 2.6.1.2 Protein Precipitation The protein samples were transferred to 10 mL centrifuge tubes and precipitated in 80% (v/v) acetone overnight at -20°C. The samples were then centrifuged at 3000 × g for 10 min and the supernatant was discarded. The resulting pellets were washed three times with ice-cold 80% (v/v) acetone, centrifuged at 3000 × g for 10 min and the acetone was discarded. The pellets were then covered with 50 µL of 80% (v/v) acetone and couriered to Durham University, United Kingdom for use in two dimensional differential gel electrophoresis (2D- DiGE) and isobaric Tags for Relative and Absolute Quantitation (iTRAQ) analysis. 36 2.6.2 Two Dimensional - Differential Gel Electrophoresis (2D-DiGE) The precipitated root and leaf samples couriered to Durham University were re-solubilised as follows. The protein samples were centrifuged at 15 000 × g for 10 min, the acetone was discarded and the protein samples were briefly air-dried. Thereafter, 400 µL of extraction buffer was added to the protein samples and vortexed for 5 min before centrifugation at 15 000 × g for 10 min. The supernatant was then transferred to fresh 1.5 mL Eppendorf tubes and quantified using the Bradford assay (Section 2.5.2). Approximately 2.5 and 10 µg of root and leaf samples, respectively were run on a 1D SDS-PAGE (Section 2.6.1) to determine protein quality. The gels were stained with a SYPRO Ruby fluorescent stain as follows. The gels were fixed with a solution of 40% (v/v) methanol and 10% (v/v) acetic acid for 30 min before staining with a Lonza SYPRO Ruby protein gel stain (Scientific Laboratory Supplies, Nottingham, UK), for at least 90 min with gentle shaking in the dark. After the staining procedure, the gels were destained in 10% (v/v) methanol and 6% (v/v) acetic acid for 30 min and imaged using the Typhoon 9100 Variable Mode Imager (Amersham Biosciences, California, USA). 2.6.2.1 Sample Preparation and Labelling for 2D-DiGE Root and leaf protein sample preparation for 2D-DiGE labelling began with adding 2 µL of 1.5 M Tris-HCl, pH 8.8 to 98 µL of protein sample. The mixture was pulse vortexed, pulse centrifuged and adjusted to pH 8-9 by adding 0.1 M NaOH before storage at -20°C. The pH was monitored using pH Test Strips 4.5-10.0 (SIGMA, St Louis, USA). The pH adjusted samples were thawed, pulse-vortexed, pulse-centrifuged and stored in a light protected ice- bath. Subsequently, a protein sample volume equivalent to 50 µg was topped up to a volume of 48 µL with extraction buffer. The control and drought-stressed protein samples were then labelled with 800 pmol of Cy3 and Cy5 fluorescent dyes, respectively, pulse vortexed and 37 returned to the light protected ice-bath for 30 min. After exactly 30 min, 1 µL of 10 mM lysine was added to stop the labelling reaction. The labelled samples were then stored at - 20°C pending 1D and 2D SDS-PAGE analysis. 2.6.2.2 1D Gel Electrophoresis for DiGE Labelled Samples Following the root and leaf protein sample DiGE fluorescent labelling procedure, 1D SDS- PAGE was performed as described in Section 2.6.1 to check the loading quality and confirm successful labelling of the proteins. Approximately 2.5 µg of labelled sample was electrophoresed. After electrophoresis, the gels were fixed in solution 40% (v/v) methanol, 10% (v/v) glacial acetic acid for 30 min in the dark with gentle shaking. The gels were then imaged using the Typhoon 9400 Variable Mode Imager (Amersham Biosciences). 2.6.2.3 2D Gel Electrophoresis for DiGE Labelled Samples 2.6.2.3.1 Rehydration of 7 cm Immobilised pH Gradient (IPG) Strips Protein extracts of 15 µg were mixed with 0.8% (w/v) DTT, 0.2% (v/v) ampholytes (BIO- RAD), a tint of bromophenol blue and topped up to 125 µL with extraction buffer. The samples were pulse vortexed and centrifuged before being placed in different wells of an Immobiline™ Dry Strip Re-swelling Tray (GE Healthcare, Amersham, UK). Linear, 7 cm ReadyStrip™ IPG strips of pH range 4-7 (GE Healthcare) were placed on top of the sample, gel side down without trapping air bubbles. The strips were then completely covered with paraffin oil to prevent the evaporation of the sample and left to passively re-hydrate overnight at room temperature. 2.6.2.3.2 Isoelectric Focusing (IEF) of IPG Strips For the first dimension IEF, the rehydrated IPG strips were removed from the re-swelling tray, briefly rinsed with distilled water and blotted dry on moist filter paper. The strips were then placed on the focusing platform of the Ettan™ IPGPhor 3 (GE Healthcare) and damp 38 electrode wicks were placed on each end of the gel to collect salts from the samples during the focusing process. The IPG strips were then covered with paraffin oil to avoid sample evaporation during the IEF run. Isoelectric focusing was performed in three stages for the IPG strips as shown in Table 2.2. After IEF, the IPG strips were equilibrated prior to the second dimension gel electrophoresis. Table 2.2: Isoelectric focusing programme for 7 cm IPG strips. Step Volts Duration volt hrs (Vhrs) 1 200 10 2 3500 2800 3 3500 3700 2.6.2.3.3 Equilibration of IPG Strips After IEF, the IPG strips were equilibrated in 2.5 mL of 2% (w/v) DTT containing equilibration buffer [6 M urea, 2% (w/v) SDS, 50 mM Tris/HCl, pH 8.8, 20% (v/v) glycerol], followed by 2.5% (w/v) iodoacetamide containing equilibration buffer for 15 min each with gentle shaking. The strips were then ready for the second dimension SDS-PAGE. 2.6.2.3.4 Second Dimension SDS-PAGE The mini format 2D SDS-PAGE gels were prepared using 12% (v/v) resolving gel as described in Appendix, Table A2. Dimensions of 10.1 cm (width) × 8.3 cm (height) spacer glass plates (BIO-RAD) mounted with 1 mm thick spacers were used. Six microliters of unstained SDS7 protein marker was spotted on separate small square pieces of filter paper and air-dried. The equilibrated 7 cm IPG strips were rinsed with MilliQ-water and placed on 39 top of mini format 12% (v/v) SDS-PAGE resolving gels avoiding trapping bubbles in the process. The dry filter paper with the unstained SDS7 protein marker was placed on the anodic side of each IPG strip. The IPG strips were then overlaid with 1 mL of 0.5% (w/v) molten agarose sealing solution prepared in electrode running buffer containing a tint of bromophenol blue. Electrophoresis was then performed using the Mini-PROTEAN® system (BIO-RAD) using a Power Pac™ basic (BIO-RAD) as described in Section 2.6.1. After electrophoresis, the gels were scanned and imaged using the Typhoon 9400 Variable Mode Imager (Amersham Biosciences). 2.7 iTRAQ Analysis 2.7.1 Sample Preparation for iTRAQ Analysis The sorghum re-solubilised root TSP (Section 2.6.2) were prepared for iTRAQ analysis as follows. MilliQ-water was added to 50 µg of protein extracts to make a total volume of 100 µL. This was followed by the addition of 5 µL of 1.5 M Tris HCl pH 8.8 and 400 µL of 100% acetone and pulse vortexing. The protein samples were left to stand at room temperature for 2 hrs and centrifuged at 15 000 × g for 10 min. The resulting pellet was washed twice with 500 µL 80% (v/v) acetone. Five microliters of 2% (w/v) SDS was added and the samples were incubated at 60°C on a heat block for 1 hr. Samples were labelled using 8-plex iTRAQ kit (AB Sciex) as follows. A volume of 95 µL of dissolution buffer (500 mM Triethylammonium bicarbonate) was added and the samples were incubated for 20 min on a shaker. The protein samples were reduced by adding 2 µL of reducing agent [1mM Tris(2-carboxyethyl)phosphine)] and incubating the samples at 60°C on a heat block for 1 hr. After reduction, 1 µL of blocking agent (methyl methanethiosulfonate) was added to alkylate the proteins and the samples were incubated at room temperature for 10 min. Thereafter, the proteins were digested by adding 10 µL of 0.5 40 µg/µL of trypsin (PROMEGA, Madison, USA). The proteins were incubated at 37°C overnight and vacuum freeze dried before sample labelling with an 8-plex iTRAQ reagent kit (AB Sciex) according to manufacturer’s instructions. 2.7.2 Sample Labelling and iTRAQ Analysis The protein samples were re-suspended in 50 µL of sterile MilliQ-water before the addition of the label and isopropanol. The control samples were labelled with tags of molecular weights 113, 114, 115 and 116 whilst the drought stressed treatment samples were labelled with 117, 118, 119 and 121 molecular weight tags. The pH of the labelled samples was adjusted to 7.5 by adding dissolution solution and the pH range was checked using pH Test Strips 4.5-10.0 (SIGMA). All the eight samples of control and drought stressed samples of each sorghum variety were separately combined into one tube to make one composite sample, redistributed back in equal amounts into eight portions and vacuum-dried. Samples were cleaned-up using HILIC SPE cartridges (PolyLC Inc.), containing 300 mg of 12 µm polyhydroxyethyl-A, to remove unincorporated label and buffer salts. The cartridges were equilibrated by sequential addition of 4 × 3 mL releasing solution (5% ACN, 30 mM ammonium formate pH 3.0) followed by 4 × 3 mL binding solution (85% ACN, 30 mM ammonium formate pH 3.0). The dried iTRAQ-labelled peptide residue was dissolved in 75 µL of 3% acetonitrile (ACN), 0.1% formic acid (FA) followed by 150 µL of 0.3 M ammonium formate, pH 3. The pH of the mixture was checked and adjusted to 3.0 using trifluoroacetic acid (TFA). After clarifying by centrifugation (10,000 × g, 10 mins), the samples were mixed with 1275 µL ACN. The resulting 1.5 mL sample was added to the SPE cartridge and the flow-through retained and passed through a second time. The column was then washed twice with 2 mL binding solution. Finally, the peptides were eluted with 2 × 1 41 mL releasing solution. The eluate was freeze-dried and re-suspended in 3% ACN, 0.1% formic acid for liquid chromatography-mass spectrometry (LC-MS). After freeze-drying, and subsequent resuspension, the samples were separated into 56 fractions on the Poly-LC strong cation exchange column (200 × 2.1 mm) at 200 µL/min on an Ettan LC (GE Healthcare) high pressure liquid chromatography system. A biphasic gradient of 0-150 mM KCI over 11.25 column volumes and 150-500 mM KCI in buffer A over 3.25 column volumes was used for performing peptide separation. Over this gradient, 56 fractions were collected and reduced to a total of 30 fractions by pooling those with low peptide concentration. The 30 fractions were dried and re-suspended in 90 µL of 2% acetonitrile/0.1% formic acid of which 20 µL of each fraction was analysed by LC-MS/MS using a nano-flow Ettan MDLC system (GE Healthcare) attached to a hybrid quadrapole- TOF mass spectrometer (QStar Pulsar i, Applied Biosystems, Foster City) coupled to a nanospray source (Protana) and a PicoTip silica emitter (New Objective, Woburn, MA). Samples were loaded and washed on a Zorbax 300SB-C18, 5 mm, 5 × 0.3 mm trap column (Agilent, Stockport, UK) and online chromatographic separation performed over 2 hrs on a Zorbax 300SB-C18 capillary column (3.5 × 75 µm) with a linear gradient of 0 – 40% acetonitrile, 0.1% formic acid at a flow rate of 200 nl/min. Applied Biosystems Analyst software version 1.1 was used acquire all MS and MS/MS data switching between the survey scan (1 × 1 s MS) and three product ion scans (3 × 3 s MS/MS) every 10 sec. Ions in the range of 2+ to 4+ charge state and with TIC ˃ 10 counts selected for fragmentation. 42 2.7.3 Mass Spectra Data Analysis Data analyses for mass spectrometry were performed as previously described (Smith et al., 2015) with minor modifications. Briefly, ProteinPilot™ 5.0.1 software version 4895 incorporating the Paragon™ Algorithm 5.0.1.0.4874 (AB Sciex) was used for data analysis against a TrEMBL database (downloaded October 2013) sequences of Sorghum bicolor only. An iTRAQ 8-plex (peptide-labelled). For each identified peptide, a minimum threshold of 1.3 at 95% confidence interval was set whilst a minimum protein score threshold of 2.0 at 99% confidence interval was set for protein identification. Proteins identified on the basis of a single peptide were removed from the dataset. For quantitative analysis, the abundance of each protein per sample was obtained as a ratio to the 113-tagged sample. Across the four biological replicates, the averages of the ratios for each protein in control and drought stressed samples were calculated. The fold-change in protein expression was denoted by the ratio of control average to drought stressed sample average. A negative sign denoted down- regulation for the drought stress responsive proteins and the drought stress sample averages were the numerators with the controls as the denominators. The Student’s t-test was used for computing the probability values for the comparison of control averages and drought stress sample averages. 2.7.4 Bioinformatic analysis The mass spectrometry identified proteins were functionally annotated using the UniProt database (http://www.uniprot.org). The database was used to search for the Gene Ontology (GO) analysis using three key terms, Biological Process, Biological Function and Cellular Component. The conserved domains and family names of the identified proteins were determined using the Interpro database (http://www.ebi.ac.uk/interpro/). The Arabidopsis homologues of the sorghum genes were obtained from the Arabidopsis Information Resource 43 (TAIR) (www.arabidopsis.org/Blast) using the sorghum protein sequence to query the Arabidopsis protein sequences. 2.8 Gene Expression Analysis 2.8.1 Total RNA Extraction and Analysis Total RNA was extracted from sorghum root and leaf samples and sorghum and Arabidopsis cell suspension cultures using the Spectrum™ Plant Total RNA Kit (SIGMA) according to the manufacturer’s instructions. Briefly, 500 µL of Lysis solution was added to 100 mg of ground sample and immediately vortexed vigorously for at least 30 sec. The sample was incubated at 56°C for 3-5 min and centrifuged at 15 000 × g for 3 min to pellet cellular debri. The lysate supernatant was collected by filtering through a filtration column. The RNA was bound by adding 500 µL of Binding Solution to the lysate supernatant, gently mixing by pipetting and briefly vortexing. The mixture was passed through a binding column and centrifuged at 15 000 × g. The bound RNA was washed with 300 µL of Wash Solution 1 to remove impurities and the DNA in- column digestion was performed as follows. A mixture of DNase 1 (10 µL) and DNase digestion buffer (70 µL) was placed directly onto the binding column and incubated for 15 min at room temperature. Thereafter, Wash Solution 2 was passed through the column twice by centrifugation at 15 000 × g to remove the digested DNA. This was followed by centrifugation at 15 000 × g for 1 min to dry the column and the addition of 50 µL of the Elution Solution directly onto the centre of the binding matrix, centrifuging at 15 000 × g for 1 min and collecting the purified total RNA. The RNA was quantified using a Nano Drop® ND 1000 Spectrophotometer (NanoDrop Technologies, Inc., Wilmington, USA). 44 2.8.2 Agarose Gel Electrophoresis of RNA A total of 300 ng of RNA was electrophoresed on 1.2% (w/v) agarose gels in 3-(N- Morpholino)-propanesulfonic acid (MOPS) buffer [20 mM MOPS pH 7.0, 2 mM sodium acetate pH 7.0, 1 mM (Ethylenedinitrilo)-tetraacetic acid (EDTA) pH 8.0]. The RNA samples were prepared by adding 5 µL of RNA loading buffer [63.7% (v/v) formamide, 1.14 M formaldehyde, 6.4% (v/v) MOPS buffer, 50 µg/mL ethidium bromide] to 300 ng of RNA and incubating at 65°C for 10 min on a heat block. The mixture was then pulse centrifuged and loaded on the agarose gel and run at 50V for 20 min in MOPS buffer. The gel image was taken using the Ingenius Bio-Imager (SynGene, Cambridge, UK). 2.8.3 Complementary Deoxy-ribonucleic Acid (cDNA) Synthesis First strand cDNA synthesis was performed using the GoScript™ Reverse Transcriptase System (PROMEGA) according to the manufacturer’s instructions. Briefly, 2 µg of total RNA, 1 µL of Oligo(dt) and nuclease free water were mixed to a total volume of 11.8 µL and incubated on a heat block at 70°C for 5 min. The mixture was chilled on ice water for at least 5 min, centrifuged at 15 000 × g for 10 sec and stored on ice whilst the reverse transcriptase mixture was being prepared. A total volume of 8.2 µL of the reverse transcriptase mixture was made by combining 1 µL of GoScript™ 5X Reaction buffer, 1.8 µL of 1.5-5.0 mM MgCl2, 1 µL of 0.5 M dNTPs, 0.4 µL of Recombinant RNasin® Ribonuclease inhibitor and 1 µL of GoScript™ Reverse Transcriptase. The reverse transcription mixture was added to 11.8 µL of the RNA reaction mixture, annealed at 25°C for 5 min, extended at 42°C for 1 hr and denatured at 70°C for 15 min in a thermal cycler. The cDNA was diluted in the ratio 1:7 with sterile MilliQ-water and stored at -20°C prior to polymerase chain reaction (PCR) analysis. 45 2.8.4 Primer Designing The primers of target genes were designed on the National Centre for Biotechnology Information (NCBI) database using the Primer-BLAST software (Ye et al., 2012) with the following specifications: the primers were selected from the Sorghum bicolor (taxid:4558) database, GC clamp 1, maximum primer size 150 bp, where the primer must span an exon- exon junction. All the genes tested in this study, as well as their specific primers used are shown in Tables 2.3 and 2.4. Two sets of genes were used in this study. The first set consisted of 10 genes selected from drought stress responsive gene identities (Table 2.3) in the iTRAQ experiment and two reference genes. The second set consisted of three sorghum genes selected from a sorbitol-induced osmotic stress experiment (Ngara et al., 2018), their Arabidopsis homologues and two reference genes (Table 2.4). The Arabidopsis genes were selected by blasting the three sorghum genes from the sorbitol-induced osmotic stress experiment on the Arabidopsis Information Resource (TAIR) database and selecting four homologues with the highest sequence identities per sorghum gene identity. 46 Table 2.3: List of sorghum target genes and primer sequences used in qRT-PCR analysis. Target Gene Identity Gene name Primer Name Forward Primer (5'>3') Reverse Primer(5'>3') SORBI_3009G190800 Thioredoxin Thioredoxin TCAGCTTCTCAAGCGGTAGC CTGTTGGCCTCCTCGATCTG SORBI_3007G172100 Peptidase Peptidase TCCTACCCCGTCAAGACCTC ATGCGATTCAGAGCTCGTCG SORBI_3002G302000 Xyloglucanase Xyloglucanase GCGTATCGCCAACAAAGTGG ATGAATCATGGACGCTTGCC SORBI_3008G134700 Aspartic peptidase SORBI_3008G134700 CCCATCCACTGGAACGAGAC ACTGCGTCCAGATGAGGTTG SORBI_3001G514200 Thioredoxin SORBI_3001G514200 GGAACATCCTGGCGCATTTG AGAGCCAGACCGACACAAAC SORBI_3002G217200 Peptidase SORBI_3002G217200 GCCCAGAAATGGCTAGTGTTC AATTCTGGCTCCGCCACTAC SORBI_3003G136200 Germin SORBI_3003G136200 TGCCACGCACAGAATACGAG TACACATGCCGGAGAATCGG SORBI_3006G135500 Galactose oxidase SORBI_3006G135500 AGGGAAATGGTTGTACGGGC GTGCTGGTGCACAAAATATATAGC SORBI_3007G149600 Histone H2B SORBI_3007G149600 TGCTCTCGTGAGATCGCTTG GCAATACCACTGCGTATCTGTTG SORBI_3001G313200 Histone H4 SORBI_3001G313200 GGATCCCTGGATCTTCTGGAG TTTGGTGGCTCTGTGGTCAG SORBI_3001G073900 Malate dehydrogenase SORBI_3001G073900 CTTCTTCAACACACGCGACC GTGTGGTGACGGATGGTAGG SORBI_3003G322400 Ribosomal protein S25 SORBI_3003G322400 AACGAGTATATGCATCCCCG AGCAGGCTCTAGGGTTTTGAG SORBI_3002G049300 Ribosomal protein S25 SORBI_3002G049300 GGTCAAAACAGCCCGTTCAG TAGTTGGGAGGGTGATGGGG *Ref. Sb910 Sb03g038910 Uncharacterised protein TCCTGAAGCATCTTTCCCTCC ACAGCCTGATTAGTTGGGGG *Ref. EIF4A Sb04g003390 Eukaryotic initiation factor-4A GATGAGATGCTCTCCCGTGG TGATCTCTAGGGCCTCTGGG *Reference genes 47 Table 2.4: List of Arabidopsis target genes and primer sequences used in qRT-PCR analysis. Target Gene Identity Gene name Primer Name Forward Primer (5'>3') Reverse Primer(5'>3') AT5G42980.1 Thioredoxin TRX3 ACCTTGCCGTTTCATTGCAC TCAGCAACAGTGTTCAATTCGTC AT1G45145.1 Thioredoxin TRX5 GATTGCTTGCCATACCCTCG AAGTCTATCACAATCAGTTTCTTGG AT3G51030.1 Thioredoxin TRX1 ATTGAAGTCGGTGGCAAGTG AGCCAAGTGTTTGGCAATGG AT1G19730.1 Thioredoxin ATTRX4 TGTTGATGAACTTCAGAGTGTTGC GCTTATCCACAACTTCGCCG AT5G50260.1 Peptidase CEP1 TGCTCCGGTAGTTTCAATCG CCGGTAAACACTCCCTCGG AT3G48340.1 Peptidase CEP2 TGGGCATGAAGATGTACCCG TCCCGTAAACACTCCCTCTG AT4G36880.1 Peptidase XCP1 AATTTCGACCGGTGGTCTCC CGCTGATTGTCACACGTTCC AT1G20850.1 Peptidase XCP2 TTACAGGGACGTCGAAGCTG CGCCCAACAACTTCCACAAG AT2G36870.1 Xyloglucanase XTH32 AGCCCTAGAGAAATCATATTTTTGG TTACCGTCTTCCGTTGCCC AT3G44990.1 Xyloglucanase XTH31 TACCCGAGTTCCCGAGTACC CCCACTCCCAGTGGATTTGTC AT1G14720.1 Xyloglucanase XTH28 CGCCTTTTATTTATCAAATGGGGAC TCTGCCCAAGTGTGTGCTTC AT1G10550.1 Xyloglucanase XTH33 CGACAAATCCTCCGGAGCTG AATCCAGCGGGAAGCTTGAG *AT3G13920 Eukaryotic Initiation Factor 4A eIF4A ATGAGAGGATGCTCTGCCTTCG GCAGAGCAAACACAGCAACAG *AT3G18780 Actin 2 Actin 2 GTGGTCGTACAACCGGTATTG TCACGTCCAGCAAGGTCAAG *Reference genes 48 2.8.5 Polymerase Chain Reaction for Primer Testing PCR analysis was used to test primer specificity. A reaction mix was prepared with 25mM MgCl2 , 10mM dNTPs, 10 µM forward primer, 10 µM reverse primer, 1:7 dilution cDNA and 5 u/µL Taq polymerase topped up to 25 µL with MilliQ-water. The mixtures were transferred to the thermal block of a PCR machine and the program shown on Table 2.5 was run. Table 2.5: Thermal cycling conditions for PCR. Step Temperature (°C) Time (sec) Cycles Initial denaturation 95 60 1 Denaturation 94 30 Annealing 56 60 40 Elongation 72 60 2.8.6 Agarose Gel electrophoresis of PCR products The PCR products were resolved on a 3.5% (w/v) agarose gel. The gels were prepared adding in tris acetate-(Ethylenedinitrilo)-tetraacetic acid (TAE) buffer (40 mM Tris acetate, 1 mM EDTA, 20 mM glacial acetic acid) with 0.5 µg/mL of ethidium bromide. The gels were cast and left to solidify before loading the samples and a HyperLadder™ 25 bp DNA marker (BIOLINE). After loading the samples, the gel was run at 50V for 10-20 min in 1X TAE running buffer. Following electrophoresis, the gel image was taken using the Ingenius Bio- Imager (SynGene). 49 2.8.7 Quantitative Real Time-Polymerase Chain Reaction (qRT-PCR) analysis The qRT-PCR reactions were performed using the SensiFAST™ SYBR No-ROX Kit (BIOLINE). Briefly, 5 µL of the 8-fold dilution cDNA was added to PCR tubes in triplicate. A master mix containing 10 µL SensiFAST reagent, 0.3 nM of each of the forward primer and reverse primers and 3.4 µL nuclease free water was prepared for each cDNA sample. A volume of 15 µL of the master mix was added to each 5 µL of the 8-fold dilution cDNA. The qRT-PCR was performed on a Corbett Rotor-Gene 6000 (QIAGEN, Cambridge, UK) using the thermal cycling conditions shown in Table 2.6. Data analysis was done using the Relative Expression Software Tool 2009 (REST2009) version 2.0.13 (QIAGEN) using SbEIF4A and Sb910 as the constitutive reference control gene for sorghum samples and Actin and EIF4A for Arabidopsis. For the sorghum gene expression analyses, the relative fold changes between the respective varieties were determined and displayed graphically. For the Arabidopsis gene expression analyses, the cell suspension culture samples under osmotic stress were compared to control samples maintained under optimum conditions. The REST2009 is based on the Pfaffl equation with the formula: Gene expression ratio = (E )∆Ct goi /(E )∆Ct rggoi rg Where Egoi = the real-time PCR efficiency of target gene transcript, Erg = the real-time PCR efficiency of a referene gene and Ct = cycle threshold. The results obtained from the Rotor-Gene comparative quantitation analysis were used rather than standard curves and Ct results. The software takes into account the different PCR efficiencies of the gene of interest and reference genes. In this study, two reference genes were used for normalisation in order to improve the reliability of results. For efficiency, 50 REST2009 uses amplification which has a value between 1 and 2. In addition, the randomisation algorithm uses take-off rather than the Ct values. The method used utilises relative gene expression rather than probes for which R-squared values are part of the output. The genes were analysed by copying the take off and amplification control values of the reference gene and pasting it in the reference gene section of the REST2009 RG mode. This was followed by entering the corresponding treatment values of the reference gene in the treatment rows of the software. The control sample gene of interest values were then added to the control rows whilst the treatment values were entered in the corresponding treatment rows. Two reference genes were used per analysis in this study. In cases where the relative gene expression of two varieties was required, one variety was used as a baseline control to standardise the gene expression. The output gene expression values were then exported to Microsoft Excel and the Student’s t-test was used to compare the gene expression means at a 5% level of significance. Table 2.6: Thermal cycling conditions for qRT-PCR. Step Temperature (°C) Time (sec) Cycles Initial denaturation 95 90 1 Denaturation 95 10 Annealing 56 15 45 Elongation 72 25 51 2.9 Statistical Analyses For statistical analysis, a Mann-Whitney test was used to compare the means for physiological and biochemical results at a 5% level of significance, using the GraphPad Prism 5.00 software. The REST2009 software version 2.0.13 was used for gene expression analysis. The Student’s t-test was used to compare the gene expression means at a 5% level of significance, using Microsoft Excel. 52 CHAPTER 3 COMPARATIVE PHYSIOLOGICAL AND BIOCHEMICAL RESPONSES OF SORGHUM TO DROUGHT STRESS 3.1 Introduction When exposed to drought stress, synthesis of the phytohormone abscisic acid (ABA) increases, which triggers stomatal closure and numerous physiological responses. Stomatal closure in periods of drought stress reduces water loss through the leaves. However, stomatal closure also reduces the assimilation of CO2 resulting in low rates of photosynthesis (Chaves et al., 2009). Plants also respond to drought stress by synthesising osmolytes such as proline, trehalose and glycine betaine (Valadez-Bustos et al., 2016). Osmolytes are low molecular weight metabolites synthesised as an inherent mechanism of osmotic adjustment in stressed plants (Hamilton and Heckathorn, 2001). Under drought stress, the accumulation of osmolytes maintains turgidity in leaf cells, minimising interruption of cellular metabolism whilst sustaining growth (Blum, 2005; Amrhein et al., 2013). Proline is also responsible for scavenging reactive oxygen species (ROS) in plants (Smirnoff and Cumbes, 1989). Proline content increases to a higher degree in drought tolerant plants compared to susceptible ones under the same drought stress conditions (Palfi and Juhász, 1971). In a previous study, proline content was shown to increase significantly in drought resistant sorghum (Sorghum bicolor. L) crops grown in the semi-arid tropics whilst the susceptible varieties showed no significant increase (Sivaramakrishnan et al., 1988). Five days after re- watering, the proline content levels were restored to that of the well-watered controls. Waldren and Teare (1974), observed an increase in proline content under only extreme drought stress in sorghum and soybean (Glycine max) in comparison to mild stress. In a 53 sorghum callus culture experiment, the proline levels of both drought tolerant and susceptible sorghum samples significantly increased (Bhaskaran et al., 1985). However, the magnitude of increase was not correlated to the tolerance or susceptibility of the sorghum varieties in that callus study. Sorghum is one of the naturally drought tolerant grain crops with a wide genetic diversity. As such, the crop is an invaluable model system for abiotic stress studies. A comparative evaluation of various parameters from such a crop may offer a better understanding of the mechanisms of plant drought stress responses. This chapter therefore aims to comparatively evaluate the physiological and biochemical differences between two sorghum varieties with contrasting phenotypes to drought stress, namely, ICSB 338 (drought susceptible) and SA 1441 (drought tolerant) under drought stress and following re-watering. 3.2.1 Field Capacity and Seed Germination Experiment The field capacity (FC) of the potting mix soil was calculated using the formula shown in Section 2.2.1. The calculated FC value of 175.5 mL was utilized to devise an irrigation regime for maintaining the soil at field capacity before the drought stress treatment. For the germination experiment, five sorghum varieties (SA 1441, ICSV 210, ICSB 338, Ns 5511, Cap 1003, Table 2.1) were sown and germinated on potting soil mix in 10 cm diameter plastic pots at field capacity for the duration of the experiment. The germination rates of the sorghum varieties are shown in Figure 3.1. The varieties ICSB 338, Ns5511 and Cap 1003 had germination percentages of greater than 95% whilst ICSV 210 had the lowest value of 66%. The varieties ICSB 338 and SA 1441 were selected for further experiments on the basis of both germination percentage and contrasting phenotypic traits to drought stress (Table 2.1). 54 a a a 100 a 80 b 60 40 20 0 41 10 38 1 3 14 2 3 55 1 00 1 A V B S S S s P IC IC N AC Sorghum variety Figure 3.1: Germination rates of different sorghum varieties. The sorghum seeds were sown and grown on potting mix soil and germination counts were taken 6 days after sowing. Bars represent mean ±SE (n = 5). Bars with the same letter are not significantly different at p ≤ 0.05 according to the Mann-Whitney test. 3.2.2 Effects of Drought Stress on Growth Parameters The ICSB 338 and SA 1441 sorghum plants were drought stressed by withholding water at the V3 growth stage for 8 days. The plants were then re-watered to field capacity for 24 hrs. The plants began wilting and leaf rolling at day 4 of drought stress. The SA 1441 plants showed signs of wilting first compared to ICSB 338 (Figure 3.2A). However, ICSB 338 showed signs of chlorosis at day 6 of drought stress whilst for SA 1441 this symptom was observed at day 7. At day 8, the ICSB plants were highly chlorotic and smaller in overall size compared to SA 1441. SA 1441 however, showed more intense wilting at day 8 of drought 55 Germination (%) stress. Both sorghum varieties recovered to the same physiological status as the respective control plants 24 hrs after re-watering (Figure 3.2B). 56 Figure 3.2: The effects of drought on sorghum plant morphology. Picture A shows control and drought stressed and B shows control and re-watered ICSB 338 (drought susceptible) and SA 1441 (drought tolerant) sorghum varieties. Ten plants per pot were sown. The sorghum plants were watered until the V3 growth stage. Water was withheld for 8 days to induce drought stress. The plants were re-watered for 24 hrs. 57 The effects of drought stress on sorghum root and shoot length as well as 24 hrs after re- watering were evaluated in both ICSB 338 and SA 1441 sorghum varieties (Figure 3.3). Generally, the root length increased significantly in both sorghum varieties following drought stress and remained constant after re-watering (Figure 3.3A). In contrast, the shoot length decreased for the ICSB 338 drought stressed samples in comparison to the well-watered controls, and remained constant after re-watering as illustrated on Figure 3.3B. For SA 1441, there was no significant difference in shoot length between the controls, drought stressed and re-watered samples. Both ICSB 338 and SA 1441 showed significantly reduced root and shoot fresh and dry weight for both the drought-stressed and re-watered samples compared to their respective controls as shown in Figure 3.4. The only exception was SA 1441 root fresh weight which showed no significant reduction in weight after drought stress (Figure 3.4A). 58 A B 30 50 Drought stress control c c Drought stress 40 c c c c Re-watered control b b 20 b b Re-watered a a a a 30 b b 20 10 10 0 0 ICSB 338 SA 1441 ICSB 338 SA 1441 Figure 3.3: Effects of drought on sorghum shoot and root growth. Root (A) and shoot (B) length for the control, drought stressed and re-watered ICSB 338 (drought susceptible) and SA 1441 (drought tolerant) sorghum varieties. Plants were watered until the V3 growth stage. Water was withheld to induce drought stress and root and shoot length was measured for the control and drought stressed samples on the 8th day of drought stress. The plants were re-watered and the same measurements were repeated after 24 hrs. Bars represent mean ±SE (n = 5). Bars with the same letter are not significant different at p ≤ 0.05 according to the Mann-Whitney test. 59 Root length (cm) Shoot length (cm) A B 0.05 c 0.015 c Drought stress controlcd 0.04 d Drought stressed c 0.010 ac Re-watered control 0.03 a a Re-watered bd d a a 0.02 b b b b 0.005 0.01 0.00 0.000 ICSB 338 SA 1441 ICSB 338 SA 1441 C D 0.6 a a a 0.10 a c c d 0.08 0.4 c a ac b 0.06 d d 0.04 b b 0.2 0.02 0.0 0.00 ICSB 338 SA 1441 ICSB 338 SA 1441 Figure 3.4: The effects of drought on sorghum root and shoot weight. Root fresh weight (A), root dry weight (B), shoot fresh weight (C) and shoot dry weight (D) for the control, drought stressed and re-watered ICSB 338 (drought susceptible) and SA 1441 (drought tolerant) sorghum varieties. Plants were well-watered until the V3 growth stage. Water was withheld and fresh weight readings for the control and drought stressed samples were taken on the 8th day of drought stress. The samples were then oven-dried to determine the dry weight. The plants were re-watered and readings were repeated after 24 hrs. Bars represent mean ±SE (n = 5). Bars with the same letter are not significantly different at p ≤ 0.05 according to the Mann-Whitney test. 60 Shoot fresh weight (g) Root fresh weight (g) Shoot dry weight (g) Root dry weight (g) 3.2.3 Leaf Relative Water Content The 8 day drought stress treatment negatively affected the leaf relative water content (RWC) of both ICSB 338 and SA 1441 sorghum varieties (Figure 3.5). This was shown by the statistically significant reduction in RWC from 100% to 61% and 90% for ICSB 338 and SA 1441 drought-stressed samples, respectively. However, 24 hrs after re-watering the plants, the initial RWC for both sorghum varieties was restored to the levels of the controls. 