Identification of novel sources of variation for the improvement of cold germination ability and early seedling vigor in upland cotton (Gossypium hirsutum L.)

by

Ritchel Bueno Gannaban, B.S.

A Thesis

In

Plant and Soil Science

Submitted to the Graduate Faculty of Texas Tech University in Partial Fulfillment of the Requirements for the Degree of

MASTER OF SCIENCES

Approved

Dr. Rosalyn B. Angeles-Shim Chair of Committee

Dr. Benildo G. de los Reyes

Dr. Brendan R. Kelly

Dr. Endang Septiningsih

Mark Sheridan Dean of the Graduate School

August, 2019

Copyright 2019, Ritchel Bueno Gannaban

Texas Tech University, Ritchel Bueno Gannaban, August 2019

ACKNOWLEDGMENTS It is my pleasure to acknowledge everyone whose efforts and contributions led me to the completion of this research study. Without the support and encouragement, I would not have been able to complete this very important chapter of my life’s journey. I would like to thank my thesis advisor, Dr. Rosalyn B. Angeles-Shim of the Department of Plant and Soil Science at Texas Tech University. Dr. Shim provided the opportunity for me to pursue my graduate studies. With her guidance, I managed to survive the grueling life of a graduate student. Dr. Shim’s office was always open whenever I had a question about my research or writing. She consistently allowed this paper to be my own work, but steered me in the right direction whenever she thought I needed it. Not only she is a very considerate adviser but also a confidant who never fails to see the potential in every person. It is truly an honor to work under a great mentor. I would also like to acknowledge my committee members, Dr. Benildo G. de los Reyes and Dr. Brendan Kelly of the Department of Plant and Soil Science at Texas Tech University and to Dr. Endang Septiningsih of the Department of Soil and Crop Sciences at Texas A&M University for their guidance. I am gratefully indebted to the valuable comments towards the completion of my academic courses and on this thesis. I would also like to thank my dear friends and lab mates, Puneet Kaur Mangat and Joshua James Singleton for their passionate participation and input, my research experiments could not have been successfully conducted. I am also deeply appreciative of Dr. Junghyun Shim, Dr. Cade Colden, and Jake Sanchez for imparting me their expertise to process my data. Above all, I am grateful for the friendship and camaraderie. It means a lot to me. Finally, I must express my very profound gratitude to my family. To my parents, Mr. and Mrs. Fernando Gannaban and to my loving partner, Garret Greene for providing me with unfailing support and continuous encouragement throughout my years of study and through the process of researching and writing this thesis. This accomplishment would not have been possible without them. Thank you.

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TABLE OF CONTENTS ACKNOWLEDGMENTS ...... ii

ABSTRACT ...... vi

LIST OF TABLES ...... vii

LIST OF FIGURES ...... viii

CHAPTER I: LITERATURE REVIEW ...... 1

Cotton biology and economic importance ...... 1 Challenges in cotton production ...... 2 Measures to address cold sensitivity in upland cotton ...... 5 Genetic variation to improve cold tolerance in upland cotton ...... 6 Pre-breeding activities towards the use of germplasm for cold improvement of cotton ...... 8 Research Goals...... 10 CHAPTER II: IDENTIFICATION OF NOVEL SOURCES OF GENETIC VARIATION FOR THE IMPROVEMENT OF COLD GERMINATION ABILITY IN UPLAND COTTON (Gossypium hirsutum L.)...... 11

Introduction ...... 11 Materials and Methods ...... 13 Plant materials ...... 13 Plant propagation and genomic DNA extraction ...... 13 SSR genotyping ...... 14 Genetic diversity analysis ...... 14 Viability seed testing...... 15 Cold germination screening ...... 15 Cold germination parameters ...... 16 Statistical analysis ...... 16 Results and Discussion ...... 17 Genetic diversity analysis using SSR markers...... 17 Germination ability of the test germplasm under low temperature stress...... 19 Patterns of variation in the cold germination ability of the test germplasm ...... 21 Conclusion ...... 22 CHAPTER III: IDENTIFICATION OF NOVEL SOURCES OF GENETIC VARIATION TO ENHANCE SEEDLING VIGOR OF UPLAND COTTON (Gossypium hirsutum L.) UNDER LOW TEMPERATURE STRESS ...... 32

Introduction ...... 32

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Materials and Methods ...... 33 Plant materials ...... 33 Morphological measurements ...... 34 Physiological measurements ...... 34 Biochemical assays ...... 35 Statistical analysis ...... 36 Results and Discussion ...... 36 Morphological response of cotton seedlings to low temperatures ...... 36 Physiological response of cotton seedlings to low temperatures ...... 39 Cellular response of cotton seedlings to low temperatures ...... 40 Conclusion ...... 43 CHAPTER IV: COMPARATIVE TRANSCRIPTOME PROFILING OF UPLAND COTTON (Gossypium hirsutum L.) UNDER LOW TEMPERATURE STRESS...... 54

Introduction ...... 54 Materials and Methods ...... 56 Plant materials ...... 56 RNA extraction ...... 56 Data analysis ...... 57 Gene enrichment analysis ...... 57 Identification of differentially expressed genes associated with cold stress ...... 58 Results and Discussion ...... 58 RNA sequencing of different samples and data analysis ...... 58 Identification of DEGs in response to cold stress in SA-0718 and SA-3781 ...... 58 Patterns of gene expression in response to cold stress in SA-0718 and SA-3781 ...... 59 Gene ontology analysis of DEGs in cotton subjected to cold stress ...... 60 Differential gene expression patterns of Gossypium hirsutum L. in response to cold stress...... 61 Conclusion ...... 63 REFERENCES ...... 72

APPENDICES ...... 87

Appendix A Description and ontology of differentially expressed genes identified in cluster A...... 87

Appendix B Description and ontology of differentially expressed genes identified in cluster B ...... 89

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Texas Tech University, Ritchel Bueno Gannaban, August 2019

Appendix C Description and ontology of differentially expressed genes identified in cluster C ...... 93

Appendix D Description and ontology of differentially expressed genes identified in cluster D...... 95

Appendix E Description and ontology of 878 differentially expressed genes in SA-0718 and SA-3781 in response to cold stress ...... 97

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ABSTRACT Cotton production is limited by the adverse effects of low temperature stress especially at the germination and early seedling stages. Due to its tropical and sub- tropical origins, cotton is extremely sensitive to low temperatures from seed germination up to maturity. The germination process is hindered when cotton seeds are exposed below the minimum cardinal temperature of 15°C. Subsequently at the early seedling stage, cotton genotypes that are susceptible to cold exhibit poor vigor. Although several mitigating strategies that involves both agricultural practices and the application of plant exogenous regulators are available, the most economical long-term solution would be to develop cultivars and varieties with tolerance to low temperatures at the germination and early seedling stage. The main goal of this study is to identify genetic variation in the available germplasm collections which can be utilized in future breeding programs aiming to improve cold germination ability and early seedling vigor in cotton at the early seedling stage. Through genotypic and phenotypic analysis, a wide variation in response to cold stress during germination is established within the Gossypium Diversity Reference Set (GDRS) and fatty acid (FA) mutants. The screening for seedling vigor was carried out using a set of morphological, physiological, and biochemical assays. A total of 18 genotypes from both test germplasm were identified exhibiting varying responses to cold stress during the early seedling stage. In addition, morphological markers such as plant height and biomass were found to be good markers in identifying seedling vigor under cold stress in cotton. Comparative transcriptome profile analysis was conducted to confirm the differences in response to cold stress at the early seedling stage at the genic level. Through further analysis and gene validation studies, the initial results of this study can be used to identify set of genes or gene networks responsible for vigor under cold stress at the early seedling stage in cotton.

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LIST OF TABLES

2.1 GDRS accession and fatty acid mutants used in the genetic diveristy and cold germination ability analysis ...... 23 2.2 Summary statistics of the SSR markers used to genotype the subset of the GDRS accessions and FA mutants ...... 25 2.3 Calculated polymorphism information content (PIC) values for independednt SSR alleles that amplified polymorphic targets in the test germplasm ...... 26 2.4 Mean values of the germination parameters used to evaluate cold germination ability in the FA mutants and GDRS cultivars and landraces ...... 28 2.5 Two-way ANOVA of the germination parameters used to evaluate cold germination ability of the FA mutants, and GDRS cultivars and landraces ...... 29 3.1 List of selected genotypes for seedling vigor analysis...... 45 3.2 Mean values of plant height (cm) used to evaluate seedling vigor under cold stress in the FA mutants and GDRS accessions...... 46 3.3 Mean values of chlorophyll concentration (µmol/m2 of leaf) used to evaluate seedling vigor under cold stress in the FA mutants and GDRS accessions ...... 49 3.4 Mean values of leaf temperature used to evaluate seedling vigo runder cold stress in FA mutants and GDRS accessions ...... 50 4.1 An overview of RNA sequence data obtained from the aboveground tissue of SA-0718 and SA-3781 after cold stress treatment ...... 64

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LIST OF FIGURES 2.1 UPGMA and clustering of GDRS accessions and FA mutants based on Jaccard’s similarity coefficient. Red broken line indicates genetic similarity threshold for the five major groupings as indicated by the colored bards labeled I, II, III, IV and V...... 30 2.2 Patterns of variations in the cold germination ability of the test germplasm established using PCA based on the mean values of GP, MGT, MDG, PV and GI. Histogram representation of the proportion of variance contributed by each principal component (PC), with PC1 and PC2 accounting for most of the phenotypic variation observed in the test germplasm. Clustering of the FA mutants (red circle), GDRS cultivars (green triangle) and landraces (blue square) at 12°C without imbibition (B) and with imbibition (D), at 15°C without imbibition (F) and with imbibition (H) and at 30°C (J). Clustering of the FA mutants (I) is based on the mean values obtained for all parameters measured from non- hydroprimed seeds ...... 31 3.1 Mean values of the plant height (cm) used to evaluate seedling vigor under cold stress in the FA mutants and GDRS accessions. * indicate genotypes that showed <25% decrease in total aboveground biomass relative to the control ...... 47 3.2 Mean values of the total aboveground biomass (%) used to evaluate seedling vigor under cold stress in the FA mutants and GDRS accessions. * indicate genotypes that showed <25% decrease in total aboveground biomass relative to the control ...... 48 3.3 Electrolyte leakage of leaves of cotton seedlings under low temperatures (A=15°C and B=18°C) and normal (C=30°C). Each value represents the average ± standard error (±SE) for bulk measurements ...... 51 3.4 Proline content of cotton seedlings under low temperatures (A=15°C and B=18°C) and normal (C=30°C). Each value represents the average ± standard error (±SE) for three technical replicates ...... 52 3.5 Melondialdehyde content of cotton seedlings under low temperatures (A=15°C and B=18°C) and normal (C=30°C). Each value represents the average ± standard error (±SE) for three technical replicates ...... 53 4.1 (A) the overall differentially expressed genes (DEGs) retrieved from SA-3781 compared to SA-0718 at different day of intervals after cold treatment. (B) Venn diagram illustrating the differentially expressed genes at three different days of interval after cold treatment ...... 65

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4.2 Heat maps of the 878 differentially expressed genes at different time points (0d, 1d, 3d, and 5d) as observed in the susceptible (SA- 3781) and the tolerant cotton genotype (SA-0718) under cold stress at the early seedling stage. Heat maps were generated based on FPKM values. The color key shows the intensity of each gene expression based on the FPKM value. The more intense the color the higher the FPKM value. d=day ...... 66 4.3 Temporal expression of genes in four gene clusters that were upregulated at the onset of cold stress in SA-0718. The different groups were denoted as A, B, C, and D ...... 67 4.4 Temporal expression of genes in cluster A that were upregulated under cold stress in SA-0718 ...... 68 4.5 Temporal expression of genes in cluster C that were upregulated under cold stress in SA-0718 ...... 69 4.6 Temporal expression of genes of genes in cluster D that were upregulated under cold stress in SA-0718 ...... 70 4.7 GO classification of the 878 differentially expressed genes of cotton seedlings under cold stress at the early seedling stage ...... 71

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CHAPTER I REVIEW OF RELATED LITERATURE

Cotton biology and economic importance The upland cotton, Gossypium hirsutum L. belongs to the genus Gossypium of the Malvaceae family and order Marvales (Smith, 1995). It is an allotetraploid belonging to the AD genomic group (2n=4x=52) that contains a set of chromosomes from the A- genome diploids and another set from the D-genome diploids (Endrizzi et al., 1985; Wendel, 1989). In tropical climates, cotton grows as a perennial crop, reaching up to 20 feet tall and exhibiting a sympodial fruiting pattern (Mauney, 1986). In temperate climates however, it is annually cultivated as a shrub of about 3-6.5 feet in height. It grows approximately 150-180 days from seed to full maturity (Eaton, 1955). The germination of cotton seed starts with the rapid uptake of water and terminates once the radicle has ruptured the testa or the seed coat (Bewley, 1997). This event is followed by the emergence and growth of the seedling. Flowering occurs within five to seven weeks after emergence when the plant produces squares that develop into buds and eventually bloom as flowers. The cotton flower is complete, making it highly self-pollinated, although a degree of outcrossing (2-50%) can occur in open fields (Poehlman and Sleper, 1995; Xanthopoulos and Kechagia, 2000). Once pollinated, the white flower turns into a reddish-pink color. The fruits of cotton known as bolls produce the cotton fibers which mature in 40-60 days (Eaton, 1955). Given favorable moisture and temperature conditions, the cotton plant is expected to grow and thrive. The progress in the development of a cotton plant is generally measured using the concept of growing degree days (DD) (Kerby et al., 1987; Landivar and Benedict, 1996; Oosterhuis, 1990; Robertson et al., 2007). DD serves as a direct measure of heat units accumulated above the accepted critical temperature of 60°F. This is calculated by averaging the maximum and minimum temperatures for each day and subtracting the threshold temperature for cotton which is 60°F (Robertson et al., 2007). It is presumed that below this temperature, the cotton plant will not develop. Cotton growth and development is divided into five stages i.e., germination to seedling establishment, first square, first flower, open boll, and the harvest stage. Each of

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Texas Tech University, Ritchel Bueno Gannaban, August 2019 these stages require certain heat units. The understanding of the accumulated heat units and heat unit requirement for any specific growth stage of cotton can be used to explain and forecast the occurrence of physiological events or the duration of the stages of development of cotton (Kerby et al., 1987; Landivar and Benedict, 1996; Oosterhuis, 1990). Cotton is primarily grown for its fiber that is used for textile production and other industrial purposes. Other cotton products include cotton oil for human consumption and cottonseed byproducts that are usually used as ingredients for livestock feed. The cotton industry serves as a primary source of income to millions of farmers in more than 80 countries in the world including China, Australia, India, Pakistan and the US. Due to its economic value, it has been considered as one of the most valuable domesticated crop cultivated in a total land area of 79.07 million acres around the world (United States Department of Agriculture, February 2018). In the US, the cotton industry is responsible for approximately $25 billion in terms of services and products every year. The industry creates at least 250,000 jobs. In 2017, the total land area planted to US upland cotton is 9.32 million acres, producing an estimated harvest of 20.20 million bales. Texas is one of the biggest cotton producing state in the country with 10.90 million acres planted to upland cotton. From 2016 to 2017, the Southern High Plains in Texas harvested an estimated total of 3.12 million bales from 3.04 million acreage of upland cotton production (Annual Cotton Review, May 2018). In the last ten years, however, the US cotton crop has shown extreme and unpredictable year-to-year variability in yields. These inconsistencies have been attributed to genetics, management practices, and unfavorable weather conditions (Lewis, 2000).