100 a a aa ac a Drought stress control 80 Drought stressed b Re-watered control 60 Re-watered 40 20 0 ICSB 338 SA 1441 Figure 3.5: The effects of drought stress on RWC of sorghum. The RWC for the control, drought stressed and re-watered leaf samples of ICSB 338 (drought susceptible) and SA 1441 (drought tolerant) sorghum varieties. Seedlings were well-watered until the V3 growth stage. Water was withheld to induce drought stress and the RWC was estimated for the control and drought-stressed samples on the 8th day of drought stress. The seedlings were re-watered and the measurements were repeated after 24 hrs. Bars represent mean ±SE (n = 5). Bars with the same letter are not significantly different at p ≤ 0.05 according to the Mann-Whitney test. 61 Relative water content (%) 3.2.4 Leaf Stomatal Conductance and Surface Temperature The stomatal conductance remained constant until day 3 of drought stress for both sorghum varieties (Figure 3.6A). Thereafter, the drought-stressed samples experienced a significant reduction in stomatal conductance for both varieties. An increase in stomatal conductance was exhibited by only SA 1441, 24 hrs after re-watering. There was no significant difference between the control and drought-stressed leaf surface temperature of ICSB 338 for the first 4 days of drought stress. For SA 1441, the increase in leaf surface temperature began at day 3 of drought stress (Figure 3.6B). The leaf surface temperature then gradually increased for both varieties until day 8 of drought stress. The rate of temperature increase was however greater for SA 1441 compared to ICSB 338. After re- watering, the leaf surface temperatures for both sorghum varieties significantly dropped to the level of the control plants. 62 A B 40 28 ICSB 338 control ICSB 338 drought stress 30 26 SA 1441 control SA 1441 drought stress 20 24 10 22 re-water re-water 0 20 0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 Time (Days) Time (Days) Figure 3.6: The effects of drought on sorghum leaf stomatal conductance and surface temperature. Leaf stomatal conductance (A) and leaf surface temperature (B) for the control, drought-stressed and re-watered third leaf of ICSB 338 (drought susceptible) and SA 1441 (drought tolerant) sorghum varieties. Both parameters were taken simultaneously using a portable leaf porometer. Plants were well watered until the V3 growth stage. Water was withheld to induce drought stress and readings for the control and drought stressed samples were taken on the 8th day of drought stress. The plants were re- watered and the same measurements were repeated after 24 hrs. Bars represent ± SE (n = 10). Bars with the same letter are not significantly different at p ≤ 0.05 according to the Mann-Whitney test. 63 Leaf stomatal conductance (mmol/m2s) Leaf surface temperature (oC) 3.2.5 Leaf Chlorophyll Content The chlorophyll content indices for the drought-stressed ICSB 338 and SA 1441 sorghum varieties showed no significant differences with their respective controls for the first 3 days of drought stress treatment (Figure 3.7). Decreases in chlorophyll content were observed for both sorghum varieties from day 3 after the induction of drought stress. The chlorophyll content of ICSB 338 decreased at a faster rate compared to SA 1441. Twenty four hrs after re-watering, the chlorophyll content of SA 1441 increased significantly while that of ICSB 338 remained constant. 5 ICSB 338 control 4 ICSB 338 drought stress SA 1441 control 3 SA 1441 drought stress 2 1 re-water 0 0 1 2 3 4 5 6 7 8 9 Time (Days) Figure 3.7: The effects of drought on sorghum leaf chlorophyll content. Leaf chlorophyll content index for the control, drought-stressed and re-watered third leaf of ICSB 338 (drought susceptible) and SA 1441 (drought tolerant) sorghum varieties. The readings were taken using chlorophyll content meter. Plants were watered until the V3 growth stage. Water was withheld to induce drought stress and readings for the control and drought stressed samples were taken on the 8th day of drought stress. The plants were re-watered and readings were repeated after 24 hrs. Bars represent ± SE (n = 10). 64 Chlorophyll content index (CCI) 3.2.6 Root and Leaf Relative Cell Death The root and leaf relative cell death was estimated using the Evans blue assay. The drought susceptible variety ICSB 338 exhibited higher root and leaf relative cell death under drought stress compared to the drought tolerant SA 1441 as shown in Figure 3.8. 100 80 a 60 b b 40 c 20 0 ol l 8 tr 33 8 41 ro 3 41t 3 on 4 B 1 on 4 SB 1 C S SA C C SAIC I Root tissue Leaf tissue Figure 3.8: The effects of drought on root and leaf cell death. Cell death percentage for the root and leaf tissue of the control and drought stressed ICSB 338 (drought susceptible) and SA 1441 (drought tolerant) sorghum samples. Plants were well-watered until the V3 growth stage. Water was withheld for 8 days to induce drought stress and the Evans blue assay was carried out using 50 mg for each of the samples. Bars represent mean ± SE (n = 3). Bars with the same letter are not significantly different at p ≤ 0.05 according to the Mann-Whitney test. 65 Relative cell death (%) 3.2.7 Osmolyte Content Analysis 3.2.7.1 Proline and Glycine Betaine (GB) Content Analysis The proline and glycine betaine content analysis was performed using Hydrophilic Interaction Liquid Chromatography-Mass Spectrometry (HILIC-MS) at 0, 4, 8 and 12 days post drought stress. The root and leaf tissue proline content (Figures 3.9A and C, respectively) remained constant for both sorghum for the first 4 days of drought stress treatment. At day 8, there was a significant increase in proline content in SA 1441 for both root and leaf tissue whilst it remained low for ICSB 338. The root and leaf glycine betaine content (Figures 3.9B and D, respectively) showed a significant difference between ICSB 338 and SA 1441 at days 4 and 8 of drought stress for both root and leaf tissue. At day 8 of drought stress, SA 1441 exhibited significantly higher glycine betaine content for both root and leaf tissue (Figures 3.9B and D, respectively). 66 A B 15 1000 ** ICSB 338 800 SA 1441 10 ** ** 600 400 5 200 0 0 * 0 4 8 12 0 4 8 12 Time (Days) Time (Days) C D 1000 1000 800 800 ** 600 600 *** 400 400 * 200 200 0 0 0 4 8 12 0 4 8 12 Time (Days) Time (Days) Figure 3.9: Effects of drought stress on sorghum leaf proline and glycine betaine content. Sorghum root proline (A) and root glycine betaine (B) content and leaf proline (C) and leaf glycine betaine (D) content for ICSB 338 (drought susceptible) and SA 1441 (drought tolerant) sorghum varieties, respectively are shown above. Seedlings were well-watered until the V3 growth stage. Water was withheld to induce drought stress and samples were collected at 0, 4, 8 and 12 days of drought stress. HILIC-MS was used for osmolyte content analysis. Bars represent ± SE (n = 3). *, ** and *** = significance between ICSB 338 and SA 1441 at each time point at p ≤ 0.05, 0.01 and 0,001, respectively. 67 Leaf proline content Root proline content (mmol/mg fresh weight) (nmol/mg fresh weight) Leaf glycine betaine content Root glycine betaine content (mmol/mg fresh weight) (nmol/mg fresh weight) 3.3 Discussion The effects of drought stress occur at morphological, biochemical and molecular levels and may be evident at any phenological stage at which the stress is imposed (Farooq et al., 2009). Drought stress causes osmotic and oxidative stress which results in undesirable effects on plant growth. Cell division, elongation, enlargement and differentiation are the main processes necessary for growth and depend on water availability to effect growth. Any disruption of these vital plant processes can lead to stunted growth whilst severe water deprivation can result in plant death. In this study, two sorghum varieties described by plant breeders as drought susceptible and drought tolerant (Table 2.1) were used to comparatively analyse their physiological and biochemical responses to drought stress. These were the drought susceptible ICSB 338 and drought tolerant SA 1441. Currently, there is no published data confirming the drought response phenotype of these sorghum varieties. The two sorghum varieties were grown in a soil-potted experiment at field capacity and drought stress was imposed by withholding water for 8 days. Following drought stress, the plants were re-watered and measurements were repeated after 24 hrs. This allowed the evaluation of the abilities of the two varieties to recover from the imposed drought stress. The sorghum plants for osmolyte content analysis were harvested in a time course drought stress experiment at 0, 4, 8 and 12 days. The RWC of a leaf estimates the current water status of the sampled tissue relative to the maximal water content it can hold at full turgidity (Barrs and Weatherley, 1962). In essence, the RWC value reflects a measure of water balance or deficit in the leaf (González and González-Vilar, 2001). The leaf RWC decreased for both seed varieties following drought stress (Figure 3.5). Similar results were observed in both drought resistant and susceptible sorghum varieties grown in the semi-arid tropics (Sivaramakrishnan et al., 1988) as well as in 68 another study in wheat (Siddique et al., 2000). In this study, the leaf RWC was higher for the drought stressed SA 1441 compared to ICSB 338 (Figure 3.5). The RWC increased significantly for both varieties 24 hrs after re-watering which may signify the possibility of the ability of the sorghum varieties to restore leaf water status after a period of water stress before permanent cell damage occurs. One of the first responses of plants to osmotic stress is the production of the endogenous growth regulator ABA (Ackerson and Radin, 1983). ABA controls stomatal closure thus reducing transpirational water loss and its deleterious effects (Ludlow and Muchow, 1990). In this study, stomatal conductance (Figure 3.6A) and chlorophyll content (Figure 3.7) of both sorghum varieties decreased with an increase in drought stress over the 8 days of stress treatment. Significant differences between the two sorghum varieties and their respective controls were exhibited beginning at day 4 after withholding water. A corresponding increase in leaf surface temperature was observed beginning at day 3 (Figure 3.6B). The increase in leaf surface temperature can be attributed to stomatal closure which reduces gaseous exchange and ventilation thereby increasing the amount of heat available within the leaf that can be dissipated (Yokota et al., 2002). A similar increase in leaf surface temperature in response to drought stress was reported in wheat (Siddique et al., 2000). Stomatal closure possibly reduced the rate of photosynthesis since CO2 and O2 were hindered from diffusing in and out of the leaves, respectively. This was reflected by the low plant total biomass as estimated by root and shoot fresh and dry weights of the drought stressed plants compared to the well-watered controls (Figure 3.4). The chlorophyll content was also reduced with an increase in the duration of drought stress for both sorghum varieties (Figure 3.8). Similar results were reported in a sorghum drought stress experiment by Jagtap et al. (1998) in varieties with differing drought stress tolerances. 69 In this study, the drought tolerant variety SA 1441 had higher total chlorophyll content compared to the drought susceptible variety ICSB 338. There was significant recovery in both chlorophyll (Figure 3.7) and stomatal conductance (Figure 3.6A) of SA 1441 after re- watering. Beardsell and Cohen (1975) also observed the same trend in stomatal conductance in maize and sorghum after re-watering. Contrary to this result, ICSB 338 showed no significant change in the chlorophyll content and stomatal conductance 24 hrs after re- watering indicating the inability of the drought susceptible variety to increase the biosynthesis of photosynthetic pigments immediately after water supply to the plant is restored. This could have been because of the excessive oxidative damage of photosynthetic machinery due the accumulation of ROS in ICSB 338 leaf tissue during drought stress. This damage was reflected partly in higher relative cell death of ICSB 338 root and leaf tissues in comparison to SA 1441 (Figure 3.8). However, ROS levels in both root and leaf tissue would need to be measured to ascertain the level of oxidative stress in the two sorghum varieties. The plant root and shoot weight was significantly reduced for both sorghum varieties compared to their respective controls following drought stress (Figure 3.4). This could have been due to the limited production and translocation of photo-assimilates. This result is in contrast to the increased root weight in drought resistant sorghum varieties observed by Nour and Weibel, (1978). Stomatal closure could have also contributed to this phenomenon since it ultimately reduced the rate of photosynthesis. Furthermore, the increased relative cell death (Figure 3.8) due to drought stress could have also reduced the total surface area available for photosynthesis. In contrast to the observed reduced root weight (Figure 3.4A and B), root length increased significantly (Figure 3.3A) for both sorghum varieties in response to drought stress. This could have been due to the extension of roots by the sorghum plant in search of water. This 70 could possibly have been facilitated by ABA which maintains cell expansion in roots under dry conditions whilst halting that in shoots (Munns and Sharp, 1993). Nour and Weibel (1978), also observed increased root length in drought resistant sorghum varieties compared to the less drought resistant varieties whilst Merrill and Rawlins (1979), reported that sorghum plants direct resources to deeper soil penetration through higher root density in the deeper soil profile during drought stress. Furthermore, Xu and Bland (1993), demonstrated that sorghum can extract water from deep soil via an elongated root system and subsequently efflux the water to the dry upper soil layers before resuming uptake again. This adaptation ensures that the sorghum plants can access both moisture and mineral nutrients even in hot and dry conditions. Apart from the growth and physiological changes observed in ICSB 338 and SA 1441 sorghum varieties in response to drought stress, the proline and glycine betaine contents of the two varieties also differed. Proline is a compatible osmolyte that functions as an osmo- protectant involved in various important functions in osmotic balancing during stress (Di Martino et al., 2003). In this study, proline content was significantly higher for the drought tolerant variety SA 1441 compared to the drought susceptible variety ICSB 338 from day 8 of drought stress in both root and leaf tissue (Figures 3.9A and C). The same results were obtained in a comparative study using drought resistant and drought susceptible sorghum varieties (Sivaramakrishnan et al., 1988). The higher proline content reflects the ability of SA 1441 to synthesise compatible osmolytes required for osmotic adjustment and membrane protection earlier in the course of drought stress. This means that SA 1441 can better protect cellular components under drought stress compared to ICSB 338. Increases in proline content in response to drought stress were also observed in sorghum and soybean (Waldren and Teare, 1974) as well as wheat (Triticum vulgare), plantago (Plantago 71 ovata), papavar (Papaver somnifera) and mustard (Brassica juncea) (Patel and Vora, 1985). Proline is also known for its role in ROS scavenging (Hare and Cress, 1997). Reactive oxygen species are chemically reactive molecules or free radicals which contain oxygen (Hayyan et al., 2016). The ROS are synthesised in high quantities in response to drought stress and this is detrimental to plant growth since they degrade proteins and nucleic acids (Vranova et al., 2002). The quaternary ammonium compound glycine betaine, whose main function in plants is osmotic adjustment, increased gradually in root and leaf tissue for both sorghum varieties with an increase in the duration of drought stress (Figures 3.9B and D). There was a higher increase in the glycine betaine content of SA 1441 compared to ICSB 338 in both root and leaf tissue. This is in agreement with Quan et al. (2004) who transformed maize plants with the betA gene encoding a key enzyme in glycine betaine biosynthesis and treated the plants with drought stress (Quan et al., 2004). Maize plants expressing the betA gene accumulated more glycine betaine and were more drought tolerant. Glycine betaine has also been shown to increase in abundance under dehydration stress in sorghum (Yang et al., 2003; Fracasso et al., 2016) The observed increase in both proline and glycine betaine content represent an important adaptive response of sorghum to drought stress, in particular SA 1441, the drought tolerant variety. Furthermore, the overall higher levels of proline in leaf tissue compared to roots and the inverse for glycine betaine possibly indicates the tissue specific responses of sorghum to drought stress. Based on the physiological and biochemical data obtained in this study, generally the drought susceptible variety ICSB 338 was more adversely affected by drought stress and exhibited poor recovery after re-watering compared to the drought tolerant variety. Having confirmed 72 the tolerance status of these two sorghum varieties to drought stress, they were further utilized in a sorghum drought stress proteomic study as described in the following chapter. 73 CHAPTER 4 IDENTIFICATION OF DROUGHT STRESS RESPONSIVE PROTEINS FROM SORGHUM ROOT TISSUE AND GENE EXPRESSION ANALYSIS OF SELECTED TARGETS 4.1 Introduction Under environmental stresses, plants undergo physiological and biochemical changes to restore cellular homeostasis. Drought stress also triggers gene and protein expression changes in plants. Some of these molecular responses are involved in signal transduction pathways and in the re-activation of gene expression for cellular protective functions and osmotic balance (Shinozaki et al., 2003; Shinozaki and Yamaguchi-Shinozaki, 2007). For example protein kinases and phosphatases are involved in cell signalling processes during stress response. In addition, enzymes involved in the biosynthesis of osmolytes are activated to maintain osmotic homeostasis during periods of stress. Dehydrins, late embryogenesis abundant, responsive to abscisic acid (RABs) and vegetative storage proteins are some of the known drought stress responsive proteins (Artlip and Funkhouser, 1995). However, the level of tolerance of a plant species to drought depends on its genetic make-up and the level of selective evolution to survive adverse environmental conditions. Sorghum, for instance is naturally drought tolerant (Rosenow et al., 1983). This attribute may be credited to the continuous exposure of the crop to harsh environmental conditions during its evolutionary history. Proteomics studies have been performed in various plant species using different technologies with the aim of understanding stress adaptive mechanisms. These methods range from the classical two dimensional gel electrophoresis (2DE) based methods coupled with mass 74 spectrometry (MS) to the label and label-free liquid chromatography (LC-MS) and LC- MS/MS based methods. For example, 2DE has been applied in the proteomic analysis of rice (Oryza sativa) leaf tissue (Ji et al., 2012), sugar beet (Beta vulgaris) leaf tissue (Hajheidari Mohsen et al., 2005) and wheat (Triticum aestivum) leaf and root tissues (Peng et al., 2009) under drought stress. Although 2DE is relatively inexpensive and easy to perform, it has its own limitations such as the inability to resolve hydrophobic proteins and its non-quantitative nature (Haynes and Yates III, 2000). With the development of non-gel based proteomics, an improvement in the proteome coverage is possible with high accuracy and efficiency. Examples of such gel free techniques include the isobaric Tags for Relative and Absolute Quantitation (iTRAQ) (Ross et al., 2004). In iTRAQ analysis, 4 or 8 samples can be labelled and analysed simultaneously (Pierce et al., 2008). The iTRAQ method has been used to study proteome changes in tobacco (Nicotiana tabacum) leaf tissue (Xie et al., 2016), wheat leaf tissue (Ford et al., 2011) and white clover (Trifolium repens) leaf tissue (Li et al., 2016b) under drought stress. Recently, proteomic changes in sorghum cell suspension cultures under sorbitol-induced osmotic stress have also been investigated (Ngara et al., 2018). Collectively, some of the differentially expressed drought responsive proteins identified in these studies were involved in photosynthesis, metabolism, and stress and defence-related processes. Currently there are a limited number of studies in root proteomics. Root proteomics is important in enhancing our knowledge on plant responses to water stress. This is because of the central roles played by roots in water stress perception and also in search for water deep down into the soil profile (Ghatak et al., 2016) during periods of drought. The objective of this chapter was to perform a comparative iTRAQ analysis of the root proteome of two sorghum varieties under drought stress followed by gene expression analysis 75 of a few selected targets. The sorghum varieties used, ICSB 338 and SA 1441 had contrasting phenotypic traits to drought. 4.2. One Dimensional Protein Profiles of Sorghum Root and Leaf Two sorghum varieties, ICSB 338 and SA 1441 were grown on soil and drought stressed by withholding water for 8 days as described in Section 2.2.3.1. Control plants were maintained at field capacity for the duration of the experiment. Root and leaf protein extracts were prepared and 2.5 µg and 10 µg of the root and leaf samples was electrophoresed on one dimensional sodium dodecyl sulfate-polyacrylamide gel electrophoresis (1D SDS-PAGE) for quality and quantity checks (Figures 4.1 and 4.2). For all protein samples, both the quality and loading quantities of the extracts was good with no visible signs of streaking. The protein samples were acetone precipitated and re-solubilised in extraction buffer for two dimensional-differential gel electrophoresis (2D-DiGE). 76 Figure 4.1: One dimensional SDS-PAGE analysis of sorghum root tissues. ICSB 338 is the drought susceptible variety, while SA 1441 is drought tolerant. Approximately 2.5µg of root protein of each sample was loaded on 12% (v/v) SDS-PAGE gels. Lane M is the molecular weight marker. The gels were stained with a SYPRO Ruby gel stain and images were taken using a Typhoon 9400 Variable Mode Imager. The first 4 lanes on each gel represent biological replicate protein extracts for the control root samples whilst the second 4 lanes represent the drought stressed biological replicate root protein extracts. 77 Figure 4.2: One dimensional SDS-PAGE analysis of sorghum leaf tissues. ICSB 338 is the drought susceptible variety, while SA 1441 is drought tolerant. Approximately 10µg of leaf protein of each sample was loaded on 12% (v/v) SDS-PAGE gels. Lane M is the molecular weight marker. The first 5 lanes on each gel represent biological replicate protein extracts for the control leaf samples whilst the second 5 lanes represent the drought stressed biological replicate leaf protein extracts. The gels were stained with Coomassie Brilliant Blue (CBB) R-250 stain and visualised using the Gel Doc Molecular imager. 4.3 2D-DiGE Profiles of Sorghum Root and Leaf Samples Following the acetone precipitation and re-solubilisation of the sorghum root and leaf samples in extraction buffer, the samples were labelled with Cy fluorescent dyes for 2D- DiGE analysis. The controls were labelled with Cy3 (red) whilst the drought stress samples were labelled with Cy5 (green). The quality of the labelling was assessed using 1D SDS PAGE. The results showed that the labelling was successful (data not shown). This was followed by 2D-DiGE as described in Section 2.6.2. The root and leaf proteome profiles are 78 shown in Figures 4.3 and 4.4, respectively. These profiles showed some differential protein expression on the gel overlays, while some spots remained unchanged. For example, the green spots on the gel overlay represent protein spots that were up-regulated following drought stress, while the red spots represent down-regulated proteins. The orange spots are those that remained unchanged following the stress treatment. The purpose of 2D-DiGE was to visualise the proteins and confirm their differential expression in the two sorghum tissues before proceeding with iTRAQ analysis. Due to the high cost of iTRAQ analysis and the fact that a number of studies have been performed on leaf tissue, only the root samples were analysed in this study. 79 Gel overlay Gel overlay Figure 4.3: 2D-DiGE analysis of sorghum root total soluble protein. ICSB 338 is the drought susceptible variety, while SA 1441 is drought tolerant. Approximately 20 µg of root protein of each sample was separated on IPG strips of pH range 4-7. The control and drought stress samples were labelled with Cy3 and Cy5 fluorescent dyes, respectively. The gel images of control and drought samples were overlaid and imaged using the Typhoon Variable Mode Imager. The green and red spots in the gel overlay indicate protein up-regulation and down-regulation, respectively whilst the orange spots represent unchanged proteins following drought stress. 80 Gel overlay Gel overlay Figure 4.4: 2D-DiGE analysis of sorghum leaf total soluble protein. ICSB 338 is the drought susceptible variety, while SA 1441 is drought tolerant. Approximately 20 µg of leaf protein of each sample was separated on IPG strips of pH range 4-7. The control and drought stress samples were labelled with Cy3 and Cy5 fluorescent dyes, respectively. The gel images of control and drought samples were overlaid and imaged using the Typhoon Variable Mode Imager. The green and red spots in the gel overlay indicate protein up-regulation and down-regulation, respectively whilst the orange spots represent unchangedproteinsfollowingdroughtstress . 81 4.4 iTRAQ Analysis of the Sorghum Drought Stress Responsive Root Proteins Fifty micrograms each of root samples of ICSB 338 and SA 1441 sorghum varieties were digested with trypsin and prepared for iTRAQ analysis (Section 2.7.1). The four biological control replicates were labelled with tags of molecular weights 113, 114, 115, and 116, while the drought stress samples were labelled with 117, 118, 119, and 121 molecular weight tags. The samples were subsequently fractionated and analysed by LC-MS/MS. Only proteins identified with a minimum of two sequenced peptides with a statistical confidence threshold of greater than or equal to 95% were regarded as positively identified. This gave rise to a total of 1169 and 1043 positively identified root proteins in ICSB 338 and SA 1441 sorghum varieties, respectively. A large proportion of 76% and 72% of the proteins were uncharacterised for ICSB 338 and SA 1441 sorghum varieties, respectively. A combined total of 1 341 unique proteins were identified in both sorghum varieties. Of these proteins, 871 were common to both sorghum varieties as illustrated in Figure 4.5 while 298 and 172 were unique to ICSB 338 and SA 1441, respectively The comparative fold-changes of the 871 root proteins common to both sorghum varieties is shown in Appendix, Figure A1 in the form of a heatmap. Of the 871 proteins common to both sorghum varieties, 174 and 172 were significant at 95% significance level for ICSB 338 and SA 1441, respectively. A total of 312 proteins significantly responded in at least one of the two sorghum varieties whilst there was an overlap of 34 significant proteins between ICSB 338 and SA 1441. Within the 312 significantly responding proteins in at least one sorghum variety, the highest fold change was approximately 2-fold. However, due to the large number of drought stress responsive proteins identified in this study, the heatmap (Appendix, Figure A1) does not include the actual protein names. 82 Figure 4.5: The relationship between ICSB 338 and SA 1441 identified drought stress responsive root proteins. Of the positively identified root proteins, 237 and 184 were differentially expressed in response to drought stress in ICSB 338 and SA 1441, respectively (Tables 4.1 and 4.2). For ICSB 338, 111 (47%) proteins were up-regulated whilst 126 (53%) proteins were down- regulated. For SA 1441, 113 (61%) proteins were up-regulated whilst 74 (39%) proteins were down-regulated. For ICSB 338, fold change ranged from 1.06 (protein no. 250) to 2.93 (protein no. 868) for up-regulated proteins and -3.62 (protein no 1218) to -1.06 (protein no. 76) for down-regulated proteins. For SA 1441, fold change ranged from 1.10 (protein no. 158) to 2.72 (protein no. 1798) for up-regulated proteins and -1.52 (protein no. 161) to -1.07 (protein no. 157) for down-regulated proteins. 83 4.4.1 Bioinformatic Analyses on the Identified Drought Stress Responsive Sorghum Root Proteins All the 237 and 184 differentially expressed drought stress responsive root proteins for ICSB 338 and SA 1441 respectively, were subjected to gene ontology annotation on the UniProt (http://www.uniprot.org/) database. Obsolete proteins on the UniProt database were annotated using the Morokoshi (http://sorghum.riken.jp/morokoshi/Home.htmL) database. This resulted in the determination of the cellular component, biological process, and functional process as shown in Tables 4.1 and 4.2. The conserved domains and protein family names were identified for all the drought responsive root proteins of the two sorghum varieties using the InterPro database (https://www.ebi.ac.uk/interpro/). Conserved domains assist in characterising proteins because they are distinct functional regions on proteins that can carry out a function independently (Marchler-Bauer et al., 2010). Family names on the other hand, link the proteins to their ancestral origins with the same protein structure, sequence and function (Finn et al., 2013). 