Challenges in cotton production Throughout its life cycle, the cotton plant is subject to a wide array of biotic and abiotic stresses. In Africa and Brazil for example, approximately 50% of total cotton production losses have been attributed to damages by pests and pathogens (Ramey, 1986). During the seedling stage, the plant is especially sensitive to attack by a complex of pests and diseases including boll weevils, angular leaf spot, Fusarium and Verticillium wilt, and Texas root rot (Compendium of Cotton Diseases). Similarly, cotton is

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Texas Tech University, Ritchel Bueno Gannaban, August 2019 constantly exposed to abiotic stresses such as drought, salinity, and extremes of temperature. Drought stress has been reported to significantly reduce the yields, and boll number and weight of cotton, as well as induce earliness in cotton plants (Alishah and Amadikhah, 2009). It also negatively influences leaf water content, reduce photosynthesis and water-use efficiency (Egilla et al., 2005). Similarly, cotton plants grown in saline soils exhibit reduced plant growth, and lint and yield quality. This is despite the fact that cotton is generally classified as a salt tolerant crop (Ashraf, 2002). One of the major environmental factors influencing cotton production is temperature. The cotton plant thrives best in regions around the world where warmer temperature is predominant. In actual field conditions however, cotton may be subjected to temperature extremes that can harm the plant. Although it is usually grown in warmer regions (28 to 30°C) (Waddle, 1984), exposure of the cotton plant to extremely high temperatures (≥40°C) anytime during its growth and development can cause detrimental damages (Ramey, 1986). For example, high temperature stress during the seedling stage can distort and stunt the growth and distribution of roots even if other growth determining factors are fulfilled (Zahid et al., 2016). High temperatures can also inhibit pollination and subsequent fertilization, resulting in fewer seeds per boll (Burke et al., 2004, Snider et al., 2009). Exposure of cotton plants to high temperatures at the flowering stage often results to pollen sterility and aborted squares and flowers. Likewise, a 10°C increase in the optimum temperature for cotton can significantly reduce the number of bolls per plant (Reddy et al., 1999) due to poor retention of the fruit to its main stem. The excessive shedding of fruits translates to low lint yield and fiber quality (Nichols et al., 2004). Due to its tropical and sub-tropical origins, cotton is also extremely sensitive to low temperatures from seed germination up to maturity. When the seeds are exposed below the minimum cardinal temperature of 15°C, the germination process is slowed down if not completely hindered, resulting in non-uniform seedling distribution in the field (Waddle, 1984). This is possibly due to the hampered imbibition process that results from the crystallization of water at low temperatures. High water density at low temperatures

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Texas Tech University, Ritchel Bueno Gannaban, August 2019 impedes the structural re-organization of the cellular membrane of an imbibing seed, resulting in leaching of vital cellular components (Robertson et al., 2007). At the early seedling stage, cotton genotypes that are susceptible to cold exhibit poor vigor that is characterized by stunting, chlorosis, and taproot loss (Guthrie et al., 1995). During the reproductive stage, exposure to low temperatures results in shedding of squares and abortion of flower. If cold snaps occur in the early fall during boll maturation, cellulose production is significantly reduced (Gipson et al., 1969). Like heat stress, the negative impacts of cold stress to cotton production translates into yield losses and poor fiber quality (Gipson et al., 1969). In the southwestern United States, Texas remains the leading state in terms of the size of the total area planted to cotton. A total of 120 Texas counties in six different regions from the High Plains to the Lower Rio Grande Valley produce cotton. The major setback in cotton production in this region is attributed to the fluctuating weather patterns within the short cropping window from May to October. In any cotton producing region, varietal selection is one of the most crucial decision to be made before the start of the cropping season. In the High Plains, however, this can be especially challenging because the cotton producers must select varieties that perform well within the narrow production window in the region. The cool spring conditions at planting frequently result in reductions in seedling vigor and seasonal growing degree day accumulation. In 2017 for example, cotton production across the High Plains and Rolling Plains has been reported to have encountered many weather-related challenges (Ledbetter, 2018). In the High Plains, many farmers struggled to establish a good planting schedule due to cycles of cool, wet weather followed by days of hot, dry and windy conditions. In the Rolling Plains, the in-season precipitation ranged only from 8.70 to 16.70 inches, which was 2-6 inches less than the previous year. Due to this, cotton planted late in the season (May to June) suffered from moisture deficit which adversely affected germination as well as crop establishment. In addition, flooding in many cotton fields due to a storm that occurred in September 2017 facilitated the spread of late bacterial blight. Although the late blight had minimum negative effects on final yields, some producers harvested dead cotton plants as a combined result of severe weather condition and the disease occurrence. It was also observed that the first killing frost occurred a week earlier (October 27, 2017) than the

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Texas Tech University, Ritchel Bueno Gannaban, August 2019 traditional first killing frost in the Rolling Plains. This shift in weather condition is detrimental especially to the late-planted cotton (Ledbetter, 2018).

Measures to address cold sensitivity in upland cotton Previous studies that involve shifting of planting schedule combined with various cultural practices have been found to mitigate the effects of cold stress on plants. In soybean for example, planting earlier in the season helped the plants escape exposure to high temperatures, moisture deficit and insect infestations (Heatherly and Spurlock, 2001). Most cotton growing regions practice early planting based on the minimum cardinal temperature requirement of cotton which is 15°C. Below this temperature, the growth and development of cotton is effectively impeded. In a five-year study by Pettigrew (2002), the author showed that early planting of cotton resulted in a 10% increase in yield compared to the normal planting schedules. To ensure the benefits of early planting practice during the growing season, various measures of alleviating the adverse effects of cold stress have been utilized including seed priming and exogenous application of compatible solutes, mineral nutrients, and plant growth regulators as foliar treatments during the early seedling stage. Seed priming is an established technique used to improve the tolerance of various crops to chilling temperatures during germination. The method regulates seed temperature and moisture content. The process involves advancing the seed to an equal stage of the germination process to enable fast and uniform emergence when planted. Under cold stress, seed priming has been reported to significantly improve the germination and early growth of some important crops such as tobacco (Xu et al., 2011), rice (Hussain et al., 2016), wheat (Singh and Usha, 2003), tomato and bean seeds (Senaratna et al., 2000). At the seedling stage, leaf treatment with proline and polyamines, soluble sugars, and glycine betaine offers protection to the plant from osmotic stress that affects enzymes activities, membrane integrity and other macromolecules (Kiani et al., 2007). In maize, proline has been found to provide chilling tolerance to the plant by relieving injuries due to low temperature (Songstad et al., 1990). Farooq and his colleagues (2009b) found that

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Texas Tech University, Ritchel Bueno Gannaban, August 2019 the osmotic potential of the plant is reduced under limited water conditions through the treatment of soluble sugars to plants. In potato and A. thaliana plants, growth improved at low and freezing temperatures through the application of exogenous glycine betaine which provides cold tolerance by modifying antioxidant activities and membrane integrity (Somersalo et al., 1996, Xing and Rajashekar, 2001). In cotton, foliar application of the plant growth regulator mepiquat chloride increased the boll weight of cotton planted early in the season. Plant growth hormones such as abscisic acid, jasmonates, and salicylic acid have been reported to increase the ability of the plant to acclimatize when exposed to stressful environmental conditions (Nadeem et al., 2016, Kolaksazov et al., 2013, Farooq et al., 2009a). These hormones play a dynamic role in the activation of phosphoprotein pathways leading to the cascade of gene expressions linked to cold tolerance in plants. Application of plant nutrients such as phosphorous (K) and calcium (Ca) has also been found beneficial in enhancing cold stress tolerance in many crops. For example, the supplementation of irrigation water with K helped carnation plants avoid injuries due to low temperatures (Kafkafi, 1990). Ca application under low temperatures improved the chlorophyll content and concentration of amino acids and polyamines in red spruce (Schaberg et al., 2011). Even for cold tolerant genotypes, Ca also serves as a necessary nutrient requirement for stomatal closure (Waraich et al., 2012). Other micronutrients such as Selenium (Se) helped improve the tolerance of cucumber plants to short-term chilling (Hawrylak-Nowak et al., 2010). Despite the benefits from these practices, the most practical approach to address cold stress at both germination and early seedling stages would be to develop cultivars and varieties with tolerance to low temperatures. With the use of modern technological tools, genetic variation that can be used to improve these target traits can be mined from both wild and related cultivated crop species.

Genetic variation to improve cold tolerance in upland cotton Genetic diversity is a key determinant to the success of any crop improvement program (Govindaraj et al., 2015). It safeguards future genetic gains by providing sources of resistance to pests and diseases, as well as tolerance to adverse environmental

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Texas Tech University, Ritchel Bueno Gannaban, August 2019 conditions. Most crop improvement programs rely on the availability of genetic variation for desirable alleles and the precise characterization of the variability that is available within and among germplasm collections (Narain, 2000). To develop superior genotypes, crossing two genetically diverse parents has been a very common practice. However, the frequent use of similar parents for the development of new cultivars has led to the high levels of genetic uniformity in crops in the last 25 years (Ebsroeck and Bowman, 1998). Breeding for a small number of traits, particularly yield and other yield-related traits, has also contributed in the narrowing of genetic variation that led to the vulnerability of crops to pest and diseases, as well as to adverse climatic conditions. A significant example of this genetic bottleneck is the weevil outbreak that happened in the 1880’s that almost wiped out the entire cotton population in South America (Percival et al., 1999). In recent years, breeding strategies have shifted to the extensive use of the available genetic diversity mined from both wild and distant relatives of crop species (Govindaraj et al., 2015). The genus Gossypium has 50 known species and more than 10,276 accessions that are available for genetic improvement studies in cotton (Hinze et al., 2018). This collection contains a vast wealth of genetic variability ranging from the evolved allotetraploids to their wild diploid progenitors. Recently, the genetic diversity of a core representative of the collection known as the Gossypium Reference Diversity Set (GDRS), which is composed of 272 diploid and 1,984 tetraploids has been characterized through the use of microsatellites or simple sequence repeat (SSR) markers. This GDRS core collection has been found to encapsulate the genetic variation from the cultivated accessions with improved yield and fiber qualities to the wild accessions harboring resistance and tolerance to abiotic and biotic stresses (Hinze et al., 2015). In 2016, Hinze et al. characterized the genetic variation available in G. hirsutum and G. barbadense. Their results suggest that, on average, there is a higher level of introgression within individual accessions in the improved G. barbadense cultivars compared to the G. hirsutum cultivars. In 2016, Li and Erpelding carried out a genotyping-by-sequencing study to infer and characterize the genetic diversity and population structure available in 302 diploid accessions of the species, G. arboreum. The results of this study suggest that there is a narrow genetic base for the Asiatic cotton. However, these accessions can be

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Texas Tech University, Ritchel Bueno Gannaban, August 2019 useful for the detection and introgression of important agronomic traits such as drought tolerance and disease resistance into upland cotton. Aside from these natural sources of genetic variation, breeding programs can also tap novel variations that are available in plant mutant repositories for crop improvement. In many cases, the use of mutagenesis in breeding involves forward genetic approaches to screen and select individual mutants with improved traits for their incorporation into breeding programs. Indeed, due to the increasing availability and accessibility of genomic tools, several mutant genes have been successfully discovered to improve crop productivity. In cotton there is an available genetic resource developed through induced chemical mutagenesis that can be utilized for research and cotton improvement programs. This set of mutant lines were developed from the breeding program at Texas Tech University from 1998 (Auld et al., 1998, 2000; Bechere et al., 2009, 2012)

Pre-breeding activities towards the use of germplasm for cold tolerance improvement of cotton

The prevalence of abiotic stresses brought about by the changing climate has spurred the initiation of breeding programs that are geared towards the development of climate-smart varieties. One of the most economically important abiotic stresses that accounts for serious losses in the agricultural sector is low temperature stress. In the US, damages caused by freezing temperatures account for majority of crop losses compared to any other weather hazard (Snyder and de Melo-Abreu, 2005). Efforts to address damages in crops due to cold stress has brought the identification and mapping of QTLs regulating cold tolerance into the mainstream. Futsuhara and Toriyama (1969) carried out QTL analysis using rice populations with large levels of genetic variation for cold tolerance. In 2006, Han et al. mapped two QTLs for low temperature germination using a set of F2:3 populations derived from a cross between indica and japonica varieties ‘Milyang 23’ and ‘Jileng 1’. Fujino et al. (2008), identified a major QTL for low temperature germination ability (qLTG3–1) on chromosome 3 using backcross inbred lines derived from a cross between cultivars ‘Italica Livorno’ and ‘Hayamasari’. qLTG3–1 explained 30% of the total phenotypic variation for low-temperature germination observed in their mapping population. In

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2009, Baruah et al. identified two major QTLs (qCTP11 and qCTP12) for cold tolerance at the plumule stage in genetic stocks derived from 34 cultivated (Oryza sativa) and 23 wild (O. rufipogon) rice strains. Andaya and Mackill (2003) mapped the major QTL, qCTS12a, to chromosome 12 that accounted for 41% of the phenotypic variation in seedling growth after cold stress using recombinant inbred lines. Identification of these QTLs are of paramount importance in developing a marker-assisted breeding program to improve cold tolerance in crops. MAS is a useful tool for crop improvement because it allows the transfer of a specific trait to a specific background. Usually it involves the use of molecular markers linked to specific traits of interest. An advantage of MAS is that it offers the potential to assemble target traits in the same genotype more precisely, with less unintentional losses and in fewer selection cycles (Devi and Ngachan, 2017). In sorghum for example, MAS was used to introgress genes for early-season vigor from the Chinese landrace ‘Shan Qui Red’ (SQR) possessing cold tolerance into elite sorghum lines (Knoll and Ejeta, 2007). In rice, extensive efforts have been made to improve its tolerance to cold. In cotton, breeding for cold tolerant cultivars is on a standstill because the primary focus of most breeding programs is on yield and fiber quality. To establish an efficient breeding program to improve cold tolerance in cotton, it is important to first dissect the genetic basis of cold stress-mediated morphological, physiological and biochemical changes in the crop. Mining for favorable QTLs and alleles should provide useful information to accelerate the progress in breeding for cold-tolerant cotton. To date, the use of improved varieties together with good planting practices such as early season planting significantly increases the yield potential of some of the economically important agricultural crops such as soybean and rice. This strategy can also be adapted for cotton production. However, unfavorable growing conditions early in the season often leads to the extreme sensitivity of the crop to lower temperatures. To maximize the full genetic potential of cotton, it is important to highlight the need to develop cultivars and varieties that can provide a certain degree of tolerance to cold specifically at the germination and early seedling stages. The goal of this research is to identify genetic variation in germplasm collections that can be tapped to improve cold germination ability and early seedling vigor in cotton that are planted early in the season.

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Research Goals The specific objectives of this study are as follows: 1. To assess the genetic variation in a panel of G. hirsutum accessions as well as ethylmethylsulfonate (EMS)-induced mutants using simple sequence repeats (SSR) markers. 2. Evaluate the germination ability of the G. hirsutum accessions and EMS- induced mutants based on established germination parameters. 3. Screen representative genotypes from the panel of G. hirsutum accessions based on the wide variation in response to cold germination for seedling vigor ability through the evaluation of morpho-physiological and biochemical response to cold stress. 4. Establish a comparative transcriptome profile of cotton accessions with contrasting response to low temperature stress during early seedling stage.

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CHAPTER II

IDENTIFICATION OF NOVEL SOURCES OF GENETIC VARIATION FOR THE IMPROVEMENT OF COLD GERMINATION ABILITY IN UPLAND COTTON (Gossypium hirsutum L.)

Introduction Upland cotton (Gossypium hirsutum) is an annual crop that originated from the tropical regions. It thrives well under an optimum temperature of 28-30°C (Lehman, 1925; Ludwig, 1932; Stanway, 1960) and is largely cultivated in regions with dominantly warm temperatures. The inherent sensitivity of cotton to cold stress can adversely affect every aspect of the physiological growth of the crop starting from germination to early seedling growth, reproductive stage and maturation (Kittock et al., 1986; Speed et al., 1996; Krzyzanowski and Delouche, 2010). Exposure of emerging seedlings to low temperature stress early in the spring have been shown to induce chilling injuries leading to taproot loss, seedling malformations, reduced seedling vigor and ultimately, to significant yield losses (Kittock et al., 1986; Speed et al., 1996; Krzyzanowski and Delouche, 2010). Conversely, a slight decrease in temperature that coincides with the reproductive stage of plants have been reported to reduce the number of flowers and size of bolls, as well as lint production in cotton. This is despite the fact that planting was carried out in mid-May when temperature requirements were optimum for planting (Cathey and Meredith, 1988). In temperate regions where the cotton season is short, late season planting may provide an optimum range of temperature for seed germination. However, this runs the danger of having the crop mature late in the fall when unexpected cold snaps below 15°C are very likely to occur and negatively impact yield potential, as well as fiber and seed quality (Gipson et al., 1969). Conversely, early season planting to ensure maturation of the crop under warmer temperatures risks poor germination, emergence, and stand establishment due to cold stress in the early spring (Christiansen and Thomas, 1969; Pettigrew, 2002; Buxton et al., 1977).

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Although early season planting presents obvious disadvantages on the overall growth and development of the plant, several studies have shown the benefits of this cultural practice in improving the yield potential of cotton (Pettigrew 2002; Bange and Milroy, 2004). Various strategies that ensures the yield benefits of early season planting in cotton have been employed and proven effective. For example, chemical priming of seeds including the exogenous application of abscisic acid and mefluidide before planting have been shown to provide cold tolerance to seeds during germination (Rikin et al., 1979, 1984; Li, 1994). Once the seedlings emerge, foliar spraying with plant growth regulators such as ethephon, mefluidide, and/or diethanolamine efficiently reduced seedling damage due to cold. Despite the benefits of these chemical mitigation strategies to the growth, development and agronomic performance of cotton planted early in the spring, the costs required for these additional inputs constrain the economic productivity of the crop. In the long-term, identification of genetic variation that can be incorporated into existing varieties to improve cold germination in the crop would be the most economical approach to ensure the production stability of early planted cotton. Natural populations of cotton hold a tremendous amount of genetic variation that can be harnessed to increase crop productivity under a wide range of agricultural ecosystems. The US National Cotton Germplasm Collection holds more than 10,000 cotton accessions representing 45 Gossypium species (Campbell et al., 2010; Hinze et al., 2015). A subset of this collection makes up the Gossypium Diversity Reference Set (GDRS) which represents 70% of the genetic diversity present in the total cotton germplasm collection. Molecular characterization of the 1982 accessions of the GDRS using 105 SSR markers indicate the presence of genetic variation that can be used for trait improvement in upland cotton (Hinze et al., 2015). Alongside natural populations, mutant libraries constitute a valuable genetic resource that can be utilized to increase variability in cultivated crops that have been severely limited by the bottleneck effects of modern plant breeding such as in the case of cotton (Shim et al., 2018). Efforts to widen the genetic base of the crop have led to the generation and consequent availability of mutant libraries that have been developed for target trait improvement (Auld et al., 2000; Bechere et al., 2009; Bechere et al., 2012) or

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Texas Tech University, Ritchel Bueno Gannaban, August 2019 as genetic tools for the functional analysis of genes controlling various biological traits (Auld et al., 1998; Aslam et al., 2016). In this study, we aim to (1) assess the genetic diversity in a panel of G. hirsutum accessions and fatty acid mutants based on DNA marker profile, (2) determine phenotypic variation for cold germination ability in the test germplasm and (3) establish an association between the observed genotype and phenotype based on principal component analysis. The results of this study will provide basis for the identification of germplasm that can be used as potential donors to improve cold stress tolerance in cotton during germination.

Materials and Methods Plant materials A subset of the Gossypium Diversity Reference Set (GDRS) composed of 30 G. hirsutum accessions from the variety-sub collection (denoted as SA-) and landrace sub- collection (denoted as TX-) was obtained from the National Cotton Germplasm Center (Hinze et al., 2015, 2017). The selected accessions have been shown to exhibit variable responses to abiotic stresses such as heat, drought and salinity (personal communication; BG de los Reyes). In addition, a total of 20 ethylmethanesulfonate (EMS)-induced fatty acid (FA) mutants that were generated at Texas Tech University from 1997-2008 were included in the genetic diversity analysis. The FA mutants were identified to have reduced levels of palmitic acid (C16:0) and higher proportions of linoleic acid (C18:2) compared to traditional cotton varieties. Table 2.1 shows the summary information for the GDRS accessions and FA mutants used in the study.

Plant propagation and genomic DNA extraction Seeds of all GDRS accessions and FA mutants were sown approximately half an inch deep in 72-round plug trays (6 x 12 configuration; 1.25” cell top diameter; 1.5” cell depth; 0.50” drain diameter) filled with potting mix (Sun Gro 900 Grower Mix) and supplemented with basal fertilizer (14-14-14 Osmocote classic). Seedlings were maintained under controlled conditions at the Horticultural Gardens of the Department of Plant and Soil Science at Texas Tech University. Twelve days after sowing, an inch of

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Texas Tech University, Ritchel Bueno Gannaban, August 2019 the first true leaf of each line were collected in 2 µl tubes for DNA extraction using the TPS method (Miura et al., 2010). Briefly, leaf tissues were cut into smaller pieces in a 96- deep-well plate with two beads each well. TPS extraction buffer at 300 µl was added into the wells before homogenization of the samples at 900 rpm for two minutes using the Geno/Grinder (SPEX, USA). The homogenized samples were centrifuged at 2300 rpm for 10 minutes before transferring100 µl of the supernatant of each sample into 96-well plates containing 100 µl isopropanol. The solution was mixed by trituration before centrifugation at 3000 rpm for 10 minutes to precipitate the DNA. The solution was then poured off and the DNA pellets were washed with 150 µl of 70% ethanol. The plates were air-dried for 15 minutes and the pelleted DNAs were re-suspended in 15 µl 1X TE Buffer with RNAse. Plates were labeled, covered with a plastic seal and stored at 4°C until further use.

SSR genotyping A total of 105 SSR markers that are distributed across the 26 chromosomes of cotton and that have been used to characterize genetic diversity within the Gossypium species were screened for their ability to amplify targets in the test germplasm (Yu et al., 2012;Hinze et al., 2015, 2012). Polymerase chain reaction (PCR) technique was used to evaluate the available genetic variation within the selected GDRS accessions and FA mutants. PCR for all samples was carried out in 10 µl reactions composed of 2 µl genomic DNA, 2.9 µl of sterile distilled water, 1.0 µl of 10X PCR buffer, 1.0 µl dNTPs, 1.0 µl of forward + reverse primers, 2.0 µl of 50% glycerol and 0.1µl of commercial Taq polymerase. The amplified PCR products were resolved in 3% agarose gel in 1X TBE buffer, supplemented with 5 µl of SYBR Safe. Gel electrophoresis was carried out at 220V and 300 milliamp for 60 minutes. Gel documentation was conducted using the UVP GelStudio Imaging System (Analytik Jena, Germany).

Genetic diversity analysis The PCR-amplified DNA fragments were scored as either present (1) or absent (0). Since cotton is an allotetraploid crop, each of the alleles amplified by the SSRs were

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Texas Tech University, Ritchel Bueno Gannaban, August 2019 considered as an independent locus and scored as a dominant marker. The polymorphic 2 information content (PIC) was calculated for each allele using the formula PIC=ΣPi to determine the ability of each primer to detect genetic variability. GenAlEx v6.5 (Peakall and Smouse, 2012) was used to determine allelic frequencies, heterozygosity, genetic diversity and Nei’s genetic distance. The genetic distance generated was exported into the MEGA software to run the clustering analysis using the Unweighted Pair Group Method with Arithmetic mean (UPGMA) (Kumar et al., 2018).

Viability seed testing Viability testing was carried out to ensure that the observed germination is due to the genetic variation in the test germplasm and not because of the poor viability of the seed lot. Briefly, thirty seeds of each test germplasm were surface sterilized, rolled in moist blotting paper, placed inside a sealed container and kept inside a growth chamber set at 30°C. Seeds with 2 mm radicle protrusion after 4, 8 and 12 days were considered germinated. Determination of seed viability was based on ≥80% germination.

Cold germination screening Cold germination experiments were set-up in plant growth chambers (Thermo Scientific™ Precision™) with dual fluorescent lighting for uniform illumination and a programmable temperature setting. The treatments comprised of three temperatures namely12°C, 15°C and 30°C (control) at 12:12 light:dark cycles. Screening for cold tolerance during germination was carried out following the protocol by Rapahel et al. (2017). Briefly, twenty-five seeds of each of the GDRS and FA mutants were placed in individual petri plates (100 x 150 mm diameter) (Fisherbrand™) lined with sterile filter papers. Three more layers of filter paper were placed on top of the seeds before spraying the plates with 6 ml sterile distilled water and sealing them with parafilm. A total of six plates per accession were prepared. The plates were placed in a plastic tray and covered with foil before placing them inside the growth chambers programmed at different temperatures. Seeds with 2 mm radicle protrusion were counted and recorded at a 24-h interval for 14 days. Seeds that were germinated at low temperatures were transferred at 30°C after 14 days to determine the germination recovery of each accession.

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The effects of imbibition prior to cold temperature germination were also evaluated. Seeds of the GDRS accessions and mutant lines were allowed to imbibe water for 8 h at 30°C before cold treatment. Hydropriming allows each accession to be placed in an equal stage of germination (Hossein, 2013) before exposure to cold temperatures. Cold germination experiments after hydropriming were as previously described.

Cold germination parameters To phenotypically characterize the germination ability of the GDRS accessions and FA mutants in response to low temperatures, five germination parameters were used. Data on germination percentage (GP), mean germination time (MGT), mean daily germination (MDG), peak value (PV) and germination index (GI) were calculated for each test germplasm. GP was calculated by dividing the total of number of seeds that germinated by the total number of seeds used and multiplying the quotient by 100. MDG was the quotient derived from dividing the germination percentage at the end of the germination test by the number of days to the end of the germination test. PV was the cumulative full-seed germination percentages on any day/the number of days to reach these percentages (Djavanshir and Pourbeik, 1976; Orchard, 1977). MGT was calculated using the formula MGT = ∑ f ∙ x ∕ ∑ where f = seeds that germinated on day x. GI was established using the formula GI = (10xn1) + (9xn2) + ∙ ∙ ∙ ∙ + (1xn10), where n1, n2…n14 refer to the number of germinated seeds on the first, second, and subsequent days until the 14th day. The numbers 14, 2 and 1 are weights given to the number of germinated seeds on the first, second and subsequent days, respectively (Scott et al, 1984; Bench et al., 1991).

Statistical analysis A two-way analysis of variance (ANOVA) and a post hoc Tukey’s test at p<0.05 significance level was carried out to examine the effects of temperature, genotype and interaction of both factors on the cold germination ability of the experimental materials. Principal component analysis (PCA) (RStudio Inc, 2015) was conducted to establish the patterns of variations in the cold germination ability of the test germplasm using the absolute mean values obtained for the five germination parameters.

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Results and Discussion Genetic diversity analysis using SSR markers Genetic diversity analysis was conducted to determine the availability of genetic variation for cold germination ability across a panel of GDRS accessions and FA mutants. The GRDS accessions SA 0881, SA 1055, SA 1156, and TX-2150 did not germinate and thus were not included in the genetic diversity analysis. Among the 105 markers screened, only 51 consistently amplified clear bands. The 51 markers generated a total of 88 alleles. Twenty-nine markers were monomorphic, whereas 22 were polymorphic, generating 1-4 alleles each. The average number of alleles that each marker detected was 1.6. The calculated number of effective alleles (Ne) ranged from 1.02 to 2.00 with a mean Ne of 1.5±0.05 (Table 2.2). A total of 42 independent polymorphic loci were generated by the 22 polymorphic markers. Of the 22 polymorphic primers, BNL2960 and JESPR153 detected four distinct bands, whereas JESPR065 detected three distinct band patterns. Six loci that were generated by the SSR markers BNL2960, BNL1047, BNL1531, JESPR153, CIR218 and BNL1673 recorded a band frequency of ≤5%, indicating the presence of rare, informative bands that are unique to an accession or a mutant line. The expected heterozygosity (He) of markers used across all accessions ranged from 0.02 to 0.49 with an average He of 0.30±0.02. The PIC values obtained for each loci ranged from 0.04 to 0.50, with an average of 0.26 (Table 2.3). PIC is a function of the number of alleles and their relative frequencies in a locus. As such, it provides a good measure of the informativeness of a marker and reflects the degree of genetic diversity based on a given locus (Smith et al., 1997; Bolstein et al., 1980; Chesnokov and Artemyva, 2015). In a dominant marker system, the PIC values can range only from 0 to 0.5 since dominant markers cannot clearly discriminate between homozygotes and heterozygotes. Based on the calculated PIC values, the 22 markers used were determined to be suitable in assessing the degree of genetic variation in the test germplasm that can be used to improve cold germination ability in upland cotton. The high mean PIC value obtained for the SSR markers, which coincides with the mean He value, suggest a high degree of genetic variation in the test germplasm.

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The genetic distances was computed based on the SSR marker profiles to establish the genetic similarities among the test germplasm. Using the GenAlEx software, the calculated genetic distance ranged from 0.01 to 0.02 with an average of 0.093. UPGMA clustering based on a minimum threshold level of 38% grouped the test germplasm into five distinct clusters namely Clusters I, II, III, IV, and V (Figure 2.1). Cluster I grouped together two landraces from France (TX 1801) and from the Dominican Republic (TX 1566). Cluster II consisted of the GDRS accession, SA 0165 and the GDRS landrace, TX112. Cluster III grouped the GDRS accessions SA1232, SA0857, and SA3781. Cluster IV was composed mostly of the GDRS germplasm including the landrace, TX0307. Cluster V grouped all the twenty FA mutants plus the GDRS accessions, SA2580 and SA1406. The genetic similarity among the test germplasm in Cluster IV ranged from 42 to 85%, whereas that of the germplasm that grouped in Cluster V ranged from 50 to 100%. Genetic similarity in Clusters I, II and III ranged from 38 to 60%. Compared to the germplasm that grouped in Clusters IV and V, the lines that grouped in Clusters I, II and III showed high degree of genetic variation which may be attributed to the very nature of landraces. Landraces are population complexes that are not necessarily high-yielding but have adaptations to the edaphic and climatic conditions, as well as to the traditional farming practices of a localized area. Landraces also lack formal crop improvement and therefore maintain a high degree of genetic heterogeneity (Mercer and Perales, 2010; Frankel et al., 1998; Casañas et al., 2017). Compared to Cluster V, the germplasm that grouped into Cluster IV shared a slightly lower genetic similarity. The accessions that grouped together in Cluster IV were collected from different geographic locations around the world including US, China, Mexico, Russia, Algeria, Chad, El Salvador and Argentina (Hinze et al., 2015). The geographic center of origin for G. hirsutum is North and Central America and Mexico (Frankel et al., 1998). The degree of genetic similarity of the GDRS accessions in Cluster IV may be attributed to the shared progenitor of the upland cotton believed to have been domesticated first by the Pueblo Indians in the southwest USA as early as the first century AD (Frankel et al., 1998). The intra-cluster variation observed in these accessions can be attributed to the result of the selection pressure imposed as

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Texas Tech University, Ritchel Bueno Gannaban, August 2019 domestication progressed. This include selection for favorable agronomic traits such as reduced seed dormancy, annual growth habit and photoperiod-independent flowering that are characteristic of modern cultivars. The FA mutants are in the genetic background of obsolete upland cotton cultivars that are highly adapted to the agricultural ecosystem of the Texas High Plains. This shared adaptation may account for the high genetic similarity within the FA mutants. Conversely, the observed intra-cluster variation may be attributed to incidences of cross- pollination observed suggest that during the propagation and generation advance of these

FA mutants (up to M5 generation) in the open fields. Natural outcrossing in upland cotton have been reported to occur at a frequency of 5- 50% (Turner, 1950; Moffet, 1980). These occurrences most likely have contributed to the observed variations within the FA mutants. Within Cluster V, the GDRS accession SA-1406 grouped at 68% similarity with the FA mutant 103-1, whereas SA-2580 grouped at 78% similarity with FA 110-5. SA- 2580 is the cultivar Acala 1517-99 which is one of the cultivars used to generate the FA mutants. Acala 1517-99 originated from an earlier version of Acala that was first cultivated in Texas in the early 1900s. Since then, Acala has been used extensively in breeding programs as a source of better fiber quality, higher yield and their adaptability to the environmental conditions at Texas High Plains region (Turner, 1914).