84 Table 4.1: List of drought stress responsive proteins identified from ICSB 338 root samples using the iTRAQ and database searches. Prot Accessionb Prot Name Scorc % Seq Ratioh SDg p-valf GO Analysisi Conserved domains and Noa Pepe family namej Covd P F C Metabolism 1 C9DHL2 Lipoxygenase OS=Sorghum 66.79 46.64 39 -1.53 0.05 9.0E-06 Oxylipin Oxidoreductase None predicted Lipoxygenase, C-terminal bicolor biosynthetic process activity domain; lipoxygenase family 5 H2ET77 Sucrose synthase 49.35 35.29 30 -1.69 0.04 2.7E-05 Sucrose metabolic Sucrose synthase None predicted Sucrose synthase domain; OS=Sorghum bicolor process activity sucrose synthase, GN=SUSY2 plant/cyanobacteria family 6 C5WNU8 Lipoxygenase OS=Sorghum 48.96 40.20 29 -1.31 0.06 5.9E-04 Fatty acid Dioxygenase None predicted Lipoxygenase, C-terminal bicolor GN= biosynthetic process activity domain; lipoxygenase SORBI_3001G125800 family 13 A0A1B6Q6S1 Uncharacterized protein 41.68 46.33 25 -1.16 0.07 9.7E-03 Glucose catabolic Phosphoglycerate Cytoplasm BPG-independent PGAM, OS=Sorghum bicolor process mutase activity N-terminal domain; GN=SORBI_3003G336000 phosphoglycerate mutase, 2,3-bisphosphoglycerate- independent family 21 A0A1B6QE21 Uncharacterized protein 36.63 46.85 23 -1.20 0.09 1.4E-02 UDP-glucose UTP:glucose-1- None predicted Domain not predicted; OS=Sorghum bicolor GN= metabolic process phosphate UTP--glucose-1-phosphate SORBI_3002G291200 uridylyltransferase uridylyltransferase family activity 17 C5XES4 Lipoxygenase OS=Sorghum 37.7 26.15 23 -1.41 0.01 4.1E-06 Fatty acid Dioxygenase None predicted Lipoxygenase, C-terminal bicolor biosynthetic process activity domain; lipoxygenase GN=SORBI_3003G385500 family 31 A0A1B6QI05 Uncharacterized protein 30.52 33.17 21 1.37 0.02 9.9E-06 Carbohydrate Hydrolase activity, None predicted Glycoside hydrolase family OS=Sorghum bicolor GN= metabolic process hydrolyzing O- 3 C-terminal domain and SORBI_3001G089100 glycosyl compounds superfamily 34 C5X3X5 Uncharacterized protein 29.92 47.92 33 1.40 0.15 4.6E-03 Glycosyl compound Hydrolase activity, None predicted Domain not predicted; OS=Sorghum bicolor GN= metabolic process acting on glycosyl glycoside hydrolase family SORBI_3002G247600 bonds 1 family 40 A0A1B6QL52 Uncharacterized protein 28.21 43.18 18 -1.28 0.06 1.7E-03 Biosynthetic process Pyridoxal phosphate None predicted Aminotransferase, class OS=Sorghum bicolor binding I/classII domain; pyridoxal GN=SORBI_3001G260800 phosphate-dependent transferase, major domain 44 C5YIC1 Aconitate hydratase 27.11 21.99 18 -1.07 0.05 3.4E-02 Metabolic process Aconitate hydratase None predicted Aconitase OS=Sorghum bicolor activity A/isopropylmalate GN=SORBI_3007G068100 dehydratase small subunit, swivel domain; aconitase/Iron-responsive element-binding protein 2 family 85 48 A0A1B6PRR1 Uncharacterized protein 26.66 31.71 17 -1.22 0.08 1.2E-02 One-carbon Hydrolase activity None predicted S-adenosyl-L- OS=Sorghum bicolor metabolic process homocysteine hydrolase, GN=SORBI_3005G112800 NAD binding domain; adenosylhomocysteinase- like family 58 C5YZJ4 S-adenosyl methionine 25.03 54.04 21 1.45 0.11 1.6E-03 S-adenosyl Methionine Cytosol S-adenosyl methionine synthase OS=Sorghum methionine adenosyl transferase synthetase, central domain; bicolor biosynthetic process activity s-adenosyl methionine GN=SORBI_3009G033600 synthetase family 60 A0A194YGV9 Uncharacterized protein 24.58 22.03 15 -1.19 0.05 1.4E-03 Metabolic process Transketolase None predicted Transketolase, C-terminal OS=Sorghum bicolor activity domain; transketolase GN=SORBI_3010G023700 family 72 C5WXJ1 Sucrose synthase 22.8 27.57 21 -1.37 0.10 3.6E-02 Sucrose metabolic Sucrose synthase None predicted Sucrose synthase domain; OS=Sorghum bicolor process activity sucrose synthase, GN=SORBI_3001G344500 plant/cyanobacteria family 76 C5Z7L1 Pyrophosphate--fructose 6- 22.34 31.15 14 -1.06 0.03 3.3E-02 Fructose 6- Diphosphate- Cytoplasm Phosphofructokinase phosphate 1- phosphate metabolic fructose-6- domain; ATP-dependent 6- phosphotransferase subunit process phosphate 1- phosphofructokinase beta OS=Sorghum bicolor phosphotransferase family GN=PFP-BETA activity 87 C5Y2F0 6-phosphogluconate 20.89 27.18 16 -1.19 0.04 2.3E-03 D-gluconate Phosphogluconate None predicted 6-phosphogluconate dehydrogenase, metabolic process dehydrogenase dehydrogenase, NADP- decarboxylating (decarboxylating) binding domain; 6- OS=Sorghum bicolor activity phosphogluconate GN=SORBI_3005G115600 dehydrogenase family 96 A0A194YT53 Uncharacterized protein 20.14 33.91 14 -1.11 0.03 1.5E-03 Metabolic process Transferase activity, None predicted Thiolase, N-terminal OS=Sorghum bicolor transferring acyl domain; thiolase family GN=SORBI_3004G345800 groups other than amino-acyl groups 109 A0A1B6Q7Z8 Uncharacterized protein 18.42 28.04 10 -1.15 0.05 1.5E-02 Carbohydrate Sedoheptulose-7- Mitochondrion Domain not predicted; OS=Sorghum bicolor metabolic process phosphate:D- transaldolase/Fructose-6- GN=SORBI_3003G410500 glyceraldehyde-3- phosphate aldolase family phosphate glyceronetransferase activity 110 A0A1B6PEZ5 Uncharacterized protein 18.4 34.73 11 -1.25 0.10 1.6E-02 Fructose metabolic Phosphotransferase Cytosol Carbohydrate kinase PfkB OS=Sorghum bicolor process activity, alcohol domain; ribokinase-like GN=SORBI_3007G014700 group as acceptor superfamily 111 C5Z0G8 Uncharacterized protein 18.39 22.67 18 1.13 0.05 4.6E-03 Lipid metabolic Hydrolase activity None predicted Fungal lipase-like domain; OS=Sorghum bicolor process Alpha/Beta hydrolase fold GN=SORBI_3009G048100 superfamily 115 Q9XE77 Patatin OS=Sorghum bicolor 18.2 31.04 13 -1.47 0.10 4.9E-03 Lipid catabolic Hydrolase activity None predicted Patatin-like phospholipase process domain; acyl transferase/acyl hydrolase/lysophospholipa se superfamily 128 A0A194YKZ5 Uncharacterized protein 17.68 17.58 8 1.18 0.08 1.1E-02 Carbohydrate Hydrolase activity, Extracellular Domain not predicted; OS=Sorghum bicolor metabolic process hydrolyzing O- Glycosyl hydrolases family GN=SORBI_3010G227400 glycosyl compounds 31 86 131 Q94EL4 Chitinase-B (Fragment) 17.56 43.89 18 1.55 0.24 6.9E-03 Chitin catabolic Chitin binding None predicted Chitin-binding, type 1 OS=Sorghum arundinaceum process domain; glycoside hydrolase, family 19 133 C5Y397 Alpha-mannosidase 17.32 9.42 9 1.18 0.12 2.8E-02 Mannose metabolic Alpha-mannosidase Apoplast Glycoside hydrolase family OS=Sorghum bicolor process activity 38, central domain and GN=SORBI_3005G132400 superfamily 138 C5YSN7 Beta-galactosidase 16.71 13.87 10 1.23 0.09 2.1E-03 Carbohydrate Hydrolase activity, Vacuole Glycoside hydrolase 35, OS=Sorghum bicolor metabolic process acting on glycosyl catalytic domain; glycoside GN=SORBI_3008G052200 bonds hydrolase, family 35 155 C5Y1P4 Uncharacterized protein 16.08 38.78 10 -2.10 0.06 2.1E-02 Chitin catabolic Chitin binding Extracellular Glycoside hydrolase family OS=Sorghum bicolor process region 18, catalytic domain; GN=SORBI_3005G099000 glycoside hydrolase superfamily 162 C5YB69 Uncharacterized protein 15.74 17.04 14 -1.21 0.06 9.7E-03 Metabolic process 4 iron, 4 sulfur None predicted Aconitase OS=Sorghum bicolor cluster binding A/isopropylmalate GN=SORBI_3006G000100 dehydratase small subunit, swivel domain; aconitase/Iron-responsive element-binding protein 2 family 178 C5Y4G6 Uncharacterized protein 14.66 19.19 9 -1.17 0.07 1.0E-02 Carboxylic acid Transferase activity, None predicted 2-isopropylmalate synthase OS=Sorghum bicolor metabolic process transferring acyl LeuA, allosteric GN=SORBI_3005G030100 groups, acyl groups (dimerisation) domain; 2- converted into alkyl isopropylmalate synthase, on transfer bacterial-type family 187 C5WWZ1 Uncharacterized protein 14.16 19.48 9 -1.11 0.07 4.3E-02 L-serine O-phospho-L- None predicted Aminotransferase class V OS=Sorghum bicolor biosynthetic process serine:2- domain; phosphoserine GN=SORBI_3001G498500 oxoglutarate aminotransferase family aminotransferase activity 197 C5Y093 Formate dehydrogenase, 13.73 26.04 9 -1.28 0.04 1.6E-03 Formate catabolic Formate Mitochondrion D-isomer specific 2- mitochondrial OS=Sorghum process dehydrogenase hydroxyacid bicolor (NAD+) activity dehydrogenase, NAD- GN=SORBI_3004G265000 binding domain; NAD- dependent formate dehydrogenase family 229 A0A194YR85 Uncharacterized protein 12.37 17.88 7 1.10 0.04 2.9E-02 Carbohydrate Hydrolase activity, None predicted Glycosyl hydrolase family OS=Sorghum bicolor metabolic process hydrolyzing O- 32, N-terminal domain; GN=SORBI_3004G166700 glycosyl compounds glycoside hydrolase, family 32 240 C5Y8Y2 Uncharacterized protein 12.07 12.52 8 1.28 0.06 7.2E-04 Carbohydrate Hydrolase activity, None predicted Glycoside hydrolase, OS=Sorghum bicolor metabolic process hydrolyzing O- family 3, N-terminal GN=SORBI_3006G235600 glycosyl compounds domain; glycoside hydrolase superfamily 249 C5XES7 Lipoxygenase OS=Sorghum 11.83 13.40 9 -1.16 0.02 1.6E-03 Oxylipin Oxidoreductase None predicted Lipoxygenase, C-terminal bicolor biosynthetic process activity domain; lipoxygenase GN=SORBI_3003G385900 family 250 A0A1B6Q495 Uncharacterized protein 11.83 21.74 6 1.06 0.03 4.2E-03 Metabolic process IAA-Ala conjugate Plasma Domain not predicted; OS=Sorghum bicolor hydrolase activity membrane Sec1 family 87 GN=SORBI_3003G190500 258 C5Y5U9 Uncharacterized protein 11.58 33.88 8 -1.97 0.01 1.9E-02 Chitin catabolic Chitin binding Extracellular Glycoside hydrolase family OS=Sorghum bicolor process region 18, catalytic domain; GN=SORBI_3005G177500 Glycoside hydrolase superfamily 289 C5YYX0 Beta-hexosaminidase 10.86 12.64 5 1.22 0.11 1.7E-02 Carbohydrate Beta-N- Cytosol Beta-hexosaminidase, OS=Sorghum bicolor metabolic process acetylhexosaminidas eukaryotic type, N- GN=SORBI_3009G017500 e activity terminal domain; Beta- hexosaminidase family 294 B5B9V8 Glutamine synthetase 10.77 28.01 12 -1.60 0.06 3.4E-04 Glutamine Glutamate-ammonia None predicted Glutamine synthetase, OS=Sorghum bicolor GN=gs biosynthetic process ligase activity catalytic domain; Glutamine synthetase, N- terminal domain superfamily 319 C5Z1E3 ATP-dependent 6- 10.27 9.77 4 -1.18 0.08 1.7E-02 Fructose 6- 6- Cytoplasm Phosphofructokinase phosphofructokinase phosphate metabolic phosphofructokinase domain; ATP-dependent 6- OS=Sorghum bicolor process activity phosphofructokinase GN=PFK family 325 A0A1B6QG06 Uncharacterized protein 10.11 4.14 5 -1.28 0.10 1.4E-02 Glutamate Glutamate synthase None predicted Glutamine OS=Sorghum bicolor biosynthetic process activity amidotransferase type 2 GN=SORBI_3002G402700 domain; Glutamate synthase, alpha subunit, C- terminal domain superfamily 327 C5YMM3 Patatin OS=Sorghum bicolor 10.1 20.69 6 -1.42 0.08 1.7E-03 Lipid catabolic Acylglycerol lipase Membrane Patatin-like phospholipase GN=SORBI_3007G158600 process activity domain; Acyl transferase/acyl hydrolase/lysophospholipa se superfamily 414 C5XZW8 Uncharacterized protein 8.25 23.19 5 -1.25 0.04 1.5E-04 Nucleotide Ribose phosphate Cytosol Ribose-phosphate OS=Sorghum bicolor biosynthetic process diphosphokinase pyrophosphokinase, N- GN=SORBI_3004G261500 activity terminal domain; Ribose- phosphate pyrophosphokinase family 449 C5XKA7 3-methyl-2-oxobutanoate 7.74 16.58 7 1.27 0.10 1.2E-02 Pantothenate 3-methyl-2- None predicted Domain not predicted; hydroxymethyltransferase biosynthetic process oxobutanoate Ketopantoate OS=Sorghum bicolor hydroxymethyltransf hydroxymethyltransferase GN=SORBI_3003G011600 erase activity family 482 C5XCA6 Amine oxidase OS=Sorghum 7.32 7.89 3 1.24 0.18 4.4E-02 Amine metabolic Primary amine None predicted Copper amine oxidase, bicolor process oxidase activity catalytic domain; Copper GN=SORBI_3002G347300 amine oxidase family 621 C5XHG5 Uncharacterized protein 5.72 10.19 3 -1.26 0.13 1.8E-02 Cellular amino acid Threonine synthase Cytoplasm Domain not predicted; OS=Sorghum bicolor metabolic process activity Pyridoxal-phosphate GN=SORBI_3003G265400 dependent enzyme 647 C5YD28 Glutamate dehydrogenase 5.4 14.84 5 -1.38 0.07 2.7E-02 Cellular amino acid Glutamate Vacuolar NAD(P) binding domain of OS=Sorghum bicolor metabolic process dehydrogenase membrane glutamate dehydrogenase GN=SORBI_3006G165400 (NAD+) activity domain; Glutamate dehydrogenase family 711 A0A1B6QME4 Uncharacterized protein 4.72 3.74 3 -1.22 0.09 2.2E-02 Pyruvate metabolic Pyruvate, phosphate None predicted Pyruvate phosphate 88 OS=Sorghum bicolor process dikinase activity dikinase, PEP/pyruvate- GN=SORBI_3001G326900 binding domain; Pyruvate/Phosphoenolpyru vate kinase-like domain superfamily 729 C5YUA1 Phosphotransferase 4.52 8.28 2 -1.75 0.18 1.1E-02 Glucose 6- Phosphotransferase Cytosol Hexokinase, C-terminal OS=Sorghum bicolor phosphate metabolic activity, alcohol domain; Hexokinase GN=SORBI_3009G069800 process group as acceptor family 735 C5YMI0 Uncharacterized protein 4.44 5.19 3 -1.26 0.10 1.7E-02 None predicted Modified amino acid None predicted Initiation factor eIF-4 OS=Sorghum bicolor binding gamma, MA3 domain; GN=SORBI_3007G023700 MIF4G-like domain superfamily 767 C5YBH7 Uncharacterized protein 4.19 6.39 2 1.56 0.19 3.1E-03 None predicted None predicted None predicted Galactose oxidase-like, OS=Sorghum bicolor Early set domain; GN=SORBI_3006G135500 Galactose oxidase, central domain superfamily 774 C5XY71 Nitrilase OS=Sorghum 4.15 9.70 3 1.13 0.05 2.8E-02 Nitrogen compound Hydrolase activity, None predicted Carbon-nitrogen hydrolase bicolor GN=nit metabolic process acting on carbon- domain; Carbon-nitrogen nitrogen (but not hydrolase superfamily peptide) bonds 783 C5WV22 Chalcone-flavonone 4.1 14.29 2 1.31 0.20 3.2E-02 Flavonoid Chalcone isomerase Endoplasmic Chalcone isomerase isomerase family protein biosynthetic process activity reticulum domain; Chalcone OS=Sorghum bicolor isomerase superfamily GN=SORBI_3001G035600 791 C5YN64 Uncharacterized protein 4.07 5.75 2 -1.17 0.03 4.1E-02 Branched-chain Dihydroxy-acid Chloroplast Domain not predicted; OS=Sorghum bicolor amino acid dehydratase activity stroma Dihydroxy-acid GN=SORBI_3007G167800 biosynthetic process dehydratase family 814 A0A1B6Q8W1 Uncharacterized protein 4 5.66 2 -1.47 0.08 9.5E-03 Fructose 6- 6- Cytoplasm Phosphofructokinase OS=Sorghum bicolor phosphate metabolic phosphofructokinase domain; ATP-dependent 6- GN=SORBI_3002G026300 process activity phosphofructokinase family 828 C5Z263 Uncharacterized protein 4 4.85 3 1.16 0.09 1.1E-02 Carbohydrate Hydrolase activity, Extracellular Domain not predicted; OS=Sorghum bicolor metabolic process acting on glycosyl region Glycoside hydrolase, GN=SORBI_3010G005000 bonds family 28 family 924 C5YYK2 Uncharacterized protein 3.27 11.27 2 -1.71 0.22 3.1E-02 Lipid metabolic Phosphatidylcholine None predicted Fungal lipase-like domain; OS=Sorghum bicolor process 1-acylhydrolase Phospholipase A1-II GN=SORBI_3009G014600 activity family 933 C5YMM5 Patatin OS=Sorghum bicolor 3.16 9.95 4 1.26 0.14 3.2E-02 Lipid metabolic Hydrolase activity None predicted Ca2+-independent GN=SORBI_3007G158800 process phospholipase A2 domain; Patatin-like phospholipase 975 Q41290 Dhurrinase OS=Sorghum 2.82 5.31 3 1.15 0.07 3.3E-02 Carbohydrate Hydrolase activity, None predicted Domain not predicted; bicolor metabolic process acting on glycosyl Glycoside hydrolase family bonds 1 1010 C5XEU4 Uncharacterized protein 2.6 4.21 2 1.26 0.08 2.4E-02 RNA catabolic RNA binding None predicted Domain not predicted; OS=Sorghum bicolor process Ribonuclease T2 family GN=SORBI_3003G388200 1029 C5WP07 Aldose 1-epimerase 2.5 5.93 2 1.29 0.19 4.6E-02 Hexose metabolic Aldose 1-epimerase Cytosol Domain not predicted; OS=Sorghum bicolor process activity Aldose 1-epimerase family GN=SORBI_3001G265600 89 Disease/defence 10 C5XX52 Glyceraldehyde-3-phosphate 44.17 75.07 48 -1.59 0.01 3.3E-04 Oxidation-reduction Oxidoreductase None predicted Glyceraldehyde 3- dehydrogenase OS=Sorghum process activity phosphate dehydrogenase, bicolor domain and family GN=SORBI_3004G205100 25 C5XXT8 Phenylalanine ammonia- 33.23 28.84 19 1.40 0.18 6.2E-03 L-phenylalanine Phenylalanine Cytoplasm Domain not predicted; lyase OS=Sorghum bicolor catabolic process ammonia-lyase phenylalanine ammonia- GN=SORBI_3004G220300 activity lyase family 43 C5WNL8 Uncharacterized protein 27.28 27.28 25 1.31 0.06 5.2E-04 Hydrogen peroxide L-ascorbate Chloroplast Haem peroxidase, plant OS=Sorghum bicolor catabolic process peroxidase activity domain; class I peroxidase GN=SORBI_3001G410200 family 11 A0A1B6PQR2 Uncharacterized protein 42.68 42.68 22 -1.18 0.10 3.7E-02 Cell redox Protein disulfide Endoplasmic Thioredoxin domain; OS=Sorghum bicolor homeostasis isomerase activity reticulum lumen protein disulphide GN=SORBI_3005G074400 isomerase family 43 C5WNL8 Uncharacterized protein 27.28 62.40 25 1.31 0.06 5.2E-04 Hydrogen peroxide L-ascorbate Chloroplast Haem peroxidase, plant OS=Sorghum bicolor catabolic process peroxidase activity domain; class I peroxidase GN=SORBI_3001G410200 family 53 C5WWQ2 Uncharacterized protein 25.79 31.16 13 -1.17 0.09 1.7E-02 Cellular oxidant Thioredoxin- Cytoplasm Thioredoxin domain; OS=Sorghum bicolor detoxification disulfide reductase thioredoxin-like GN=SORBI_3001G342600 activity superfamily 70 C5X1Q1 Uncharacterized protein 23.37 45.58 17 1.23 0.17 4.1E-02 None predicted None predicted None predicted Domain not predicted; OS=Sorghum bicolor ricin B-like lectins family GN=SORBI_3001G385900 82 C5XGS0 Uncharacterized protein 21.42 8.04 11 -1.21 0.02 1.9E-03 Oxidation-reduction Oxidoreductase Cytoplasm FAD/NAD(P)-binding OS=Sorghum bicolor process activity, acting on domain; Glutamate GN=SORBI_3003G258800 the CH-NH2 group synthase, NADH/NADPH, of donors, NAD or small subunit 1 family NADP as acceptor 83 A0A1B6Q513 Uncharacterized protein 21.09 30.43 13 -1.13 0.04 2.9E-02 Oxidation-reduction Magnesium ion Plasma Isocitrate/isopropylmalate OS=Sorghum bicolor process binding membrane dehydrogenase family GN=SORBI_3003G241500 93 C5Y949 Uncharacterized protein 20.57 22.42 10 -1.17 0.06 4.3E-03 Oxidation-reduction Oxidoreductase None predicted D-isomer specific 2- OS=Sorghum bicolor process activity, acting on hydroxyacid GN=SORBI_3006G243400 the CH-OH group of dehydrogenase, NAD- donors, NAD or binding domain; D-3- NADP as acceptor phosphoglycerate dehydrogenase family 95 C5YB27 Peroxidase OS=Sorghum 20.25 44.22 18 -1.27 0.06 5.0E-03 Hydrogen peroxide Peroxidase activity Extracellular Secretory peroxidase bicolor catabolic process region domain; plant peroxidase GN=SORBI_3006G277800 family 101 O82430 Putative alcohol 19.05 31.66 11 -1.64 0.03 3.4E-03 Oxidation-reduction Oxidoreductase None predicted Alcohol dehydrogenase, N- dehydrogenase 1 process activity terminal domain; NAD(P)- OS=Sorghum bicolor binding domain GN=Adh1 superfamily 108 C5XFP2 Uncharacterized protein 18.43 38.61 12 -1.12 0.06 2.4E-02 Oxidation-reduction Oxidoreductase None predicted Alcohol dehydrogenase, C- OS=Sorghum bicolor process activity terminal domain; NAD(P)- GN=SORBI_3003G110200 binding domain superfamily 90 126 A0A1B6QM42 Uncharacterized protein 17.77 35.37 10 1.23 0.10 1.4E-02 None predicted Protein binding None predicted Glutathione S-transferase, OS=Sorghum bicolor C-terminal-like domain; GN=SORBI_3001G310300 thioredoxin-like superfamily 134 A0A1B6Q818 Uncharacterized protein 17.27 68.04 15 -1.31 0.11 8.2E-03 None predicted Protein binding None predicted Glutathione S-transferase, OS=Sorghum bicolor C-terminal and superfamily GN=SORBI_3003G416300 148 C5YB22 Peroxidase OS=Sorghum 16.29 33.70 11 -1.37 0.05 2.1E-03 Hydrogen peroxide Peroxidase activity Extracellular Secretory peroxidase bicolor catabolic process region domain; Plant peroxidase GN=SORBI_3006G277500 family 149 A0A194YU12 Uncharacterized protein 16.29 21.01 8 1.07 0.02 4.1E-03 Cellular oxidant Oxidoreductase Peroxisome FAD/NAD(P)-binding OS=Sorghum bicolor detoxification activity, acting on a domain; Glutathione- GN=SORBI_3004G341200 sulfur group of disulphide reductase family donors, NAD(P) as acceptor 173 C5Z864 Peroxidase OS=Sorghum 14.82 25.98 8 1.23 0.13 1.5E-02 Hydrogen peroxide Peroxidase activity Extracellular Secretory peroxidase bicolor catabolic process region domain; Plant peroxidase GN=SORBI_3010G232500 family 232 C5XIX9 Peroxidase OS=Sorghum 12.33 19.95 7 1.16 0.11 3.3E-02 Hydrogen peroxide Peroxidase activity Extracellular Secretory peroxidase bicolor catabolic process region domain; plant peroxidase GN=SORBI_3003G151900 family 234 C5WZ12 Uncharacterized protein 12.3 34.88 12 1.40 0.12 1.5E-03 Glutathione Glutathione Cytoplasm Glutathione S-transferase, OS=Sorghum bicolor metabolic process transferase activity C-terminal domain; GN=SORBI_3001G514400 thioredoxin-like superfamily 235 C5Z469 Peroxidase OS=Sorghum 12.25 33.12 8 1.66 0.28 4.2E-03 Hydrogen peroxide Peroxidase activity Extracellular Secretory peroxidase bicolor catabolic process region domain; plant peroxidase GN=SORBI_3010G161600 family 259 A0A1B6PL71 Uncharacterized protein 11.53 20.85 7 1.25 0.13 1.2E-02 Oxidation-reduction Oxidoreductase None predicted NADP-dependent OS=Sorghum bicolor process activity oxidoreductase domain; GN=SORBI_3006G096400 Aldo/keto reductase family 273 C5WNE0 Uncharacterized protein 11.31 32.55 7 1.21 0.15 3.7E-02 None predicted None predicted None predicted Alpha crystallin/Hsp20 OS=Sorghum bicolor domain; Small heat shock GN=SORBI_3001G264300 protein HSP20 family 292 C5Y1X4 Uncharacterized protein 10.79 26.39 11 -2.60 0.00 2.8E-04 Oxidation-reduction Oxidoreductase None predicted Alcohol dehydrogenase, N- OS=Sorghum bicolor process activity terminal domain; NAD(P)- GN=SORBI_3005G103300 binding domain superfamily 295 A0A1B6QJR7 Uncharacterized protein 10.77 18.95 7 1.16 0.07 4.9E-02 Hydrogen peroxide Peroxidase activity Extracellular Secretory peroxidase OS=Sorghum bicolor catabolic process region domain; Plant peroxidase GN=SORBI_3001G189000 family 298 C5XWZ7 Uncharacterized protein 10.76 23.36 6 1.23 0.16 3.7E-02 Response to stress None predicted Endoplasmic Domain not predicted; OS=Sorghum bicolor reticulum Putative cyclase family GN=SORBI_3004G069900 329 C5WZ08 Uncharacterized protein 10.05 30.53 5 1.77 0.35 1.0E-02 Glutathione Glutathione Cytoplasm Glutathione S-transferase, OS=Sorghum bicolor metabolic process transferase activity N-terminal domain; GN=SORBI_3001G514200 Thioredoxin-like superfamily 354 C5XKN3 Uncharacterized protein 9.41 17.94 5 1.15 0.08 2.1E-02 Ubiquinone-6 NADH None predicted NAD-dependent 91 OS=Sorghum bicolor biosynthetic process dehydrogenase epimerase/dehydratase GN=SORBI_3003G166500 activity domain; NAD(P)-binding domain superfamily 360 C5XHV4 Uncharacterized protein 9.28 23.79 5 -1.14 0.06 4.9E-02 Glutathione Glutathione Cytoplasm Glutathione S-transferase, OS=Sorghum bicolor metabolic process transferase activity N-terminal domain; GN=SORBI_3003G426000 Thioredoxin-like superfamily 367 C5XF87 Ketol-acid reductoisomerase 9.06 10.02 9 -1.20 0.04 3.3E-04 Oxidation-reduction Ketol-acid None predicted Ketol-acid OS=Sorghum bicolor process reductoisomerase reductoisomerase, N- GN=SORBI_3003G239900 activity terminal domain; Ketol- acid reductoisomerase family 389 A0A1B6Q6I7 Uncharacterized protein 8.73 46.31 12 1.47 0.21 4.4E-03 Oxidation-reduction NAD(P)H None predicted NADPH-dependent FMN OS=Sorghum bicolor process dehydrogenase reductase-like domain; GN=SORBI_3003G319700 (quinone) activity Flavoprotein WrbA-like family 400 A0A1B6PEV7 Uncharacterized protein 8.52 12.59 4 -1.11 0.06 4.2E-02 Oxidation-reduction Oxidoreductase Mitochondrion Polyketide synthase, OS=Sorghum bicolor process activity enoylreductase domain; GN=SORBI_3007G009100 NAD(P)-binding domain superfamily 443 C5Z240 Uncharacterized protein 7.85 11.36 5 1.12 0.03 2.2E-02 Oxidation-reduction Oxidoreductase None predicted Ascorbate oxidase OS=Sorghum bicolor process activity, oxidizing homologue, first GN=SORBI_3010G003100 metal ions cupredoxin domain; Cupredoxin superfamily 446 C5Z0N9 Peroxidase OS=Sorghum 7.77 17.63 10 -1.19 0.09 1.9E-02 Hydrogen peroxide Peroxidase activity Extracellular Secretory peroxidase bicolor catabolic process region domain; Plant peroxidase GN=SORBI_3009G055300 family 454 C5WZ26 Inosine-5'-monophosphate 7.69 16.17 4 -1.09 0.06 4.7E-02 Oxidation-reduction Oxidoreductase Cytoplasm CBS domain; Inosine-5'- dehydrogenase OS=Sorghum process activity monophosphate bicolor dehydrogenase family GN=SORBI_3001G069900 466 C5XVU9 S- 7.5 14.70 4 1.25 0.09 1.4E-02 Oxidation-reduction S-(hydroxymethyl) None predicted Alcohol dehydrogenase, C- (hydroxymethyl)glutathione process glutathione terminal domain; Alcohol dehydrogenase OS=Sorghum dehydrogenase dehydrogenase class III bicolor activity family GN=SORBI_3004G343200 532 C5Z0L4 Uncharacterized protein 6.59 38.32 4 -1.26 0.10 1.2E-02 Response to stress None predicted None predicted UspA domain; Universal OS=Sorghum bicolor stress protein A family GN=SORBI_3009G052600 558 C5YD83 Uncharacterized protein 6.28 24.09 5 1.29 0.17 2.0E-02 None predicted None predicted None predicted Thioredoxin-like fold OS=Sorghum bicolor domain; Thioredoxin-like GN=SORBI_3006G031200 superfamily 592 A0A1B6QGT1 Uncharacterized protein 6 21.40 7 -1.43 0.04 4.4E-03 None predicted Protein binding None predicted Glutathione S-transferase, OS=Sorghum bicolor C-terminal-like domain; GN=SORBI_3001G012500 Thioredoxin-like superfamily 623 C5X768 Uncharacterized protein 5.64 10.86 3 -1.25 0.07 7.4E-03 Response to Catalytic activity Cytosol Domain not predicted; OS=Sorghum bicolor oxidative stress Dirigent-like protein GN=SORBI_3002G005600 family 92 631 A0A1B6QKQ9 Uncharacterized protein 5.58 19.89 3 -1.23 0.08 2.2E-02 Oxidation-reduction Acireductone Cytoplasm Domain not predicted; OS=Sorghum bicolor process dioxygenase Acireductone dioxygenase GN=SORBI_3001G243100 [iron(II)-requiring] ARD family activity 689 C5WRM3 Uncharacterized protein 4.92 11.49 3 -1.58 0.09 3.2E-03 Oxidation-reduction 3''-deamino-3''- None predicted NADP-dependent OS=Sorghum bicolor process oxonicotianamine oxidoreductase domain; GN=SORBI_3001G443100 reductase activity Aldo/keto reductase family 708 C5YGF5 Peroxidase OS=Sorghum 4.73 14.33 3 -1.51 0.06 8.8E-04 Hydrogen peroxide Peroxidase activity Extracellular Secretory peroxidase bicolor catabolic process region domain; Plant peroxidase GN=SORBI_3006G079300 family 715 A0A1B6QNZ7 Uncharacterized protein 4.7 12.37 2 1.14 0.10 5.0E-02 None predicted Protein binding None predicted Glutathione S-transferase, OS=Sorghum bicolor C-terminal-like domain; GN=SORBI_3001G412800 Thioredoxin-like superfamily 731 C5WXD7 Uncharacterized protein 4.47 29.33 6 1.41 0.16 1.1E-02 Defence mechanism None predicted None predicted Domain not predicted; OS=Sorghum bicolor against pathogens Uncharacterised protein GN=SORBI_3001G209300 family, basic secretory protein family 766 C5X763 Dirigent protein 4.21 18.11 3 1.20 0.13 2.3E-02 Biosynthetic process Enzyme regulator None predicted Domain not predicted; OS=Sorghum bicolor activity Dirigent-like protein GN=SORBI_3002G005100 777 A0A1B6PQP5 Uncharacterized protein 4.14 12.27 2 1.35 0.17 1.0E-02 None predicted None predicted None predicted Domain not predicted; OS=Sorghum bicolor Ricin B-like lectins GN=SORBI_3005G073000 superfamily 780 C5YF15 Uncharacterized protein 4.1 9.52 2 1.23 0.08 3.5E-03 Defence response None predicted None predicted Bet v I/Major latex protein OS=Sorghum bicolor domain; START-like GN=SORBI_3006G198800 domain superfamily 785 C5WMM0 Uncharacterized protein 4.08 70.63 18 -1.30 0.12 1.1E-02 Defence response None predicted None predicted Bet v I/Major latex protein OS=Sorghum bicolor domain; Bet v I type GN=SORBI_3001G400900 allergen family 794 C5Y360 Peroxidase OS=Sorghum 4.05 11.59 3 1.17 0.08 1.1E-02 Hydrogen peroxide Peroxidase activity Extracellular Secretory peroxidase bicolor catabolic process region domain; Plant peroxidase GN=SORBI_3005G011300 family 831 A0A194YQ30 Uncharacterized protein 4 10.41 5 1.37 0.18 1.1E-02 Oxidation-reduction Oxidoreductase Membrane Rieske [2Fe-2S] iron- OS=Sorghum bicolor process activity, acting on sulphur domain; Rieske GN=SORBI_3004G159000 diphenols and iron-sulphur protein family related substances as donors 845 A0A194YLK4 Uncharacterized protein 3.9 2.75 2 -1.21 0.05 5.9E-03 Oxidation-reduction Prephenate None predicted Prephenate dehydrogenase OS=Sorghum bicolor process dehydrogenase domain; NAD(P)-binding GN=SORBI_3010G258500 (NAD+) activity domain superfamily 876 C5X4Y7 Uncharacterized protein 3.66 8.91 2 1.26 0.08 4.1E-02 Lipid particle None predicted Lipid droplet Domain not predicted; OS=Sorghum bicolor organisation rubber elongation factor GN=SORBI_3002G412800 family 880 A2SXR8 Uricase OS=Sorghum bicolor 3.62 7.52 2 -1.34 0.10 7.4E-03 Urate catabolic Urate oxidase Peroxisome Domain not predicted; process activity uricase family 918 C5YWZ7 Uncharacterized protein 3.3 11.67 3 1.31 0.10 3.6E-03 None predicted None predicted None predicted Alpha crystallin/Hsp20 OS=Sorghum bicolor domain; HSP20-like GN=SORBI_3009G255200 chaperone superfamily 93 977 C5X765 Dirigent protein 2.82 23.67 3 1.27 0.13 1.9E-02 None predicted None predicted None predicted Domain not predicted; OS=Sorghum bicolor Dirigent protein family GN=SORBI_3002G005300 1051 C5XMC9 Uncharacterized protein 2.35 3.68 2 1.22 0.15 3.3E-02 Oxidation-reduction Catechol oxidase None predicted Polyphenol oxidase, OS=Sorghum bicolor process activity central domain; Polyphenol GN=SORBI_3003G322100 oxidase family 1102 C5Z026 Uncharacterized protein 2.13 11.06 4 -1.33 0.16 3.3E-02 Glutathione Glutathione Cytoplasm Glutathione S-transferase, OS=Sorghum bicolor metabolic process transferase activity N-terminal domain; GN=SORBI_3009G043600 Thioredoxin-like superfamily 1216 Q4VQB5 Pathogenesis-related protein 2 76.88 23 -1.43 0.13 1.7E-02 Response to biotic None predicted None predicted Bet v I/Major latex protein 10a OS=Sorghum bicolor stimulus domain; Bet v I type GN=PR10 allergen family 1218 A0A1B6PRF2 Uncharacterized protein 2 31.72 10 -3.62 0.04 5.2E-03 Oxidation-reduction None predicted None predicted Alcohol dehydrogenase, C- OS=Sorghum bicolor process terminal domain; NAD(P)- GN=SORBI_3005G103500 binding domain superfamily 1228 A0A1B6QQQ9 Uncharacterized protein 2 9.15 4 -1.61 0.16 2.4E-02 Hydrogen peroxide Peroxidase activity None predicted Catalase core domain; OS=Sorghum bicolor catabolic process Catalase, mono-functional, GN=SORBI_3001G517700 haem-containing family Energy 3 A0A194YGY2 Uncharacterized protein 52.35 78.48 60 -1.29 0.10 5.8E-03 Glycolytic process Phosphopyruvate Cytosol Enolase, C-terminal TIM OS=Sorghum bicolor hydratase activity barrel domain; enolase GN=SORBI_3010G027000 family 12 A0A194YMV2 Uncharacterized protein 42.27 63.43 37 -1.27 0.07 4.4E-03 Glycolytic process ATP binding None predicted Domain not OS=Sorghum bicolor predicted;Phosphoglycerat GN=SORBI_3004G055200 e kinase family 14 C5XYZ9 Uncharacterized protein 40.48 26.67 24 -1.20 0.01 3.6E-06 Tricarboxylic acid Phosphoenolpyruvat Chloroplast Domain not OS=Sorghum bicolor cycle e carboxylase predicted;Phosphoenolpyru GN=SORBI_3004G106900 activity vate carboxylase family 15 C5XFH6 Fructose-bisphosphate 39.96 65.92 40 -1.64 0.03 4.9E-04 Glycolytic process Fructose- None predicted Domain not predicted; aldolase OS=Sorghum bisphosphate fructose-bisphosphate bicolor aldolase activity aldolase, class-I family GN=SORBI_3003G393900 37 C5WSL1 Uncharacterized protein 28.63 34.58 18 -1.24 0.02 4.3E-03 Electron transfer Ion-ion binding Mitochondrion Domain not predicted; OS=Sorghum bicolor activity Cytochrome P450 family GN=SORBI_3001G449700 121 C5XQ07 Uncharacterized protein 18 62.06 15 -1.32 0.10 4.1E-03 Glyceraldehyde-3- Triose-phosphate Cytosol Domain not predicted; OS=Sorghum bicolor phosphate isomerase activity triosephosphate isomerase GN=SORBI_3003G072300 biosynthetic process family 135 C5XY37 Acetyltransferase component 17.2 20.04 11 -1.14 0.06 2.0E-02 Pyruvate metabolic Transferase activity, Mitochondrion 2-oxoacid dehydrogenase of pyruvate dehydrogenase process transferring acyl acyltransferase, catalytic complex OS=Sorghum groups domain; bicolor dihydrolipoyllysine-residue GN=SORBI_3004G093600 acetyltransferase component of pyruvate dehydrogenase complex family 94 144 A0A1B6QHQ8 Uncharacterized protein 16.45 17.81 9 -1.08 0.04 3.1E-02 Gluconeogenesis Glucose-6- None predicted Phosphoglucose isomerase, OS=Sorghum bicolor phosphate isomerase SIS domain 1; GN=SORBI_3001G071800 activity phosphoglucose isomerase (PGI) family 147 C5XZ73 Pyruvate dehydrogenase E1 16.3 25.13 8 -1.07 0.04 3.5E-02 Acetyl-CoA Pyruvate Intracellular Dehydrogenase, E1 component subunit alpha biosynthetic process dehydrogenase membrane- component domain; OS=Sorghum bicolor from pyruvate (acetyl-transferring) bounded pyruvate dehydrogenase GN=SORBI_3004G244000 activity organelle (acetyl-transferring) E1 component, alpha subunit, subgroup y family 181 C5YAI8 Pyruvate kinase 14.61 15.40 8 -1.27 0.02 5.4E-04 Glycolytic process Pyruvate kinase Cytoplasm Pyruvate kinase, C- OS=Sorghum bicolor activity terminal domain; pyruvate GN=SORBI_3006G267200 kinase family 191 A0A1B6P9F4 Uncharacterized protein 14.03 50.49 18 1.25 0.14 1.6E-02 Oxidation-reduction FMN protein None predicted Flavodoxin/nitric oxide OS=Sorghum bicolor process binding synthase domain; GN=SORBI_3009G188000 flavoprotein WrbA-like family 206 C5X2Z4 Glucose-6-phosphate 13.36 14.96 7 -1.08 0.02 2.2E-02 Gluconeogenesis Glucose-6- None predicted Phosphoglucose isomerase, isomerase OS=Sorghum phosphate isomerase SIS domain 1; bicolor activity phosphoglucose isomerase GN=SORBI_3002G230600 (PGI) family 220 A0A1B6PFE9 Uncharacterized protein 12.68 21.23 7 -1.11 0.07 4.2E-02 Oxidation-reduction Flavin adenine Chloroplast FAD/NAD(P)-binding OS=Sorghum bicolor process dinucleotide protein domain; FAD/NAD(P)- GN=SORBI_3007G038600 binding binding domain superfamily 261 A0A1B6QIA3 Uncharacterized protein 11.52 37.56 7 -1.16 0.09 3.3E-02 Glycolytic process None predicted Cytoplasm CBS domain; family not OS=Sorghum bicolor predicted GN=SORBI_3001G098500 436 C5XH93 Pyruvate kinase 7.94 12.97 6 -1.31 0.15 4.2E-02 Glycolytic process Pyruvate kinase Cytoplasm Pyruvate kinase, C- OS=Sorghum bicolor activity terminal domain; Pyruvate GN=SORBI_3003G130800 kinase 503 C5Z6W2 Uncharacterized protein 6.99 24.64 4 1.98 0.36 1.8E-03 Electron transport Electron transfer Anchored Phytocyanin domain; OS=Sorghum bicolor chain activity component of Cupredoxin superfamily GN=SORBI_3010G086800 plasma membrane 525 C5WY84 Ferredoxin--NADP reductase 6.75 10.00 4 -1.21 0.07 7.8E-03 Oxidation-reduction Ferredoxin-NADP+ Chloroplast Ferredoxin reductase-type OS=Sorghum bicolor process reductase activity FAD-binding domain; GN=SORBI_3001G067100 Ferredoxin--NADP reductase family 543 C5WWE9 Uncharacterized protein 6.46 34.53 4 1.11 0.07 4.6E-02 None predicted Heme protein None predicted Cytochrome b5-like OS=Sorghum bicolor binding heme/steroid binding GN=SORBI_3001G202400 domain; Cytochrome b5- like heme/steroid binding domain superfamily 549 C5YPW0 Uncharacterized protein 6.41 10.43 4 -1.24 0.13 3.4E-02 Acetyl-CoA ATP citrate synthase Cytosol ATP-citrate synthase, OS=Sorghum bicolor biosynthetic process activity citrate-binding domain; GN=SORBI_3008G132700 Succinyl-CoA synthetase- like superfamily 594 C5XW40 Uncharacterized protein 6 24.68 4 1.11 0.05 3.0E-02 Glycine ATP binding Mitochondrion Biotin/lipoyl attachment 95 OS=Sorghum bicolor decarboxylation via domain; Glycine cleavage GN=SORBI_3004G055700 glycine cleavage system H-protein family system 806 C5Y4L1 Pyruvate kinase 4 20.30 8 -1.51 0.06 2.9E-03 Glycolytic process Pyruvate kinase Cytoplasm Pyruvate kinase, barrel OS=Sorghum bicolor activity domain; Pyruvate kinase GN=SORBI_3005G034400 family 1110 C5XD18 Uncharacterized protein 2.1 38.22 2 1.29 0.16 2.2E-02 Electron transport Oxidoreductase None predicted Domain not predicted; OS=Sorghum bicolor chain activity, acting on NADH dehydrogenase GN=SORBI_3002G356400 NAD(P)H ubiquinone Fe-S protein 4, mitochondrial family Protein destination and storage 166 C5Y8M5 Uncharacterized protein 15.66 20.75 10 1.24 0.19 4.9E-02 Proteolysis Serine-type None predicted Peptidase S8/S53 domain; OS=Sorghum bicolor endopeptidase peptidase S8, subtilisin- GN=SORBI_3006G082000 activity related family 196 C5YBS5 Uncharacterized protein 13.76 11.11 6 -1.30 0.08 1.5E-02 Proteolysis Serine-type None predicted Peptidase S8/S53 domain; OS=Sorghum bicolor endopeptidase Peptidase S8, subtilisin- GN=SORBI_3006G011400 activity related family 225 C5XVC9 Uncharacterized protein 12.54 15.54 6 -1.23 0.05 3.8E-02 Chaperone-mediated Unfolded protein