Germination ability of the test germplasm under low temperature stress Seed viability testing of the test germplasm was conducted to eliminate the possible effects of seed lot quality and other postharvest factors that can affect germination ability. Viability testing showed 80% germination of all the FA mutants and 13 GDRS accessions, and only 34-75% germination of the remaining 17 GDRS accessions (Table 2.1). With this observation, seed viability testing was carried out again for the 17 accessions using a different seed lot. The results were consistent with the first trial, indicating that the low germination rate is inherent to these accessions. Like most agricultural crops, the ability of the seed to germinate uniformly in the shortest possible time is an important factor for a successful crop establishment (Orchard, 1977; Scott et al., 1984; Kader, 1998). In this study, a time-to-event approach was used to

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Texas Tech University, Ritchel Bueno Gannaban, August 2019 determine the cold germination ability of the test germplasm. This means taking into consideration other parameters that provided a measure of the speed of germination in terms of spread (MGT), duration (MDG) and high/low event (PV), alone or in combination with final germination percentage (GI) (Kader, 2005). The test germplasm showed a general reduction in germination expressed in terms of GP, MGT, MDG, PV and GI with decreasing temperatures from 30°C to 12°C (Table 2.4). These results are consistent with findings of similar studies that focused on establishing the effects of low temperature on the germination of cotton seeds (Krzyzanowski and Delouche, 2011; Khetran et al., 2015; Cole and Wheeler, 1974). Significantly higher mean values for GP, MDG, PV, and GI were observed in the FA mutants across all temperatures. The FA mutants germinated uniformly within a shorter period compared to the GDRS accessions. Among the GDRS accessions, a significantly higher germination was observed in the landraces than in the cultivars, particularly at 15°C and 30°C. Imbibition at 30°C before exposure to low temperatures improved the uniformity and speed of germination of the GDRS accessions at 12°C and 15°C, although germination of the landraces were more spread within the 14-day experiment period. Given the intrinsically higher germination ability of the FA mutants at 15°C, imbibition test was no longer carried out for these lines. Imbibition had no significant effects on the germination ability of the FA mutants at 12°C. Even with the significant effects of imbibition on the overall germination ability of the GDRS accessions at 15°C, it is still not comparable to the germination ability of FA mutants at 15°C without imbibition. Most of the seeds that did not germinate within 14 days at low temperatures were able to recover and germinate within 5-7 days at 30°C. The FA mutants recorded the highest recovery at both low temperatures, with or without hydropriming, followed by the GDRS cultivars and then the landraces (Table 2.5). Significant effects of temperature, genotype and the interaction of both factors on all the germination parameters measured were established using a two-way ANOVA (Table 2.5). The germination response of the FA mutants at 12°C after hydropriming can be attributed to the fact that these mutant lines have higher proportions of unsaturated fatty acid. It has been studied in corn that incorporation of unsaturated fatty acid in the cell

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Texas Tech University, Ritchel Bueno Gannaban, August 2019 membrane increases membrane fluidity, thereby reducing electrolyte leakage during imbibition (Thakur et al., 2010). Unsaturated and saturated fatty acids are the chief lipid components of a cellular membrane. These two fatty acids differ from one another in the number of carbon atoms they contain as well as the number of carbon-carbon double bonds. The unsaturated fatty acids are composed of one or more double bonds between two carbon atoms (-CH=CH-) whereas the carbon in the saturated fatty acids are surrounded with only hydrogen atoms (-CH2-CH2-) (Harwood et al., 1994; Williams et al., 1988). Lipids consisting high proportions of unsaturated fatty acids can resist solidifying at lower temperatures thereby influencing membrane fluidity. Chilling resistant species have higher proportion of unsaturated fatty acids compared to those chilling sensitive plant species (Falcone et al., 2004).

Patterns of variation in the cold germination ability of the test germplasm The PCA analysis was carried out to establish patterns of variation in the test germplasm in terms of their response to cold germination. Using the mean values for GP, MGT, MDG, PV and GI, five principal components (PC) were established. Of the five, PC1 and PC2 explains 86.70-99.29% of the observed variation in the germination ability of the test germplasm under different treatments (Figure 2B, 2F, 2J, 2D and 2H). The variation in the germination ability observed at 12°C in both FA mutants and GDRS accessions was defined by PC1 contributing to the separation of the two groups into slightly overlapping clusters (Figure 2A). However, at 15°C (Figure 2F) and 30°C (Figure 2J), it can be clearly observed that PC1 contributed more on the differentiation of the FA mutants from the GDRS accessions, whereas PC2 contributed more to the differences within the GDRS accessions. In the PCA biplot, it can be observed that there is more scattered distribution of the GDRS accessions. This is reflective of the wider variability in the ability of the accessions to germinate at 15°C and 30°C. In addition, the grouping of the landraces closer to the FA mutants at both axis at 15°C suggests a lesser variation in the germination ability within these accessions but at a range that is more similar to the FA mutants than the rest of the GDRS cultivars. In comparison, there is relatively more uniform distribution of the FA mutants against the PC2 axis but not in PC1 at both 15°C

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Texas Tech University, Ritchel Bueno Gannaban, August 2019 and 30°C (Figure 2F and 2J). This distribution agrees to the observed higher germination rate and narrower GP range of the FA mutants within a fewer number of compared to the GDRS accessions. At 12°C, the observed patterns of variation in the cold germination ability of the test germplasm was significantly reduced by hydropriming. This is indicated by the overlapping clusters of GDRS accessions and FA mutants with few outliers from the GDRS lines in the PCA biplot (Figure 2D). This reduction in variation in the germination ability of the test germplasm effect of hydropriming can also be observed at 15°C, although a clear separation of the FA mutants from the GDRS accession was still maintained. In this study, it is observed that the differentiation of the FA mutants from the GDRS cultivars and landraces based on the phenotypic measurements for cold germination ability is reflective of the results of the clustering of the test germplasm into separate clades based on genetic similarities that was established by using the SSR markers. Overall, the degree of germination ability of the FA mutants and GDRS accessions at low temperature stress correlates with the degree of genetic variability established for the test germplasm.

Conclusion This study was carried out to screen and evaluate both genetic and phenotypic variations in a set GDRS accessions and chemically induced FA mutants based on SSR marker profiles and physiological responses to cold germination. The results showed a wide range of genetic variation which can be utilized in improving cold germination ability in upland cotton. At low temperatures (12°C and 15°C), the FA mutants showed robust germination ability even without imbibition treatment compared to the GDRS accessions. The imbibition treatment significantly improved the germination ability of the GDRS accessions at 15°C. At 12°C, imbibition helped improve the germination of GDRS accessions by 50%. The FA mutants and select GDRS accessions can be used as possible donors in breeding programs to help improve the cold germination ability of the upland cotton. In addition, these test germplasms can also be used for further genetic studies aimed at identifying the molecular basis underlying cold germination ability in cotton.

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Table 2.1. GDRS accessions and fatty acid mutants used in the genetic diversity and cold germination ability analysis. No. Inventory No. Species Accession name Country of Origin 1 SA-0002 G. hirsutum Algerian Brown Algeria 2 SA-0033 G. hirsutum Hopi USA 3 SA-0165a G. hirsutum M.U. 3 UA 7-41 Trinidad and Tobago 4 SA-0298 G. hirsutum Wonder Wilt Wannamaker's USA 5 SA-0300a G. hirsutum Rowden #2 USA 6 SA-0369a G. hirsutum D and PL 10-1 USA 7 SA-0582 G. hirsutum USA 8 SA-0718 G. hirsutum Arkansas 11 Nucala X Rowden 20-4 USA 9 SA-0857a G. hirsutum Acala Original USA 10 SA-0881b G. hirsutum Missdel USA 11 SA-1055ab G. hirsutum M 100 Uzbekistan 12 SA 1106ab G. hirsutum 13 SA 1156a G. hirsutum 14 SA-1232 G. hirsutum AC 134 CB 4029 Pakistan 15 SA-1330a G. hirsutum Reba P 279 (Reba B-50 X Dpl Smo.) Chad 16 SA-1406 G. hirsutum S4727 Russia 17 SA-1412a G. hirsutum Chung Mein-Jue #7 China 18 SA-1512 G. hirsutum Deltapine 50 USA 19 SA-1759 G. hirsutum Chaco 510 Inta Argentina 20 SA-1766 G. hirsutum Ceix El Salvador 21 SA-2580 G. hirsutum Acala 1517-99 USA 22 SA-2895 G. hirsutum Lambright 2020A USA 23 SA-3284a G. hirsutum VIR-6654 SAC-24-4 Russia (Mexico) 24 SA-3403a G. hirsutum VIR-7137 Coker 201 Russia (USA) 25 SA-3781 G. hirsutum Acala Royale USA 26 TX-0307a G. hirsutum Mexico 27 TEX 112a G. hirsutum Guatemala 28 TEX 1556 a G. hirsutum Dominican Republic

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Table 2.1 Continued 29 TEX 1801 a G. hirsutum France 30 TEX 2150ab G. hirsutum Trinidad and Tobago 31 FA101-1 G. hirsutum EMS Mutant (M5 generation) USA 32 FA103-1 G. hirsutum EMS Mutant (M5 generation) USA 33 FA110-5 G. hirsutum EMS Mutant (M5 generation) USA 34 FA110-6 G. hirsutum EMS Mutant (M5 generation) USA 35 FA110-8 G. hirsutum EMS Mutant (M5 generation) USA 36 FA110-9 G. hirsutum EMS Mutant (M5 generation) USA 37 FA210-2 G. hirsutum EMS Mutant (M5 generation) USA 38 FA210-4 G. hirsutum EMS Mutant (M5 generation) USA 39 FA210-7 G. hirsutum EMS Mutant (M5 generation) USA 40 FA210-10 G. hirsutum EMS Mutant (M5 generation) USA 41 FA301-1 G. hirsutum EMS Mutant (M5 generation) USA 42 FA301-3 G. hirsutum EMS Mutant (M5 generation) USA 43 FA302-3 G. hirsutum EMS Mutant (M5 generation) USA 44 FA303-1 G. hirsutum EMS Mutant (M5 generation) USA 45 FA303-3 G. hirsutum EMS Mutant (M5 generation) USA 46 FA304-1 G. hirsutum EMS Mutant (M5 generation) USA 47 FA304-2 G. hirsutum EMS Mutant (M5 generation) USA 48 FA306-8 G. hirsutum EMS Mutant (M5 generation) USA 49 FA307-3 G. hirsutum EMS Mutant (M5 generation) USA 50 FA309-3 G. hirsutum EMS Mutant (M5 generation) USA aaccessions exhibiting 34-75% germination in replicated viability testing baccessions excluded from genetic diversity analysis due to several missing data

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Table 2.2. Summary statistics of the SSR markers used to genotype the subset of GDRS accessions and FA mutants. Descriptive statistics Value Total no. of markers used 105 Total no. of markers that amplified in >50% of the experimental materials 51 Average no. of observed alleles per SSR 1.6 Average no. of effective alleles (Ne) 1.5±0.05 Average expected heterozygosity (He) 0.30±0.02 No. of polymorphic SSR markers 22 No. of independent alleles amplified by polymorphic SSRs 88 No. of polymorphic alleles amplified by polymorphic SSRs 42 PIC range of alleles that were considered as independent markers 0.04-0.50 Average PIC of alleles that were considered as independent markers 0.26

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Table 2.3. Calculated polymorphism information content (PIC) values for independent SSR alleles. Locus Allele No. of present Total no. Frequency of present PIC No. SSR Marker no. allele samples allele (2PiQi) 1 BNL3545 1 37 45 0.82 0.292 2 2 20 45 0.44 0.494 3 BNL4071 1 4 46 0.09 0.159 4 BNL2960 1 40 46 0.87 0.227 5 2 35 46 0.76 0.364 6 3 9 46 0.20 0.315 7 4 1 46 0.02 0.043 8 BNL0530 1 41 46 0.89 0.194 9 2 6 46 0.13 0.227 10 NAU2140 1 30 46 0.65 0.454 11 2 16 46 0.35 0.454 12 BNL3474 1 36 46 0.78 0.340 13 2 10 46 0.22 0.340 14 MUSB1015 1 43 46 0.93 0.122 15 DPL0541 1 41 46 0.89 0.194 16 2 44 46 0.96 0.083 17 BNL1673 1 2 46 0.04 0.083 18 2 2 46 0.04 0.083 19 BNL3090 1 44 46 0.96 0.083 20 BNL1495 1 28 46 0.61 0.476 21 2 18 46 0.39 0.476 22 BNL4061 2 26 46 0.57 0.491 23 DPL0135 1 10 46 0.22 0.340 24 2 36 46 0.78 0.340 25 JESPR220 1 21 46 0.46 0.496 26 BNL1521 1 43 46 0.93 0.122

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Table 2.3 Continued 27 2 3 46 0.07 0.122 28 BNL1047 1 45 46 0.98 0.043 29 2 1 46 0.02 0.043 30 JESPR065 1 25 46 0.54 0.496 31 2 21 46 0.46 0.496 32 3 36 46 0.78 0.340 33 BNL1531 1 45 46 0.98 0.043 34 2 1 46 0.02 0.043 35 BNL4030 1 26 46 0.57 0.491 36 2 3 46 0.07 0.122 37 JESPR119 1 10 46 0.22 0.340 38 CIR218 2 2 46 0.04 0.083 39 JESPR153 1 10 46 0.22 0.340 40 2 38 46 0.83 0.287 41 3 44 46 0.96 0.083 42 4 1 46 0.02 0.043

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Table 2.4. Mean values of the germination parameters used to evaluate cold germination ability in the FA mutants and GDRS cultivars and landraces. Temperature Treatment Germination parameter/group 12°C 12°C+ 15°C 15°C+ 30°C Germination percentage (GP; %) Fatty acid mutants 33.32a 30.18b 94.73a -c 100.00a GDRS accessions 13.74b 53.05a 36.20b 92.40a 70.03b GDRS landraces 11.13b 30.18b 42.87b 74.81b 76.65ab Mean germination time (MGT) Fatty acid mutants 12.22a 11.33a 5.88a - 1.90b GDRS accessions 12.42a 10.78a 5.09a 5.41a 5.16a GDRS landraces 12.14a 11.33a 7.28a 6.82a 4.49a Mean daily germination (MDG) Fatty acid mutants 0.07a 0.065a 0.81a - 4.91a GDRS accessions 0.03b 0.082a 0.10b 0.31a 0.82b GDRS landraces 0.03b 0.065a 0.14b 0.21a 0.51b Peak value (PV) Fatty acid mutants 0.49a 0.46a 2.96a - 8.14a GDRS accessions 0.20b 0.59a 0.68b 1.68a 3.03b GDRS landraces 0.19b 0.46a 0.73b 1.27a 1.99b Germination index (GI) Fatty acid mutants 0.57a 0.48b 4.81a - 10.96a GDRS accessions 0.21b 0.78a 1.12b 4.39a 4.76b GDRS landraces 0.21b 1.07a 0.98b 3.15a 4.84b +with hydropriming treatment *different letters indicate significant differences at P<0.05. Cno data. Effects of hydropriming not tested because of high GP obtained at 15℃ even without hydropriming.