Chaperonin- Domain not predicted; OS=Sorghum bicolor protein folding binding containing T- chaperonin Cpn60/TCP-1 GN=SORBI_3004G047200 complex family 242 C5WW86 Uncharacterized protein 12.01 9.15 9 1.29 0.16 1.5E-02 Proteolysis Dipeptidyl-peptidase None predicted Peptidase S28; alpha/Beta OS=Sorghum bicolor activity hydrolase fold superfamily GN=SORBI_3001G196700 272 C5WV27 Uncharacterized protein 11.31 15.71 6 -1.21 0.05 5.0E-03 Peptide catabolic Metalloaminopeptid Cytosol Peptidase M1, membrane OS=Sorghum bicolor process ase activity alanine aminopeptidase, N- GN=SORBI_3001G036300 terminal domain; Peptidase M1, alanine aminopeptidase/leukotriene A4 hydrolase family 293 C5XDR4 Uncharacterized protein 10.78 17.36 14 1.69 0.51 3.7E-02 Proteolysis Cysteine-type Extracellular Peptidase C1A, papain C- OS=Sorghum bicolor endopeptidase space terminal domain; Peptidase GN=SORBI_3002G217200 activity C1A family 304 C5XUD0 Uncharacterized protein 10.7 11.38 5 -1.14 0.06 9.3E-03 Chaperone-mediated Unfolded protein Chaperonin- Domain not predicted; OS=Sorghum bicolor protein folding binding containing T- Chaperonin Cpn60/TCP-1 GN=SORBI_3004G326600 complex family 315 C5X455 Carboxypeptidase 10.42 15.59 5 1.24 0.13 1.5E-02 Proteolysis Serine-type None predicted Domain not predicted; OS=Sorghum bicolor carboxypeptidase Serine carboxypeptidase GN=SORBI_3002G401200 activity family 363 C5XXJ0 Uncharacterized protein 9.2 16.83 5 1.13 0.06 1.4E-02 Proteolysis Aspartic-type Cytosol Aspartic peptidase, DDI1- OS=Sorghum bicolor endopeptidase type domain; DNA GN=SORBI_3004G081800 activity damage-inducible protein 1 family 369 A0A1B6PNM7 Uncharacterized protein 9.01 18.22 11 1.26 0.08 4.0E-03 Proteolysis Cysteine-type None predicted Peptidase C1A, papain C- OS=Sorghum bicolor peptidase activity terminal domain; Peptidase GN=SORBI_3006G242000 C1A family 392 C5Y171 Uncharacterized protein 8.71 16.71 5 1.43 0.21 8.4E-03 Proteolysis involved Cysteine-type Extracellular Peptidase C1A, papain C- OS=Sorghum bicolor in cellular protein endopeptidase space terminal domain; Peptidase GN=SORBI_3004G142800 catabolic process activity C1A family 96 404 A0A1B6Q8T4 Uncharacterized protein 8.41 16.30 5 1.11 0.03 3.7E-02 Proteolysis Serine-type Vacuole Domain not predicted; OS=Sorghum bicolor carboxypeptidase Serine carboxypeptidase GN=SORBI_3002G024400 activity family 582 C5Z8D3 Uncharacterized protein 6.1 10.33 9 -2.06 0.04 6.5E-04 Proteolysis involved Cysteine-type Extracellular Peptidase C1A, papain C- OS=Sorghum bicolor in cellular protein endopeptidase space terminal domain; Peptidase GN=SORBI_3010G239400 catabolic process activity C1A family 627 C5YRI0 Uncharacterized protein 5.61 10.09 3 -1.14 0.03 6.2E-03 Proteasome Protein binding Proteasome Proteasome component OS=Sorghum bicolor assembly, ubiquitin- regulatory (PCI) domain; GN=SORBI_3008G161800 dependent protein particle, lid Tetratricopeptide-like catabolic process subcomplex helical domain superfamily 722 C5Z8D2 Uncharacterized protein 4.62 6.99 5 -1.41 0.10 5.3E-03 Proteolysis Cysteine-type Extracellular Peptidase C1A, papain C- OS=Sorghum bicolor endopeptidase space terminal domain; Peptidase GN=SORBI_3010G239300 activity C1A family 790 A0A1B6P5Q3 Uncharacterized protein 4.07 14.61 4 1.29 0.08 6.4E-04 Proteolysis Cysteine-type None predicted Peptidase C1A, papain C- OS=Sorghum bicolor peptidase activity terminal domain; Peptidase GN=SORBI_3009G008800 C1A family 841 C5XZT7 Uncharacterized protein 3.95 8.95 3 1.37 0.13 2.1E-03 Protein catabolic Aspartic-type None predicted Peptidase family A1 OS=Sorghum bicolor process endopeptidase domain; Aspartic peptidase GN=SORBI_3004G257700 activity A1 family 868 C5YPX8 Uncharacterized protein 3.74 8.91 2 2.93 0.54 4.3E-04 Protein catabolic Aspartic-type None predicted Peptidase family A1 OS=Sorghum bicolor process endopeptidase domain; Aspartic peptidase GN=SORBI_3008G134700 activity A1 family 934 A0A1B6QEG2 Uncharacterized protein 3.16 8.73 3 1.31 0.20 3.3E-02 Proteolysis Cysteine-type None predicted Peptidase C1A, papain C- OS=Sorghum bicolor endopeptidase terminal domain; Peptidase GN=SORBI_3002G315800 activity C1A family 980 C5XUD8 Uncharacterized protein 2.8 4.39 2 -1.37 0.14 1.5E-02 Proteolysis Dipeptidyl-peptidase Cytoplasm G-patch domain; Family OS=Sorghum bicolor activity not predicted GN=SORBI_3004G327500 Protein synthesis 80 A0A194YLC1 Uncharacterized protein 21.66 23.99 14 -1.41 0.15 2.3E-02 rRNA maturation Nucleotide binding Intracellular Domain not predicted; OS=Sorghum bicolor DEAD-box superfamily GN=SORBI_3010G251100 246 A0A1B6Q0S3 Uncharacterized protein 11.92 13.16 6 -1.11 0.04 3.4E-02 Translation Aminoacyl-tRNA Cytoplasm Aminoacyl-tRNA OS=Sorghum bicolor ligase activity synthetase, class II domain; GN=SORBI_3003G012200 aspartate-tRNA synthetase, type 2 family 330 C5YIU0 Uncharacterized protein 10.05 39.23 6 1.13 0.07 3.9E-02 Translation Structural Ribosome Ribosomal protein S8 OS=Sorghum bicolor constituent of domain and superfamily GN=SORBI_3007G212600 ribosome 355 A0A1B6PQ87 Uncharacterized protein 9.31 12.08 6 -1.38 0.04 2.6E-03 Translation Structural Ribosome Domain not predicted; OS=Sorghum bicolor constituent of Ribosomal protein L3 GN=SORBI_3005G050500 ribosome family 396 C5XE07 Uncharacterized protein 8.59 25.99 5 -1.15 0.07 2.1E-02 Translation Translation Eukaryotic Translation elongation OS=Sorghum bicolor elongation factor translation factor EF1B, beta/delta GN=SORBI_3002G373800 activity elongation subunit, guanine nucleotide factor 1 exchange domain; complex translation elongation factor eEF-1beta-like 97 superfamily 483 C5XIN8 Uncharacterized protein 7.31 30.17 5 1.15 0.06 1.8E-02 Ribosomal small Structural Cytosolic small Plectin/S10, N-terminal OS=Sorghum bicolor subunit assembly constituent of ribosomal domain; 40S Ribosomal GN=SORBI_3003G436700 ribosome subunit protein S10 520 C5XGD3 60S ribosomal protein L13 6.81 24.04 4 -1.29 0.04 8.3E-03 Translation Structural Cytosolic large Domain not predicted; OS=Sorghum bicolor constituent of ribosomal Ribosomal protein L13e GN=SORBI_3003G408100 ribosome subunit family 522 C5YAR4 Uncharacterized protein 6.77 9.23 3 1.24 0.11 2.2E-02 Translational Translation None predicted Domain not predicted; OS=Sorghum bicolor initiation initiation factor Plant specific eukaryotic GN=SORBI_3006G121100 activity initiation factor 4B family 707 C5Z466 Uncharacterized protein 4.75 8.78 2 1.16 0.03 8.6E-03 Translation Structural Large ribosomal Ribosomal protein OS=Sorghum bicolor constituent of subunit L1/ribosomal biogenesis GN=SORBI_3010G161500 ribosome protein domain; Ribosomal protein L1-like superfamily 816 C5XDZ7 Uncharacterized protein 4 15.17 2 -1.66 0.06 1.4E-03 Cytoplasmic Structural Cytosolic large Ribosomal protein OS=Sorghum bicolor translation constituent of ribosomal L18e/L15P domain; GN=SORBI_3002G372400 ribosome subunit Ribosomal protein L15 family 930 C5YZW6 Uncharacterized protein 3.2 14.91 2 -1.48 0.13 5.0E-03 Translation Structural Cytosolic large TRASH domain; OS=Sorghum bicolor constituent of ribosomal Ribosomal protein L24e- GN=SORBI_3009G177700 ribosome subunit related family 958 C5XPN5 60S ribosomal protein L36 2.95 16.96 2 -1.30 0.06 9.7E-03 Translation Structural Cytosolic large Domain not predicted; OS=Sorghum bicolor constituent of ribosomal Ribosomal protein L36e GN=SORBI_3003G351000 ribosome subunit family 964 C5YNS4 40S ribosomal protein S6 2.91 9.56 2 -1.28 0.07 4.7E-02 Ribosomal small Structural Cytosolic small Domain not predicted; OS=Sorghum bicolor subunit biogenesis constituent of ribosomal Ribosomal protein S6e GN=SORBI_3008G098800 ribosome subunit family 1022 C5Z5W5 Ribosomal protein L19 2.52 15.87 3 -1.38 0.09 2.3E-02 Translation Structural Cytosolic large Ribosomal protein OS=Sorghum bicolor constituent of ribosomal L19/L19e domain and GN=SORBI_3010G069400 ribosome subunit superfamily 1074 C5WRR1 Uncharacterized protein 2.22 5.30 2 -1.23 0.13 4.7E-02 Translational Ribosomal small Intracellular RNase L inhibitor RLI, OS=Sorghum bicolor initiation subunit binding possible metal-binding GN=SORBI_3001G447300 domain; RLI1 family 1077 A0A1B6QMW1 Uncharacterized protein 2.21 3.55 2 -1.24 0.08 1.7E-02 None predicted Nucleic acid binding Cytosol Acinus, RNA recognition OS=Sorghum bicolor motif domain; RNA- GN=SORBI_3001G354600 binding domain superfamily 1223 C5YMC5 Uncharacterized protein 2 34.44 5 1.43 0.29 3.3E-02 Translation Small ribosomal Cytosolic small Ribosomal protein OS=Sorghum bicolor subunit rRNA ribosomal S13/S15, N-terminal GN=SORBI_3007G019000 binding subunit domain; Ribosomal protein S15 family 1410 A0A194YL15 Uncharacterized protein 1.72 8.33 2 -1.57 0.27 4.9E-02 Translation Structural Ribosome KOW domain; Ribosomal OS=Sorghum bicolor constituent of protein L27e family GN=SORBI_3010G230700 ribosome 1454 C5WZW1 Uncharacterized protein 1.51 7.62 2 -1.21 0.06 1.3E-02 Ribosomal large 5S rRNA binding Cytosolic large Ribosomal protein L5 OS=Sorghum bicolor subunit assembly ribosomal eukaryotic/L18 archaeal, GN=SORBI_3001G517600 subunit C-terminal domain; Ribosomal protein L5 98 eukaryotic/L18 archaeal family Transporters 4 C5Y9I0 Plasma membrane ATPase 50.02 31.44 26 -1.18 0.02 8.0E-03 Proton export across Proton-exporting Plasma Cation-transporting P-type OS=Sorghum bicolor plasma membrane ATPase activity membrane ATPase, N-terminal GN=SORBI_3006G247100 domain; P-type ATPase family 64 C5XHF1 Uncharacterized protein 24.05 67.12 47 1.65 0.15 2.7E-04 Plasmodesmata- Manganese ion Extracellular Cupin 1 domain; germin OS=Sorghum bicolor mediated binding region family GN=SORBI_3003G136200 intercellular transport 189 C5WNH9 Uncharacterized protein 14.11 14.92 10 -1.71 0.04 3.2E-04 None predicted Thiamine Intracellular Thiamine pyrophosphate OS=Sorghum bicolor pyrophosphate enzyme, central domain; GN=SORBI_3001G405500 binding, magnesium thiamine pyrophosphate ion binding (TPP)-dependent enzyme family 290 C5XSW1 Uncharacterized protein 10.81 17.52 6 -1.14 0.07 3.1E-02 Phosphate ion Phosphate ion Mitochondrial Domain not predicted; OS=Sorghum bicolor transmembrane transmembrane inner membrane Mitochondrial carrier GN=SORBI_3004G310300 transport transporter activity domain superfamily 299 C5XFB9 Uncharacterized protein 10.75 22.61 5 1.52 0.30 1.6E-02 ATP hydrolysis Proton-transporting Membrane Domain not predicted; V- OS=Sorghum bicolor coupled proton ATPase activity, type ATPase subunit E GN=SORBI_3003G244200 transport rotational family mechanism 332 C5XXV1 Uncharacterized protein 10.04 25.26 5 -1.22 0.06 7.6E-03 Ion transmembrane Water channel Integral Domain not predicted; OS=Sorghum bicolor transport activity component of Aquaporin transporter GN=SORBI_3004G222000 plasma family membrane 346 A0A1B6Q2N0 Uncharacterized protein 9.72 18.67 5 1.33 0.20 1.7E-02 None predicted Manganese ion Extracellular Cupin 1 domain; Germin OS=Sorghum bicolor binding region family GN=SORBI_3003G112200 448 C5YUI7 Uncharacterized protein 7.74 33.70 6 1.32 0.07 8.6E-03 Anion Voltage-gated anion Mitochondrial Domain not predicted; OS=Sorghum bicolor transmembrane channel activity outer membrane Porin, eukaryotic type GN=SORBI_3009G212400 transport family 463 C5Y1D9 Uncharacterized protein 7.51 22.15 9 -1.16 0.07 1.2E-02 Ion transmembrane Water channel Integral Domain not predicted; OS=Sorghum bicolor transport activity component of Aquaporin transporter GN=SORBI_3004G288700 plasma family membrane 523 C5YBI6 Uncharacterized protein 6.76 4.17 3 1.18 0.12 3.6E-02 Transport of virus in RNA binding None predicted Initiation factor eIF-4 OS=Sorghum bicolor host, cell to cell gamma, MA3 domain; GN=SORBI_3006G136400 MIF4G-like domain superfamily 585 C5XHX2 Uncharacterized protein 6.04 31.25 4 1.41 0.07 1.9E-02 Plasmodesmata- Manganese ion Extracellular Cupin 1 domain; Germin OS=Sorghum bicolor mediated binding region family GN=SORBI_3003G427700 intercellular transport 680 C5YSH7 Uncharacterized protein 5.09 56.32 3 1.22 0.08 2.7E-03 Metal ion transport Metal ion binding Cytoplasm Heavy metal-associated OS=Sorghum bicolor domain, HMA; Heavy GN=SORBI_3008G179500 metal-associated domain 99 superfamily 704 C5YIX1 Mitochondrial pyruvate 4.76 35.45 3 1.32 0.17 2.0E-02 Mitochondrial Pyruvate Mitochondrial Domain not predicted; carrier OS=Sorghum bicolor pyruvate transmembrane inner membrane Mitochondrial pyruvate GN=SORBI_3007G078100 transmembrane transporter activity carrier family transport 846 C5XHX3 Uncharacterized protein 3.89 18.22 3 1.34 0.12 1.3E-03 Plasmodesmata- Manganese ion Extracellular Cupin 1 domain; Germin OS=Sorghum bicolor mediated binding region family GN=SORBI_3003G427800 intercellular transport Signal transduction 7 A0A1B6QGY0 Uncharacterized protein 48.46 30.96 32 -1.39 0.03 2.2E-06 None predicted GTP binding None predicted Small GTP-binding protein OS=Sorghum bicolor domain; translation GN=SORBI_3001G021300 protein, beta-barrel domain superfamily 33 A0A1B6Q7A0 Uncharacterized protein 29.94 38.81 21 1.13 0.05 4.7E-03 Phosphorylation Phosphatidylinositol Cytoplasm Phosphatidylinositol-4- OS=Sorghum bicolor phosphate kinase phosphate 5-kinase, core GN=SORBI_3003G373500 activity domain; Chaperonin Cpn60/TCP-1 family 77 C5XES8 Uncharacterized protein 22.33 48.92 12 -1.20 0.12 4.8E-02 Phosphorylation Phosphotransferase None predicted Carbohydrate kinase PfkB OS=Sorghum bicolor activity, alcohol domain; GN=SORBI_3003G386000 group as acceptor ribokinase/fructokinase family 92 A0A1B6PIK2 Uncharacterized protein 20.66 21.73 17 1.36 0.23 2.0E-02 Dephosphorylation acid phosphatase None predicted Domain not predicted; OS=Sorghum bicolor activity Calcineurin-like GN=SORBI_3007G190200 phosphoesterase family 190 A0A194YKQ5 Uncharacterized protein 14.05 52.12 22 -1.30 0.05 2.4E-02 Phosphorylation Phosphoglycerate None predicted Domain not predicted; OS=Sorghum bicolor kinase activity Phosphoglycerate kinase GN=SORBI_3010G221800 family 199 A0A1B6P7A4 Uncharacterized protein 13.6 31.35 11 1.55 0.34 1.8E-02 Dephosphorylation Acid phosphatase None predicted Domain not predicted; acid OS=Sorghum bicolor activity phosphatase, plant family GN=SORBI_3009G070800 262 A0A1B6PBB3 Uncharacterized protein 11.5 24.69 6 1.23 0.04 3.6E-04 Nucleoside Nucleoside None predicted Nucleoside diphosphate OS=Sorghum bicolor diphosphate diphosphate kinase kinase-like domain; GN=SORBI_3009G259200 phosphorylation activity Nucleoside diphosphate kinase family 526 C5XQD5 Uncharacterized protein 6.72 8.99 4 1.25 0.07 4.6E-03 Protein Protein kinase Integral Protein kinase domain; OS=Sorghum bicolor phosphorylation activity component of Protein kinase-like domain GN=SORBI_3003G214900 membrane superfamily 638 C5Y2R8 Uncharacterized protein 5.52 16.60 4 1.28 0.12 6.8E-03 Protein Transmembrane Plasma Leucine-rich repeat- OS=Sorghum bicolor phosphorylation receptor protein membrane containing N-terminal, GN=SORBI_3005G126200 serine/threonine plant-type domain; kinase activity Leucine-rich repeat domain superfamily 776 A0A1B6PPB3 Uncharacterized protein 4.14 12.15 2 -1.27 0.07 2.5E-02 Phosphate- Inorganic Cytoplasm Inorganic pyrophosphatase OS=Sorghum bicolor containing diphosphatase domain; Inorganic GN=SORBI_3006G276700 compound activity pyrophosphatase metabolic process superfamily 100 921 C5XR62 Uncharacterized protein 3.28 5.71 4 -1.14 0.09 4.5E-02 Protein Calmodulin- Cytoplasm Protein kinase domain; OS=Sorghum bicolor autophosphorylation dependent protein Protein kinase-like domain GN=SORBI_3003G225200 kinase activity superfamily 1393 C5YAK6 Uncharacterized protein 1.84 6.31 2 1.38 0.14 1.3E-02 Clathrin-dependent Clathrin heavy chain Membrane Domain not predicted; OS=Sorghum bicolor endocytosis binding, structural Clathrin light chain family GN=SORBI_3006G268900 molecule activity 1425 C5XTL6 UMP-CMP kinase 1.65 14.55 2 1.20 0.10 1.8E-02 Phosphorylation Kinase activity Nucleus Domain not predicted; OS=Sorghum bicolor Adenylate kinase/UMP- GN=SORBI_3004G318100 CMP kinase family Transcription 57 C5Y220 Uncharacterized protein 25.17 37.16 24 1.26 0.07 2.7E-03 Methylation O-methyltransferase None predicted Plant methyltransferase OS=Sorghum bicolor activity dimerization domain; o- GN=SORBI_3005G107900 methyltransferase COMT- type family 395 C5Z4W3 Uncharacterized protein 8.63 24.90 5 1.43 0.16 4.7E-03 Methylation O-methyltransferase None predicted Domain not predicted; O- OS=Sorghum bicolor activity methyltransferase, family 3 GN=SORBI_3010G052200 518 A0A1B6QMW0 Uncharacterized protein 6.81 21.83 5 -2.02 0.07 1.2E-02 Methylation O-methyltransferase None predicted Plant methyltransferase OS=Sorghum bicolor activity dimerization domain; O- GN=SORBI_3001G354200 methyltransferase COMT- type family 533 C5Y9F6 Uncharacterized protein 6.59 11.72 3 1.29 0.12 5.8E-03 Nucleic acid Endonuclease None predicted S1/P1 nuclease domain; OS=Sorghum bicolor phosphodiester bond activity Phospholipase C/P1 GN=SORBI_3006G244400 hydrolysis nuclease domain superfamily 702 C5Z0T1 Uncharacterized protein 4.77 29.17 4 1.25 0.07 2.7E-03 Pseudouridine Pseudouridine Nucleus Zinc finger, RING-H2-type OS=Sorghum bicolor synthesis synthase activity domain; Zinc finger, GN=SORBI_3009G193800 RING/FYVE/PHD-type superfamily 787 A0A1B6Q2Y5 Uncharacterized protein 4.08 5.22 2 -1.32 0.12 2.4E-02 RNA editing Protein binding None predicted Pentacotripeptide-repeat OS=Sorghum bicolor region of PRORP domain; GN=SORBI_3003G134000 Tetratricopeptide-like helical domain superfamily 836 C5WVZ7 Uncharacterized protein 4 23.85 2 -1.41 0.07 3.4E-02 mRNA splicing, via RNA binding Nucleus RNA recognition motif OS=Sorghum bicolor spliceosome domain; RNA-binding GN=SORBI_3001G041100 domain superfamily 865 C5YH53 Uncharacterized protein 3.74 17.96 3 1.25 0.06 1.8E-03 RNA modification RNA binding Intracellular C2 domain; C2 domain OS=Sorghum bicolor superfamily GN=SORBI_3007G179900 Cell structure 563 C5YYR1 Uncharacterized protein 6.24 26.06 7 -1.18 0.07 4.6E-02 None predicted ATP binding None predicted Domain not predicted; OS=Sorghum bicolor Actin family GN=SORBI_3009G153000 804 A0A1B6PBJ7 Uncharacterized protein 4 57.56 31 1.22 0.17 4.8E-02 None predicted ATP binding None predicted Domain not predicted; OS=Sorghum bicolor Actin family GN=SORBI_3008G047000 907 C5YXZ2 Uncharacterized protein 3.41 7.02 2 1.53 0.31 1.6E-02 Regulate the actin Protein binding None predicted Leucine-rich repeat- OS=Sorghum bicolor and microtubule containing N-terminal, 101 GN=SORBI_3009G002400 cytoskeleton plant-type; leucine-rich repeat domain superfamily 1127 A0A1B6PCX7 Uncharacterized protein 2.07 5.41 2 1.66 0.44 2.5E-02 Lignan biosynthetic None predicted Plasmodesmata NmrA-like domain; OS=Sorghum bicolor process NAD(P)-binding domain GN=SORBI_3008G106700 superfamily 1378 A0A1B6Q2X9 Uncharacterized protein 1.89 33.18 24 1.87 0.39 5.6E-03 Cytoskeleton Structural Cytoskeleton Tubulin/FtsZ, GTPase OS=Sorghum bicolor organization constituent of domain; Tubulin family GN=SORBI_3003G135400 cytoskeleton Unclear classification 19 C5YBP8 Uncharacterized protein 36.79 55.68 32 -1.25 0.05 8.3E-04 None predicted None predicted None predicted E2F/DP family winged- OS=Sorghum bicolor helix DNA-binding GN=SORBI_3006G009000 domain; von Willebrand factor A-like domain superfamily 75 C5YBP9 Uncharacterized protein 22.35 24.11 12 -1.19 0.06 9.5E-03 None predicted Protein binding None predicted Domain not predicted; six- OS=Sorghum bicolor bladed beta-propeller, GN=SORBI_3006G009100 TolB-like superfamily 251 A0A1B6PH65 Uncharacterized protein 11.83 20.75 6 1.15 0.10 4.4E-02 None predicted None predicted None predicted Domain not predicted OS=Sorghum bicolor domain; Short-chain GN=SORBI_3007G112100 dehydrogenase/reductase SDR family 286 C5YWC5 Uncharacterized protein 10.93 18.37 7 -1.10 0.03 2.7E-02 None predicted None predicted None predicted Peptidase M24 domain; OS=Sorghum bicolor Peptidase M24, methionine GN=SORBI_3009G104100 aminopeptidase family 303 C5WMM5 Uncharacterized protein 10.71 35.36 8 1.08 0.03 2.6E-02 None predicted None predicted None predicted Remorin, C-terminal OS=Sorghum bicolor domain; family not GN=SORBI_3001G535400 predicted family 384 C5WY51 Uncharacterized protein 8.84 12.03 4 1.42 0.22 8.6E-03 None predicted None predicted Cytoplasm Fasciclin domain and OS=Sorghum bicolor superfamily GN=SORBI_3001G063500 465 C5YAK8 Uncharacterized protein 7.5 17.87 4 -1.29 0.06 4.2E-03 None predicted None predicted Cytoplasm Domain of unknown OS=Sorghum bicolor function DUF3700; GN=SORBI_3006G269200 Nucleophile aminohydrolases, N- terminal 467 C5XJI8 Uncharacterized protein 7.49 37.67 5 1.19 0.06 6.6E-03 None predicted None predicted Cytosol Domain not predicted; OS=Sorghum bicolor Nucleic acid-binding, OB- GN=SORBI_3003G003000 fold superfamily 514 C5XKH1 Uncharacterized protein 6.88 25.64 7 1.23 0.13 4.5E-02 None predicted None predicted Vacuole Domain not predicted; DJ- OS=Sorghum bicolor 1/PfpI family GN=SORBI_3003G017700 601 C5X978 Uncharacterized protein 5.96 47.37 9 1.42 0.13 9.3E-03 None predicted None predicted None predicted C2 domain domain; C2 OS=Sorghum bicolor domain superfamily GN=SORBI_3002G307600 609 C5XNE6 Uncharacterized protein 5.89 28.04 7 1.57 0.38 4.2E-02 None predicted Protein binding None predicted Domain not predicted; OS=Sorghum bicolor Embryo-specific ATS3 GN=SORBI_3003G050200 family 102 652 C5XHE5 Uncharacterized protein 5.35 11.03 2 -1.28 0.06 6.3E-03 None predicted None predicted Cytoplasm Domain not predicted; OS=Sorghum bicolor trimeric LpxA-like GN=SORBI_3003G135600 superfamily 723 C5XTT3 Uncharacterized protein 4.62 32.06 4 1.20 0.09 2.2E-02 None predicted Protein binding None predicted SWIB domain; OS=Sorghum bicolor SWIB/MDM2 domain GN=SORBI_3004G028400 superfamily 818 C5YI64 Uncharacterized protein 4 15.89 2 1.52 0.08 2.7E-04 None predicted None predicted Membrane DOMON domain; family OS=Sorghum bicolor not predicted GN=SORBI_3007G198000 1173 C5WPI6 Uncharacterized protein 2.02 15.35 2 1.30 0.17 3.1E-02 None predicted None predicted None predicted Domain not predicted; OS=Sorghum bicolor Protein of unknown GN=SORBI_3001G132100 function DUF4370 1412 A0A1B6QD95 Uncharacterized protein 1.72 9.66 2 1.48 0.13 5.0E-03 None predicted None predicted Cytoplasm FAS1 domain; FAS1 OS=Sorghum bicolor domain superfamily GN=SORBI_3002G244300 a Protein number assigned in ProteinPilot software. b Protein accession numbers obtained from the TrEMBL database [incorporated within the UniProt database (http://www.uniprot.org)] searches against sequences of Sorghum bicolor only. c Protein score generated by ProteinPilot software relating to the confidence of protein identification. A protein identification threshold of 1.3 was applied to the data, which only retains proteins identified with a 95% confidence. d Percentage coverage is determined by the number of amino acids of sequenced peptides against the total length of the protein, with a threshold of at least 95% confidence. e Sequenced peptide refers to the number of peptide that were sequenced and gave rise to protein identity. All proteins that were identified by means of a single peptide were filtered out of the dataset. f Probability value of the quantitative difference between the treatment and control protein abundance being due to chance alone. gStandard deviation of the ratios of drought stressed samples (n=4). h Ratio represents the average fold-change (n = 4) induced by treatment relative to control. Positive values indicate an up-regulation. i Gene ontology analysis as predicted on the IntePro database (https://www.ebi.ac.uk/interpro/) and UniProt database (http://www.uniprot.org). P denotes Biological Process, F denotes Functional Process, and C denotes Cellular Component. j Conserved domains and family name as predicted by InterPro database (http://www.ebi.ac.uk/interpro/). 103 Table 4.2: List of drought stress responsive proteins identified from SA 1441 root samples using the iTRAQ and database searches. Prot Accessionb Protein Name Scorc % Seq Ratioh SDg p-valf GO Analysisi Conserved domains and Noa Covd Pepe family namej P F C Disease/defence 5 C5YPZ1 Uncharacterized protein 41.13 41.14 29 -1.25 0.03 1.16E-04 None predicted ATP binding None predicted Domain not predicted, heat OS=Sorghum bicolor shock protein 70 family GN=SORBI_3008G136000 36 C5WNL8 Uncharacterized protein 26.13 56 20 1.21 0.05 2.71E-02 Cellular response Peroxidase Chloroplast Haem peroxidase, plant OS=Sorghum bicolor to oxidative stress activity domain, class I peroxidase GN=SORBI_3001G410200 family 38 C5XIN9 Peroxidase OS=Sorghum 25.77 43.24 34 1.33 0.14 8.38E-03 Cellular oxidant Oxidoreductase Extracellular Haem peroxidase, plant bicolor detoxification activity region domain; plant peroxidase GN=SORBI_3003G436800 family 46 A0A1B6QHY1 Uncharacterized protein 24.35 31.25 16 -1.27 0.10 3.41E-02 Oxidation- Oxidoreductase None predicted UDP-glucose/GDP-mannose OS=Sorghum bicolor reduction process activity, acting on dehydrogenase, N-terminal GN=SORBI_3001G084100 the CH-OH group domain; UDP-glucose 6- of donors, NAD dehydrogenase, eukaryotic or NADP as type family acceptor 57 Q4VQB2 Pathogenesis-related 22.93 89.38 33 1.38 0.21 1.63E-02 None predicted Defence response None predicted Bet v I/Major latex protein protein 10b OS=Sorghum domain; Bet v I type allergen bicolor GN=PR10 family 66 C5YB27 Peroxidase OS=Sorghum 21.8 42.49 26 -1.26 0.03 3.52E-03 Response to Oxidoreductase Extracellular Secretory peroxidase domain, bicolor oxidative stress activity region plant peroxidase family GN=SORBI_3006G277800 78 A0A194YLC1 Uncharacterized protein 20.1 22.84 11 -1.14 0.04 1.03E-02 None predicted Hydrolase activity Cytoplasm Ubiquitin-conjugating OS=Sorghum bicolor enzyme family GN=SORBI_3010G251100 97 C5Z475 Peroxidase OS=Sorghum 18.25 48.31 20 1.28 0.12 3.81E-03 Oxidation- Peroxidase Extracellular Secretory peroxidase bicolor reduction process activity region domain; plant peroxidase GN=SORBI_3010G162000 family 102 C5YU67 Uncharacterized protein 17.35 12.23 9 -1.14 0.03 1.85E-02 None predicted ATP binding None predicted Domain not predicted; heat OS=Sorghum bicolor shock protein 70 family GN=SORBI_3009G067000 119 C5Y2Z7 Uncharacterized protein 16.38 12.45 10 -1.17 0.05 5.66E-03 Ubiquitin- Ubiquitin Cytoplasm Ubiquitin-activating enzyme OS=Sorghum bicolor dependent protein activating enzyme E1, FCCH domain; GN=SORBI_3005G004400 catabolic process activity Ubiquitin/SUMO-activating enzyme E1 family 130 C5XI24 Peroxidase OS=Sorghum 15.35 33.52 15 1.11 0.06 4.98E-02 Hydrogen Peroxidase Extracellular Secretory peroxidase bicolor peroxide catabolic activity region domain; plant peroxidase GN=SORBI_3003G140700 process family 131 J7FJG8 Late embryogenesis 15.33 47.29 10 2.15 0.92 4.85E-02 Positive None predicted None predicted Domain not predicted; late 104 abundant protein 3 regulation of embryogenesis abundant OS=Sorghum bicolor response to water protein family GN=Lea3 deprivation 159 C5XGH3 Peroxidase OS=Sorghum 13.41 23.05 7 1.18 0.07 1.15E-02 Cellular oxidant Peroxidase Extracellular Secretory peroxidase bicolor detoxification activity region domain; plant peroxidase GN=SORBI_3003G121100 family 161 C5Y1X4 Uncharacterized protein 13.34 25.86 8 -1.52 0.07 1.01E-02 Oxidation- Oxidoreductase None predicted Alcohol dehydrogenase OS=Sorghum bicolor reduction process activity domain; NAD(P)-binding GN=SORBI_3005G103300 domain superfamily 187 C5XMX0 Peroxidase OS=Sorghum 12.12 42.27 13 1.16 0.08 2.21E-02 Hydrogen Peroxidase Extracellular Secretory peroxidase bicolor peroxide catabolic activity region domain; plant peroxidase GN=SORBI_3003G183300 process family 207 C5Z864 Peroxidase OS=Sorghum 11.43 25.68 8 1.15 0.03 5.76E-03 Hydrogen Peroxidase Extracellular Secretory peroxidase bicolor peroxide catabolic activity region domain; plant peroxidase GN=SORBI_3010G232500 process family 214 C5WNE0 Uncharacterized protein 11.17 32.08 7 1.20 0.08 1.39E-02 None predicted None predicted None predicted Alpha crystallin/Hsp20 OS=Sorghum bicolor domain; small heat shock GN=SORBI_3001G264300 protein HSP20 family 267 C5YYX3 Uncharacterized protein 9.89 33.18 5 1.31 0.10 1.72E-02 Glutathione Glutathione Cytoplasm Glutathione S-transferase, N OS=Sorghum bicolor metabolic process transferase and C-terminal domain; GN=SORBI_3009G017800 activity Thioredoxin-like superfamily 287 C5YZI4 Peroxidase OS=Sorghum 9.48 22.87 6 1.19 0.07 3.64E-03 Hydrogen Peroxidase Extracellular Secretory peroxidase bicolor peroxide catabolic activity region domain; plant peroxidase GN=SORBI_3009G032600 process family 306 U5U008 Betaine-aldehyde 9.12 20.79 5 -1.10 0.05 3.15E-02 Oxidation- Oxidoreductase Cytoplasm Aldehyde dehydrogenase dehydrogenase 2 reduction process activity domain; Aldehyde/histidinol OS=Sorghum bicolor dehydrogenase superfamily GN=BADH2 307 C5Z469 Peroxidase OS=Sorghum 9.06 28.34 7 1.29 0.15 3.09E-02 Hydrogen Peroxidase Extracellular Secretory peroxidase bicolor peroxide catabolic activity region domain; plant peroxidase GN=SORBI_3010G161600 process family 324 C5WPD8 Uncharacterized protein 8.61 32.97 20 1.24 0.16 3.75E-02 None predicted None predicted None predicted Ginkbilobin-2-homologous OS=Sorghum bicolor domain and superfamily GN=SORBI_3001G418200 336 C5XF22 Uncharacterized protein 8.39 22.11 4 1.40 0.18 5.74E-03 None predicted None predicted None predicted Ricin B-related lectin domain OS=Sorghum bicolor and family GN=SORBI_3003G105700 371 C5XVU9 S- 7.94 14.7 4 -1.22 0.02 2.29E-02 Oxidation- S- None predicted Alcohol dehydrogenase, N- (hydroxymethyl)glutathion reduction process (hydroxymethyl) terminal domain; alcohol e dehydrogenase glutathione dehydrogenase class III OS=Sorghum bicolor dehydrogenase family GN=SORBI_3004G343200 activity 385 C5WZ12 Uncharacterized protein 7.71 25.25 10 1.24 0.07 8.95E-03 Glutathione Glutathione Cytoplasm Glutathione S-transferase, C- OS=Sorghum bicolor metabolic process transferase terminal-like domain; GN=SORBI_3001G514400 activity thioredoxin-like superfamily 403 A0A1B6P5R2 Uncharacterized protein 7.44 29.11 3 1.68 0.30 4.26E-03 Negative Serine-type None predicted Domain not predicted; OS=Sorghum bicolor regulation of endopeptidase proteinase inhibitor I13, GN=SORBI_3009G009600 endopeptidase inhibitor activity potato inhibitor I superfamily activity 105 412 C5YD83 Uncharacterized protein 7.29 28.18 7 1.57 0.09 3.80E-05 None predicted None predicted None predicted Thioredoxin-like fold; OS=Sorghum bicolor thioredoxin-like superfamily GN=SORBI_3006G031200 429 A0A1B6QJR7 Uncharacterized protein 7.05 11.97 4 1.19 0.09 3.67E-02 Hydrogen Peroxidase Extracellular Secretory peroxidase OS=Sorghum bicolor peroxide catabolic activity region domain; plant peroxidase GN=SORBI_3001G189000 process family 437 C5XIY0 Peroxidase OS=Sorghum 6.85 15.66 12 1.18 0.10 4.48E-02 Hydrogen Peroxidase Extracellular Secretory peroxidase bicolor peroxide catabolic activity region domain; plant peroxidase GN=SORBI_3003G152000 process family 468 C5WQF6 Uncharacterized protein 6.42 47.18 5 1.17 0.08 3.06E-02 None predicted None predicted None predicted Vicinal oxygen chelate OS=Sorghum bicolor (VOC) domain; GN=SORBI_3001G147300 glyoxalase/Bleomycin resistance protein/Dihydroxybiphenyl dioxygenase superfamily 569 C5Z0G0 Uncharacterized protein 5.32 15 3 1.27 0.19 4.76E-02 Response to stress None predicted None predicted UspA domain; universal OS=Sorghum bicolor stress protein A family GN=SORBI_3009G188300 622 C5XIY1 Peroxidase OS=Sorghum 4.6 9.244 4 1.45 0.27 2.76E-02 Hydrogen Peroxidase Extracellular Secretory peroxidase bicolor peroxide catabolic activity region domain; plant peroxidase GN=SORBI_3003G152100 process family 630 C5X760 Uncharacterized protein 4.5 15.74 3 -1.14 0.04 2.61E-02 Oxidation- Oxidoreductase Cytoplasm NADPH-dependent FMN OS=Sorghum bicolor reduction process activity, reductase-like domain; GN=SORBI_3002G004700 NAD(P)H flavoprotein WrbA-like dehydrogenase family (quinone) activity 641 C5XIY2 Peroxidase OS=Sorghum 4.32 13.21 8 1.27 0.13 1.13E-02 Hydrogen Peroxidase Extracellular Secretory peroxidase bicolor peroxide catabolic activity region domain; plant peroxidase GN=SORBI_3003G152200 process family 742 C5YXZ4 Uncharacterized protein 3.93 5.865 3 1.24 0.03 1.41E-02 None predicted Protein binding None predicted Leucine-rich repeat- OS=Sorghum bicolor containing N-terminal, plant- GN=SORBI_3009G002600 type domain; leucine-rich repeat domain superfamily 779 C5X040 Peroxidase OS=Sorghum 3.58 10.18 2 1.24 0.11 1.72E-02 Hydrogen Peroxidase Extracellular Secretory peroxidase bicolor peroxide catabolic activity region domain; plant peroxidase GN=SORBI_3001G080300 process family 794 C5X764 Dirigent protein 3.48 10.95 2 1.27 0.07 3.87E-04 None predicted None predicted None predicted Domain not predicted; OS=Sorghum bicolor dirigent protein family GN=SORBI_3002G005200 885 A0A1B6QFH9 Uncharacterized protein 2.78 10.42 2 1.22 0.09 4.41E-02 None predicted None predicted None predicted Stress responsive alpha-beta OS=Sorghum bicolor barrel domain; dimeric alpha- GN=SORBI_3002G371400 beta barrel superfamily 909 A0A1B6P5R5 Uncharacterized protein 2.56 43.66 2 1.31 0.17 3.38E-02 Negative Serine-type None predicted Domain not predicted; OS=Sorghum bicolor regulation of endopeptidase proteinase inhibitor I13, GN=SORBI_3009G009700 endopeptidase inhibitor activity potato inhibitor I family activity 972 Q8W0Q8 Small heat shock-like 2.19 8.503 2 1.44 0.12 1.11E-03 None predicted None predicted None predicted Alpha crystallin/Hsp20 protein OS=Sorghum domain; HSP20-like 106 bicolor GN=SB32H17.11 chaperone superfamily 998 A0A1B6PJ81 Uncharacterized protein 2.11 6.548 2 1.73 0.33 1.06E-02 Oxidation- Nitronate None predicted Nitronate monooxygenase OS=Sorghum bicolor reduction process monooxygenase domain; aldolase-type TIM GN=SORBI_3007G225300 activity barrel superfamily 1014 A0A1B6QKE9 Uncharacterized protein 2.08 5.133 2 -1.36 0.14 2.11E-02 Protein Ubiquitin-protein Prp19 complex Pre-mRNA-splicing factor 19 OS=Sorghum bicolor ubiquitination transferase domain; pre-mRNA- GN=SORBI_3001G226000 activity processing factor 19 family Metabolism 2 C9DHL2 Lipoxygenase 51.44 37.51 33 -1.35 0.04 7.90E-03 Oxylipin Oxidoreductase None predicted Lipoxygenase, C-terminal OS=Sorghum bicolor biosynthetic activity domain; lipoxygenase family process 9 C5WNU8 Lipoxygenase 37.53 28.94 20 -1.25 0.04 1.66E-02 Oxylipin Oxidoreductase None predicted Lipoxygenase, C-terminal OS=Sorghum bicolor biosynthetic activity domain; lipoxygenase family GN=SORBI_3001G125800 process 19 A0A1B6QC86 Uncharacterized protein 32.99 32.11 20 -1.16 0.04 2.48E-03 None predicted Glucan endo-1,3- None predicted Domain not predicted, endo- OS=Sorghum bicolor beta-glucanase 1,3(4)-beta-glucanase family GN=SORBI_3002G189100 activity 21 A0A1B6QI05 Uncharacterized protein 31.2 37.36 27 1.36 0.08 2.21E-04 Carbohydrate Hydrolase activity None predicted Glycoside hydrolase family OS=Sorghum bicolor metabolic process 3 C-terminal domain; GN=SORBI_3001G089100 glycoside hydrolase superfamily 32 C5XES4 Lipoxygenase 28.09 20.85 17 -1.33 0.05 2.59E-02 Oxylipin Oxidoreductase None predicted Lipoxygenase, C-terminal OS=Sorghum bicolor biosynthetic activity domain; lipoxygenase family GN=SORBI_3003G385500 process 34 C5XYP5 Uncharacterized protein 26.35 22.58 14 1.19 0.12 2.60E-02 Carbohydrate Hydrolase None predicted Glycoside hydrolase family 3 OS=Sorghum bicolor metabolic process activity, C-terminal domain,; GN=SORBI_3004G233700 hydrolyzing O- glycoside hydrolase glycosyl superfamily compounds 45 A0A125QUG5 Uncharacterized protein 24.43 24.22 12 1.23 0.10 6.47E-03 Carbohydrate Hydrolase None predicted Glycosyl hydrolase family OS=Sorghum bicolor GN= metabolic process activity, 32, C-terminal domain; SORBI_3006G070298 hydrolyzing O- glycoside hydrolase, family glycosyl 32 compounds 70 C5XRJ6 L-lactate dehydrogenase 21.32 39.89 12 -1.23 0.13 4.52E-02 Carbohydrate Oxidoreductase Cytoplasm Lactate/malate OS=Sorghum bicolor metabolic activity, acting on dehydrogenase, N-terminal GN=SORBI_3004G004400 processes the CH-OH group domain and family of donors, NAD or NADP as acceptor 76 C5WXJ1 Sucrose synthase 20.21 25.25 21 -1.35 0.04 3.49E-02 Sucrose metabolic Sucrose synthase None predicted Sucrose synthase/ glycosyl OS=Sorghum bicolor process activity transferase, family 1 GN=SORBI_3001G344500 domain; sucrose synthase, plant/cyanobacteria family 81 A0A1B6PEZ5 Uncharacterized protein 19.86 37.72 13 -1.28 0.05 2.07E-02 Fructose Fructokinase Cytosol Carbohydrate kinase PfkB 107 OS=Sorghum bicolor metabolic process activity domain; ribokinase-like GN=SORBI_3007G014700 superfamily 99 C5XB38 Uncharacterized protein 18 36.48 12 1.35 0.13 2.82E-03 Chitin catabolic Chitin binding Extracellular Glycoside hydrolase family OS=Sorghum bicolor process region 18, catalytic domain; GN=SORBI_3002G055600 glycoside hydrolase superfamily 142 C5XY37 Acetyltransferase 14.42 16.51 9 -1.12 0.04 6.00E-03 Pyruvate Dihydrolipoyllysi Mitochondrial 2-oxoacid dehydrogenase component of pyruvate metabolic process ne-residue matrix acyltransferase, catalytic dehydrogenase complex acetyltransferase domain; dihydrolipoyllysine- OS=Sorghum bicolor activity residue acetyltransferase GN=SORBI_3004G093600 component of pyruvate dehydrogenase complex family 157 A0A194YT53 Uncharacterized protein 13.51 23.11 9 -1.07 0.03 3.37E-02 Metabolic process Transferase None predicted Thiolase domain and family OS=Sorghum bicolor activity, GN=SORBI_3004G345800 transferring acyl groups other than amino-acyl groups 158 A0A194YQ33 Uncharacterized protein 13.44 11.88 6 1.10 0.02 8.84E-04 Carbohydrate Hydrolase None predicted Glycosyl hydrolase family OS=Sorghum bicolor metabolic process activity, domain 32; N-terminal GN=SORBI_3004G166700 hydrolyzing O- glycoside hydrolase, family glycosyl 32 compounds 166 C5YF84 Uncharacterized protein 13.1 9.446 7 -1.13 0.02 5.27E-03 Protein metabolic ATP binding None predicted Domain not predicted; OS=Sorghum bicolor process ClpA/B family GN=SORBI_3006G058100 231 C5XAC2 Uncharacterized protein 10.71 16.12 6 1.29 0.11 3.27E-03 Carbohydrate Hydrolase Anchored X8 domain; glycoside OS=Sorghum bicolor metabolic process activity, component of hydrolase family 17 GN=SORBI_3002G045800 hydrolyzing O- plasma glycosyl membrane compounds 247 C5XWE5 Uncharacterized protein 10.32 10.76 6 -1.16 0.07 1.63E-02 Lipid metabolic Phosphoric diester None predicted Glycerophosphodiester OS=Sorghum bicolor process hydrolase activity phosphodiesterase domain; GN=SORBI_3004G197600 family not predicted 276 C5WNH9 Uncharacterized protein 9.71 11.48 6 -1.29 0.09 1.07E-02 None predicted Thiamine Intracellular Thiamine pyrophosphate OS=Sorghum bicolor pyrophosphate enzyme, N-terminal TPP- GN=SORBI_3001G405500 binding binding domain; thiamine pyrophosphate (TPP)- dependent enzyme family 285 C5XF87 Ketol-acid 9.49 12.61 6 -1.34 0.03 1.37E-02 Branched-chain Ketol-acid None predicted Ketol-acid reductoisomerase, reductoisomerase amino acid reductoisomerase N and C-terminal domain OS=Sorghum bicolor biosynthetic activity and family GN=SORBI_3003G239900 process 288 C5Y4R7 Glycosyltransferase 9.45 14.17 4 -1.20 0.07 4.77E-02 Metabolic process Transferase Intracellular UDP-glycosyltransferase OS=Sorghum bicolor activity, membrane- family, conserved site GN=SORBI_3005G156500 transferring bounded domain; UDP- glycosyl groups organelle glucuronosyl/UDP- glucosyltransferase family 338 C5XSC5 Uncharacterized protein 8.33 28.05 14 -1.15 0.07 2.20E-02 UDP-glucose dUTP:glucose-1- Cytoplasm Domain not predicted; UTP-- 108 OS=Sorghum bicolor metabolic process phosphate glucose-1-phosphate GN=SORBI_3004G013500 uridylyltransferase uridylyltransferase family activity 382 C5YBH7 Uncharacterized protein 7.78 10.48 4 1.57 0.23 2.75E-03 None predicted None predicted None predicted Galactose oxidase-like, early OS=Sorghum bicolor set domain; galactose GN=SORBI_3006G135500 oxidase, central domain superfamily 390 C5Z1D3 Phosphotransferase 7.64 12.03 4 -1.29 0.10 7.09E-03 Glucose 6- Phosphotransferas Mitochondria Hexokinase, N- and C- OS=Sorghum bicolor phosphate e activity, alcohol terminal domain; hexokinase GN=SORBI_3009G203500 metabolic process group as acceptor family 422 C5Z1F9 Uncharacterized protein 7.13 19.18 6 1.29 0.19 2.38E-02 None predicted Quercetin 3-O- Intracellular Domain and family not OS=Sorghum bicolor glucosyltransferas membrane- predicted GN=SORBI_3009G209100 e activity bounded organelle 463 C5X255 Uncharacterized protein 6.47 8.788 4 -1.22 0.08 1.12E-02 Cellular amino Formate- Intracellular Domain not predicted; OS=Sorghum bicolor acid metabolic tetrahydrofolate Tetrahydrofolate GN=SORBI_3002G220200 process ligase activity dehydrogenase/cyclohydrolas e family 494 C5YMM3 Patatin OS=Sorghum 6.13 12.41 3 -1.39 0.10 4.79E-03 Lipid catabolic Acylglycerol Cytoplasm Patatin-like phospholipase bicolor process lipase activity, domain; patatin family GN=SORBI_3007G158600 hydrolase activity 568 C5YLQ3 D-3-phosphoglycerate 5.32 6.947 3 -1.19 0.06 2.60E-02 Cellular amino Phosphoglycerate None predicted D-isomer specific 2- dehydrogenase acid biosynthetic dehydrogenase hydroxyacid dehydrogenase, OS=Sorghum bicolor process activity NAD-binding domain; D-3- GN=SORBI_3007G145000 phosphoglycerate dehydrogenase family 649 A0A1B6PIY5 Uncharacterized protein 4.28 3.728 2 1.25 0.09 2.25E-02 Carbohydrate Glycerophosphodi Plasmodesma Glycerophosphoryl diester OS=Sorghum bicolor metabolic process ester phosphodiesterase domain GN=SORBI_3007G213300 phosphodiesterase and family activity 691 A0A1B6PNS7 Uncharacterized protein 4.05 14.09 7 -1.21 0.12 4.56E-02 Glutamine Glutamate- Chloroplast Glutamine synthetase, OS=Sorghum bicolor biosynthetic ammonia ligase thylakoid catalytic domain; Glutamine GN=SORBI_3006G249400 process activity membrane synthetase, N-terminal domain superfamily 755 C5WTM9 UDP-glucose 6- 3.78 18.54 9 -1.25 0.06 2.13E-02 Glycosaminoglyca UDP-glucose 6- Cytosol UDP-glucose/GDP-mannose dehydrogenase n biosynthetic dehydrogenase dehydrogenase, dimerization OS=Sorghum bicolor process activity domain; UDP-glucose 6- GN=SORBI_3001G459800 dehydrogenase, eukaryotic type family 785 A0A1B6P878 Uncharacterized protein 3.54 13.33 2 1.51 0.07 5.95E-03 Carbohydrate Hydrolase None predicted Domain not predicted; OS=Sorghum bicolor metabolic process activity, glycoside hydrolase family GN=SORBI_3009G119200 hydrolyzing O- 17 glycosyl compounds 848 C5XL11 Uncharacterized protein 3.03 6.562 2 1.37 0.13 5.48E-03 None predicted Hydrolase None predicted GDSL lipase/esterase-like, OS=Sorghum bicolor activity, acting on plant domain; SGNH GN=SORBI_3003G020500 ester bonds hydrolase superfamily 851 C5YIY2 Uncharacterized protein 2.99 8.482 2 -1.20 0.04 3.00E-02 Fatty acid Hydro-lyase Cytoplasm Beta-hydroxydecanoyl thiol OS=Sorghum bicolor biosynthetic activity ester dehydrase, FabA/FabZ 109 GN=SORBI_3007G079600 process domain; beta-hydroxyacyl- (acyl-carrier-protein) dehydratase FabZ family 856 C5XLZ0 ATP-dependent 6- 2.97 5.283 2 -1.26 0.09 2.37E-02 Fructose 6- 6- Cytoplasm Phosphofructokinase domain; phosphofructokinase phosphate phosphofructokina ATP-dependent 6- OS=Sorghum bicolor metabolic process se activity phosphofructokinase family GN=PFK 872 C5YDX7 Uncharacterized protein 2.87 5.263 2 1.32 0.17 3.21E-02 Carbohydrate Alpha-L- Vacuole Domain not predicted; OS=Sorghum bicolor metabolic process fucosidase activity Glycoside hydrolase, family GN=SORBI_3006G177300 29 874 A0A1B6QHU0 Uncharacterized protein 2.85 10.41 5 2.01 0.36 3.48E-03 Malate metabolic Malate None predicted Lactate/malate OS=Sorghum bicolor process dehydrogenase dehydrogenase, N and C- GN=SORBI_3001G073900 activity terminal domain; malate dehydrogenase, type 1 family 942 A0A194YSX5 Uncharacterized protein 2.34 6.972 2 -1.15 0.06 3.02E-02 Cellular amino Pyridoxal None predicted Aminotransferase, class OS=Sorghum bicolor acid metabolic phosphate binding I/classII domain; GN=SORBI_3004G331700 process aspartate/other aminotransferase family 1294 C5XGS0 Uncharacterized protein 1.73 1.063 2 -1.24 0.10 3.23E-02 Nitrogen Oxidoreductase Cytoplasm Glutamate synthase domain; OS=Sorghum bicolor compound activity, acting on glutamate synthase, GN=SORBI_3003G258800 metabolic process the CH-NH2 NADH/NADPH, small group of donors subunit 1 family Protein synthesis 54 C5WW48 Uncharacterized protein 23.5 47.08 17 1.53 0.07 5.48E-04 Translation Structural Cytosolic small Ribosomal protein S5, N- OS=Sorghum bicolor constituent of ribosomal terminal domain; ribosomal GN=SORBI_3001G046300 ribosome subunit protein S5 family 118 C5YH46 Uncharacterized protein 16.4 42.76 9 1.33 0.17 9.52E-03 Translation Structural Cytosolic small Domain not predicted; OS=Sorghum bicolor constituent of ribosomal ribosomal protein S19e GN=SORBI_3007G178600 ribosome subunit family 133 C5Z622 Uncharacterized protein 15.24 48.59 14 1.26 0.11 4.61E-03 Translation Structural Cytosolic small Domain not predicted; OS=Sorghum bicolor constituent of ribosomal ribosomal protein S17e GN=SORBI_3010G076400 ribosome subunit family 134 C5Y9U0 Uncharacterized protein 15.1 46 9 1.19 0.09 1.36E-02 Translation Structural Cytosolic small Domain not predicted; OS=Sorghum bicolor constituent of ribosomal ribosomal protein S11 family GN=SORBI_3006G106100 ribosome subunit 255 C5X0P2 Uncharacterized protein 10.17 39.39 5 1.29 0.16 2.77E-02 Translation Structural Cytosolic large Domain not predicted; OS=Sorghum bicolor constituent of ribosomal ribosomal protein L22e GN=SORBI_3001G376600 ribosome subunit family 260 C5YR16 Uncharacterized protein 10.08 38.93 6 1.36 0.18 1.44E-02 Translation Structural Cytosolic small Domain not predicted; OS=Sorghum bicolor constituent of ribosomal ribosomal protein S9 family GN=SORBI_3008G021100 ribosome subunit 263 C5YF43 Uncharacterized protein 10.03 36.14 6 1.25 0.17 3.82E-02 Translation rRNA binding Cytosolic large Ribosomal protein L11 L and OS=Sorghum bicolor ribosomal C terminal domains; GN=SORBI_3006G201500 subunit Ribosomal protein L11/L12 family 290 C5XAE4 Uncharacterized protein 9.44 32.24 5 1.79 0.39 1.23E-02 Translation Structural Small ribosomal Domain not predicted; OS=Sorghum bicolor constituent of subunit ribosomal protein S13 family GN=SORBI_3002G049300 ribosome 110 291 C5YIU0 Uncharacterized protein 9.43 39.23 7 1.29 0.11 2.18E-02 Translation Structural Ribosome Domain not predicted; OS=Sorghum bicolor constituent of ribosomal protein S8 family GN=SORBI_3007G212600 ribosome 351 C5YGH8 Uncharacterized protein 8.1 25.54 6 1.42 0.20 1.62E-02 Ribosomal small Structural Cytosolic small Plectin/S10, N-terminal OS=Sorghum bicolor subunit assembly constituent of ribosomal domain; 40S Ribosomal GN=SORBI_3006G081100 ribosome subunit protein S10 family 374 C5YR01 Uncharacterized protein 7.91 10.02 4 1.13 0.05 9.29E-03 None predicted RNA binding None predicted K Homology domain, type 1 OS=Sorghum bicolor domain; K Homology GN=SORBI_3008G153500 domain, type 1 superfamily 419 C5YAD0 Uncharacterized protein 7.2 29.22 8 1.25 0.03 2.70E-03 Translation Structural Cytosolic large Domain not predicted; 60S OS=Sorghum bicolor constituent of ribosomal ribosomal protein L6E GN=SORBI_3006G116700 ribosome subunit 502 C5WYX6 Uncharacterized protein 6.07 25.71 5 1.45 0.28 2.02E-02 Translation Structural Cytosolic large Domain not predicted; OS=Sorghum bicolor constituent of ribosomal ribosomal protein L14P GN=SORBI_3001G225700 ribosome subunit family 525 C5XMD2 Uncharacterized protein 5.93 35.19 5 1.82 0.57 3.17E-02 None predicted None predicted None predicted Domain not predicted; OS=Sorghum bicolor Ribosomal protein S25 GN=SORBI_3003G322400 family 530 C5Z0J3 Uncharacterized protein 5.86 18.09 3 1.22 0.12 2.93E-02 Translation RNA binding Cytosolic large Ribosomal protein OS=Sorghum bicolor structural ribosomal L18e/L15P domain; GN=SORBI_3009G050300 constituent of subunit ribosomal protein L18e ribosome family 538 C5X548 Uncharacterized protein 5.68 17.59 3 -1.47 0.10 1.36E-02 Translation RNA binding Cytosolic large Ribosomal protein OS=Sorghum bicolor structural ribosomal L18e/L15P domain; GN=SORBI_3002G124700 constituent of subunit ribosomal protein L18e ribosome 563 C5WT03 Uncharacterized protein 5.38 15.79 3 1.44 0.26 2.03E-02 Translation Structural Large ribosomal Domain not predicted; OS=Sorghum bicolor constituent of subunit ribosomal protein L22/L17 GN=SORBI_3001G016400 ribosome family 611 C5XBK5 Elongation factor 1-alpha 4.77 30.2 16 -1.19 0.10 4.83E-02 Translational Translation Cytoplasm Transcription factor, GTP- OS=Sorghum bicolor elongation elongation factor binding domain; translation GN=SORBI_3002G339600 activity elongation factor EF1A, eukaryotic/archaeal family 648 C5Z466 Uncharacterized protein 4.28 8.78 2 1.23 0.07 4.93E-02 Translation RNA binding Large ribosomal Domain not predicted; OS=Sorghum bicolor subunit ribosomal protein GN=SORBI_3010G161500 L1/ribosomal biogenesis protein family 667 C5XQP0 Uncharacterized protein 4.16 20.38 2 1.21 0.12 1.48E-02 Ribosomal large RNA binding Cytosolic large KOW domain; ribosomal OS=Sorghum bicolor subunit biogenesis ribosomal protein L26/L24, GN=SORBI_3003G078200 subunit eukaryotic/archaeal family 793 C5XD92 40S ribosomal protein S3a 3.48 26.24 5 1.23 0.06 2.34E-03 Cytoplasmic Structural Cytoplasm Domain not predicted; 40S OS=Sorghum bicolor translation constituent of ribosomal protein S1/3, GN=SORBI_3002G364500 ribosome eukaryotes family 710 C5X7L1 Uncharacterized protein 4 39.55 6 1.14 0.09 4.55E-02 Ribosomal large RNA binding Cytosolic large Ribosomal protein L14e OS=Sorghum bicolor subunit biogenesis ribosomal domain; translation protein GN=SORBI_3002G286900 subunit SH3-like domain superfamily 806 A0A1B6PFH1 Uncharacterized protein 3.38 6.59 4 -1.11 0.04 1.90E-02 None predicted Structural Cytoplasm Domain not predicted; OS=Sorghum bicolor constituent of Ribosomal protein GN=SORBI_3007G023400 ribosome S14p/S29e family 111 883 C5Z0D7 Uncharacterized protein 2.79 19.13 2 1.36 0.10 2.05E-02 Translational Translation None predicted SUI1 domain; eukaryotic OS=Sorghum bicolor initiation initiation factor translation initiation factor GN=SORBI_3009G185800 activity SUI1 family 1103 C5WW47 Uncharacterized protein 2 47.08 17 1.37 0.18 2.07E-02 Translation RNA binding Cytosolic small Ribosomal protein S5, N and OS=Sorghum bicolor ribosomal C-terminal domain; GN=SORBI_3001G046100 subunit ribosomal protein S5 family 1186 C5XVB4 Uncharacterized protein 2 17.39 3 1.18 0.08 1.84E-02 Translation Structural Ribosome Domain not predicted; OS=Sorghum bicolor constituent of ribosomal protein L37ae GN=SORBI_3004G045400 ribosome family Transporters 12 C5Y9I0 Plasma membrane ATPase 36.96 22.71 19 -1.24 0.03 1.50E-02 Proton export ATP binding Plasma Cation-transporting P-type OS=Sorghum bicolor across plasma membrane ATPase, N-terminal domain; GN=SORBI_3006G247100 membrane P-type ATPase family 51 C5X0G5 Uncharacterized protein 23.77 22.35 18 -1.17 0.06 1.53E-02 None predicted Nucleotide None predicted AAA+ ATPase domain; OS=Sorghum bicolor binding AAA ATPase, CDC48 GN=SORBI_3001G236300 family 52 A0A194YJS5 Uncharacterized protein 23.77 35.32 20 -1.15 0.07 3.70E-02 ATP hydrolysis ATP binding Proton- ATPase, F1/V1/A1 complex, OS=Sorghum bicolor coupled proton transporting V- alpha/beta subunit, N- GN=SORBI_3010G172500 transport type ATPase, terminal domain; V-type V1 domain ATP synthase regulatory subunit B/beta family 61 C5XHF1 Uncharacterized protein 22.39 59.82 66 1.53 0.14 2.66E-04 Plasmodesmata- Metal ion binding Extracellular Cupin 1 domain; germin OS=Sorghum bicolor mediated region family GN=SORBI_3003G136200 intercellular transport 88 A0A194YRR0 Uncharacterized protein 18.97 29.2 13 -1.36 0.10 4.65E-02 Transmembrane Transmembrane Mitochondrial Domain not predicted; OS=Sorghum bicolor transport transporter inner mitochondrial carrier protein GN=SORBI_3004G259200 activity membrane, family integral component of membrane 156 C5WST3 Uncharacterized protein 13.52 26.67 7 -1.24 0.08 1.00E-02 ATP hydrolysis Proton- Proton- Domain not predicted; ATP OS=Sorghum bicolor coupled cation transporting ATP transporting synthase, F1 complex, GN=SORBI_3001G457000 transmembrane synthase activity, ATP synthase gamma subunit family transport rotational complex, mechanism catalytic core F(1) 215 C5XFB9 Uncharacterized protein 11.14 26.52 6 1.29 0.10 1.81E-02 ATP hydrolysis Proton- Proton- Domain not predicted; V- OS=Sorghum bicolor coupled proton transporting transporting type ATPase subunit family GN=SORBI_3003G244200 transport ATPase activity two-sector ATPase complex 346 C5XTE7 Uncharacterized protein 8.18 18.22 4 1.24 0.17 4.02E-02 None predicted Manganese ion Extracellular Cupin 1 domain; Germin OS=Sorghum bicolor binding region family GN=SORBI_3004G165300 373 A0A1B6PAV5 Uncharacterized protein 7.91 3.099 4 -1.30 0.10 9.46E-03 Intracellular Clathrin light Clathrin coat of Clathrin, heavy chain, linker, OS=Sorghum bicolor protein transport chain binding trans-Golgi core motif domain; clathrin, GN=SORBI_3008G003700 network vesicle heavy chain family 112 381 C5XB21 Uncharacterized protein 7.8 22.94 5 1.17 0.10 3.98E-02 Plasmodesmata- Manganese ion Extracellular Cupin 1 domain; germin OS=Sorghum bicolor mediated binding region family GN=SORBI_3002G053900 intercellular transport 515 A0A1B6Q2N0 Uncharacterized protein 6 16.14 5 1.49 0.28 1.53E-02 None predicted Manganese ion Extracellular Cupin 1 domain; germin OS=Sorghum bicolor binding region family GN=SORBI_3003G112200 516 C5YSH7 Uncharacterized protein 6 56.32 3 1.20 0.14 4.24E-02 Metal ion Transition metal Cytoplasm Heavy metal-associated OS=Sorghum bicolor transport ion binding domain, HMA; heavy metal- GN=SORBI_3008G179500 associated domain superfamily 657 C5YC92 Uncharacterized protein 4.21 20.96 6 1.43 0.26 1.79E-02 None predicted Manganese ion Extracellular Cupin 1 domain; germin OS=Sorghum bicolor binding region family GN=SORBI_3006G018100 675 C5YG34 Uncharacterized protein 4.13 30.04 8 1.19 0.12 2.54E-02 None predicted Manganese ion Extracellular Cupin 1 domain; germin OS=Sorghum bicolor binding region family GN=SORBI_3006G217100 693 C5Y1D9 Uncharacterized protein 4.05 11.42 4 -1.32 0.09 2.25E-02 Ion Water channel Integral Domain not predicted; OS=Sorghum bicolor transmembrane activity component of aquaporin transporter family GN=SORBI_3004G288700 transport plasma membrane 920 C5X999 Uncharacterized protein 2.47 11.06 2 -1.33 0.09 6.51E-03 Intra-Golgi GTP binding Golgi apparatus Small GTP-binding protein OS=Sorghum bicolor vesicle-mediated domain; small GTPase GN=SORBI_3002G309500 transport superfamily 1065 C5XHX2 Uncharacterized protein 2.02 12.95 2 1.26 0.10 4.77E-02 Plasmodesmata- Manganese ion Extracellular Cupin 1 domain; germin OS=Sorghum bicolor mediated binding region family GN=SORBI_3003G427700 intercellular transport 1107 C5WZZ8 Uncharacterized protein 2 17.89 8 -1.18 0.03 1.66E-03 Regulation of ATP:ADP Mitochondrial Domain not predicted; OS=Sorghum bicolor mitochondrial antiporter activity inner membrane mitochondrial carrier protein GN=SORBI_3001G522000 membrane family permeability Energy 4 A0A1B6Q5E4 Uncharacterized protein 41.86 53.79 43 -1.24 0.08 3.07E-02 ATP biosynthetic ATP binding Mitochondrial AAA+ ATPase domain; OS=Sorghum bicolor process proton- ATP synthase, F1 complex, GN=SORBI_3003G260600 transporting beta subunit family ATP synthase complex, catalytic core F(1) 27 C5WSL1 Uncharacterized protein 28.85 36.85 21 -1.30 0.05 2.09E-03 Oxidation- Electron transfer Chloroplast Domain not predicted; OS=Sorghum bicolor reduction process activity Cytochrome P450 family GN=SORBI_3001G449700 121 C5XQ07 Uncharacterized protein 16.17 57.31 13 -1.15 0.04 1.39E-02 Glycolytic process Triose-phosphate Cytosol Domain not predicted; OS=Sorghum bicolor isomerase activity triosephosphate isomerase GN=SORBI_3003G072300 family 143 A0A1B6P9F4 Uncharacterized protein 14.41 47.55 15 1.21 0.08 1.69E-02 Oxidation- FMN activity None predicted Flavodoxin/nitric oxide OS=Sorghum bicolor reduction process synthase domain; 113 GN=SORBI_3009G188000 flavoprotein WrbA-like family 244 C5Y4L1 Pyruvate kinase 10.35 14.99 6 -1.35 0.10 7.10E-03 Glycolytic process Pyruvate kinase Cytoplasm Pyruvate kinase domain and OS=Sorghum bicolor activity family GN=SORBI_3005G034400 277 A0A1B6Q6I7 Uncharacterized protein 9.68 50.25 10 1.38 0.23 2.71E-02 Oxidation- FMN binding None predicted Flavodoxin/nitric oxide OS=Sorghum bicolor reduction process synthase domain; GN=SORBI_3003G319700 flavoprotein WrbA-like family 297 A0A1B6QPV9 Uncharacterized protein 9.34 15.26 6 -1.12 0.05 4.87E-02 Tricarboxylic acid Transferase Intracellular Dihydrolipoamide OS=Sorghum bicolor cycle activity, succinyltransferase domain; GN=SORBI_3001G465400 transferring acyl Urb2/Npa2 family groups 304 C5XBD4 Uncharacterized protein 9.16 15.06 6 -1.11 0.03 2.89E-03 Glycolytic process Phosphopyruvate Cytosol Enolase, C and N-terminal OS=Sorghum bicolor hydratase activity TIM barrel domain; enolase GN=SORBI_3002G186900 family 359 C5Z7L1 Pyrophosphate--fructose 6- 8.02 12.57 5 -1.40 0.07 1.16E-03 Glycolytic process Diphosphate- Cytoplasm Phosphofructokinase domain; phosphate 1- through fructose- fructose-6- pyrophosphate-dependent phosphotransferase subunit 6-phosphate phosphate 1- phosphofructokinase PfpB beta OS=Sorghum bicolor phosphotransferas family GN=PFP-BETA e activity 387 C5WN51 Uncharacterized protein 7.69 38.28 9 1.36 0.20 1.28E-02 Electron transport Electron transfer Anchored Phytocyanin domain; OS=Sorghum bicolor chain activity component of cupredoxin superfamily GN=SORBI_3001G119000 plasma membrane 489 C5Z8E7 Uncharacterized protein 6.19 36.3 4 1.58 0.31 1.17E-02 Electron transport Electron transfer Anchored Phytocyanin domain; OS=Sorghum bicolor chain activity component of cupredoxin superfamily GN=SORBI_3010G110800 plasma PE=4 SV=1 membrane 491 C5XWJ7 Uncharacterized protein 6.17 16 3 -1.22 0.02 2.20E-02 Tricarboxylic acid Oxidoreductase None predicted Isopropylmalate OS=Sorghum bicolor cycle activity dehydrogenase-like domain; GN=SORBI_3004G202100 isocitrate dehydrogenase NAD-dependent family 701 C5XC95 Uncharacterized protein 4.02 18.95 6 1.42 0.25 3.02E-02 Electron transport Electron transfer Anchored Phytocyanin domain; OS=Sorghum bicolor chain activity component of cupredoxin superfamily GN=SORBI_3002G345800 plasma membrane 871 C5Z0R1 Uncharacterized protein 2.87 2.983 2 -1.23 0.03 1.02E-03 Sulfate reductions Sulfite reductase None predicted Nitrite/Sulfite reductase OS=Sorghum bicolor (ferredoxin) ferredoxin-like domain; GN=SORBI_3009G189400 activity Sulphite reductase, ferredoxin dependent family 1111 A0A1B6PBV5 Uncharacterized protein 2 12.22 5 -1.24 0.13 4.24E-02 Glycolytic process Pyruvate kinase None predicted Pyruvate kinase, barrel OS=Sorghum bicolor activity domain; pyruvate kinase GN=SORBI_3008G033600 family 1114 C5WN52 Uncharacterized protein 2 27.91 5 1.68 0.42 2.21E-02 Electron transport Electron transfer Anchored Phytocyanin domain; OS=Sorghum bicolor chain activity component of cupredoxin superfamily GN=SORBI_3001G119100 plasma membrane 114 Transcription 173 C5YM38 Histone H2B OS=Sorghum 12.62 34.44 21 2.13 0.46 5.03E-03 None predicted DNA binding Nucleosome Histone H2A/H2B/H3 bicolor domain; histone H2B family GN=SORBI_3007G149600 184 C5YHS2 Uncharacterized protein 12.34 42.51 10 1.12 0.06 2.33E-02 None predicted None predicted Cytoplasm Nascent polypeptide- OS=Sorghum bicolor associated complex NAC GN=SORBI_3007G192700 domain; nascent polypeptide- associated complex subunit alpha family 196 C5XID4 Uncharacterized protein 11.84 27.15 7 1.16 0.05 1.89E-03 None predicted None predicted None predicted Hyaluronan/mRNA-binding OS=Sorghum bicolor protein domain, family not GN=SORBI_3003G279800 predicted 245 C5YL42 Uncharacterized protein 10.34 24.24 5 1.36 0.20 1.43E-02 DNA-templated, DNA binding Nucleosome Linker histone H1/H5, OS=Sorghum bicolor nucleosome domain H15; high mobility GN=SORBI_3007G132900 assembly group protein HMGA family 284 C5WYC3 Nascent polypeptide- 9.51 45.18 8 1.29 0.16 1.48E-02 Regulation of None predicted None predicted Nascent polypeptide- associated complex subunit transcription, associated complex NAC beta OS=Sorghum bicolor DNA-templated domain; NAC A/B domain GN=SORBI_3001G216200 superfamily 362 C5WTL6 Histone H4 OS=Sorghum 8.01 42.72 5 2.06 0.33 7.98E-04 Nucleosome Histone binding Nuclear CENP-T/Histone H4, histone bicolor assembly nucleosome fold domain; histone H4 GN=SORBI_3001G313200 family 500 C5XAT9 Histone H2A OS=Sorghum 6.09 34.81 5 1.58 0.24 7.85E-03 Chromatin DNA binding Nuclear Histone H2A/H2B/H3 bicolor silencing chromatin domain; histone H2A family GN=SORBI_3002G330000 579 C5XX54 Histone H2A OS=Sorghum 5.15 23.91 4 1.73 0.27 4.03E-03 Chromatin DNA binding Nuclear Histone H2A, C-terminal bicolor silencing chromatin domain; histone H2A family GN=SORBI_3004G205300 597 C5WMP6 Nascent polypeptide- 4.94 40.72 6 1.29 0.13 3.99E-03 Regulation of None predicted None predicted Nascent polypeptide- associated complex subunit transcription, associated complex NAC beta OS=Sorghum bicolor DNA-templated domain; NAC A/B domain GN=SORBI_3001G537200 superfamily 627 C5XYG2 Uncharacterized protein 4.53 7.267 4 1.35 0.13 2.17E-03 None predicted mRNA binding Cytosol K Homology domain; K OS=Sorghum bicolor Homology domain, type 1 GN=SORBI_3004G096500 superfamily 953 C5XAT8 Histone H2A OS=Sorghum 2.3 29.85 3 1.42 0.20 6.61E-03 Chromatin DNA binding Nuclear Histone H2A, C-terminal bicolor silencing chromatin domain; Histone H2A family GN=SORBI_3002G329900 1112 C5WPC4 Histone H2A OS=Sorghum 2 28.75 5 2.23 0.75 1.98E-02 Chromatin DNA binding Nucleosome Histone H2A, C-terminal bicolor silencing domain; histone H2A family GN=SORBI_3001G416900 Protein destination and storage 90 A0A1B6PKJ4 Uncharacterized protein 18.86 22.33 13 -1.17 0.02 4.88E-03 Protein folding Unfolded protein Endoplasmic Domain not predicted; OS=Sorghum bicolor binding reticulum, calreticulin/calnexin family GN=SORBI_3006G062500 integral component of 115 membrane 135 C5WV27 Uncharacterized protein 14.98 22.42 10 -1.14 0.01 2.38E-02 Proteolysis Metalloaminopept Cytoplasm Peptidase M1, membrane OS=Sorghum bicolor idase activity alanine aminopeptidase, N- GN=SORBI_3001G036300 terminal domain; peptidase M1, alanine aminopeptidase/leukotriene A4 hydrolase family 224 C5XDR4 Uncharacterized protein 10.89 15.43 9 1.47 0.17 3.24E-03 Proteolysis Cysteine-type Extracellular Peptidase C1A, papain C- OS=Sorghum bicolor endopeptidase space terminal; peptidase C1A GN=SORBI_3002G217200 activity family 233 C5Z4C7 Proteasome subunit beta 10.69 28.57 6 -1.22 0.07 4.62E-03 Proteolysis Threonine-type Cytoplasm Domain not predicted; type OS=Sorghum bicolor endopeptidase peptidase T1A, proteasome GN=SORBI_3010G042800 activity beta-subunit family 254 B3GQV9 Peptidyl-prolyl cis-trans 10.19 34.3 11 1.12 0.05 1.51E-02 Protein peptidyl- Peptidyl-prolyl None predicted Cyclophilin-type peptidyl- isomerase OS=Sorghum prolyl cis-trans prolyl cis-trans isomerase bicolor GN=Cyp isomerization isomerase activity domain and family 272 A0A1B6PHE0 Uncharacterized protein 9.8 7.306 5 -1.11 0.04 4.38E-02 Proteolysis Aminopeptidase None predicted Peptidase M1, membrane OS=Sorghum bicolor activity alanine aminopeptidase, N- GN=SORBI_3007G120800 terminal domain; peptidase M1, alanine aminopeptidase/leukotriene A4 hydrolase family 296 C5XM19 Uncharacterized protein 9.35 18.47 6 -1.21 0.04 1.55E-03 None predicted Transferase None predicted Domain not predicted; OS=Sorghum bicolor activity, transferase family GN=SORBI_3003G037800 transferring acyl groups other than amino-acyl groups 311 C5XUD0 Uncharacterized protein 8.97 10.83 4 -1.30 0.08 7.37E-03 Chaperone- ATP binding Chaperonin- Domain not predicted; OS=Sorghum bicolor mediated protein containing T- chaperone tailless complex GN=SORBI_3004G326600 folding complex polypeptide 1 (TCP-1) family 319 C5Y8G7 Uncharacterized protein 8.75 39.19 15 1.29 0.16 3.44E-02 Proteolysis Aspartic-type None predicted Peptidase family A1 domain; OS=Sorghum bicolor endopeptidase aspartic peptidase A1 family GN=SORBI_3005G224500 activity 322 C5Y4Y9 Uncharacterized protein 8.66 11.31 5 -1.09 0.05 3.10E-02 Chaperone- Protein binding Chaperonin- Domain not predicted; OS=Sorghum bicolor mediated protein involved in containing T- chaperone tailless complex GN=SORBI_3005G163700 folding protein folding complex polypeptide 1 (TCP-1) family 536 C5WTN6 T-complex protein 1 5.7 5.376 3 -1.25 0.08 1.13E-02 Chaperone- Protein binding Chaperonin- Domain not predicted; T- subunit gamma mediated protein involved in containing T- complex protein 1, gamma OS=Sorghum bicolor folding protein folding complex subunit family GN=SORBI_3001G460500 668 C5X6A0 Uncharacterized protein 4.16 5.607 2 -1.23 0.13 4.05E-02 Chaperone- Protein binding Chaperonin- Domain not predicted; OS=Sorghum bicolor mediated protein involved in containing T- chaperonin Cpn60/TCP-1 GN=SORBI_3002G423500 folding protein folding complex family Signal transduction 42 A0A1B6PIK2 Uncharacterized protein 24.58 21.73 18 1.17 0.09 1.11E-02 None predicted Acid phosphatase None predicted Purple acid phosphatase OS=Sorghum bicolor activity domain; Calcineurin-like GN=SORBI_3007G190200 phosphoesterase family 116 43 A0A1B6Q0N0 Uncharacterized protein 24.57 41.09 12 1.66 0.18 4.54E-03 Cellular response None predicted Anchored Domain not predicted; OS=Sorghum bicolor to stimulus component of DREPP family GN=SORBI_3003G005800 plasma membrane 89 C5YU10 Uncharacterized protein 18.89 43.15 12 1.30 0.12 9.71E-03 Nucleotide Adenylate kinase None predicted Adenylate kinase, active site OS=Sorghum bicolor phosphorylation activity lid domain; adenylate GN=SORBI_3008G084600 kinase/UMP-CMP kinase family 150 A0A1B6P7A4 Uncharacterized protein 13.98 31.35 12 1.37 0.10 6.51E-03 Dephosphorylation Acid phosphatase None predicted Domain not predicted; acid OS=Sorghum bicolor activity phosphatase, class B-like GN=SORBI_3009G070800 family 252 A0A1B6PBB3 Uncharacterized protein 10.2 20.99 5 1.19 0.03 8.22E-03 Nucleoside Nucleoside None predicted Nucleoside diphosphate OS=Sorghum bicolor diphosphate diphosphate kinase-like domain; GN=SORBI_3009G259200 phosphorylation kinase activity nucleoside diphosphate kinase family 855 C5X044 Uncharacterized protein 2.97 8.571 2 1.57 0.26 2.47E-02 Protein Protein None predicted Protein kinase domain; OS=Sorghum bicolor phosphorylation serine/threonine protein kinase-like domain GN=SORBI_3001G080700 kinase activity, superfamily ATP binding 1039 C5YQC5 Purple acid phosphatase 2.04 2.585 2 -1.23 0.07 1.65E-02 Dephosphorylation Acid phosphatase None predicted Purple acid phosphatase OS=Sorghum bicolor activity domain; N-terminal family GN=SORBI_3008G140100 20 C5WW94 Uncharacterized protein 32.48 56.76 28 -1.24 0.11 1.46E-02 None predicted ATP binding None predicted Domain not predicted; actin OS=Sorghum bicolor family GN=SORBI_3001G197400 55 C5Z7S0 Tubulin beta chain 23.35 37.22 23 -1.31 0.05 3.30E-02 Cytoskeleton Structural Cytoplasm Tubulin/FtsZ, GTPase OS=Sorghum bicolor organization constituent of domain; tubulin family GN=SORBI_3010G224900 cytoskeleton 567 C5Z4B6 Profilin OS=Sorghum 5.34 30.53 5 1.12 0.03 2.44E-02 Sequestering of Actin monomer Cell cortex Domain not predicted; bicolor actin monomers binding profilin family GN=SORBI_3010G041600 1104 C5WTY3 Uncharacterized protein 2 36.07 17 -1.37 0.19 4.76E-02 None predicted ATP binding None predicted Domain not predicted; actin OS=Sorghum bicolor family 786 C5Z7L3 Uncharacterized protein 3.53 2.629 2 1.12 0.09 5.00E-02 Mitochondrial GTP binding Cytoplasm Dynamin-type guanine OS=Sorghum bicolor fission nucleotide-binding (G) GN=SORBI_3010G101800 domain; dynamin superfamily Unclear classification 50 C5YCX2 Uncharacterized protein 23.79 50.47 11 1.74 0.23 1.23E-03 None predicted None predicted None predicted Remorin, N-terminal domain; OS=Sorghum bicolor family not predicted GN=SORBI_3006G159300 64 C5YBP8 Uncharacterized protein 22.2 37.19 16 -1.34 0.03 3.86E-03 None predicted None predicted None predicted E2F/DP family winged-helix OS=Sorghum bicolor DNA-binding domain; 117 GN=SORBI_3006G009000 family not predicted 175 A0A1B6Q9X2 Uncharacterized protein 12.59 29.38 9 -1.13 0.06 1.79E-02 None predicted Protein binding None predicted Domain not predicted; OS=Sorghum bicolor Kelch-type beta propeller GN=SORBI_3002G082000 superfamily 469 C5X978 Uncharacterized protein 6.39 43.86 7 1.38 0.09 1.05E-03 None predicted None predicted None predicted C2 domain; C2 domain OS=Sorghum bicolor superfamily GN=SORBI_3002G307600 643 C5YVJ8 Uncharacterized protein 4.32 8.633 2 1.11 0.06 4.54E-02 None predicted None predicted None predicted FAS1 domain; FAS1 domain OS=Sorghum bicolor superfamily GN=SORBI_3009G232200 808 C5XFL0 Uncharacterized protein 3.34 20.65 3 2.00 0.48 8.56E-03 None predicted None predicted None predicted Conserved hypothetical OS=Sorghum bicolor protein GN=SORBI_3003G397500 824 C5XEU4 Uncharacterized protein 3.22 4.207 2 1.38 0.03 3.83E-04 None predicted RNA binding None predicted Ribonuclease T2 domain and OS=Sorghum bicolor family GN=SORBI_3003G388200 956 A0A1B6PFS5 Uncharacterized protein 2.29 26.76 2 1.47 0.16 2.16E-03 None predicted None predicted None predicted Expressed protein OS=Sorghum bicolor GN=SORBI_3007G057100 a Protein number assigned in ProteinPilot software. b Protein accession numbers obtained from the TrEMBL database [incorporated within the UniProt database (http://www.uniprot.org)] searches against sequences of Sorghum bicolor only. c Protein score generated by ProteinPilot software relating to the confidence of protein identification. A protein identification threshold of 1.3 was applied to the data, which only retains proteins identified with a 95% confidence. d Percentage coverage is determined by the number of amino acids of sequenced peptides against the total length of the protein, with a threshold of at least 95% confidence. e Sequenced peptide refers to the number of peptide that were sequenced and gave rise to protein identity. All proteins that were identified by means of a single peptide were filtered out of the dataset. f Probability value of the quantitative difference between the treatment and control protein abundance being due to chance alone. gStandard deviation of the ratios of drought stressed samples (n=4). h Ratio represents the average fold-change (n = 4) induced by treatment relative to control. Positive values indicate an up-regulation. i Gene ontology analysis as predicted on the IntePro database (https://www.ebi.ac.uk/interpro/) and UniProt database (http://www.uniprot.org). P denotes Biological Process, F denotes Functional Process, and C denotes Cellular Component. j Conserved domains and family name as predicted by InterPro database (http://www.ebi.ac.uk/interpro/). 