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Table 2.5. Two-way ANOVA of the germination parameters used to evaluate cold germination ability of the FA mutants and GDRS cultivars and landraces. Source DF Sum of Mean Squares F Squares Germination percentage (GP; %) Genotype 2 6604.71 3302.35 8.937* Temperature 4 103688.45 25922.11 70.16* Genotype x Temperature 7 47160.231 6737.18 18.23* Mean germination time (MGT) Genotype 2 96.98 48.49 72.25* Temperature 4 2001.48 500.37 745.52* Genotype x Temperature 7 462.61 66.09 98.46* Mean daily germination (MDG) Genotype 2 71.56 35.78 98.65* Temperature 4 185.53 46.38 127.88* Genotype x Temperature 7 135.60 19.37 53.41* Peak value (PV) Genotype 2 162.28 81.14 136.29* Temperature 4 616.57 154.14 258.92* Genotype x Temperature 7 238.60 34.09 57.26* Germination index (GI) Genotype 2 197.03 98.51 312.62* Temperature 4 1376.12 344.03 1091.72* Genotype x Temperature 7 449.69 56.21 178.38* *significant at P<0.001

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Figure 2.1 UPGMA and clustering of GDRS accessions and FA mutants based on Jaccard’s similarity coefficient. Red broken line indicates genetic similarity threshold for the five major groupings as indicated by the colored bards labeled I, II, III, IV and V.

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Figure 2.2. Patterns of variations in the cold germination ability of the test germplasm established using PCA based on the mean values of GP, MGT, MDG, PV and GI. Histogram representation of the proportion of variance contributed by each principal component (PC), with PC1 and PC2 accounting for most of the phenotypic variation observed in the test germplasm. Clustering of the FA mutants (red circle), GDRS cultivars (green triangle) and landraces (blue square) at 12°C without imbibition (B) and with imbibition (D), at 15°C without imbibition (F) and with imbibition (H) and at 30°C (J). Clustering of the FA mutants (I) is based on the mean values obtained for all parameters measured from non-hydroprimed seeds.

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CHAPTER III IDENTIFICATION OF NOVEL SOURCES OF GENETIC VARIATION TO ENHANCE SEEDLING VIGOR OF UPLAND COTTON (Gossypium hirsutum L.) UNDER LOW TEMPERATURE STRESS

Introduction Seedling vigor is a key component of crop yield. Agricultural crops with vigorous seedlings can provide uniform stands which translates into higher yields at the end of each growing season (Ritchie et al., 2004). While seedling vigor is largely a product of genetics, environmental influences, and the interaction of both (Wittington, 1973), it can also be manipulated through proper crop management. Low temperature stress is one of the major environmental factors limiting the growth and productivity of crops (Yadav, 2010). In addition, sub-optimal temperatures negatively affect plant growth. The severity of damage also depends on the developmental time point which the stress occurs (Enders et al., 2019). Exposure of plants to cold stress at the early seedling stage can result in growth inhibition, development delays, and even to plant death (Yadav, 2010). Low temperature stress can also disrupt photosynthesis, transpiration, and enzyme activity in plants (Marocco et al., 2004), which then manifest as poor agronomic performance characterized by decreased biomass accumulation and growth rate, and leaf chlorosis and necrosis (Miedema, 1982). Other manifestations of cold stress can be determined by the biochemical changes within the cell such as increased electrolyte leakage, accumulation of reactive oxygen species, malondialdehyde, sucrose, lipid peroxidase, proline and other metabolites (Ruelland and Zachowski, 2010). Agricultural crops that are well-adapted to warmer conditions are often more prone to the negative impacts of cold stress exposure at the early seedling stage compared to temperate crops. Rice for example, exhibits stunted growth and high death rate when exposed to low temperature (Zhang et al., 2014). Upland cotton (Gossypium hirsutum L.) is native to the tropical and sub-tropical regions of the world and is extremely sensitive to low temperatures. Owing to this intrinsic characteristic, cotton production in temperate regions is significantly limited by prevalent occurrences of low temperature that bookend the growing season. Especially

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Texas Tech University, Ritchel Bueno Gannaban, August 2019 with early season planting, cotton plants that are exposed to temperature dips below 15°C exhibit poor vigor, resulting in an uneven distribution of seedlings in the field. In worst cases, poor seedling vigor can eventually lead to stunting, chlorosis, and taproot loss (Perry, 1980). Despite the risk of exposing young cotton seedlings to cold stress with early season planting, this practice has been reported to facilitate the more effective utilization of late spring and early summer rainfall by the cotton plants. Access to these supplementary water resources early in the growth of cotton consequently enhances the yield potential of the crop. Increasing the seeding rate and foliar applications of plant growth regulators such as ethephon and mefluidide efficiently provide protection against cold stress, thereby ensuring the yield stability of early planted cotton (Cathey and Meredith, 1988). Although these practices offer reasonable solution to the problem, ultimately the most economical strategy is to develop and breed cultivars with innate tolerance to cold stress during the early seedling stage. The goal of this study is to identify germplasm exhibiting seedling vigor under low temperature stress at the early seedling stage. To achieve this, the following specific objectives were set: 1) Assess the morphological, physiological and biochemical performance of select GDRS accessions and FA mutants in response to low temperature exposure at the early seedling stage. 2) Identify a signature set of parameters that can be used effectively as markers for seedling vigor under cold stress at the early seedling stage in cotton. 3) Select possible donor lines that can be used for upland cotton breeding for cold tolerance at the seedling stage.

Materials and Methods Plant Materials Twelve GDRS accessions and six fatty acid (FA) mutants that were identified in Chapter I to represent the full spectrum of genetic variation for cold germination ability were selected and evaluated for seedling vigor under cold stress (Table 3.1). Seeds of the selected materials were germinated in 72-round plug trays (6 x 12 configuration; 1.25”

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Texas Tech University, Ritchel Bueno Gannaban, August 2019 cell top diameter; 1.5” cell depth; 0.50” drain diameter) filled with potting mix (Sun Gro 900 Grower Mix) and supplemented with basal fertilizer (14-14-14 Osmocote classic). Twelve plants per genotype were maintained in triplicates under controlled conditions at the Horticultural Gardens of the Department of Plant and Soil Science at Texas Tech University. At the appearance of the first true leaves, two sets of seedlings were transferred to 15°C and 18°C, whereas one set was maintained in the greenhouse (30°C) as control. Except for biomass which was taken 15 days after cold stress treatment, plant height, chlorophyll content and leaf temperature were recorded after 6, 9, 12 and 15 days of cold treatment. The morphological and physiological performance in response to cold stress of six genotypes (SA-0033, SA-0718, SA-0881, SA-1766, SA-2895, SA-3781) out of the initial 18 genotypes were validated following the experimental set-up describe above. In addition, biochemical assays such as electrolyte leakage, lipid peroxidase and free proline content analysis were recorded at 0, 1, 3 and 5 days of cold treatment. The six genotypes were identified to represent the full spectrum of variation for seedling vigor based on morphological and physiological evaluations. Twenty-four plants per genotype was maintained for the biochemical assays set-up to measure cold tolerance of cotton at the early seedling stage.

Morphological measurements Plant height (cm) was measured from the base of the plant to the tip of the fully expanded first true leaf of ten individual plants for each genotype. The accumulated biomass of each genotype was determined 15 days after cold treatment. Ten seedlings were uprooted, washed and cut from the base to get rid of the root system. Fresh weight measurement was based on the bulked plants. Dry weight of the bulked plants was measured after drying of the aboveground mass at 60°C for five days. The relative water and biomass content were calculated using the formula: dry weight (g)/fresh weight (g) x 100% (Franks, 1997).

Physiological measurements Leaf temperature (°C) were measured from the first true leaf of seven plants of

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Texas Tech University, Ritchel Bueno Gannaban, August 2019 each genotype using the SC-1 leaf porometer (MeterGroup). Chlorophyll content index (CCI) was measured from the first true leaf of ten individual plants per genotype using the Apogee chlorophyll meter (Apogee Instruments, USA). Actual chlorophyll concentration (µmol/m2) of leaves was calculated using the conversion formula 84.3+98.6*(CCI)0.505 since CCI only gives the relative indication of leaf chlorophyll content (Parry et al., 2014).

Biochemical assays Electrolyte leakage. Leaf discs were punched from the edges of the first true-leaf of five plants per genotype and immediately placed in a 50 ml Falcon tube with 30 ml of deionized, sterile, distilled water. The tubes were placed in a shaker for 30 minutes at 1500 rpm before measuring the electrical conductivity of the water using the Star A211 pH BT Meter (Thermo Scientific). The samples were then boiled at 100°C for 1 h and cooled off to 50°C before measuring the final electrical conductivity of the water. Lipid peroxidase assay. Approximately 75 mg of flash frozen leaf tissue was placed in a 2 ml tube (without hole) with 1.75 ml of 0.1% Trichloroacetic acid. The tube was vortexed for 1 minute and then centrifuged at 10,000 x g for 15 minutes. The supernatant (357 µl) was pipetted into a second 2 ml tube with punctured cap and mixed with 730 µl of 29% TCA and 750 µl of 0.5% w/v Thiobarbaturic acid. The tube was incubated in a water bath set at 95°C for 30 minutes and then immediately transferred on ice for 5 minutes to terminate the reaction. A 200 µl solution was pipetted on to an ELISA assay plate (Thermo Scientific, USA) for absorbance reading at 532-600 nm using the ELISA assay plate reader (Thermo Scientific, USA). Absorbance is read at 532 nm subsequent to subtraction of non-specific absorption at 600 nm. The malondialdehyde (MDA) concentration is calculated using its extinction coefficient ε =155 nM-1 cm-1. The MDA equivalents are calculated as follows:

MDA ∆Acorrected ∗ 3.5 ∗ x ∗ 1000 nmol = g DW ε ∗ b ∗ y Where: ∆Acorrected=A523-A600 corrected with ∆A of the blank sample

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b=light path length (0.56 cm for 200 µl) ε=millimolar extinction coefficient (155 nM-1 cm-1) 3.5=dilution factor from 400 µl extract + 1 ml TBA/TCA solution x=(ml) TCA 0.1% used for extraction (1ml) y=(g) DW used for extraction 1000=conversion factor (nmol to µmol) Proline assay. Approximately 100 mg of flash frozen tissue samples were placed in a 2 ml tube with 400 µl of 0.5 w/v toluene. Samples were vortexed for 1 minute, incubated at room temperature for 1 h, and centrifuged at 12,000 rpm for 10 minutes at 25°C. Approximately 100 µl of the solution was pipetted into a new tube containing 200 µl of glacial acetic acid and 200 µl of acid ninhydrin. The tubes were then punctured with a hypodermic needle and immediately incubated at 95°C for 1 h. The reaction was quenched in ice for 5 minutes before adding 1 ml of pure toluene onto the solution. A 100 µl organic phase (top phase) from each sample was transferred into a 96-well plate for absorbance reading at 520 nm. Proline concentration was determined from a standard curve and calculated on a fresh weight basis using the formula: [(µg proline/ml × ml toluene) / 115.5 µg/µmole]/ [(g sample)/5] = µmoles proline/g of fresh weight material.

Statistical analysis Student’s t-test was conducted to establish differences in the parameters measured in response to the different temperature treatments. One-way analysis of variance was carried out to test the significance of treatment and genotypic effects. In conjunction, Tukey’s test was performed at p≥0.05 significance as a post-hoc analysis to find means that are significantly different from each other.

Results and Discussion Morphological response of cotton seedlings to low temperatures Plant height. Plant height is an agro-morphological trait directly related to the growth and development of plants. Exposure of plants at the early seedling stage to abiotic stresses such as cold, drought and salinity has been reported to cause stunting (Mahajan and Tuteja, 2005).

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In this study, the plant height of all 18 genotypes decreased with decreasing temperature from 30°C to 15°C (Table 3.2; Figure 3.1). At 18°C, the plant height reduction of the FA mutants was 32.50 to 39.98% compared to the plant height of the mutants under controlled condition. At 15°C, the reduction in plant height was 35.95 to 44.23% compared to the control. The GDRS accessions recorded significant reductions of 20.10 to 44.59% and 18.22 to 39.60% in plant height at 15°C and 18°C, respectively, compared to plants in the control set-up. The growth rate of the cotton seedlings under both low temperature treatments was initially the same and remained steady from 6 to 8 days before it started leveling off from the 9th up to the 15th day. Among the six FA mutants, FA304-1 and FA306-8 grew the fastest, whereas FA101-1 grew the slowest. Of the twelve GDRS accessions, SA-0002 and SA-0881 maintained a steady growth rate while SA-3781 and SA-2895 grew slowly at both 15°C and 18°C. SA-0718 and SA-0033 were the tallest at 18°C. The GDRS accessions exhibited a wider range of variation in plant height in response to cold stress, especially at 18°C. The results of our study is consistent with previous reports on the effects of cold stress to plant height (Suzuki et al., 2008). Different crops have optimum requirements for temperature to thrive and grow. When this is not met early on during the growth and development of the plant, the growth of the seedlings are compromised, resulting in poor vigor. When exposed to low temperatures, the seedlings of the test germplasm were still able to grow although not as vigorous as the seedlings grown in the normal conditions. Plant biomass. Plant biomass is the total amount of organic material contained in a living plant and is highly correlated to physiological growth. Under optimum conditions, the total plant biomass is largely defined by the synthesis and accumulation of photoassimilates that are transported throughout the plant (Lima et al., 2017). Exposure to low temperature, especially at the seedling stage have been reported to significantly reduce plant biomass (Leonardos et al., 2003). In our study, the total aboveground biomass was determined for the 18 genotypes in response to cold temperature after 15 days of treatment (Figure 3.2). The observed total biomass of plants exposed to 15°C and 18°C were significantly lower compared to the observed total biomass of plants maintained at 30°C. The average aboveground biomass for the six FA mutants and twelve GDRS accessions maintained at 15°C were

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Texas Tech University, Ritchel Bueno Gannaban, August 2019 lower by 11.91% and 12.41%, respectively, compared to the control. At 18°C, the average aboveground biomass were 10.09% and 10.67% lower in FA mutants and GDRS accessions, respectively, compared to the control. The mutant FA306-8 has a comparable aboveground biomass at 15°C and 18°C while the other five mutants showed higher biomass at 15°C than at 18°C. Of the twelve GDRS genotypes, SA-1156, SA-1759 and SA-3781 recorded the highest aboveground biomass at 15°C, whereas SA-0002 and SA- 0033 recorded the highest biomass at 18°C. In our study, the effects of cold stress after 15 days of cold treatment resulted in a general decrease in the aboveground biomass, with the reduction being higher in the test germplasm maintained at 18°C than 15°C. The percent decrease in the biomass of the FA mutants at 15°C and 18°C was 26.90-47.00% and 40.60-55.70%, respectively. The reduction in biomass of the GDRS accessions ranged from 4.50 to 40.10% at 15°C, and from 29.40 to 52.50% at 18°C. These observations suggest a wider genetic variation in terms of the response of the GDRS accessions to lower temperatures compared to the FA mutants. Plants that are exposed to low temperatures usually exhibit reduction in both photosynthesis rate and the green leaf area index, resulting in decreased total aboveground biomass accumulation (Subedi et al., 1998). In the current study, the biomass of the test germplasm that were exposed to 15°C and 18°C were lower compared to those maintained under the control temperature of 30°C. This observation coincides with previous reports on the effects of cold stress on biomass accumulation (Leonardos et al., 2003; Subedi et al., 1998). Interestingly, plants that were exposed to 15°C recorded higher biomass compared to plants exposed to 18°C. The discrepancy between the observed and expected response of the test germplasm to varying low temperatures based on the established effects of cold stress to plant biomass may be attributed to the effects of light intensity in the growth chambers used for the experiments. The recorded light intensity in the growth chambers set at 18°C was lower (10.47:0.15 µmol·s-1·m-2 day:night cycle) compared to the recorded light intensity in the growth chamber set at 15°C (20.07:0.16 µmol·s-1·m-2 day:night cycle). The higher light intensity in the growth chambers set at 15°C may have facilitated a higher photosynthetic rate despite the colder

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Texas Tech University, Ritchel Bueno Gannaban, August 2019 temperature. This is reflective of the accumulated total biomass of the test germplasm at the end of the 15-day period for the 15°C treatment.

Physiological response of cotton seedlings to low temperatures Chlorophyll concentration. The primary function of chlorophyll is to absorb light for energy transformation. When plants are subjected to low temperatures, photosynthetic rate is significantly reduced through photoinhibition (Liu et al., 2013; Glazsman et al., 1990; Wu et al., 2006). In rice for example, low temperature stress has been found to inhibit chlorophyll synthesis and chloroplast formation in the leaves leading to the reduction in the chlorophyll content (Sharma et al., 2005). In the current study, the chlorophyll concentration in the leaves of the seedlings of both the FA mutants and GDRS accessions exposed to cold was relatively lower compared to the control (Table 3.3). Small incremental increases and/or decreases in chlorophyll concentration were observed in the FA mutants and GDRS accessions that have been exposed to 15°C and 18°C for 6-15 days. The initial chlorophyll concentration after 6 days of exposure to cold was relatively low for both FA mutants and GDRS accessions compared to the control. After the 15-day period of the experiment, the average chlorophyll concentration of the FA mutants was 221.00 µmol/m2 leaf at 15°C and 239.20 µmol/m2 leaf at 18°C, which were significantly lower compared to that of the control (358.80 µmol/m2 leaf). Similarly, the GDRS accessions recorded lower average chlorophyll concentrations of 218.20 µmol/m2 at 15°C and 237.20 µmol/m2 at 18°C compared to the control (355.20 µmol/m2). In general, prolonged exposure of the seedlings to 15°C and 18°C decreased chlorophyll concentration, consistent with previous findings on the effects of cold stress on chlorophyll concentration in plants (Sharma et al., 2005). ANOVA results showed that the effects of genotype, temperature, and the interaction between these two factors significantly contributed to the observed variability in the chlorophyll concentration of the test germplasm. Leaf temperature. The leaf temperature of young cotton seedlings at both cold and control treatments was also determined (Table 3.4). The average leaf temperature of the FA mutants at 15°C and 18°C were 26.60°C and 24.50°C, respectively. The average leaf

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Texas Tech University, Ritchel Bueno Gannaban, August 2019 temperature of the GDRS accessions at 15°C and 18°C were 26.20°C and 24.60°C, respectively. In both FA mutants and GDRS accessions, leaf temperature was lower at 18°C than 15°C. The mutants FA 110-9 and FA 303-1, and the GDRS accession SA3781 recorded the lowest reduction in leaf temperature at both cold treatments compared to the control.

Cellular response of cotton seedlings to low temperatures In this study, the relative electrolyte leakage (REL) measured in terms of electrical conductivity, MDA concentration, and free proline content were assayed from the seedlings of six GDRS accessions. These parameters were measured to determine cellular injury caused by cold temperature treatment at the early seedling stage in upland cotton. The six accessions were selected to represent the spectrum of variation for seedling vigor based on the plant height, biomass, and chlorophyll concentration of the 18 accessions that were initially screened. Relative electrolyte leakage. REL is one of the indices that has been used to measure the cellular integrity of plant cells under cold stress. Cold temperatures cause disruptions and damages in the cell membrane resulting in the leakage of solutes (Daw et al., 1973). For the REL analysis only SA-0033, SA-0718, SA-0881, and SA-1766 were included in the 15°C treatment, whereas SA-0033, SA-0718, SA-0881, SA-1766, and SA-3781 were used for the 18°C and 30°C set-up (Figure 3.3). SA-2895 was not included in the analysis due to missing data. An initial decrease in REL values was observed from 0 to 1 day of treatment, after which the values increased up to the 7th day of cold stress treatment (Figure 3.3). The same pattern was observed for the REL of cotton seedlings at the control temperature, although the REL values of the test germplasm maintained at 30°C was generally lower than the observed REL at both 15°C and 18°C. SA-0033 recorded the highest REL (93.02%) after 7 days in 15°C treatment. SA-0718 showed a decreasing REL value from 3 to 7 days of cold stress treatment, as well as in the control set-up. SA-1766 showed a decrease in REL value from 3 to 7 days at 15°C but not at 18°C and 30°C. The rest of the genotypes showed an increasing REL value after 1 to 7 days in the cold treatments as well as in the control set-up.

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Intracellular electrolyte leakage has been widely used as an indicator of the extent of cellular damage in plants due to exposure to low temperature (Steponkus et al., 1990). In the current study, the electrolyte leakage of young cotton seedlings increased after 3 days of exposure to cold temperatures. It is possible that the extent of cellular damage after 1 day of exposure to cold stress is no longer manageable for the seedling to repair up to the third day thus the increase in REL. After 7 days of treatment however, REL values in the test germplasm remained within 80-95%, regardless of the temperature indicating severe cellular damage and possibly cellular deaths. The higher values of REL observed in the control condition (30°C) may be attributed to the fact that leaf samples were taken from the same set of individual plants that has already incurred damages from the start of sampling at 0 day. Proline content. When a plant is exposed to stressful conditions including low temperature stress, it accumulates metabolites, including amino acids. Proline is an amino acid that acts as an excellent osmolyte and therefore plays a highly beneficial role in plants exposed to various abiotic stresses (Hayat et al., 2012). It can act as a metal chelator, an antioxidative defense molecule and/or a signaling molecule (Hayat et al., 2012). Previous studies have shown that plants exposed to stressful environments overproduce proline which acts to maintain cell turgor or osmotic balance. This stabilizes cell membranes, thereby preventing electrolyte leakage. This also brings down the concentrations of reactive oxygen species (ROS) within the normal range, thus preventing oxidative burst in plants (Serraj and Sinclair, 2002; Ashraf and Fooland, 2007; Madan et al., 1995). In many plant species, proline accumulation has been correlated with cold and salinity stress tolerance (Fougère et al., 1991; Petrusa and Winicov, 1997; Naidu et al., 1991). In the present study, a general increase in proline content was observed in the test germplasm after exposure to cold stress (Figure 3.4). In addition, the variable rates of proline accumulation in the test germplasm indicate genetic variation for the trait in response to cold stress. After 3 days at 15°C, proline concentration increased in all genotypes except for SA-0718. SA-3781 recorded the most significant increase in proline content (456.70 µmol/g), whereas SA-0033 showed the lowest proline accumulation (63.30 µmol/g) after 3 days at 15°C. Proline accumulation was higher in SA-0033, SA-

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0718, and SA-1766 at 18°C than 15°C after 3 days of exposure to cold stress. These results of the study coincides with previous reports of proline accumulation in plants in response to cold stress (Hayat et al., 2012). In our result, we observed that after 3 days at 15°C, proline accumulation is lower compared to the 18°C treatment. This may be accounted to the fact that since 15°C is pegged as the physiological zero for cotton, this temperature is already critical for the seedlings to survive. The higher proline accumulation observed in plants maintained at the control temperature (30°C) may be due to the technical errors that were made during the sampling process such as using the same individual plant for the collection of leaf sample at the succeeding time point. It may also be attributed to the fluctuations in the temperature that occurred in the greenhouse during the sampling period around the late fall period (October) in 2018. In using proline as an indicator of stress tolerance, it is important to set the threshold for the basal accumulation of the metabolite especially because proline concentration have been reported to significantly increase even under normal physiological conditions in actively dividing cells, senescing tissue, and in reproductive and desiccating tissue (Kishor and Sreenivasulu, 2014). Malondialdehyde content. MDA is the final product of lipid peroxidation and therefore is an indicator of cell membrane injuries and cell weakening (Fan et al., 2012; Hodges et al., 1991). To determine the extent of cold injury due to increased cellular penetrability, we measured MDA content in cotton seedlings exposed to cold stress. The average MDA contents of SA-0718, SA-1766, and SA-3781 after 1 day at 15°C and 18°C were higher compared to the control (Figure 3.5). The average MDA levels in SA-0033, SA-0718, SA-2895, and SA-3781 were higher after 3 days of exposure at 15°C and 18°C than at 30°C. This result suggests the upregulation of genes involved in lipid peroxidation within 3 days of exposure to cold. In addition, this indicate that 3 days is a critical time point for the upregulation in the expression of such genes. Increase in MDA concentration in the test germplasm under lower temperatures was indicative of the cellular damage accumulated by the plant. The varying responses of each genotype to lower temperatures suggest that each genotype may have different mechanisms for cold tolerance. The higher MDA content in plants exposure to15°C than those exposed to 18°C indicate more cellular damage with exposure to lower

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Texas Tech University, Ritchel Bueno Gannaban, August 2019 temperatures. This means that due to the inability of the plant to cope with colder temperatures, cellular membrane integrity was compromised. In rice, MDA has been used as a parameter to measure cellular damage due to cold stress (Zhang et al., 2014; Kim and Tai, 2011). When plants are continuously subjected to low temperature, they suffer cellular membrane damage because it is the primary site of freezing injury in plants (Hodgson et al., 1991). In our study, the increase in MDA can also be the result of the damage to the plants during the sampling process. Due to the limited number of individuals in our experimental set-up, we sampled the same individual in more than one time point.

Conclusion The results of our study indicate the presence of genetic variation in the morphological, physiological and biochemical responses of the FA mutants and GDRS accessions to cold stress. Although, no correlation between plant height and biomass was established, a clear genotype-specific response to cold temperature was observed among the test germplasm. Based on the morphological assessment conducted to determine the effect of cold stress in the test germplasm, significant differences in the height and biomass were observed among the genotypes. These parameters may potentially be used as phenotypic markers in addition to chlorophyll concentration to evaluate and determine cold tolerance in cotton seedlings because these morpho-physiological traits are direct, physical manifestations of the physiological and biochemical changes occurring within the plant during their exposure to cold stress. In terms of the biochemical assays, we can recommend REL and MDA to be used as indices in determining the degree of cellular membrane damage due to cold stress since these has been utilized in previous screening studies for cold tolerance in other crops. The results of our biochemical assays, especially at the control temperature, were confounded by technical errors committed during tissue sampling and failure to ensure the consistency in the settings of the growth chambers. For future studies that aim to re- confirm the results of this study, it is strongly recommended to address these issues by increasing the number of biological replicates, as well as by using growth chambers with similar configurations. In addition, the control set-up for the experiments were

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Texas Tech University, Ritchel Bueno Gannaban, August 2019 maintained in the greenhouse from early fall when dips in temperature resulted in fluctuations in temperature outside of the 28-30°C range. For future studies, it is also recommended to place control set-ups in growth chambers where temperature is easily maintained. On the basis of morpho-physiological parameters alone used to screen seedling vigor at the early seedling stage of cotton in response to low temperature stress, three potential genotypes namely SA-0033, SA-0718, and SA0881 may be used as potential donors to improve seedling vigor in early planted cotton.

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Table 3.1. List of selected genotypes for seedling vigor analysis. Entry Classification Accession or Line Number no. 1 FA mutant FA101-1 2 FA mutant FA110-9 3 FA mutant FA210-7 4 FA mutant FA303-1 5 FA mutant FA304-1 6 FA mutant FA306-8 7 GDRS SA-0002 8 GDRS SA-0033 9 GDRS SA-0298 10 GDRS SA-0718 11 GDRS SA-0881 12 GDRS SA-1156 13 GDRS SA-1232 14 GDRS SA-1406 15 GDRS SA-1759 16 GDRS SA-1766 17 GDRS SA-2895 18 GDRS SA-3781

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Table 3.2. Mean values of plant height (cm) used to evaluate seedling vigor under cold stress in the FA mutants and GDRS accessions. Temperature 15°C 18°C 30°C Genotype 6d* 9d 12d 15d 6d 9d 12d 15d 6d 9d 12d 15d FA mutants FA101-1 15.07 15.93 15.93 17.35 16.18 17.33 17.90 18.26 21.01 23.57 26.21 28.09 FA110-9 15.17 16.73 16.73 17.77 18.02 19.41 19.56 21.33 25.43 27.03 28.85 31.54 FA210-7 16.82 17.58 17.65 18.66 17.20 18.79 19.31 19.65 25.78ab 28.66ab 30.88 32.43 FA303-1 16.90 17.90 17.66 18.88 19.02 20.46ab 20.83 21.08 26.42a 29.59a 32.03a 33.85a FA304-1 17.46ab 18.50ab 19.00ab 20.25ab 19.06 20.35ab 20.65 20.93 26.10ab 30.73a 33.41a 34.31a FA306-8 17.94ab 19.19a 19.23ab 20.32ab 18.68 19.47 20.12 20.06 23.86 27.92 29.63 31.74 GDRS accessions SA-0002 18.83a 18.05 19.59a 21.24a 19.32 20.55ab 20.30 20.52 22.30 24.66 26.35 26.58 SA-0033 16.02 16.58 16.76 19.39 20.44a 21.87a 22.75a 23.46a 27.13a 29.90a 31.22 33.71a SA-0298 17.67ab 17.49 18.39 19.59 19.93ab 21.90a 23.34a 22.34 22.93 26.67 31.69ab 33.56ab SA-0718 17.87ab 18.77ab 19.12ab 20.41ab 20.51a 20.64ab 22.10ab 22.77 21.51 26.76 29.67 32.55 SA-0881 18.27ab 18.37 19.45ab 20.98a 18.53 19.65 20.45 20.76 22.65 27.70 30.37 33.33ab SA-1156 16.71 17.69 17.68 19.78 17.09 17.54 17.62 17.93 21.58 24.42 26.70 29.07 SA-1232 15.93 14.36 14.04 15.18 17.83 20.74ab 21.42 20.11 21.06 23.01 24.41 24.63 SA-1406 14.12 14.78 14.80 14.91 19.42 20.37ab 20.95 21.34 19.08 21.28 26.01 26.09 SA-1759 15.02 15.32 15.25 16.92 15.92 15.81 16.46 17.32 16.87 21.40 24.07 26.07 SA-1766 14.90 14.78 15.90 16.19 17.02 15.29 16.93 17.08 18.76 22.14 24.40 27.11 SA-2895 11.79 11.76 11.98 14.46 15.58 16.49 16.92 16.92 17.84 21.33 24.73 26.09 SA-3781 11.86 12.01 12.82 13.19 12.13 13.65 13.91 13.98 15.70 16.95 19.40 23.15 *d=day values within a column followed by different letters indicate significant differences at p<0.05

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40.0

15°C 18°C 30°C

35.0

30.0

25.0 * * * * * 20.0

(cm) height Plant 15.0

10.0

5.0

0.0

Genotype

Figure 3.1. Mean values of the plant height (cm) after 15 days of cold treatment used to evaluate seedling vigor under cold stress in the FA mutants and GDRS accessions. * indicate genotypes that showed <25% decrease in plant height relative to the control.