118 4.4.2 Gene ontology analysis The 237 and 184 drought stress responsive differentially expressed proteins were submitted for GO annotation. This resulted in the determination of their cellular component, biological process and molecular function. From the results obtained, 50.2% and 44.6% of the root proteins had no predicted cellular component for ICSB 338 and SA 1441 sorghum varieties, respectively. These proteins comprised the greatest proportion for both sorghum varieties. The most highly represented cell components included the cytoplasm and cytosol with 17.7% and 15.2%, extracellular region with 8.9% and 12.5%, ribosome with 5.1% and 9.2%, mitochondrion with 4.2 and 2.7%, and chloroplast with 2.5% and 1.6% for ICSB 338 and SA 1441, respectively. Other cellular components such as the nucleus, plasma membrane, Golgi apparatus, endoplasmic reticulum, cytoskeleton were represented to a lower extent within each sorghum variety (Figure 4.6). The 237 and 184 drought stress responsive root proteins identified for ICSB 338 and SA 1441 were annotated across a wide range of biological functions. Amongst the identified proteins, 15.6% and 22.8% had no predicted biological processes for ICSB 338 and SA 1441, respectively (Figure 4.7). Proteins predicted to be involved in metabolic processes comprised the largest proportion compared to other categories. In this category, ICSB 338 and SA 1441 totalled 17.7% and 13.6%, respectively. Other highly represented categories were oxidation reduction processes with 11.8% and 8.2%, catabolic processes with 12.2% and 7.1%, translation with 5.9% and 10.9%, biosynthetic processes with 8% and 4.9%, proteolysis with 5.9% and 2.7%, glycolytic processes 3% and 3.3%, phosphorylation with 3.4% and 2.7%, and transmembrane transport with 2.1% and 1.6% for ICSB 338 and SA 1441, respectively. The remaining proteins were distributed across several other biological processes (Figure 4.7). 119 Figure 4.6: Cellular component predictions of the identified sorghum root drought stress responsive total soluble protein based on GO annotation. 120 Figure 4.7: Biological process predictions of the identified sorghum root drought stress responsive protein based on GO annotation. 121 The identified drought stress responsive root proteins were annotated across a wide range of molecular functions as well (Figure 4.8). Proteins with binding activity which includes protein, nucleotide, ATP, GTP, ion-ion, FMN, heme- binding comprised 17.3% and 27.2% for ICSB 338 and SA 1441, respectively. Relatively high activity was also observed for electron transfer, peptidase, peroxidase, isomerase, kinase, dehydrogenase, hydrolase, transferase, synthase and oxidoreductase activity in both sorghum varieties. 122 Figure 4.8: Molecular process predictions of the identified sorghum root drought stress responsive proteins based on GO annotation. 123 4.4.3 Functional Categories of Differentially Expressed Drought Responsive Proteins The differentially expressed proteins in ICSB 338 and SA 1441 sorghum varieties were grouped into 11 functional categories according to Bevan et al. (1998) and other literature sources as shown in Tables 4.1 and 4.2, respectively. Functional classification was also carried out using GO datasets as well as the conserved domains/protein families, especially for the uncharacterized proteins. A graphical representation of the functional groups is shown in Fig 4.9. For ICSB 338, the most highly represented groups of differentially expressed root proteins in response to drought stress were disease/defense (26%), metabolism (25%), energy (9%), protein synthesis (8%) and protein destination and storage (8%). For SA 1441, disease/defense (23%), metabolism (21%), protein synthesis (14%), transporters (10%) and energy (9%) were highly represented. A brief description of some proteins in these functional groups is given below. 124 ICSB 338 SA 1441 Figure 4.9: Functional classification of the differentially expressed sorghum root drought stress responsive proteins. 125 4.4.3.1 Metabolism Of the 237 differentially expressed ICSB 338 sorghum drought stress responsive root proteins, 59 (25%) were involved in metabolism. For SA 1441, 38 (21%) proteins were assigned to the metabolism group out of a total of 184 drought responsive proteins (Figure 4.9). Of these proteins, a total of 30 and 25 were uncharacterised for ICSB 338 and SA 1441 sorghum varieties, respectively. The proteins with the highest fold change were protein no. 767 (1.56) and 874 (2.01) for ICSB 338 and SA 1441, respectively. Conversely, proteins with the lowest fold change were protein no. 155 (-2.1) and 494 (-1.39) for ICSB 338 and SA 1441, respectively. Collectively some of the characterized proteins families in both sorghum varieties included lipoxygenase, sucrose synthase, aconitate hydratase, s-adenosyl methionine synthase, pyrophosphate--fructose 6-phosphate 1-phosphotransferase subunit beta, 6-phosphogluconate dehydrogenase, patatin, chitinase, alpha-mannosidase, beta-galactosidase, formate dehydrogenase, beta hexosaminidase, glutamine synthase, ATP-dependent 6- phosphofructokinase, 3-methyl-2-oxobutanoate hydroxymethyltransferase, amine oxidase, glutamate dehydrogenase, phosphotransferase, nitrilase, chalcone-flavonone, dhurrinase, aldose 1-epimerase, L-lactate dehydrogenase, acetyltransferase component of pyruvate dehydrogenase complex, ketol-acid reductoisomerase, glycosyltransferase, phosphotransferase, D-3-phosphoglycerate dehydrogenase and UDP-glucose 6- dehydrogenase (Table 4.1 and 4.2). The group of proteins over-represented in both sorghum varieties was lipoxygenase (protein no. 1, 6, 17, 249 for ICSB 338 and protein no. 2, 9 and 32 for SA 1441). All the lipoxygenase proteins were down-regulated in both ICSB 338 and SA 1441. Lipoxygenases constitute a large gene family of non-heme, iron containing fatty acid dioxygenases which are ubiquitous 126 in plants (Brash, 1999; Feussner and Wasternack, 2002). They are involved in the catalysis of hydroperoxidation of polyunsaturated fatty acids in plants. Products of this pathway such as jasmonates function in signalling whilst leaf aldehydes have antimicrobial and antifungal properties (Feussner and Wasternack, 2002). Other proteins in this functional category were involved in the metabolism of a range of carbohydrates (protein no. 5, 72, 76, 87, 131, 133, 138, 289, 319, 319, 729, 975, and 109 for ICSB 338 and protein no. 70, 76, 142, 288, 390, 755 and 856 for SA 1441), lipids and fatty acids (protein no. 115 and 933 for ICSB 338 and protein no. 494 for SA 1441) and amino acids (protein no 58, 294, 482, 647 and 774 for ICSB 338 and 568, for SA 1441). Carbohydrate metabolism, amino acid metabolism and lipid metabolism proteins were also differentially expressed in tomato root tissue in a comparative study between a drought tolerant Solanum chilense and a susceptible Solanum lycopersicum tomato plants (Zhou et al., 2013). 4.4.3.2 Disease/defence A total of 61 (26%) of the 237 ICSB 338 proteins that were responsive to drought stress were involved in the disease and defence (Figure 4.9). For SA 1441, 38 (21%) of the 184 drought responsive proteins were involved in this functional category. Of these proteins, 43 and 23 were uncharacterized for ICSB 338 and SA 1441, respectively (Table 4.1 and 4.2). The proteins with the highest fold change were protein no. 329 (1.77) and 998 (1.73) for ICSB 338 and SA 1441, respectively. Conversely, proteins with the lowest fold change were protein no. 1218 (-3.62) and 161 (-1.52) for ICSB 338 and SA 1441, respectively. For ICSB 338, the peroxidase group of proteins (protein no. 95, 148, 173, 232, 235, 446, 708, 794) was most common. Of these, a total of 4 proteins (protein no. 173, 232, 235, 794) were up-regulated whilst the remaining 4 (protein no. 95, 148, 446, 708) were down-regulated. Peroxidases are known to utilize hydrogen peroxide as the optimal substrate although others 127 utilize organic hydroperoxides such as lipid peroxides (Elstner, 1987). The main function of peroxidases is in antioxidant defences by scavenging and detoxifying reactive oxygen species (ROS) present in plants. The major detoxification of hydrogen peroxide occurs through the ascorbate-glutathione cycle, in which the substrate is converted into water with the use of ascorbate as the electron donor (Elstner, 1987). Other functions of peroxidases in plants include lignin biosynthesis, which renders the cell wall rigid thereby forming a defensive barrier against biotic and abiotic stress (Boudet, 1998; Kawano, 2003). Peroxidases were also up-regulated in wild wheat (Triticum aestivum) root tissue under drought stress (Liu et al., 2015). The other characterized proteins identified include the up-regulated proteins phenylalanine ammonia lyase (protein no. 25), S- (hydroxymethyl) glutathione dehydrogenase (protein no. 466), dirigent protein (protein 766 and 977), and the down-regulated glyceraldehyde-3-phosphate dehydrogenase (protein no. 10), putative alcohol dehydrogenase 1 (protein no. 101), ketol-acid reductoisomerase (protein no, 367), inosine-5'-monophosphate dehydrogenase (protein no. 454), uricase (protein no. 880) and the pathogenesis related protein 10a (protein no. 1216). For SA 1441, the peroxidases (protein no. 38, 66, 97, 130, 159, 187, 207, 287, 307, 437, 622, 641, 779) were also the most common amongst the disease and defence functional group. Of these, a total of 12 proteins (protein no. 38, 97, 130, 159, 187, 207, 287, 307, 437, 622, 641, 779) were up-regulated whilst only one protein (protein no. 66) was down-regulated. This indicates that a larger number of peroxidase proteins responded to drought stress in SA 1441 in comparison to ICSB 338. An increase in abundance of peroxidases was also observed in Arabidopsis root tissue in response to salinity stress (Jiang et al., 2007). The other characterised up-regulated SA 1441 proteins were pathogenesis-related protein 10b (protein no. 57), late embryogenesis abundant protein (LEA) 3 (protein no. 131), dirigent protein 128 (protein no. 794), as well as small heat shock-like protein (protein no. 972). LEA proteins function as cellular protectants under water loss (Chakrabortee et al., 2007). Group 2 LEAs were up-regulated in maize (Benešová et al., 2012) and wheat (Ford et al., 2011) in response to drought stress. The down-regulated characterised SA 1441 proteins were betaine-aldehyde dehydrogenase 2 (protein no. 306) and S-(hydroxymethyl) glutathione dehydrogenase (protein no. 371). 4.4.3.3 Protein Destination and Storage Figure 4.9 shows that 20 proteins (8%) of the 237 drought responsive root proteins function in protein destination and storage for ICSB 338. Im comparison, 12 proteins (6%) had functions in protein destination and storage for SA 1441. A total of 19 and 9 proteins were uncharacterised for ICSB 338 and SA 1441, respectively (Table 4.1 and 4.2). The protein with the highest fold change was protein no. 868 (2.93) and 224 (1.47) for ICSB 338 and SA 1441, respectively. Conversely, proteins with the lowest fold change were protein no. 582 (- 2.06) and 311 (-1.3) for ICSB 338 and SA 1441, respectively. It is important to note that from this functional group, all the ICSB 338 proteins are involved in proteolysis except for protein no. 225 and 304 which are involved in chaperone mediated protein folding. Of the proteins involved in proteolysis, 70% were up-regulated with the remaining proportion being down- regulated. The two proteins involved in protein mediated protein folding were down- regulated. The only characterized ICSB 338 protein that was up-regulated was carboxypeptidase (protein no. 315), involved in protein catabolism and post-translational modification (Fraser et al., 2005). For SA 1441, the down-regulated proteasome subunit beta type (protein no. 233), up- regulated peptidyl-prolyl cis-trans isomerase (protein no. 254) and the down-regulated T- complex protein 1 subunit gamma (protein no. 536) involved in proteolysis, protein 129 isomerisation and chaperone mediated protein folding, respectively were the only characterised proteins. The remaining proteins were uncharacterised and involved protein folding (protein no. 90, 311,322, 668), proteolysis (protein no, 135, 224, 233, 272, 319), protein binding and transferase activity (protein no. 296). All the proteins involved in protein folding were down-regulated whilst two of the five proteolysis proteins were up-regulated. Chaperonins play an important role in facilitating newly synthesised translocated proteins to obtain their native form (Wang et al., 2004). A chaperonin protein (chaperonin subunit beta) was down-regulated in response to drought stress in Kentucky bluegrass (Poa pratenis) leaf tissue in response to drought stress (Xu and Huang, 2010). In the current study, proteins involved in chaperone-mediated protein folding were also down-regulated. 4.4.3.4 Signal Transduction A total of 13 (6%) proteins out of the 237 ICSB 338 and 7 (4%) of the total 184 SA 1441 drought responsive root proteins were signal transduction related proteins (Figure 4.9). A total of 12 and 6 of these proteins were uncharacterized for ICSB 338 and SA 1441, respectively (Table 4.1 and 4.2). The proteins with the highest fold change were protein no. 199 (1.55) and 43 (1.66) for ICSB 338 and SA 1441, respectively. Conversely, proteins with the lowest fold change were protein no. 1218 (-1.39) and 1039 (-1.23) for ICSB 338 and SA 1441, respectively. In this functional category, only UMP-CMP kinase and purple acid phosphatase were characterised for ICSB 338 and SA 1441, respectively. UMP-CMP kinase was up-regulated whilst purple acid phosphatase was down-regulated. All the SA 1441 proteins were up- regulated except for purple acid phosphatase. The main function of UMP-CMP kinase is to catalyse the phosphorylation of pyrimidine nucleoside monophosphates using ATP. In this way, it plays a vital role in de novo pyrimidine nucleoside biosynthesis. Purple acid 130 phosphatase belongs to a family of dinuclear metalloenzymes (LeBansky et al., 1992), and catalyses phosphate ester hydrolysis. Kaida et al. (2010) showed that purple acid phosphatase can catalyse the dephosphorylation of cell wall proteins in tobacco. The remaining ICSB 338 proteins were uncharacterized. These uncharacterized proteins were involved in kinase and phosphatase molecular activity except for protein no. 7 and 1393 whose function are GTP binding and clathrin heavy chain binding, respectively. Of the proteins involved in phosphorylation, 56% were up-regulated. The two proteins involved in dephosphorylation were both down-regulated. In SA 1441, the uncharacterized proteins were either kinases or phosphatases except for protein no. 43 which had no predicted molecular function but predicted to be involved in cellular response to stimuli. Protein kinases and phosphatases are responsible for signal transduction and regulation (Shinozaki and Yamaguchi-Shinozaki, 1997). Protein kinases were also up-regulated in sorghum leaf tissue in response to salt stress (Swami et al., 2011). The GTP binding protein was down-regulated in ICSB 338, whereas the protein was up-regulated in SA 1441. GTP binding proteins were also identified in Arabidopsis root tissue under salt stress (Jiang et al., 2007). 4.4.3.5 Energy A total of 22 (9%) and 16 (9%) of the total drought responsive proteins for ICSB 338 and SA 1441 were annotated in the energy functional group (Figure 4.9). Of these proteins, 14 were uncharacterized for both ICSB 338 and SA 144 (Table 4.1 and 4.2). The protein with the highest fold change was protein no. 503 (1.98) and 1114 (1.68) for ICSB 338 and SA 1441, respectively. Conversely, proteins with the lowest fold change were protein no. 15 (-1.64) and 359 (-1.4) for ICSB 338 and SA 1441, respectively. In this functional category, the majority of the proteins were electron transport chain and glucose metabolism related for both sorghum varieties. These processes are responsible for the production of ATP required 131 for cellular function. All of the proteins involved in the glycolytic and gluconeogenesis processes were down-regulated in ICSB 338 and SA 1441. However, electron transport chain proteins were up-regulated in both sorghum varieties. The plasma membrane ATPase, involved in proton export across plasma membrane was down-regulated in both ICSB 338 and SA 1441. All of the other characterised proteins in ICSB 338 were down-regulated. These include fructose-bisphosphate aldolase (protein no. 15), acetyltransferase component of pyruvate dehydrogenase complex (protein no. 135), pyruvate dehydrogenase E1 component subunit alpha (protein no. 147), pyruvate kinase (protein no. 181 and 806), glucose-6-phosphate isomerase (protein no. 206) and ferredoxin-NADP reductase (protein no. 525). In SA 1441, all the proteins under the energy functional group were uncharacterised except for pyruvate kinase (protein no. 244) and pyrophosphate-fructose 6-phosphate 1-phosphotransferase subunit beta (protein no. 359) which were down-regulated as well. 4.4.3.6 Protein Synthesis Since their discovery in 1955, ribosomes are known as cell structures that synthesise proteins as directed by mRNA using aminoacyl-tRNA, GTP and ATP substrates (Zorca and Zorca, 2011). A total of 80 unique ribosomal proteins have been reported, with 32 being predicted to be present in the 40S ribosomal subunit whilst 48 are in the 60S subunit (Carroll et al., 2008; Chang et al., 2005). Figure 4.9 illustrates that 19 proteins (8%) and 26 (14%) of the total 237 and 184 proteins for ICSB 338 and SA 1441 drought responsive root proteins, respectively, were involved in protein synthesis. Of these, 15 and 24 proteins were uncharacterized for ICSB 338 and SA 1441, respectively (Table 4.1 and 4.2). The protein with the highest fold change was protein no. 1223 (1.43) and 525 (1.82) for ICSB 338 and SA 132 1441, respectively. Conversely, those with the lowest fold change were protein no. 816 (- 1.66) and 538 (-1.47) for ICSB 338 and SA 1441, respectively. In ICSB 338, 60S ribosomal protein L13 and L36 (protein no. 520 and 958), 40S ribosomal protein S6 (protein no. 964) and ribosomal protein L19 (protein no. 1022) all of which are involved in translation, were the only characterised proteins. For SA 1441 the elongation factor 1-alpha (protein no. 611) and 40S ribosomal protein S3a (protein no. 793) were the identified characterised proteins. Of all the characterised proteins for both sorghum varieties, only 40S ribosomal protein S3a (protein no. 793) was up-regulated. Ribosomal proteins were aslo down-regulated in Arabidopsis root tissue under salinity stress (Jiang et al., 2007). In agreement to the findings in this study, the translation elongation factor 1-alpha was down- regulated in response to drought stress in barley leaf tissue (Śniegowska-Świerk et al., 2015). The same protein was however up-regulated in sorghum leaf tissue in response to drought stress (Jedmowski et al., 2014). The uncharacterised proteins were mainly involved in the translation and ribosomal large and small subunit biogenesis and assembly for both sorghum varieties. The results of this current study are consistent with Jiang et al. (2007) where translation related proteins were also identified in Arabidopsis root tissue under salinity stress. In ICSB 338, 62% of the proteins involved in translation were up-regulated. The remaining uncharacterised proteins included those involved in rRNA maturation (protein no. 80) which was down-regulated, ribosomal small subunit assembly (protein no. 483) which was up- regulated and ribosomal large subunit assembly which was down-regulated. In SA 1441, all the uncharacterised proteins involved in translation were up-regulated except for protein no. 538. All the remaining uncharacterised proteins, that is, those involved in ribosomal small 133 subunit assembly (protein no. 35), ribosomal large subunit biogenesis (protein no. 667 and 710) were up-regulated. 4.4.3.7 Transporters Of the 237 drought responsive root proteins for ICSB 338, 14 proteins (6%) with putative functions as transporters were identified (Figure 4.9). For SA 1441, 18 proteins (10%) were identified in this category. For ICSB 338, 12 proteins were uncharacterized (Table 4.1) whilst 17 proteins were uncharacterized for SA 1441 (Table 4.2). The protein with the highest fold change was protein no. 64 (1.65) and 61 (1.53) for ICSB 338 and SA 1441, respectively. Conversely, proteins with the lowest fold change were protein no. 189 (-1.71) and 88 (-1.36) for ICSB 338 and SA 1441, respectively. The plasma membrane ATPase (protein no. 4) which was down-regulated, and the mitochondrial pyruvate carrier (protein no. 704) which was up-regulated, were the only characterised proteins in ICSB 338. These proteins are both important in transmembrane transport. In SA 1441, plasma membrane ATPase (protein no. 12), which was down- regulated, was the only characterised protein. Of the ICSB 338 uncharacterised proteins in this category, 67% were up-regulated and involved in various biological functions including intra- and inter-cellular transport. For SA 1441, 53% of the uncharacterised proteins were up- regulated and also involved in intra- and intercellular transport. 4.4.3.8 Other Functional Groups The remaining functional groups with relatively low numbers of proteins were cell structure, transcription and cell growth/division (Tables 4.1 and 4.2). All the identified cell structure related proteins were uncharacterized for ICSB 338. SA 1441 had only two characterized proteins, namely, tubulin beta chain (protein no. 55) responsible for cytoskeleton organisation 134 and profilin (protein no. 567) important for sequestering actin monomers. Tubulin beta chain was down-regulated whilst profiling was up-regulated. Amongst the five uncharacterized ICSB 338 cell structure proteins, protein no. 563 and 804 belonged to the actin family whilst the other three were involved in cytoskeleton organization. Only one protein (protein no. 563) was down-regulated in ICSB 338. Of the uncharacterized proteins, only one protein was also in the actin family for SA 1441. This protein was also down-regulated. This was in agreement with Śniegowska-Świerk et al. (2015) where actin was down-regulated in response to drought stress in barley leaf tissue. In contrast to this result, actin was up-regulated in response to drought stress in Kentucky bluegrass leaf tissue in response to drought stress (Xu and Huang, 2010). All the transcription related proteins identified in ICSB 338 were uncharacterized. Two of the three proteins predicted to be involved in methylation (protein no. 57 and 395) were up- regulated. Proteins involved in nucleic acid phosphodiester bond hydrolysis (protein no. 533), pseudouridine synthesis (protein no.702) and RNA modification (protein no. 865) were also up-regulated. However, those involved in RNA editing (protein no. 787) and mRNA splicing (protein no. 836) were down-regulated. Proteins involved in RNA related processes were also down-regulated in Kentucky bluegrass leaf tissue in response to drought stress (Xu and Huang, 2010) as well as in the resurrection plant (Xerophyta viscosa) (Ingle et al., 2007). The RNA binding protein was down-regulated in sorghum leaf tissue in a drought sensitive sorghum variety (Jedmowski et al., 2014). For SA 1441, histone H2B (protein no. 173), nascent polypeptide associated complex (protein no. 284), histone H4 (protein no. 362), histone H2A (protein no. 500, 579, 953 and 1112) were the SA 1441 characterised proteins. The most represented protein was histone H2A which is responsible for chromatin silencing (protein no. 500, 579,953 and 1112), all of 135 which were up-regulated. Chromatin silencing is an essential process in plants and it results in repression of transcription (Li et al., 2007). In order to initiate gene expression, chromatin must be modified to allow transcription factors, co-activators and RNA polymerase to access DNA (Thomas and Chiang, 2006). In other studies, histone H2Bs were down-regulated in rockrose (Cistus albidus) (Brossa et al., 2015) and oilseed rape (Brassica napus) (Koh et al., 2015) in response to drought stress. Mitochondrial fission is a process that results in the formation of two daughter mitochondria. Mitochondrial fission and fusion are important processes because they allow the sharing of internal materials between mitochondria (Arimura et al., 2004a and b). One uncharacterized protein (protein no. 786) identified in SA 1441 was predicted to be responsible for mitochondrial fission. This protein is in the dynamin superfamily with a dynamin-type guanine nucleotide-binding domain. Dynamin-like proteins are a requirement in mitochondrial fission in Arabidopsis (Arimura et al., 2004a) and rice (Fujimoto et al., 2004). The protein was up-regulated in SA 1441, the drought tolerant variety. 4.4 Drought-Induced Gene Expression Analysis in Sorghum Root and Leaf Tissue Five proteins with the highest fold-change were selected from each of the ICSB 338 and SA 1441 iTRAQ datasets (Tables 4.1 and 4.2) to make a total of 10 protein targets for gene expression analysis in sorghum plants (Table 4.3). Primers were designed for each of the genes using the Primer-BLAST tool on the NCBI database (https://www.ncbi.nlm.nih.gov). Following this, the two sorghum varieties were grown at field capacity until the V3 growth stage after which water was withheld for 12 days to impose drought stress (Figure 4.10). At 4 days of drought stress there were no visible signs of drought stress for both sorghum varieties. From 4 to 8 days of drought stress, SA 1441 showed a higher degree of wilting and leaf rolling compared to ICSB 338. ICSB 338 however showed greater leaf chlorosis during 136 this time period. From 8 to 12 days of drought stress the ICSB 338 leaf tissue became extremely chlorotic and the plants collapsed whilst SA 1441 plants maintained an upright position but the older leaves senesced whilst the new leaves experienced leaf rolling. Table 4.3: Drought stress responsive proteins selected for primer designing. Protein no. Accession Gene Identity Fold Change Protein Family ICSB 338 868 C5YPX8 SORBI_3008G134700 2.93 Aspartic peptidase 329 C5WZ08 SORBI_3001G514200 1.77 Thioredoxin 293 C5XDR4 SORBI_3002G217200 1.69 Peptidase 64 C5XHF1 SORBI_3003G136200 1.65 Germin 767 C5YBH7 SORBI_3006G135500 1.56 Galactose oxidase SA 1441 874 A0A1B6QHU0 SORBI_3001G073900 2.32 Malate dehydrogenase 173 C5YM38 SORBI_3007G149600 2.13 Histone H2B 362 C5WTL64 SORBI_3001G313200 2.06 Histone H4 525 C5XMD2 SORBI_3003G322400 1.82 Ribosomal protein S25 290 C5XAE4 SORBI_3002G049300 1.79 Ribosomal protein S25 137 Figure 4.10: ICSB 338 and SA 1441 sorghum plants during the drought stress period. Seedlings were watered until the V3 growth stage. Water was withheld and root and leaf tissue samples were collected at 0, 4, 8 and 12 days after drought stress for gene expression analysis. ICSB 338 and SA 1441 sorghum root and leaf samples were collected at 0, 4, 8 and 12 days after drought stress for RNA extraction and RNA gel electrophoresis (Figure 4.11). The RNA integrity was good for 0, 4 and 8 day time points for root and leaf tissue of both sorghum varieties. However, at 12 days of drought stress, RNA was somewhat degraded except for the SA 1441 root tissue. This could have possibly been due to leaf chlorosis observed in the plants during the drought stress treatment (Figure 4.10). This was followed by cDNA 138 synthesis and a standard PCR to assess the specificity of the 10 primers (SORBI_3008G134700, SORBI_3001G514200, SORBI_3002G217200, SORBI_3003G136200, SORBI_3006G135500, SORBI_3007G149600, SORBI_3001G313200, SORBI_3001G073900, SORBI_3003G322400, SORBI_3002G049300) to their intended targets (Figure 4.12). All the gene specific primers yielded PCR product except for SORBI_3002G217200 (Figure 4.12) which was subsequently discarded from the study. The primer SORBI_3002G049300 was also excluded because of non-specificity as observed in Fig 4.12 where no specific amplicon was observed, instead, multiple amplicons of the same resolution were observed. In addition, the melt curve of the gene showed two peaks, signifying the possibility of primer dimers (Appendix, Figure C9 and C12). Ultimately, eight genes were used in the study, four of which showed single amplicons (SORBI_3001G514200, SORBI_3003G136200, SORBI_3006G135500 and SORBI_3001G313200) whilst the other four exhibited double amplicons (SORBI_3008G134700, SORBI_3007G149600, SORBI_3001G073900 and SORBI_3003G322400). Of the genes with double amplicons, it was noted that the most prominent amplicon was of the expected size and also, the melt curves showed specific products (Appendix, Figure C7, C8 and C9), therefore the primers were selected for further analyses. Consequently, qRT-PCR was used to analyse the expression of the selected target eight genes. 139 Figure 4.11: Sorghum root and leaf total RNA gel. Approximately 300 ng of RNA for each sample was loaded on a 1.2% agarose gel. Images were taken using an Ingenius Bio-Imager. Four lanes, each in succession, represent ICSB 338 and SA 1441 total RNA at 0, 4, 8 and 12 days of drought stress, respectively. 140 Figure 4.12: Primer analysis of sorghum root drought responsive genes. Sorghum PCR products were loaded on a 3.5% agarose gel. Images were taken using an Ingenius Bio-Imager. The first lane represent a HyperLadder™ 25 bp DNA marker. The following lanes represent amplicons of each of the drought responsive genes. 141 The gene expression values were generated by entering take off and amplification control values of the reference gene and pasting it in the reference gene section of the REST2009 RG mode. This was followed by entering the corresponding treatment values of the reference gene in the treatment rows of the software. The control sample gene of interest values were then added to the control rows whilst the treatment values were entered in the corresponding treatment rows. Two reference genes were used per analysis in this study. SA 1441 was used as a baseline control to standardise the gene expression for comparison with ICSB 338. The output gene expression values were then exported to Microsoft Excel and the Student’s t-test was used to compare the gene expression means at a 5% level of significance. All the sorghum genes except for SORBI_3003G136200 showed significant differences in expression on at least one time point between ICSB 338 and SA 1441 root tissue in response to drought stress (Figure 4.13). There was a significantly higher expression of SORBI_3001G073900 and SORBI_3003G322400 in SA 1441 compared to ICSB 338 at 12 hrs of drought stress. In contrast, SORBI_3008G134700, SORBI_3006G135500 and SORBI_3001G313200 showed significantly higher expression levels in ICSB 338 compared to SA 1441 at the same time point. Of the 8 genes analysed, 6 showed a peak in gene expression in root tissue at 12 days of drought stress (Figure 4.13). In the sorghum leaf tissue, a consistent trend of gradual increase in gene expression peaking at 12 days of drought stress was exhibited by all the 8 genes (Figure 4.14). Most of the genes showed a gradual decrease in expression in both sorghum varieties until day 8 of drought stress. This was followed by a massive increase in gene expression at day 12 of drought stress (Figure 4.14). 142 SORBI_3008G134700 SORBI_3007G149600 Aspartic peptidase Histone H2B 20 40 ICSB 338 SA 1441 15 30 10 20 5 10 *** ** 0 0 0 4 8 12 0 4 8 12 SORBI_3001G514200 SORBI_3001G313200 Thioredoxin Histone H4 2.0 150 1.5 100 1.0 ** *** **50 0.5 *** ** 0.0 00 4 8 12 0 4 8 12 SORBI_3003G136200 SORBI_3001G073900 Germin Malate dehydrogenase 50 8 * 40 6 30 4 20 10 2 * 0 * 00 4 8 12 0 4 8 12 SORBI_3006G135500 SORBI_3003G322400 Galactose oxidase Ribosomal protein S25 6 80 60 * 4 40 2 ** *** 20 *** 0 0 * * 0 4 8 12 0 4 8 12 Time (Days) Time (Days) Figure 4.13: Drought stress-induced gene expression in sorghum root tissue. ICSB 338 (drought susceptible) and SA 1441 (drought tolerant) sorghum plants were exposed to drought by withholding water for 12 days. The root samples were harvested at the indicated time points and gene expression analysis was performed using qRT-PCR. Control plants were maintained at field capacity. Bars represent mean ± SE (n = 3). *, ** and *** = significance at p ≤ 0.05, 0.01 and 0,001, respectively. 143 Relative fold change Relative fold change Relative fold change Relative fold change SORBI_3008G134700 SORBI_3007G149600 Aspartic peptidase Histone H2B 600 300 ICSB 338 SA 1441 400 200 200 100 0 *** *** ** 0 *** ** * 0 4 8 12 0 4 8 12 SORBI_3001G514200 SORBI_3001G313200 Thioredoxin Histone H4 15 300 10 * 200 5 100 *** *** ** 0 0 *** *** * 0 4 8 12 0 4 8 12 SORBI_3003G136200 SORBI_3001G073900 Germin Malate dehydrogenase 250 200 200 150 150 100 100 50 50 0 * * *** ** *** *0 0 4 8 12 0 4 8 12 SORBI_3006G135500 SORBI_3003G322400 Galactose oxidase Ribosomal protein S25 250 400 200 300 150 200 100 50 100 0 *** *** *** 0 *** *** ** 0 4 8 12 0 4 8 12 Time (Days) Time (Days) Figure 4.14: Drought stress-induced gene expression in sorghum leaf tissue. ICSB 338 (drought susceptible) and SA 1441 (drought tolerant) sorghum plants were exposed to drought by withholding water for 12 days. The leaf samples were harvested at the indicated time points and gene expression analysis was performed using RT-qPCR. Control plants were maintained at field capacity. Bars represent mean ± SE (n = 3). *, ** and *** = significance at p ≤ 0.05, 0.01 and 0,001, respectively. 144 Relative fold change Relative fold change Relative fold change Relative fold change 4.5 Discussion Plants continuously synthesise proteins to maintain growth and development processes. When exposed to drought, stress response genes are activated which trigger a network of molecular responses in a bid to restore homeostasis and adapt to the imposed stress. In this study, a comparative root proteomic analysis of two sorghum varieties was conducted. The varieties used, ICSB 338 and SA 1441, had contrasting phenotypic traits to drought. ICSB 338 is drought susceptible while SA 1441 is drought tolerant. These varieties were used to identify changes in the total soluble protein profiles of roots following an 8-day drought stress treatment using iTRAQ analysis. This was followed by qRT-PCR analysis of 9 drought stress responsive target genes. Roots are important in that they are responsible for the primary perception of soil water deficits and subsequent signalling processes toward the shoots. This set of events results in the regulation of gene expression and subsequent modulation of drought stress response pathways (Ghatak et al., 2016). Fewer studies on root proteome profiles under drought stress have been performed on C4 perennial grasses (Zhao et al., 2011), soybean (Mathesius et al., 2011), and tomato (Zhou et al., 2013). There has been no report on comparative sorghum root proteomics thus making this study relevant. This study also focuses on sorghum because it is an important naturally drought tolerant cereal crop with a wide genetic diversity. A total of 237 and 184 unique root proteins were identified as drought responsive for ICSB 338 and SA 1441, respectively (Table 4.1 and 4.2). Of the iTRAQ identified drought responsive root protein, 76% and 72% were uncharacterised in ICSB 338 and SA 1441, respectively (Table 4.1 and 4.2). Uncharacterised proteins that are responsive to stress may be important is that they may possess relevant function, which can only be assessed through further experimentation via gene and protein function validation studies. It is therefore important to extend proteomics and genomics studies on sorghum under environmental 145 stresses. The drought responsive proteins were classified into functional groups on the basis of gene ontology, domain and protein family data obtained from the UniProt and InterPro databases. In response to stress, plants recruit a network of pathways involved in energy and carbohydrate metabolism (Zadražnik et al., 2013). Energy is vital in plant growth and developmental processes. In this study, the expression of energy and metabolism proteins was altered by drought stress for both sorghum varieties. The main pathways affected by drought stress in ICSB 338 under the energy functional group were glycolysis, gluconeogenesis and the electron transport chain (Table 4.1). For SA 1441, the tricarboxylic acid (TCA) as well as glycolytic and the electron transport chain proteins were differentially expressed (Table 4.2) in response to the drought stress. Glycolysis is responsible for converting glucose into pyruvate liberating ATP and NADH. Under drought stress, the glycolysis-related proteins were down-regulated for both sorghum varieties. Gluconeogenesis proteins, which are involved in the generation of glucose from substrates of a non-carbon origin, were down-regulated as well in ICSB 338 in this study. This is in agreement with Zhou et al. (2013), who reported the down-regulation of glycolytic and gluconeogenesis proteins in tomato root tissue under drought stress. A down-regulation of glycolytic proteins was also observed in wild wheat (Triticum boeoticum) root tissue in response to drought stress (Liu et al., 2015). Zhao et al. (2011) and Mathesius et al. (2011) also reported an up-regulation of glycolysis proteins in Cynodon sp and soybean (Glycine max) root tissue under drought stress, respectively. The results may suggest that sorghum or specifically sorghum root tissue conserves energy more efficiently in response to drought stress through reduced glucose catabolism. 146 The up-regulation of the drought responsive electron chain transport proteins was observed for both sorghum varieties in this study. The electron chain transport system is important for the generation of energy in the form of ATP. The TCA related proteins in the drought susceptible sorghum variety ICSB 338 were down-regulated. The TCA cycle is the primary driver of cellular respiration, which results in energy production through oxidative phosphorylation when the reduced NAD and FAD are fed in the electron transport chain (Stern et al., 2000). In this study, TCA related proteins were down-regulated in SA 1441 possibly as a way of limiting energy production under drought stress conditions. As indicated in Figure 4.9, 25% and 21% of the drought responsive ICSB 338 and SA 1441 proteins respectively, were involved in metabolism. Of these, 13% and 24% are involved in carbohydrate metabolic processes for ICSB 338 and SA 1441 sorghum varieties, respectively. All these proteins were up-regulated. The up-regulation of carbohydrate metabolism proteins suggests that sorghum plants require a certain level of energy to fuel defence mechanisms and cellular repair in the root tissue in response to drought stress. The other up-regulated proteins for SA 1441 include those involved in glutathione, malate and protein metabolic processes. Lipoxygenase and other proteins involved in lipid metabolism were down-regulated for both sorghum varieties except ICSB 338 protein no. 111 which is an uncharacterised protein. The main functions of lipids in plants include membrane synthesis and energy storage. Of interest is the down-regulation of patatin, in both sorghum varieties in response to the drought stress. Papatin is a storage protein and also responsible for lipid catabolism (Andrews et al., 1988). Its down-regulation could be a mechanism for conserving cellular energy stores. A total of 61 (26%) and 42 (23%) drought responsive proteins were disease and defence related for ICSB 338 and SA 1441 sorghum varieties, respectively (Figure 4.9). These proteins can further be classified into programmed cell death (PCD), redox homeostasis, and 147 molecular chaperones related proteins. Programmed cell death is an important component of defence (Lam, 2004), as it is selective and inactivates less useful cells and tissues in preference to life sustaining ones (Reape et al., 2008). Under PCD, the ubiquinone-6 biosynthetic process putative protein was up-regulated whilst the ubiquitin activating enzyme was down-regulated in SA 1441. Ubiquitin is responsible for tagging proteins for degradation prior to cellular repair (Gray and Estelle, 2000). A previous study of the grass Sporobolus stapfianus associated ubiquitin with desiccation tolerance mostly in the signalling processes and cellular protection and repair mechanisms (O’Mahony and Oliver, 1999). The down- regulation of the ubiquitin activating enzyme in this study may be due to the ability of the sorghum plant to limit cellular damage under drought stress. This would therefore result in less need of protein degradation for cellular repair processes. Zhou et al. (2013) observed that ubiquitin was induced only in drought tolerant tomato plants in comparison to the drought susceptible variety. Drought stress disrupts cellular redox homeostasis resulting in excessive ROS accumulation (Mittler, 2002). This results in damage of cellular components. The production of antioxidant enzymes by plants acts as the main mechanism in the restoration of redox homeostasis. In this study, peroxidase enzymes, mainly responsible for the hydrogen peroxide catabolic process were highly represented in the disease and defence functional category for both sorghum varieties. For ICSB 338, some of the peroxidases were up-regulated (protein no. 173, 232, 235, 794) whilst others were down-regulated (protein no. 95, 148, 446, 708). On the other hand, only one peroxidase enzyme was down-regulated for SA 1441 (protein no.66) whilst the rest were up-regulated (protein no. 38, 97,130, 159, 187, 207, 287, 307, 437, 622, 641, 779). The up-regulation of peroxidases in response to abiotic stress was also reported in sorghum under salt stress (Swami et al., 2011). These results suggest that under drought 148 stress, the drought tolerant SA 1441 is able to synthesise more peroxidases to reduce cellular damage by ROS compared to the drought susceptible variety ICSB 338. Heat shock proteins are known to be highly responsive to drought stress. In this study, one uncharacterised protein in the HSP20-like chaperone superfamily was up-regulated in both ICSB 338 (protein no. 214) and SA 1441 (protein no. 273) (Table 4.1 and 4.2). For SA 1441, the characterised small heat shock-like protein was up-regulated as well (protein no. 972). The up-regulation of the small heat shock proteins may play a role in preventing protein denaturation in response to stress thus improving the tolerance of sorghum to drought stress. Two uncharacterised proteins, which belong to the ricin-B like lectin family were assigned to the disease and defence functional category in ICSB 338. Lectins have been previously demonstrated to be responsive to abiotic stress in rice exposed to salt stress (Zhang et al., 2000). Lectins are carbohydrate binding proteins that agglutinate cells and glycoconjugates (Goldstein, 1980). One of their functions is defence against biotic and abiotic stresses (Etzler, 1986). In this study, the lectins were up-regulated in ICSB 338 possibly as a defence mechanism against drought stress. Uricase is involved in liberating the much needed nitrogen to plants (Damsz et al., 1994; Capote-Mainez and Sánchez, 1997). The presence of different types of uricase enzymes has been previously established in roots of maize and tobacco (Theimer and Beevers, 1971). In this study, there was a down-regulation of this protein in ICSB 338. This could be for limiting the amount of nitrogen available to the plant as the plant lowers its metabolic activities in response to drought stress. Pathogenesis related proteins are defined as proteins that are induced in plant defence against pathogens (Van Loon et al., 1994). The pathogenesis related protein 10b was down-regulated in ICSB 338 but up-regulated in SA 1441. This suggests that the drought tolerant sorghum 149 variety triggers a wide range of stress responses as an adaptive mechanism to increase chances of survival. Kim et al. (2014) also showed that pathogenesis related proteins were up-regulated in maize under drought stress. In another study, Mirzaei et al. (2011) demonstrated that pathogenesis-related proteins were generally increased in rice root tissue exposed to drought stress. Since their discovery, dirigent proteins have been shown to be involved in lignin biosynthesis through phenoxy-radical coupling (Davin and Lewis, 2000). In this study, dirigent proteins were differentially expressed and up-regulated in both sorghum varieties. This is essential in plants in maintaining cell wall integrity (Paniagua et al., 2017). LEA proteins function as cellular protectants under water loss (Chakrabortee et al., 2007). The LEA3 was up-regulated in SA 1441 in response to drought stress. This is in agreement with Xu et al. (1996), where the expression of LEA proteins in rice plants conferred both water and salinity stress tolerance. The LEA proteins were however, not identified in ICSB 338 in the current study. Drought stress can repress protein synthesis (Good and Zaplachinski, 1994). A reduction in protein synthesis in response to heat (Duncan and Hershey, 1989) and drought (Good and Zaplachinski, 1994) stresses has been reported. In this study, all the characterised proteins involved in protein synthesis, that is, 60S ribosomal protein L13, 60S ribosomal protein L36, 40S ribosomal protein S6 and ribosomal protein L19 were down-regulated in ICSB 338 (Table 4.1). However, in SA 1441, the elongation factor 1-alpha was down-regulated whereas the 40S ribosomal protein S3a was up-regulated (Table 4.2). All the remaining proteins were uncharacterised and mainly predicted to be involved in translation and ribosome biogenesis and organisation. The presence of protein destination and storage proteins in high abundance could signify protein mobility both within the cell and across the cell wall into the extracellular matrix. 150 Protein movement is also vital in cell-cell communication and signal transduction (O’Connor et al., 2010). Proteins involved in protein catabolic processes dominated the protein destination and storage functional group with 75% of the identified proteins being involved in proteolysis for ICSB 338 and 53% of these being up-regulated (Table 4.1). The up-regulated proteolysis related proteins for ICSB 338 were uncharacterised proteins, but predicted to be involved in serine-type endopeptidase, dipeptidyl-peptidase, cysteine-type endopeptidase and aspartic type endopeptidase activities. On the other hand, only 33% of the proteins assigned to the protein destination and storage functional group were involved in proteolysis for SA 1441 with only the cysteine type endopeptidase being up-regulated (Table 4.2). These results are in agreement with Good and Zaplachinski (1994), who attributed the reduced protein synthesis in response to drought in oilseed rape (Brassica napus) to high protease activity and the enhanced synthesis of stress adaptive proteins at the expense of other proteins. The other protein destination and storage uncharacterised proteins in ICSB 338 and SA 1441 were involved in chaperone-mediated protein folding although all of them were down-regulated. Proteases, involved in the degradation of unfolded proteins were up- regulated, possibly to maintain homeostasis through the release of amino acids for recycling (Zou et al., 2011). The initial perceived water deficits resulting from drought stress must be transmitted within the plant by signalling molecules. This occurs as a result of a process termed signal transduction. The transmission of signals to cellular machinery consequently triggers various signalling processes and transcription controls, which maintain homeostasis by activating adaptive stress responsive processes (Xiong and Zhu, 2001). These processes are directed towards cellular membrane and protein repair. In this study, purple acid phosphatase, which is involved in protein-protein interactions resulting in stress perception was down-regulated 151 in SA 1441 (Table 4.2). The other drought responsive proteins identified under this functional category for both sorghum varieties were mainly kinases and phosphatase activity proteins. Protein kinases and phosphatases are thought to be responsive to environmental stresses (Hardie, 1999). Membrane receptors receive signals and utilise receptor-coupled protein phosphorylation signalling modes resulting in a wide range of cellular processes including enzyme activation and protein degradation (Xiong and Zhu, 2001). It is however unclear why the purple acid phosphatase was down-regulated. Cell structure related proteins play an important role in cell shape organisation and cellular signalling and have been reported to undergo rigorous changes when under abiotic stress (Abdrakhamanova et al., 2003). In this study, chitinase-B, involved in the chitin catabolic process, was up-regulated in ICSB 338 but absent in SA 1441. In drought stressed rice root tissue, chitinases were increased in response to drought stress (Mirzaei et al., 2011). The tubulin beta chain was down-regulated in SA 1441 but absent in ICSB 338. An up-regulation of profilin was observed in SA 1441 but absent in ICSB 338. Profilin is an important cell component that plays a role in actin microfilament growth both spatially and temporally resulting in cell shape alterations and locomotion (Gunning et al., 2015). In another study, actin was up-regulated in response to drought stress in Kentucky bluegrass in response to drought stress (Xu and Huang, 2010). The result of this study suggests that ICSB 338 may be unable to organise its cell cytoskeleton efficiently under drought stress resulting in its inability to confer drought tolerance compared to SA 1441. Other proteins identified are those involved in transport and transcription. The mitochondrial protein carriers were up-regulated in ICSB 338. The rest of the differentially expressed proteins in this category were uncharacterised. The histone and nascent polypeptide- associated complex subunit beta proteins involved in chromatin silencing and regulation of 152 transcription, respectively were up-regulated in SA 1441. This suggests that drought stress was regulated at the transcription level in the drought tolerant sorghum variety. Following the proteomic analysis, gene expression analysis of the eight targets with the largest fold changes selected from both ICSB 338 and SA 1441 was performed. RNA degradation was observed at 12 days in the leaf tissue of both sorghum varieties at 12 days of drought stress (Figure 4.11) possibly due to the observed leaf chlorosis. For the root tissue, ICSB 338 showed similar RNA degradation result on day 12 whilst in SA 1441 the RNA remained intact throughout the duration of the experiment. Despite the non-specific binding, the eight genes were used because the melt curves showed the presence of specific PCR product (Appendix, Figure C7, C8 and C9). Significant differences in expression profiles was noted between ICSB 338 and SA 1441 root and leaf tissue in all genes except SORBI_3003G136200 in leaf tissue (Figures 4.13 and 4.14). The differential expression of the genes in the two sorghum varieties with contrasting responses to drought stress suggests that these genes can differentiate respective drought tolerance levels of the sorghum varieties. The gene expression levels were much higher in leaf tissue compared to root tissue for all of the genes analysed. The SA 1441 leaf tissue had an up-regulation of the genes starting at 8 days. The expression levels of most of these sorghum genes peaked at 12 days of drought stress, which could indicate a delayed drought stress response in aerial parts of the plant. This could be an advantage to the sorghum plants since an early response in mild drought stress unnecessarily consumes energy. In conclusion, the two sorghum varieties showed different protein profiles for most of the assigned functional groups. This may be due to the differences in response mechanisms between the two sorghum varieties in response to drought stress. The results indicate that the drought tolerant variety SA 1441 expressed proteins aimed at establishing stability and also 153 conferring adaptive responses to drought stress to ensure continued function and survival in comparison to the drought susceptible ICSB 338. In addition, a greater proportion of the differentially expressed proteins were uncharacterised. As such, further experiments are recommended for the characterisation of these proteins since they may possess novel drought stress adaptive characteristics important to cereal crops. The gene expression analysis also validated the drought responsiveness of the protein targets selected from the two sorghum varieties. This suggests that the genes could be potentially utilised in developing drought markers for use in selecting drought tolerant varieties in plant breeding programs. 154 CHAPTER 5 DEVELOPING MOLECULAR MARKERS FOR DROUGHT TOLERANCE 5.1 Introduction Classical plant breeding involves crossing closely or distantly related species in an effort to create variability and produce plants with desirable traits (Acquaah, 2012). Variability forms the basis for plant breeding (Messmer et al., 2015). In order to identify new traits, large populations of diverse germplasm are continuously crossed over several generations to achieve a certain combination of alleles (Poehlman et al., 1995). This is followed by screening of the progeny to identify individuals with the desired trait. This process is carried out in the field over several years. Thereafter, the plants with the desirable trait are crossbred to introduce the new trait into a widely accepted variety. Generations of backcrossing would then be performed to maintain the characteristics of the widely accepted variety (Acquaah, 2012). As the duration and cost of the selection process is high, it is desirable to develop molecular markers which can assist in screening for a particular trait at the seedling stage to increase the efficiency and effectiveness of the process (Acquaah, 2012). Genes that would serve as high- fidelity molecular markers should have their expression tightly linked to the phenotype (Yang et al., 2015a). In the case of drought, the progeny of genetic crosses can be germinated in the confines of growth rooms or greenhouses and gene expression evaluated at the seedling stage. Only plants that pass the screen test will be transferred to the field to produce seed. This expedites the breeding program by reducing the space, time, water, agrochemical, and labour required to identify new crop varieties with desirable traits (Messmer et al., 2015). 155 Overall, this project aimed to develop a toolkit of drought marker genes to rapidly screen plants for drought tolerance at an early developmental stage. The objective of this chapter was to explore the utility of genes, identified in Chapter 4 and by Ngara et al. (2018), as putative marker genes for use as a screening tool in breeding platforms developing drought- tolerant crops. 5.2 Drought Marker Gene Expression in ICSB 338 and White Sorghum Compared to other cereals, sorghum is highly drought tolerant, maintaining its yield potential under severe drought conditions. However, different sorghum varieties have different levels of drought tolerance, which can be physiologically distinguished (Chapter 3). The eight drought-responsive sorghum genes identified in Chapter 4 (Section 4.4) have already been shown to be differentially expressed between ICSB 338 and SA 1441 sorghum varieties (Figures 4.13 and 4.14). Expression of the same genes was then evaluated in another sorghum variety, White sorghum, relative to the ICSB 338 using cell suspension cultures developed by our research group (Ramulifho, 2017). Total RNA was extracted from ICSB 338 and White sorghum cell cultures grown under optimal conditions and analysis of gene expression was performed using real-time quantitative polymerase chain reaction (qRT-PCR). According to the results, six genes, namely SORBI_3001G514200, SORBI_3003G136200, SORBI_3007G149600, SORBI_3001G313200, SORBI_3001G073900 and SORBI_3003G322400, showed significant differences between the two sorghum varieties (Figure 5.1). Of these differentially expressed genes, SORBI_3001G313200 and SORBI_3001G073900 exhibited the highest difference in fold-change between White sorghum and ICSB 338 cell suspension cultures. SORBI_3001G514200 and SORBI_3003G322400 showed higher gene expression in White sorghum whilst the remaining 5 genes showed the opposite. Due to time constraints, physiological experiments to 156 verify this in whole plants could not be conducted within the PhD project. All the nine genes also showed significant differences between the two sorghum varieties in response to sorbitol-induced osmotic stress on at least one time point in a 72-hr time-course experiment (Appendix, Figure B1). 4 White sorhum *** ** ICSB 338 3 ** ** 2 * 1 * *** 0 se in in se B 4 se 25 25 da x m a 2 H a S S ti ed o er id e H ne en n n ep r G o x n to g e i ei o p i e st o h s i s dr o ro t ot tic T o i H t H y l p l p r r c eha a a ap al ds te so m om A G s al a ib o ib o M R R Gene name Figure 5.1: Gene expression in ICSB 338 and White sorghum cell cultures without osmotic stress. Gene identities are SORBI_3008G134700 (Aspartic peptidase), SORBI_3001G514200 (Thioredoxin), SORBI_3002G217200 (Peptidase), SORBI_3003G136200 (Germin), SORBI_3006G135500 (Galactose oxidase), SORBI_3007G149600 (Histone H2B), SORBI_3001G313200 (Histone H4), SORBI_3001G073900 (Malate dehydrogenase), SORBI_3003G322400 (Ribosomal protein S25) and SORBI_3002G049300 (Ribosomal protein S25) Gene expression analysis was performed using qRT- PCR. Bars represent mean ± SE (n = 3). *, ** and *** represent a significant difference between means at p ≤ 0.05, 0.01, and 0.001, respectively. 157 Fold change Overall, the gene expression results (Figure 5.1 and Appendix, Figure B1) showed that the drought-responsive genes can be used to distinguish between two sorghum varieties, even in cell suspension cultures either with or without osmotic stress. Therefore, at least four of the genes (SORBI_3003G136200, SORBI_3007G149600, SORBI_3001G313200, and SORBI_3001G073900) can be possibly incorporated into a screen to identify sorghum seedlings likely to have better drought tolerance. 5.3 Heat Stress Activates Drought-Responsive Genes Drought stress is inseparably associated with heat stress in most agro-ecological zones of sub-Sahara Africa. This raises the possibility that drought-responsive genes may also be responsive to temperature. Therefore, the effects of temperature on the expression of the nine sorghum drought responsive genes (Chapter 4, Section 4.4) were evaluated. White sorghum cell suspension cultures (Ngara et al., 2008; Ramulifho, 2017) were grown at either 22°C or 30°C for 8 days and cells harvested for RNA extraction. Gene expression analysis was conducted on the nine drought-responsive genes and sorghum homologues of Arabidopsis DREB2A and ERD1 drought markergenes - SbDREB2A-1 (SORBI_3009G101400), SbERD1- 1 (SORBI_3004G162400) and SbERD1-2 (SORBI_3006G065100). Expression of all the genes was significantly different between the two temperatures, indicating that the genes were responsive to heat (Figure 5.2). All the genes were activated in response to heat except for SORBI_3002G049300 (Figure 5.2), which was down-regulated. The nine sorghum genes except SORBI_3002G049300 exhibited the same up-regulation expression trend as the three drought marker genes. Of all the genes evaluated SORBI_3003G322400 showed the greatest gene expression. Overall, the results show that the majority of drought-responsive genes are triggered by heat stress as well. 158 15 22 °C *** 30 °C 10 5 * * *** *** * *** ** ** * *** * 0 se in in se 2B 4 a x m H s e 25 25 -1 -1 -2A 1 1 id do er id a H e na S Sn n n 2 D De B ep t e G e R R or i i o x o p i e to n t g e e E E E is ro ot ot R tic Th s i s H d r r D r ct o H hy l p p a a e a a l p al d om ms G te o A la bo s os a i ib M R R Gene name Figure 5.2: Drought-responsive genes [SORBI_3008G134700 (Aspartic peptidase), SORBI_3001G514200 (Thioredoxin), SORBI_3002G217200 (Peptidase), SORBI_3003G136200 (Germin), SORBI_3006G135500 (Galactose oxidase), SORBI_3007G149600 (Histone H2B), SORBI_3001G313200 (Histone H4), SORBI_3001G073900 (Malate dehydrogenase), SORBI_3003G322400 (Ribosomal protein S25) and SORBI_3002G049300 (Ribosomal protein S25)] are also responsive to high temperature. White sorghum cell cultures were grown at 22°C or 30°C and harvested 4 days later for RNA extraction. Gene expression analysis was performed using qRT-PCR. Bars represent mean ± SE (n = 3). *, ** and *** = a significant difference between means at p ≤ 0.05, 0.01 and 0.001, respectively. 5.4: Identifying Homologues of Sorghum Drought Marker Genes in a Different Species Because sorghum is a naturally drought tolerant crop, breeding platforms for drought tolerance tend to target other drought-sensitive crops, such as maize (Zea mays) and rice (Oryza sativa). Thus, the knowledge obtained from sorghum has to be transferred to other 159 Fold change species to improve their drought tolerance through classical breeding platforms or genetic engineering. To explore how this might be achieved, three sorghum drought genes previously identified by the Ngara and Chivasa research groups (Ngara et al., 2018) were selected for proof-of-concept using Arabidopsis (Arabidopsis thaliana). These are thioredoxin (SORBI_3009G190800), peptidase (SORBI_3007G172100) and xyloglucanase (SORBI_3002G302000). These genes are also responsive to sorbitol-induced osmotic stress in sorghum cell cultures and they are differentially expressed in ICSB 338 and SA 1441 sorghum plants exposed to drought stress (Ngara et al., 2018). Putative Arabidopsis homologues of these genes were identified in the Arabidopsis Information Resource (TAIR) database (www.arabidopsis.org/Blast) using the sorghum protein sequences as bait in BLAST searches. It became clear that the equivalent gene families in Arabidopsis are very large, each consisting of over 50 genes (Appendix, Table B1). The top four hits per gene family were selected for further gene expression analysis. Four-day-old Arabidopsis cell suspension cultures grown separately in continuous darkness or in a 16 hr-photoperiod were treated with 0.4 M sorbitol and samples for RNA extraction were collected at 0, 6, 24, 48 and 72 hrs (Section 2.1.2). The light and dark growth conditions were used to check if there are difference in gene expression under these conditions. The RNA was extracted and separated by electrophoresis in a 1.2% agarose gel. RNA of good integrity was obtained (Figure 5.3). This was followed by cDNA synthesis and standard PCR to assess the specificity of primers (Table 2.4) designed to amplify fragments of the target Arabidopsis genes. Primers that did not yield a product (CEP2 and XTH28) were excluded from the study (Figure 5.4). Consequently, qRT-PCR was used to investigate the response of the selected Arabidopsis genes to osmotic stress using the eukaryotic Initiation Factor 4A (eIF4A) and Actin 2 as reference control genes. 160 Figure 5.3: Arabidopsis RNA gel. Arabidopsis cells grown in total darkness (dark) or a 16 hr- photoperiod (light) were treated with 0.4 M sorbitol for the indicated time prior to harvesting and RNA extraction. Approximately 300 ng of RNA from each sample were separated in a 1.2% agarose gel. 161 Figure 5.4: PCR-amplification of fragments of Arabidopsis genes. Arabidopsis cDNA was used as template to PCR-amplify the indicated genes to test the specificity of the primers. The PCR products were separated in a 3.5% agarose gel. The lane on the extreme right contain the HyperLadder™ 25 bp DNA marker. 