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Texas Tech University, Ritchel Bueno Gannaban, August 2019

25.00

15°C 18°C 30°C

20.00

*

15.00 * * * *

10.00

Biomass Biomass (%)

5.00

0.00

Genotype Figure 3.2. Mean values of the total aboveground biomass (%) used to evaluate seedling vigor under cold stress in the FA mutants and GDRS accessions. * indicate genotypes that showed <25% decrease in total aboveground biomass relative to the control

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Table 3.3. Mean values of chlorophyll concentration (µmol/m2 of leaf) used to evaluate seedling vigor under cold stress in the FA mutants and GDRS accessions. Temperature 15°C 18°C 30°C Genotype 6d* 9d 12d 15d 6d 9d 12d 15d 6d 9d 12d 15d FA mutants FA101-1 194.04 224.88 215.19 206.53 276.96 261.51 260.499 241.82 347.59ab 373.13a 392.56ab 389.85 FA110-9 201.56 206.67 193.34 195.24 227.03 216.55 211.397 207.74 350.72ab 361.67ab 379.79ab 359.48 FA210-7 227.97 234.83 222.10 228.86 227.01 218.28 210.682 211.25 304.94 331.74 364.76 340.13 FA303-1 218.27 278.59ab 257.37 251.16 262.82 266.87 267.847 245.92 317.14 368.60a 406.59ab 368.13 FA304-1 258.38 274.01ab 244.20 226.88 286.98ab 252.59 247.299 244.02 347.19ab 356.40 370.72 349.40 FA306-8 226.72 219.95 212.03 217.46 299.56a 284.13a 258.190 284.58 340.19 332.60 387.22ab 339.70 GDRS accessions SA-0002 231.87 242.00 248.56 238.94 265.51 265.59 274.855 290.73a 371.81a 373.35a 416.03a 414.17ab SA-0033 186.73 198.77 197.71 197.36 198.64 216.02 208.186 223.11 261.09 278.25 274.51 275.21 SA-0298 194.99 201.92 182.51 174.02 224.85 204.46 204.516 213.93 287.23 304.64 343.24 333.86 SA-0718 269.05 282.46ab 257.23 259.05 233.50 235.13 233.851 242.56 279.78 280.49 314.19 307.55 SA-0881 273.82 293.55a 288.26a 297.28a 227.55 221.14 217.820 226.92 318.55 333.68 355.86 356.16 SA-1156 218.00 236.74 226.91 234.12 206.79 210.95 226.193 231.32 348.64ab 359.90 379.43ab 387.66 SA-1232 175.03 173.87 176.41 173.07 184.12 188.26 217.124 244.27 289.02 288.26 313.11 319.56 SA-1406 211.45 212.76 213.89 270.66ab 220.55 232.90 246.112 270.47 366.39ab 357.50 381.29ab 430.63a SA-1759 202.13 239.03 225.39 224.87 263.15 202.02 223.934 225.72 311.98 336.02 343.29 373.37 SA-1766 185.04 186.34 174.99 175.40 220.75 215.52 201.924 207.81 322.91 316.96 329.69 334.63 SA-2895 210.05 226.73 233.44 236.37 210.68 215.29 225.776 240.91 293.33 295.89 323.36 346.02 SA-3781 200.06 188.32 177.64 159.47 219.19 205.76 201.169 228.06 311.72 293.53 347.24 383.15 *d=day values within a column followed by different letters indicate significant differences at p<0.05

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Table 3.4. Mean values of leaf temperature used to evaluate seedling vigor under cold stress in the FA mutants and GDRS accessions. Temperature 15°C 18°C 30°C Genotype 6d* 9d 12d 15d 6d 9d 12d 15d 6d 9d 12d 15d FA mutants FA101-1 25.79 25.10 25.86 28.20ab 25.01a 23.51 26.01b 26.40a 29.300 27.80 38.89 27.59 FA110-9 26.17 25.69 26.04 27.87 24.38b 23.40 26.20a 25.27 30.886 28.66 38.47 28.53 FA210-7 27.24a 25.90 26.62 28.18ab 24.11b 23.24 25.90 25.29 31.286 28.54 38.27 29.06 FA303-1 27.34a 26.22a 26.69 28.37a 23.74 23.07 25.80 24.86 31.443 29.21 38.51 29.59 FA304-1 26.73 26.15ab 26.90ab 27.80 23.46 22.90 25.60 24.96 31.971 28.93 38.66 29.93 FA306-8 26.89 26.41a 26.95ab 26.74 23.11 22.80 25.50 25.04 32.557 29.54 40.82a 30.31 GDRS accessions SA-0002 26.40 26.21a 24.54 26.96 22.70 28.37a 25.23 25.81 30.686 30.97 39.57b 30.39 SA-0033 26.27 25.91 25.77 27.23 22.74 28.05b 25.09 25.91 31.271 31.23 38.24 30.44 SA-0298 26.01 25.60 26.47 27.46 22.37 27.71 25.10 25.06 31.886 31.87a 38.07 30.96 SA-0718 25.73 25.29 26.80 27.37 22.59 27.56 25.03 25.61 31.871 30.94 37.64 30.74 SA-0881 26.07 25.13 26.97ab 27.39 22.50 27.47 24.90 25.17 32.029 31.47b 37.79 31.11ab SA-1156 26.16 24.91 27.08a 27.56 23.24 27.33 24.90 25.24 32.300 31.94a 38.26 31.34a SA-1232 25.27 24.47 25.23 27.06 22.74 23.66 24.47 22.87 31.700 29.20 38.36 28.43 SA-1406 25.75 24.25 25.54 26.93 22.96 23.69 24.24 23.17 30.414 30.10 38.30 29.30 SA-1759 26.26 24.12 25.52 27.20 22.93 23.96 24.24 24.27 30.300 29.73 38.21 29.47 SA-1766 26.44 24.03 25.24 27.56 24.06 23.86 24.27 25.14 30.386 30.26 38.26 29.86 SA-2895 26.82 24.06 25.13 27.48 24.32b 23.67 24.33 26.11ab 30.557 30.70 37.96 30.13 SA-3781 26.97ab 24.28 25.23 27.61 25.05a 23.66 24.40 25.54 30.914 30.60 37.47 30.21 *d=day values within a column followed by different letters indicate significant differences at p<0.05.

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120.00 A )

100.00 µS/cm 80.00 SA0033 SA0718 60.00 SA0881 40.00 SA1766

20.00 0.00 ( leakage Electrolyte 0d 1d 3d 7d Number of days

B 120.00 ) 100.00

µS/cm SA0033

80.00 SA0718

60.00 SA0881 SA1766 40.00 SA3781

20.00

( leakage Electrolyte 0.00 0d 1d 3d 7d Number of days C 120.00 ) 100.00 SA0033 µS/cm 80.00 SA0718 SA0881 60.00 SA1766 40.00 SA3781

20.00

( leakage Electrolyte 0.00

0d 1d 3d 7d Number of days Figure 3.3. Electrolyte leakage of leaves of cotton seedlings under low temperatures (A=15°C and B=18°C) and normal (C=30°C). Each value represents the average ± standard error (±SE) for bulk measurements.

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A 1000.0

800.0

SA0033

DW) 1 600.0 - SA0718

400.0 SA0881 SA1766 200.0 SA2895 SA3781

(µg/g Proline 0.0

-200.0 0d 1d 3d 5d Number of days B 1000.0

800.0 SA0033 1 DW) 1 600.0 - SA0718 400.0 SA0881 SA1766 200.0 SA2895 SA3781

Proline (µg/g Proline 0.0

-200.0 0d 1d 3d 5d Number of days C 1000.0

800.0 SA0033

1 DW) 1 600.0 SA0718 - SA0881 400.0 SA1766 SA2895 200.0 SA3781

(µg/g Proline 0.0

-200.0

0d 1d 3d 5d Number of days Figure 3.4. Proline content of cotton seedling leaves under low temperatures (A=15°C and B=18°C) and normal (C=30°C). Each value represents the average ± standard error (±SE) for three technical replicates.

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A 1300.0

SA0033 800.0 SA0718 SA0881 SA1766 SA2895 300.0 SA3781

DW) (nmol/g MDA

-200.0

0d 1d 3d 5d Number of days

B 1300.0

SA0033 SA0718 800.0 SA0881 SA1766 SA2895 300.0 SA3781

DW) (nmol/g MDA

-200.0 0d 1d 3d 5d Number of days

1300.0 C SA0033 SA0718 800.0 SA0881 SA1766 SA2895 300.0 SA3781

DW) (nmol/g MDA -200.0 0d 1d 3d 5d Number of days

Figure 3.5. Malondialdehyde content of cotton seedling leaves under low temperature stress (A=15°C and B=18°C) and normal (C=30°C). Each value represents the average ± standard error (±SE) for three technical replicates.

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CHAPTER IV COMPARATIVE TRANSCRIPTOME PROFILING OF UPLAND COTTON (Gossypium hirsutum L.) UNDER LOW TEMPERATURE STRESS

Introduction The upland cotton (Gossypium hirsutum L.) is extremely sensitive to cold given its tropical origin. This effectively restricts the geographical distribution, as well as regions where it can be successfully cultivated. Exposure of the cotton plant to low temperatures at the early stages of growth and development can result to arrested growth. This can consequently lead to unequal crop stand in the field and ultimately, to significant losses in yield and reduction in fiber quality (Kittock et al., 1986; Speed et al., 1996; Krzyzanowski and Delouche, 2010). To maximize cotton production in regions where cold snaps are a common occurrence in the beginning and end of the growing season, breeding for cotton cultivars that have tolerance to low temperatures presents the most economical and efficient strategy. Screening of a wide range of germplasm for genetic variation that can be used to improve cold tolerance traits in cotton, as well as identification of potential donors that can be used in breeding programs for cold tolerance improvement constitute the initial steps towards this end. Mapping, identification and functional validation of genes genes/QTLs controlling cold tolerance in cotton will facilitate the precise transfer of such gene across existing cotton cultivars though marker- assisted breeding. Mapping and identification of causative genes or major QTLs that are associated with seedling vigor in cotton at the early seedling stage in response to cold stress can be accomplished by either a forward or reverse genetics approach. Linkage analysis, mapping and positional cloning of genes/major QTLs controlling agronomic traits using primary and secondary populations that have been developed from biparental crosses have been the mainstream approach to date. Several genes/major QTLs regulating yield and yield-related traits, biotic and abiotic stress tolerance and other traits of agronomic importance have been identified and cloned in a wide variety of crops using this approach (Ashikari et al., 2002; Hattori et al., 2009; Miura et al., 2010; Angeles-Shim 2012). While genetic resources that will allow the use of forward genetics approach to map and clone

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Texas Tech University, Ritchel Bueno Gannaban, August 2019 genes/ major QTLs in cotton are available, the limited availability of DNA markers, as well as the allotetraploid nature of the crop tend to complicate mapping of putative genes/major QTLs in the crop. Conversely, identification of causative genes and major QTLs regulating traits of agronomic importance can also be accomplished using a reverse genetics approach via mutation induction and mapping. With the recent and rapid advances in DNA sequencing, whole genome transcriptome profiling is also becoming a popular tool for gene identification and cloning. In recent years, several studies that involve the utilization of transcriptome profile analysis has been conducted to identify many functional traits associated with abiotic stresses (Zhang et al., 2017; Wang et al., 2016; Ito et al., 2006). The transcriptome represents the complete collection of transcripts in a cell at a specific developmental stage (Chen et al., 2014). This provides comprehensive and valuable information on the spatial and temporal expression and regulation of genes throughout the growth of the plant as it responds to environmental stimuli (Jiang et al., 2013). The advancement of sequencing technology has provided a novel method for the analysis of whole genome transcriptome (Morozova and Mara, 2008). In plants, RNA sequencing has accelerated the investigation of the complexity of gene transcription patterns and gene regulation networks (Wang et al., 2010). For example, several transcription factors that are involved in cold acclimation has been discovered through transcriptome analysis. In Arabidopsis, the C- repeat/dehydration-responsive element binding factors (CBFs) have been identified as key regulators that activate the expression of cold-response genes (Jaglo-Ottosen et al., 1998; Kasuga et al., 1999). These factors are also known as dehydration-responsive element binding factor proteins (DREBs). Within this gene family, ZmDREB1A has been identified to enhance freezing tolerance in Arabidopsis. An orthologue of this gene has also been cloned in maize (Qin et al., 2004). The current study aims to establish a comparative transcriptome profile between a cold-susceptible and cold-tolerant genotype of cotton that were grown under the minimum cardinal temperature of 15°C during the early seedling stage. The results of our preliminary analysis will serve as a basis for the identification and cloning of the causative genes/major QTLs that confer seedling vigor in cotton during the early seedling stage.

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Materials and Methods Plant Materials The GDRS accessions SA-0718 and SA-3781, which were identified in Chapter II to be tolerant and susceptible, respectively, to low temperature stress were selected for whole genome transcriptome profiling. Seeds of the selected materials were germinated in 72-round plug trays (6 x 12 configuration; 1.25” cell top diameter; 1.5” cell depth; 0.50” drain diameter) filled with potting mix (Sun Gro 900 Grower Mix) and supplemented with basal fertilizer (14-14-14 Osmocote classic). Twelve plants per genotype were maintained in triplicates under controlled conditions at the Horticultural Gardens of the Department of Plant and Soil Science at Texas Tech University. At the appearance of the first true leaves, two sets of seedlings were transferred to 15°C and 18°C, whereas one set was maintained in the greenhouse (30°C) as control. After 0, 1, 3 and 5 days of cold treatment, seedlings were collected, flash frozen in liquid nitrogen and stored -80°C until RNA extraction.

RNA Extraction Tissue lysis and homogenization. Approximately 1 g of plant tissue was homogenized in liquid nitrogen using a mortar and a pestle, and transferred into a 2 ml tube. Lysis buffer with 2-mercaptoethanol was added to the tissue before mixing the solution for 1 minute. The tubes were centrifuged at 12,000 rpm for 10 minutes and the supernatant was transferred into a clean 2 ml RNase-free tube for purification. RNA purification. RNAs were precipitated by adding 500 µl of 70% ethanol to the supernatant and mixing the solution by inverting the tubes repeatedly. The solution (500 µl at a time) was then transferred to a spin cartridge with a collection tube and centrifuged for 12,000 rpm to a minute. The spin cartridges were washed by adding 700 µl of Wash Buffer I and quick spinning at 12,000 rpm for 15 seconds. The flow-through was discarded and the spin cartridge was re-inserted back into the tube. Wash Buffer II with ethanol (500 µl) was added to the spin cartridge before centrifugation at 12,000 rpm for 15 seconds at room temperature. The flow-through was discarded and the spin cartridge was re-inserted into the collection tube. This washing step was repeated twice. Finally, the spin cartridge was centrifuged at 12,000 rpm for 1 minute to dry the

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Texas Tech University, Ritchel Bueno Gannaban, August 2019 membrane. The collection tube was discarded and the spin cartridge was put into a recovery tube. After 1 minute, a total of 50 µl of RNase-free water was added to the tube and incubated for 2 minutes. The tube was then centrifuged at 12,000 rpm for 2 minutes. The RNAs were quantified using Nanodrop (Thermo Scientific, USA) and stored at - 20°C. All samples were then sent to Novogene (Novogene Corporation Inc., USA) for library construction, quality control and sequencing.

Data Analysis FastQC application was used to evaluate the initial quality of the raw data. Raw reads were filtered to remove adapter sequences and low-quality reads. The clean sequence reads were mapped to the available G. hirsutum (www.cottongen.org) and Arabidobpsis genomes (https://www.arabidopsis.org/) using the HISAT2 algorithm. Gene expression levels were estimated by counting the reads that map to the genes or exons of the reference genomes. The higher the count, the higher the expression level of the genes. The read counts are proportional to the real gene expression level, as well as to the gene length and the sequencing depth. Normalization of reads to allow comparative analysis of gene expression between samples and time points was carried out using RPKM (Reads Per Kilo bases per Million reads) (Morozova and Marra, 2008). The RPKM method is able to eliminate the influence of different gene length and sequencing discrepancy on the calculation of gene expression. Therefore, the calculated gene expression can be directly used for comparing the difference of gene expression among samples. In the current study, the measure of the transcript abundance and the expression of each transcript was expressed in fragments per kilobase pair of exon model per million fragments mapped)

(FPKM) (Trapnel et al., 2010). The subsequent list of DEGs was filtered using Log2FC ≥1 (upregulated genes) or ≤1 (downregulated genes).