5.4.1: Responses of Arabidopsis Thioredoxin Homologues to Sorbitol A total of four Arabidopsis homologues of the sorghum thioredoxin gene with the highest sequence similarity were obtained from the TAIR database. The homologues were denoted TRX1 (AT3G51030.1), TRX3 (AT5G42980.1), TRX5 (AT1G45145.1) and ATTRX4 (AT1G19730.1). In cell cultures maintained in light, TRX1 was suppressed from 6 hrs to 24 hrs followed by activation at 48 and 72 hrs (Figure 5.5). TRX5 on the other hand exhibited gene activation at 6 hrs followed by suppression until the 72 hr time-point. TRX3 was up- regulated only at 6 hrs whilst for ATTRX4 there was gene activation at both 6 and 24 hrs. The cell cultures maintained in the dark also exhibited more or less the same trends as those 162 maintained in light (Figure 5.5). This shows that Arabidopsis thioredoxin homologues exhibited unique responses to osmotic stress. 163 TRX1 light TRX1 dark 1.5 1.5 *** 1.0 1.0 *** 0.5 *** 0.5 *** * *** *** 0.0 0.0 0 6 24 48 72 0 6 24 48 72 Time (Hrs) Time (Hrs) TRX3 light TRX3 dark 2.0 2.0 *** 1.5 1.5 * 1.0 1.0 *** *** 0.5 0.5 0.0 0.0 0 6 24 48 72 0 6 24 48 72 Time (Hrs) Time (Hrs) TRX5 light TRX5 dark 6 5 *** 4 *** 4 ** 3 *** 2 2 *** 1 0 0 0 6 24 48 72 0 6 24 48 72 Time (Hrs) Time (Hrs) ATTRX4 light ATTRX4 dark 2.0 1.5 ** * 1.5 ** 1.0 1.0 0.5 0.5 0.0 0.0 0 6 24 48 72 0 6 24 48 72 Time (Hrs) Time (Hrs) Figure 5.5: The transcriptional response of Arabidopsis thioredoxin genes to sorbitol. Arabidopsis cell suspension cultures were treated with 0.4 M sorbitol and samples for RNA extraction harvested at the indicated time-points. Gene expression analysis was conducted via qRT-PCR. Bars represent mean ± SE (n = 3). *, ** and *** = a significant difference between means at p ≤ 0.05, 0.01 and 0.001, respectively. 164 Fold-change Fold-change Fold-change Fold-change Fold-change Fold-change Fold-change Fold-change 5.4.2: Responses of Arabidopsis Peptidase Homologues to Sorbitol Three Arabidopsis peptidase homologues from which RT-PCR amplified a product (Figure 5.4) were used for subsequent gene expression analysis. CEP2 (AT3G48340.1) was excluded as the primers failed to yield a PCR product (Figure 5.4). In cell suspension cultures maintained in light, all the Arabidopsis peptidase homologues CEP1 (AT5G50260.1), XCP1 (AT4G36880.1) and XCP2 (AT1G20850.1) showed an initial gene activation at 6 hrs followed by different expression patterns (Figure 5.6). XCP1 gradually declined, while that of CEP1 and XCP2 dipped and 24 hrs before being re-activated again. This again is shows that gene expression of the homologues is generally different to one another. The general trends of gene expression in cell cultures maintained in light and dark conditions were similar except for CEP1. This gene exhibited a general gradual activation in light conditions with suppression under dark conditions (Figure 5.6). 165 CEP1 light CEP1 dark 20 15 * 15 *** ***10 10 *** *** *** 5 *** 5 * 0 0 0 6 24 48 72 0 6 24 48 72 Time (Hrs) Time (Hrs) XCP1 light XCP1 dark 2.0 4 ** *** 1.5 3 1.0 *** ***2 *** 0.5 *** 1 * 0.0 0 0 6 24 48 72 0 6 24 48 72 Time (Hrs) Time (Hrs) XCP2 light XCP2 dark 2.5 2.0 2.0 ** 1.5 1.5 ** * 1.0 1.0 0.5 0.5 0.0 0.0 0 6 24 48 72 0 6 24 48 72 Time (Hrs) Time (Hrs) Figure 5.6: The transcriptional response of Arabidopsis peptidase genes to sorbitol. Arabidopsis cell suspension cultures were treated with 0.4 M sorbitol and samples for RNA extraction harvested at the indicated time-points. Gene expression analysis was conducted via qRT-PCR. Bars represent mean ± SE (n = 3). *, ** and *** = a significant difference between means at p ≤ 0.05, 0.01 and 0.001, respectively. 166 Fold-change Fold-change Fold-change Fold-change Fold-change Fold-change 5.4.3: Response of Arabidopsis Xyloglucanase Homologues to Sorbitol A total of three Arabidopsis xyloglucanase homologues - XTH31 (AT3G44990.1), XTH32 (AT2G36870.1) and XTH33 (AT1G10550.1) were successfully PCR-amplified (Figure 5.4). However PCR-amplifaction of XTH28 (AT1G14720.1) failed. In Arabidopsis cell suspension cultures maintained in light conditions, XTH31 & XTH32 had gene activation peaking at 48 and 24 hrs, respectively, while XTH33 was generally suppressed throughout the time-course (Figure 5.7). A comparison between the gene expression of cell cultures maintained in light and dark conditions showed generally different expression trends for all the genes. 167 XTH31 light XTH31 dark 8 150 6 *** 100 *** 4 *** 50 2 *** *** *** *** 0 0 0 6 24 48 72 0 6 24 48 72 Time (Hrs) Time (Hrs) XTH32 light XTH32 dark 2.0 15 *** 1.5 10 ** 1.0 *** 5 0.5 *** ** *** 0.0 0 0 6 24 48 72 0 6 24 48 72 Time (Hrs) Time (Hrs) XTH33 light XTH33 dark 1.5 2.0 *** 1.5 1.0 1.0 * 0.5 ** 0.5 ** *** ** 0.0 0.0 0 6 24 48 72 0 6 24 48 72 Time (Hrs) Time (Hrs) Figure 5.7: The transcriptional response of Arabidopsis xyloglucanase genes to sorbitol. Arabidopsis cell suspension cultures were treated with 0.4 M sorbitol and samples for RNA extraction harvested at the indicated time-points. Gene expression analysis was conducted via RT-qPCR. Bars represent mean ± SE (n = 3). *, ** and *** = a significant difference between means at p ≤ 0.05, 0.01 and 0.001 respectively. Overall, these results show that Arabidopsis homologues of sorghum drought genes can be identified through database searches and that at least some of these similarly respond to osmotic stress-simulated drought. 168 Fold-change Fold-change Fold-change Fold-change Fold-change Fold-change 5.5 Discussion Marker assisted selection (MAS) refers to any selection system that depends on the selection of desired traits through markers linked to them (Eckenstorfer et al., 2014). This selection system is more reliable than the traditional visual phenotypic assessment and selection done in classical plant breeding (Eckenstorfer et al., 2014). This is because molecular markers are both tightly associated with the desirable trait and also highly heritable (Bernardo, 2002). In addition, molecular markers are not affected by environmental factors or plant developmental stages (Winter and Kahl, 1995), whereas the phenotype changes in response to changing environments. Molecular markers are thus useful in plant breeding as they reduce the duration of the selection process in the classical plant breeding method (Acquaah, 2012). The development of drought markers requires the identification and validation of drought tolerance phenotype linked genes. Most studies in molecular marker development are focusing on PCR based markers mainly for genetic diversity studies. These include random amplified polymorphic DNA (RAPD), simple sequence repeats (SSRs) and restriction fragment length polymorphism (RFLP). Some of these molecular markers can also be used for determining stress tolerant phenotypes. For example, genetic diversity analyses for drought tolerance have been carried out on sorghum using SSR markers (Rajarajan and Ganeshamurthy, 2011). In the study, the drought tolerant and drought susceptible varieties were grouped into different clusters on a molecular level of genetic diversity using SSR markers. In the current study, a different approach for the development of drought molecular markers was used. The method involves the identification of the sorghum drought stress responsive proteins (Section 4.3) and validating the expression of corresponding gene under drought conditions (Section 4.4). The proteins were identified in sorghum, a naturally drought tolerant 169 crop (Rosenow et al., 1983). Messmer et al. (2015) also suggested the use of protein targets as a reliable method of identifying effective molecular markers. Drought stress responsive genes identified in Chapter 4 as well as three genes from a previous study by Ngara et al. (2018) were assessed for their utility as putative drought markers for use as a screening tool on breeding platforms developing drought-tolerant crops. Two sorghum cell suspension cultures systems were used for the proof-of-concept. Expression analysis of the nine genes in the two sorghum cell culture lines indicated that seven of the genes showed significant expression differences between the two sorghum varieties (Figure 5.1; Appendix, Figure B1) and thus could be used to distinguish the cell lines. Although ICSB 338 is a drought susceptible sorghum variety (Table 2.1), the corresponding phenotype of White sorghum is unknown. The four genes identified in this study (SORBI_3003G136200, SORBI_3007G149600, SORBI_3001G313200 and SORBI_3001G073900) (Figure 5.1) could be incorporated into breeding programs to screen for potentially drought tolerant crop varieties. Other studies have also developed drought marker genes for use in plant breeding. For example, Arabidopsis homologues of the ABA-independent pathway genes, namely, GmaxRD20A-like, GmaxRD22-like and GmaxERD1-like, were analysed under drought stress in soybean root tissue (Neves-Borges et al., 2012). The results showed that the three genes were responsive to drought stress. The authors recommended these genes for use as drought markers in soybean. Heat stress is naturally associated with drought stress, especially in agro-ecological zones like the sub-Saharan Africa. Plant also respond to heat and drought stress using common response mechanisms (Wilkins et al., 2016). Furthermore, drought stress responsive genes such as DREB2A and OsMYB55 have been shown to be heat responsive in previous studies (Sakuma et al., 2006; Casaretto et al., 2016). Therefore, it is imperative to also evaluate the drought 170 stress responsive sorghum genes for heat stress response. In this study, eight of the nine drought responsive genes evaluated were also heat responsive (Figure 5.2). Therefore, during MAS, the markers could be used to select for drought and heat tolerant plants. A gene with a known function in one species is likely to have a homologue with similar function in another plant species (Yang et al., 2015a). Homologous genes in other species may have slight differences in gene sequence but have similar protein structure and function supporting the development of molecular markers based on one species and used in another. In this study, Arabidopsis was used to show the transcriptional responses of homologous genes found in sorghum. Arabidopsis homologues of three sorghum drought responsive genes validated by Ngara et al. (2018) were analysed. The results showed at least one homologue was up-regulated in response to simulated drought (Figures 5.5 - 5.7). The use of homologues of a gene with known function in another plant system has been practiced previously in many studies. These include the use of DREB 1 gene homologues in wheat in a drought stress experiment (Huseynova and Rustamova, 2010). Similar studies were conducted where sorghum homologues of the Arabidopsis sodium proton antiporter (NHX) and the human sodium proton exchanger (NHE) genes were evaluated under multiple stresses of drought, cold, salt and heat (Kumari et al., 2018). The study concluded that the SbNHXs and SbNHEs were up-regulated by at least one of these stresses. In a selection process for the identification of drought responsive homologues in another plant, the genes must be evaluated so that the best performing ones under drought stress are advanced to the next stage. These functional homologues would be overexpressed under stress to validate their response. Over-expression analyses have been done previously to confirm gene function (Baima et al., 2001; Pontier et al., 2001; Fan and Dong, 2002). The 171 other alternative would be to transfer the gene to the new species to create a transgenic plant with the gene of interest. In conclusion, four genes (SORBI_3003G136200, SORBI_3007G149600, SORBI_3001G313200 and SORBI_3001G073900) can be used in a classical plant breeding selection process to distinguish between sorghum varieties for drought tolerance (Figure 5.1). A total of eight genes (SORBI_3008G134700, SORBI_3001G514200, SORBI_3003G136200, SORBI_3006G135500, SORBI_3007G149600, SORBI_3001G313200, SORBI_3001G073900, SORBI_3003G322400) can also be used to screen sorghum plants for heat tolerance. Furthermore, sorghum homologues can be identified in other agriculturally important crops like maize and analysed for drought tolerance. This would assist plant breeders in MAS for drought tolerance. 172 CHAPTER 6 GENERAL DISCUSSION, CONCLUSION AND RECOMMEDATIONS Drought stress is one of the abiotic factors that is detrimental to plant growth and development. As such, it is imperative that studies are carried out to develop crops that are tolerant to drought stress. To achieve this, various studies have utilised plant model systems such as Arabidopsis (Arabidopsis thaliana) and rice (Oryza sativa) among others. These plants are however, susceptible to drought stress. Sorghum (Sorghum bicolor), a naturally drought tolerant crop (Rosenow et al., 1983), with a wide genetic diversity (Motlhaodi et al., 2017), is also an ideal model system in drought stress studies. The aim of this research was to evaluate the physiological, biochemical and molecular responses of two sorghum varieties to drought stress with the ultimate goal of working towards the development of drought markers. These drought markers would be vital in the selection of drought tolerant varieties by plant breeders at the seedling stage. The two sorghum varieties used in this study, ICSB 338 and SA 1441, are drought susceptible and drought tolerant, respectively. These phenotypic traits were assigned by plant breeders. However, no published data on their drought tolerance is currently available. This study therefore analysed various growth and physiological parameters in an effort to confirm the drought stress response phenotypes of the two sorghum varieties prior to the proteomic and genomic analyses. This evaluation confirmed that the two sorghum varieties indeed have contrasting responses to drought stress. Striking differences were observed between the two sorghum varieties for all the growth and physiological parameters evaluated. For example, the chlorophyll content was higher in the drought tolerant variety compared to the drought susceptible variety following 8 days of 173 drought stress. Furthermore, the chlorophyll content was restored 24 hrs after re-watering (Figure 3.7). This could indicate that the drought tolerant variety possesses the ability to stay green and photosynthesise under drought stress. In addition, stomatal conductance was lower in the drought tolerant variety (Figure 3.6), signifying the closure of stoma to conserve water under drought stress. This stomatal closure is reciprocated by an increase in surface temperature (Figure 3.6), since the evaporative transpiration normally dissipates heat. Collectively, the leaf surface temperature and stomatal conductance reveal that SA 1441 has superior drought performance than ICSB 338. Furthermore, the decrease in stomatal conductance of the drought tolerant SA 1441 at 5 days suggests an active mechanism of drought tolerance. Proline and glycine betaine content, which play important roles as osmoprotectants in plant cells (Hamilton and Heckathorn, 2001), increased in root and leaf tissues of both sorghum varieties in response to drought stress (Figure 3.9). Proline has an added role of scavenging for reactive oxygen species (ROS) (Smirnoff and Cumbes, 1989). This possibly reduces oxidative cellular damage in plants during periods of drought stress. Noteworthy was that the drought tolerant sorghum variety showed an early accumulation of these compatible osmolytes following stress and also accumulated higher levels compared to the drought susceptible variety (Figure 3.9). These osmolytes ultimately protect plant cells from the harmful effects of drought stress. Furthermore, the overall higher levels of proline in leaf tissue compared to roots and the inverse for glycine betaine possibly indicates the tissue specific responses of sorghum to drought stress. After confirming the drought stress phenotype of the two sorghum varieties using a range of growth, morphological and biochemical parameters, the contrasting drought tolerance responses between the two varieties was further observed at the proteome level in root tissue. 174 Only the root tissue was used for the analysis because roots are the first organs to sense water deficits in the soil before sending signals to the rest of the plant (Ghatak et al., 2016). Furthermore, no other comparative root proteome studies have been carried out in sorghum to date. Therefore, the current study will provide a foundation for comparative sorghum root proteomic analyses. The proteomic analysis was conducted using the isobaric Tags for Relative and Absolute Quantitation (iTRAQ) analysis. In this study, the 8-plex iTRAQ kit, which labels 8 samples simultaneously (Ross et al., 2004), was used. Some of the advantages of using iTRAQ analysis are that it is a more efficient way of protein identification and quantitation compared to two-dimensional gel electrophoresis (2DE). This is because it is able to identify low abundant and hydrophobic proteins compared to 2DE (Zieske, 2006). Briefly, the two sorghum varieties were exposed to drought stress by withholding water for 8 days and total soluble root protein was extracted from the control and drought stresses plants. Four biological replicates were used to increase reproducibility of the results. A total of 1169 and 1043 proteins were identified in ICSB 338 and SA 1441, respectively. Of these, 237 and 184 were drought responsive for ICSB 338 and SA 1441, respectively (Tables 4.1 and 4.2). However, a large percentage of protein were uncharacterised for both sorghum varieties, with proportions of 76% and 72% for ICSB 338 and SA 1441, respectively. The three most highly represented drought responsive proteins after functional annotation were related to disease/defence (26%), metabolism (25%) and energy (9%) for ICSB 338 (Figure 4.9). For SA 1441, these were disease/defence (23%), metabolism (21%) and protein synthesis (14%). Metabolism and disease/defence proteins were also identified as the highly represented functional categories in a wheat grain comparative proteomic study under drought stress (Ge et al., 2012). 175 Peroxidases were highly represented in both sorghum varieties in the disease/defence functional category. Their role was possibly to scavenge for, detoxify and reduce the harmful effects of ROS, thereby restoring cellular homeostasis (Elstner, 1987). Carbohydrate metabolism related proteins were highly represented in the metabolism functional category. High numbers of carbohydrate related proteins were also identified in a comparative root proteome study of two tomato (Solanum spp.) varieties (Zhou et al., 2013). The results signify that the sorghum plants dedicated cellular resources towards disease/defence and metabolism to a greater extent in root tissue in response to drought stress. The ability to synthesise disease/defence and metabolism related proteins may be one of the reasons why sorghum is highly adapted to drought stress compared to other crops. However, more functional validation studies would need to be performed. Following the comparative root proteome analysis between the two sorghum varieties, a total of 10 proteins were selected for validation by gene expression analysis (Table 4.3). The 10 target genes corresponding to these proteins were then analysed using qRT-PCR in a time- course experiment. A primer pair of one gene did not yield any PCR product and was subsequently excluded from the analysis (Figure 4.12). The gene expression analysis showed that all the nine genes were responsive to drought stress (Figures 4.13 and 4.14). In root tissue (Figure 4.13), four genes showed significantly higher gene expression in ICSB 338 at 12 days of drought stress in comparison to SA 1441 whilst two genes showed the opposite. In leaf tissue (Figure 4.14), ICSB 338 showed significantly higher gene expression at 0 and 4 days of drought stress for both root and leaf tissue. However, SA 1441 showed significantly higher gene expression at 8 days of drought stress for all genes in comparison to ICSB 338. This gene validation was an important step towards the development of drought marker genes. 176 The first step in the development of drought marker genes included expression analysis of the genes in sorghum cell suspension cultures of White sorghum and ICSB 338 varieties with and without sorbitol-induced osmotic stress stress. The result showed that seven of the genes could distinguish between the two sorghum varieties without osmotic stress (Figure 5.1). All the nine genes were also differentially expressed in the two sorghum cell suspension cultures in response to sorbitol-induced osmotic stress (Appendix, Figure B1). Since drought stress is closely associated with heat stress, the expression patterns of the 9 genes was further analysed on cell cultures maintained at 22°C and 30°C in White sorghum cell suspension cultures. The results showed that all the genes were responsive to heat stress at 30°C (Figure 5.2). This set of results led to the proposal for the use of these genes for the selection of drought tolerant varieties by plant breeders at the seedling stage. This could possibly save time, labour and space since the marker assisted selection can be carried out in a greenhouse using seedlings. A workflow was proposed in which these drought marker genes can be utilised in other crops. The workflow involves the identification of the homologues of these genes in the target species. In this study, Arabidopsis (Arabidopsis thaliana) was used as a proof of concept. After the identification of four homologues per target gene, the results showed that the homologues behaved differently in response to osmotic stress in a time course experiment. Some genes were activated whilst some were suppressed under osmotic stress. Since large numbers of homologues were identified in Arabidopsis through bioinformatics, the osmotic stress responsive ones would be selected for gene overexpression as a way of assessing gene function in transgenic plants. These genes could be used by plant breeders through introgressing them into widely accepted varieties for drought stress tolerance improvement in target crops. 177 In future studies, the measurements of photosynthetic activities of sorghum under drought stress are recommended so that the changes in important photoreceptors such as chlorophyll a and b are determined. This is because chlorophyll is responsible for absorbing light during the photosynthetic process. Reciprocal gene expression analysis of some key genes such as delta1-pyrroline-5-carboxylate synthase 1 and 2 (P5CS1 and P5CS2) and gbsAB in the biosynthetic pathways of proline and glycine betaine are also recommended. This is essential in understanding the activity of these compatible osmolytes in response to drought stress in different plant tissues. Furthermore, the use of bioinformatics and PCR to screen homologues of sorghum drought stress responsive genes in important crop species like maize (Zea mays) and wheat (Triticum aestivum) is recommended. This would assist in the transfer of knowledge obtained from sorghum to other crop species. A few target drought stress responsive homologues can then be overexpressed in the target crop parallel to the transgene in case sequence divergence in sorghum makes the gene non-functional in another species due to protein complexity. This would be important in developing drought stress tolerance in these target crops. In this study, SORBI_3001G514200, SORBI_3003G136200, SORBI_3007G149600, SORBI_3001G313200, SORBI_3001G073900 and SORBI_3003G322400 showed significant differences between the drought susceptible ICSB 338 and unknown phenotype White sorghum varieties in cell suspension cultures. Physiological experiments to verify this in whole plants are recommended. Overall, this study is important in that it created a foundation on comparative sorghum root proteomics under drought stress and developed drought marker genes for potential use by plant breeders in MAS for drought tolerance. 178 REFERENCES Abdrakhamanova, A., Wang, Q.Y., Khokhlova, L., Nick, P., 2003. Is microtubule disassembly a trigger for cold acclimation? Plant and Cell Physiology, 44, 676-686. Ackerson, R.C., Radin, J.W., 1983. Abscisic acid accumulation in cotton leaves in response to dehydration at high pressure. Plant Physiology, 71, 432-433. 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BSA final concentration BSA 5 mg/ml stock Urea extraction 0.1 M Distilled (mg/ml) solution (µl) buffer HCl (µl) water (µl) (µl) 0* 0 10 10 80 5 1 9 10 80 10 2 8 10 80 20 4 6 10 80 40 8 2 10 80 50 10 0 10 80 *blank solution Table A2: Preparation of resolving and stacking gels for gel electrophoresis. Resolving gel Stacking gel 12% (v/v) 5% (v/v) Distilled water 4.3mL 3.6mL 40% Acryl-bisacrylamide mix 3mL 0.625mL 0.5M Tris-HCL (pH 6.8) - 0.63mL 1.5M Tris-HCL (pH 8.8) 2.5mL - 10% SDS 0.1mL 0.05mL 10% APS 0.1mL 0.05mL TEMED 0.006mL 0.005mL Total volume 10 mL 5 mL 197 Heat-map showing the up-regulation and down-regulation of the 871 proteins common to both ICSB 338 and SA 1441. Figure A1: Heat-map showing the up-regulation and down-regulation of the 871 proteins common to both ICSB 338 and SA 1441. Each band shows a cluster of proteins in ICSB 338 and their corresponding SA 1441 proteins. The intensity of the red and blue colours represent down-regulation and up-regulation of proteins, respectively. 198 SORBI_3008G134700 SORBI_3001G514200 SORBI_3003G136200 1.5 1.5 2.0 * White sorghum ICSB 338 1.5 1.0 1.0 * * ** *** *** 1.0 *** * *** 0.5 0.5 0.5 * * 0.0 0.0 0.00 6 24 48 72 0 6 24 48 72 0 6 24 48 72 SORBI_3006G135500 SORBI_3007G149600 SORBI_3001G313200 2.0 2.5 4 *** ** *** 2.0 * **1.5 * 3 * 1.5 *** 1.0 2 *** 1.0 ** ** 0.5 *** ** 1 0.5 * * 0.0 0.0 0 0 6 24 48 72 0 6 24 48 72 0 6 24 48 72 SORBI_3001G073900 SORBI_3003G322400 SORBI_3002G049300 5 1.5 2.5 *** *** 4 ** *** **2.0 *** ***1.0 *** * 3 1.5 * 2 1.00.5 1 0.5 *** * *** *** *0 0.0 0.0 0 6 24 48 72 0 6 24 48 72 0 6 24 48 72 Time (Hrs) Time (Hrs) Time (Hrs) Figure B1: Sorbitol-induced gene expression in sorghum cell suspension cultures. The White sorghum (unknown) and ICSB 338 (drought susceptible) cell suspension cultures were exposed to osmotic stress by exposure to 0.4 M sorbitol and the cells were harvested at the indicated time points. Gene expression analysis was performed using RT-qPCR. Bars represent mean ± SE (n=3). *, ** and *** = significant differences between ICSB 338 and White sorghum means at each time point at p ≤ 0.05, 0.01 and 0,001, respectively. 199 Fold change Fold change Fold change Arabidopsis homologues of sorghum genes. Table B1: Arabidopsis homologues of sorghum genes. Gene Code Gene Name Expected Thioredoxin (SORBI_3009G190800) AT5G42980.1 TRX3 9e-39 AT1G45145.1 TRX5 3e-38 AT3G51030.1 TRX1 2e-34 AT1G19730.1 ATTRX4 2e-32 AT3G17880.2 ATTDX 1e-22 AT3G17880.1 ATTDX 2e-22 AT5G39950.1 TRX2 1e-21 AT3G56420.3 no symbol available 1e-21 AT3G56420.2 no symbol available 1e-21 AT3G08710.3 ATH9 2e-21 AT3G08710.2 ATH9 2e-21 AT3G08710.1 ATH9 2e-21 AT1G59730.1 TH7 2e-19 AT2G40790.2 CXXS2 2e-18 AT2G40790.1 CXXS2 2e-18 AT1G11530.1 CXXS1 3e-18 AT1G69880.1 ATH8 1e-17 AT2G35010.2 ATO1 9e-15 AT2G35010.1 ATO1 9e-15 AT4G26160.1 no symbol available 2e-14 AT1G31020.1 ATO2 3e-13 AT4G29670.2 ACHT2 6e-13 AT4G29670.1 ACHT2 6e-13 AT1G43560.1 Aty2 1e-12 AT5G16400.1 TRXF2 1e-12 AT3G15360.1 ATM4 5e-12 AT1G03680.1 ATHM1 6e-12 AT3G02730.1 TRXF1 6e-12 AT1G76760.1 ATY1 4e-11 AT5G61440.1 ACHT5 4e-11 AT4G04950.1 AtGRXS17 6e-11 200 AT1G76080.1 CDSP32 8e-11 AT4G03520.1 ATHM2 8e-11 AT5G61440.2 ACHT5 1e-10 AT3G53220.1 no symbol available 3e-10 AT4G32580.1 no symbol available 4e-09 AT4G37200.1 HCF164 7e-09 AT1G52990.1 no symbol available 8e-09 AT2G33270.1 ACHT3 2e-08 AT5G60640.3 PDI2 2e-08 AT1G08570.1 ACHT4 3e-08 AT1G08570.2 ACHT4 4e-08 AT1G08570.4 ACHT4 4e-08 AT1G08570.3 ACHT4 4e-08 AT5G60640.2 PDI2 4e-08 AT5G60640.1 PDI2 5e-08 AT1G07700.4 no symbol available 8e-08 AT1G07700.2 no symbol available 8e-08 AT1G07700.1 no symbol available 8e-08 AT1G50320.1 ATHX 8e-08 AT1G07700.3 no symbol available 1e-07 AT5G06690.1 WCRKC1 4e-07 AT2G47470.1 UNE5 4e-07 AT2G15570.2 ATHM3 5e-07 AT2G47470.4 UNE5 5e-07 AT2G47470.3 UNE5 6e-07 AT2G15570.1 ATHM3 6e-07 AT2G47470.2 UNE5 7e-07 AT1G53300.1 TTL1 9e-07 AT3G06730.1 TRX z 3e-06 AT1G77510.1 ATPDI6 5e-06 AT1G21750.1 PDIL1-1 1e-05 AT1G21750.2 PDIL1-1 4e-05 AT5G04260.1 WCRKC2 5e-05 AT2G42580.1 TTL3 2e-04 AT2G01270.1 QSOX2 5e-04 AT2G41680.1 NTRC 5e-04 201 AT3G25580.1 no symbol available 6e-04 AT4G12170.1 no symbol available 6e-04 AT3G54960.2 PDI1 6e-04 AT1G04980.1 PDIL2-2 6e-04 AT3G54960.1 PDI1 6e-04 AT3G25580.2 no symbol available 6e-04 AT4G03520.2 ATHM2 7e-04 AT3G58620.1 TTL4 7e-04 AT1G35620.1 ATPDIL5-2 7e-04 AT2G18990.1 TXND9 0.002 AT2G32920.1 PDIL2-3 0.002 AT3G14950.1 TTL2 0.003 AT3G16110.1 ATPDIL1-6 0.005 AT5G18120.2 ATAPRL7 0.007 AT5G18120.3 ATAPRL7 0.007 AT5G18120.1 ATAPRL7 0.008 AT3G03860.1 ATAPRL5 0.010 AT1G60420.1 AtNRX1 0.011 AT4G27080.2 PDIL5-4 0.011 AT4G27080.1 PDIL5-4 0.011 AT1G15020.2 QSOX1 0.012 AT1G15020.3 QSOX1 0.015 AT1G15020.1 QSOX1 0.015 AT1G52260.1 PDI3 0.074 AT4G08930.1 ATAPRL6 0.077 AT3G50960.2 PLP3a 0.11 AT3G50960.1 PLP3a 0.11 AT1G34780.1 ATAPRL4 0.17 AT5G66410.1 PLP3b 0.17 AT1G07960.4 ATPDIL5-1 0.45 AT1G07960.3 ATPDIL5-1 0.45 AT1G07960.2 ATPDIL5-1 0.45 AT1G07960.1 ATPDIL5-1 0.45 AT4G31240.2 NRX2 0.45 AT4G31240.1 NRX2 0.45 AT4G21990.1 PRH26 0.66 202 AT4G04610.1 APR 0.94 AT1G62180.2 APSR 2.0 AT1G62180.1 APSR 2.1 AT5G08290.1 YLS8 3.8 AT3G48170.1 ALDH10A9 4.2 AT3G44680.1 HDA09 4.5 AT1G56500.3 SOQ1 5.0 AT1G56500.2 SOQ1 5.0 AT1G56500.1 SOQ1 5.3 AT1G45100.1 no symbol available 9.9 Peptidase (SORBI_3007G172100) AT5G50260.1 CEP1 e-134 AT3G48350.1 CEP3 e-119 AT3G48340.1 CEP2 e-118 AT4G35350.1 XCP1 e-100 AT1G20850.1 XCP2 2e-99 AT4G36880.1 CP1 6e-99 AT1G47128.1 RD21 2e-97 AT5G43060.1 RD21B 4e-97 AT3G19390.1 no symbol available 6e-92 AT5G45890.1 SAG12 8e-90 AT3G19400.1 no symbol available 7e-86 AT4G23520.1 no symbol available 2e-85 AT3G48350.2 CEP3 3e-84 AT1G06260.1 no symbol available 5e-84 AT2G27420.1 no symbol available 8e-84 AT3G49340.1 no symbol available 1e-82 AT4G11320.2 AtCP2 1e-81 AT4G11320.1 AtCP2 1e-81 AT4G11310.1 CP1 2e-81 AT2G34080.1 no symbol available 1e-77 AT1G29080.1 no symbol available 3e-77 AT1G09850.1 XBCP3 6e-77 AT1G29090.1 no symbol available 3e-76 AT4G35350.2 XCP1 4e-71 203 AT3G43960.1 no symbol available 5e-70 AT1G29110.1 no symbol available 2e-62 AT3G19400.2 no symbol available 7e-61 AT3G45310.1 no symbol available 3e-54 AT5G60360.1 AALP 3e-54 AT3G45310.2 no symbol available 3e-52 AT5G60360.2 AALP 3e-52 AT5G60360.3 AALP 7e-51 AT4G16190.1 no symbol available 7e-48 AT4G39090.1 RD19 5e-47 AT3G54940.2 no symbol available 3e-46 AT2G21430.1 no symbol available 3e-46 AT3G54940.3 no symbol available 2e-33 AT4G01610.1 AtcathB3 4e-20 AT1G02305.1 AtcathB2 3e-19 AT4G01610.2 AtcathB3 2e-18 AT1G02300. AtcathB1 1e-13 AT2G22160.1 no symbol available 2e-12 AT1G03720.2 no symbol available 6e-06 AT1G03720.1 no symbol available 2e-04 AT5G17140.1 no symbol available 4e-04 AT5G05050.1 no symbol available 5e-04 AT5G17080.1 no symbol available 0.12 AT3G44780.1 no symbol available 0.33 AT1G58250.1 HPS4 3.0 AT1G58250.2 HPS4 3.1 AT3G19720.1 ARC5 9.5 Xyloglucanase (SORBI_3002G302000) AT2G36870.1 XTH32 e-103 AT3G44990.1 XTH31 3e-95 AT2G36870.2 XTH32 1e-56 AT1G14720.1 XTR2 1e-56 AT1G10550.1 XTH33 5e-55 AT1G32170.1 XTH30 2e-54 AT2G01850.1 XTH27 2e-54 204 AT3G25050.1 XTH3 3e-51 AT1G11545.1 XTH8 4e-51 AT2G06850.1 EXT 6e-51 AT4G18990.1 XTH29 2e-50 AT5G13870.3 EXGT-A4 3e-50 AT5G13870.2 EXGT-A4 3e-50 AT5G13870.1 XTH16 3e-50 AT3G23730.1 XTH13 3e-50 AT5G57540.1 XTR7 5e-50 AT4G14130.1 XTH12 2e-49 AT5G57530.1 XTH6 3e-49 AT5G65730.1 ATXTH14 4e-49 AT4G25820.1 XTH22 5e-49 AT5G57560.1 XTR6 7e-49 AT4G25810.1 MERI5B 2e-48 AT4G30270.1 ATXTH21 2e-47 AT2G18800.1 XTH25 2e-47 AT5G57550.1 XTH9 8e-47 AT4G03210.1 EXT 4e-46 AT2G06850.2 ATXTH20 2e-45 AT5G48070.1 XTH7 9e-45 AT4G37800.1 XTH2 9e-45 AT4G13090.1 XTH9 2e-44 AT4G03210.2 XTH1 1e-43 AT4G13080.1 ATXTH18 3e-43 AT4G30280.1 XTH26 5e-43 AT4G28850.1 XTH10 5e-43 AT2G14620.1 ATXTH19 3e-42 AT4G30290.1 XTH17 5e-42 AT1G65310.2 XTH17 4e-40 AT1G65310.1 XTH10 5e-40 AT2G14620.2 XTH29 1e-37 AT4G18990.2 XTH10 2e-35 AT2G14620.3 XTH11 2e-35 AT3G48580.1 XTH11 2e-33 AT3G48580.3 XTH11 4e-30 205 AT3G48580.2 EXPRS 2e-21 AT2G32170.1 no symbol available 0.060 AT2G32160.2 no symbol available 0.39 AT2G32160.1 no symbol available 0.41 AT2G32160.3 no symbol available 0.42 AT2G32160.4 no symbol available 0.42 AT1G76810.1 no symbol available 0.62 AT4G02040.1 no symbol available 5.2 206 Melt curves of genes used in this study. Arabidopsis sorbitol-induced osmotic stress. Figure C1: Act 2 and EIF4A Figure C2: TRX1 and TRX3 Figure C3:TRX5 and ATTRX4 207 Arabidopsis cell culture sorbitol-induced osmotic stress. Figure C4: CEP1 and XCP1 Figure C5: XCP2 and XTH31 Figure C6: XTH32 and XTH33 208 Sorghum leaf tissue Figure C7: SORBI_3008G134700, SORBI_3001G514200 and SORBI_3003G136200 Figure C8: SORBI_3006G135500, SORBI_3007G149600 and SORBI_3001G313200 Figure C9: SORBI_3001G073900, SORBI_3003G322400 and SORBI_3002G049300 209 Sorghum root tissue Figure C10: SORBI_3008G134700, SORBI_3001G514200 and SORBI_3003G136200 Figure C11: SORBI_3006G135500, SORBI_3007G149600 and SORBI_3001G313200 Figure C12: SORBI_3001G073900, SORBI_3003G322400 and SORBI_3002G049300 210 Sorghum cell cultures Figure C13: SORBI_3008G134700, SORBI_3001G514200, SORBI_3003G136200SORBI_3006G135500, SORBI_3007G149600, SORBI_3001G313200SORBI_3001G073900, SORBI_3003G322400, SORBI_3002G049300, DREB2A-1, ERD1-1, ERD1-2, Sb03g038910 and EIF4A. 211