Gene enrichment analysis GOseq which is based on Wallenius non-central hyper geometric distribution (Young et al., 2010) was used for the gene enrichment analysis of the differentially expressed genes (DEGs). Gene ontology analysis was carried out using Gene Ontology (GO, http://www.geneontology.org/).

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Identification of differentially expressed genes associated with cold stress Using the FPKM data, a stringent selection criteria was used to identify the differentially expressed genes in SA-0718 and SA-3781 that are associated with cold stress at the early seedling stage. The genes were selected if there is more than two-fold change in the FPKM values of SA-3871 and in the SA-0718 genotype for each time point for both 15 and 30 °C. From the 70487 genes identified, only 878 were selected for further analysis based on the established criteria. Heat maps of the DEG were generated using the RStudio program (RStudio Inc, 2015).

Results and Discussion RNA sequencing of different samples and data analysis The plant tissues were collected from both SA-0718 and SA-3781. Based on the results of screening for vigor at the early seedling stage under cold stress, we selected SA-0718 as the “tolerant” genotype and SA-3781 as the “susceptible” genotype. To establish a comparative gene expression profile of SA-0718 and SA-3781 in response to cold stress at the early seedling stage, RNA was extracted from the aboveground tissues of both genotypes at 0, 1, 3, and 5 days of cold stress treatment. A summary of the sequencing assembly that provides the number of raw, clean and mapped reads generated from SA-0718 and SA-3781 is presented in Table 4.1. The total number of mapped reads ranged from 85.91 to 97.34%, whereas the GC content ranged from 44.72 to 45.84%. The mapping data shows that >85% clean reads were successfully mapped to the cotton genome. The high range of genome coverage of the RNA-seq data indicate the reliability of the differentially expressed genes (DEGs) data for further bioinformatics analysis.

Identification of DEGs in response to cold stress in SA-0718 and SA-3781 An overall analysis of the differentially expressed genes in SA-0718 and SA-3781 in response to cold stress at the early seedling stage is summarized in Figure 4.1A. In general, there was a higher up-regulation of gene expression after one day at 15°C. At the third day of cold treatment, a significant decrease in the number of up-regulated and down-regulated genes was observed in both SA-0718 and SA-3781. By

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Texas Tech University, Ritchel Bueno Gannaban, August 2019 the fifth day of cold tress treatment, higher levels of down-regulation of gene expression was observed. Briefly, a total of 2865 (1185 up-regulated and 1650 down-regulated), 958 (507 up-regulated and 478 down-regulated), 3168 (1987 up-regulated and 1181 down- regulated) DEGs were identified after 1, 3, and 5 days of cold stress treatments, respectively. Additionally, overlaps in the expression of DEGs were also analyzed (Figure 4.1B). At 1 day of cold stress treatment, the total number of shared DEGs for both genotypes was 38475, with 4898 and 3004 genes being identified as unique to SA-3781 and SA-0718, respectively. After 3 days of cold treatment, the total number of shared DEGs was 40453. From this, 2170 were unique to SA-3781 and 4285 were unique to SA- 0718. After 5 days of cold stress treatment, the total number of shared DEGs was 39432, with 3619 being unique to SA-0718 and 4103 to SA-3781.

Patterns of gene expression in response to cold stress in SA-0718 and SA-3781 A total of 878 genes were selected based on the criteria established to screen the differentially expressed genes in both SA-0178 and SA-3781 (Figure 4.2). Based on the FPKM values, heat maps were generated to provide a visual representation of the differential gene expression of the susceptible and tolerant genotypes. The generated heat maps showed significant differences in the temporal expression of various genes in SA- 0178 and SA-3781 subjected to cold stress at the early seedling stage. SA-0718 showed a distinct, overall increase in gene expression with exposure to cold stress from 0 to 5 days. Conversely, SA-3781, showed a generally slight increase in gene expression after 1 day of cold treatment that was not sustained until the fifth day of cold stress. The observed upregulation of several genes in the tolerant genotype, SA-0718, after only a day of cold stress treatment indicate the importance of the activation of a network of genes at the onset of the stress. This suggests that the timing of the expression of genes may be the critical factor in defining the tolerance or susceptibility of a genotype to cold stress. The sustained upregulation of gene expression in relation to stress duration in SA-0718 compared to the susceptible genotype indicate the importance of this pattern of gene expression in conferring cold tolerance in cotton. A similar pattern of rapid

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Texas Tech University, Ritchel Bueno Gannaban, August 2019 upregulation of gene expression was also observed in a cold-tolerant genotype of Jatropha curcas L. when exposed to 12°C for 12, 24, and 48 hours (Wang et al., 2013).

Gene ontology analysis of DEGs in cotton subjected to cold stress The attributes of the 878 genes that were differentially expressed under cold stress in both SA-0718 and SA-3781 were characterized by GO analysis. The 878 differentially expressed genes were functionally assigned to three categories namely molecular function, cellular component, and biological processes (Figure 4.7). GO category for 225 genes were unknown. A total of 310 DEGs grouped under molecular function. Of the GO terms, DNA- dependent transcriptional regulation (87 genes), protein tyrosinase kinase activity (43 genes), electron carrier activity (24 genes) and metal iron-binding activity (24 genes) were significantly overrepresented. Regulatory genes such as transcription factors and kinases have been associated with abiotic stress response in a wide range of crop species. Most transcription factors are early stress-responsive genes and control the expression of a network of downstream target genes (Wang et al., 2013: Hoang et al. 2017). The identification of different families of transcription factors that are overexpressed in SA- 0718 to confer cold tolerance coincides with previous reports on the role of transcription factors in abiotic stress tolerance. Under the cellular component category, a total of 38 DEGs clustered together. The significant expression of genes corresponding to cell membrane in response to cold stress suggests that the regulation of osmotic potential may play an important role in cold stress. In addition, DEGs with GO terms nucleus and ribosome were also identified. This indicate increased transcriptional and translational activities in the cell. These results suggest that possible changes in the intercellular level might have occurred during cold stress at the early seedling stage in cotton. In turn, this allowed the re-organization of the resources outside the cell membrane towards the increase in the osmotic potential and cold-resistant substance in the inner cell (Mangelsen et al., 2011). Lastly, a total of 305 DEGs grouped under the biological process category. Of the GO terms, protein binding (70 genes), chromatin binding (34 genes) and metabolic process (27 genes) were highly expressed, indicating the involvement of signaling

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Texas Tech University, Ritchel Bueno Gannaban, August 2019 processes and inhibition of cellular activity in cotton seedlings under cold stress at the early seedling stage (Wang et al., 2013).

Differential gene expression patterns of Gossypium hirsutum L. in response to cold stress

Based on the patterns of differentially expressed genes in both SA-0718 and SA- 3781 after 0, 1, 3 and 5 days of cold treatment, four gene clusters (A, B, C, and D) were selected for further dissection of gene expression patterns in response to cold (Figure 4.3). In general, clusters A, B, C and D exhibited upregulation in gene expression at the onset of cold treatment. Closer examination of the gene groups, showed different patterns of gene expressions. These patterns include a high degree of gene expression after 1 day of exposure to cold stress followed by a gradual decrease in expression until 5 days of cold treatment. The other pattern of gene expression starts from a low level of gene expression that gradually increased with prolonged exposure to stress. By zooming into the four groups, we were able to identify genes that exhibited these patterns of expression. The genes Gh_Sca005862G01, Gh_A08G1059, and Gh_D01G1989 in the tolerant genotype SA-0718 exhibited high expression at the onset of cold stress that gradually decreased with prolonged cold exposure up to the fifth day (Figure 4.4). Whereas in the susceptible genotype there is a very low level of gene expression from the onset of stress and is sustained until the 5 day exposure to cold. The orthologues of the genes Gh_Sca005862G01, Gh_A08G1059, and Gh_D01G1989 in Arabidopsis encode proteins that are involved in calcium-mediated signaling (Yuan et al., 2017), as well as epigenetic mechanisms that are involved in chromatin binding in response to cold stress (Park et al., 2018). The transient temporal expression of these genes in SA-0718 particularly at the first day of cold stress indicate their role in conferring cold tolerance in cotton at the onset of cold stress during the early seedling stage. Conversely, the gene expression of the Gh_A02G1062 was upregulated after 1 day of cold stress and then gradually decreased with prolonged exposure to cold stress in the susceptible genotype (Figure 4.4). In the tolerant genotype, this gene exhibited an opposite pattern of expression. This gene is a transcription factor that plays a role in regulating the response

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Texas Tech University, Ritchel Bueno Gannaban, August 2019 of Arabidopsis to heat stress (Guo et al., 2014). The upregulation of this gene in SA-0718 suggests its role not only in regulating heat stress response but also cold stress response in cotton. The genes Gh_D04G1218 and Gh_A04G0742 (Figure 4.5) were highly expressed in the tolerant genotype after 5 days of cold stress while the expression of the same gene in the susceptible genotype remained unchanged throughout the different time points. This gene is an orthologue of a RING/FYVE/PHD zinc finger superfamily protein in Arabidopsis. This protein family binds to zinc ion domains of proteins found in the plasma membrane. In rice, the overexpression of a zinc-finger protein gene confers tolerance to abiotic stresses such as cold, dehydration and salt stress (Mukhopadhyay et al., 2003). Upregulation of this gene in cotton seedlings suggest a similar role of the gene in stress response in cotton. The genes Gh_D03G0541 and Gh_D06G2277 are both involved in hydrolase activity. Although these two genes are categorized to have the same function, the gene expression between these two are genes are different. Gh_D03G0541 is transiently expressed at the onset of stress but gradually decreases as the stress is prolonged (up to 5 days) while Gh_D06G2277 showed no increase in gene expression until the third day exposure to cold stress. By the 5 day, the gene expression is again similar to the 1 day. In general, hydrolases are enzymes that are responsible in catalyzing hydrolysis reactions. In a study by Liu et al. (2014), the expression of the gene IbMas (contains maspardin domain and belongs to α/β-hydrolase superfamily) in sweet potato plants was upregulated within 12 hour exposure to salt stress. This upregulation enhanced the salt tolerance of the sweet potato plants. Their findings suggest that there was an upregulation of osmotic balance that protected the integrity of the cellular membrane. The results described above show the varying response to cold stress at the early seedling stage of the susceptible and tolerant genotypes in cotton. The observed upregulation of several transcription factors at the onset of cold stress are consistent with previous reports on the early stress response of transcription factors in response to cold stress at the early seedling stage.

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Conclusion In this study, we used RNA-seq to establish a comparative transcriptome profile between two cotton genotypes with differential response to cold stress at the early seedling stage. Based on a 2-fold differential expression of genes between the susceptible and tolerant genotypes used in the study, we identified 878 genes that were differentially expressed in response to cold. GO analysis grouped these genes based on their involvement in molecular function, biological process, and cellular component. In general, the tolerant SA-0718 exhibited a temporal upregulation of gene expression, whereas the susceptible SA-3178 showed a very slight increase in gene expression at the onset of cold stress that was not sustained up to 5 days of cold treatment. In some cases, the tolerant genotype exhibited an increase in gene expression at the onset of stress, which gradually decreased with prolonged cold exposure up to 5 days. Genes that encode regulatory proteins such as transcription factors and kinases were overrepresented in transcriptome profile of the tolerant genotype, SA-0718. While this study covers only the general patterns of gene expression between a susceptible and tolerant genotype of cotton, the identification of the 878 DEGs can serve as the starting point for a more detailed interrogation of genes and gene networks that regulate cold tolerance in upland cotton.

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Table 4.1. An overview of RNA sequence data obtained from the aboveground tissue of SA-0718 and SA-3781 after cold stress treatment. Samples Raw reads Clean reads Total mapped Q value (30%) GC % SA-0718_30_0d 49819802 48544886 41705875 (85.91%) 94.60 45.35 SA-3781_30_0d 58597888 57973990 50974673 (94.04%) 95.41 45.67 SA-0718_15_1d 59879618 59365012 53457032 (90.05%) 95.46 45.84 SA-0718_15_3d 40241288 38942592 36784966 (94.46%) 95.00 44.72 SA-0718_15_5d 35973330 34165450 31245905 (91.45%) 95.25 45.18 SA-3781_15_1d 55729902 55034418 53572286 (97.34%) 95.19 44.71 SA-3781_15_3d 55409740 53649794 51144680 (95.33%) 95.51 45.24 SA-3781_15_5d 67393972 66896302 61728803 (92.28%) 94.49 45.21

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Figure 4.1. (A) The overall differentially expressed genes (DEGs) retrieved from SA-3781 compared to SA-0718 at different day of intervals after cold treatment. (B) Venn diagram illustrating the differentially expressed genes at three different days of interval after cold treatment.

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Figure 4.2. Heat maps of the 878 differentially expressed genes at different time points (0d, 1d, 3d, and 5d) as observed in the susceptible (SA-3781) and the tolerant cotton genotype (SA-0718) under cold stress at the early seedling stage. Heat maps were generated based on FPKM values. The color key shows the intensity of each gene expression based on the FPKM value. The more intense the color the higher the FPKM value. d=day

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A B

C D

Figure 4.3. Temporal expression of genes in four gene groups that were upregulated at the onset of cold stress in SA-0718. The different clusters were denoted as A, B, C, and D.

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Figure 4.4. Temporal expression of genes in cluster A that were upregulated under cold stress in SA-0718.

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Figure 4.5. Temporal expression of genes in cluster C that were upregulated under cold stress in SA-0718.

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Figure 4.6. Temporal expression of genes in cluster D that were upregulated under cold stress in SA-0718.

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Figure 4.7. GO classification of the 878 differentially expressed genes of cotton seedlings under cold stress at the early seedling stage.

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Appendix A. Description and ontology of differentially expressed genes identified in cluster A. Gene ID Gene description Gene ontology (GO) Gh_D10G0417 LEM3 (ligand-effect modulator 3) family protein membrane Gh_A04G0542 growth-regulating factor 3 - Gh_A13G0143 AP2/B3-like transcriptional factor family protein DNA binding Gh_D11G2780 eukaryotic translation initiation factor 3A protein binding Gh_D08G0698 Seed maturation protein - Gh_A02G1062 heat shock transcription factor B3 nucleus Gh_A13G2102 laccase 11 copper ion binding Gh_A12G1429 semialdehyde dehydrogenase family protein oxidoreductase activity Basic-leucine zipper (bZIP) transcription factor family Gh_A01G0888 protein regulation of transcription Gh_Sca092001G01 glucan synthase-like 12 1,3-beta-D-glucan synthase activity Gh_A10G1095 Carbohydrate-binding X8 domain superfamily protein - Gh_Sca005862G01 protein serine/threonine kinases protein tyrosine kinase activity Gh_A08G1059 myb domain protein 2 chromatin binding Gh_D09G0477 LOB domain-containing protein 42 - Gh_D01G1989 NAD(P)-binding Rossmann-fold superfamily protein metabolic process Gh_A08G0245 Protein kinase superfamily protein protein tyrosine kinase activity Gh_D11G0974 ACC synthase 1 biosynthetic process Gh_A11G0781 BON association protein 2 protein binding Gh_D06G1554 Ankyrin repeat family protein protein binding Gh_D07G1136 dsRNA-binding protein 2 double-stranded RNA binding Gh_D01G0306 protein serine/threonine kinases protein tyrosine kinase activity

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Appendix A. Continued Gh_D10G1222 PEP1 receptor 2 protein binding Gh_A05G2517 cytochrome P450, family 71, subfamily A, polypeptide 21 electron carrier activity Gh_A09G0943 plant U-box 24 ubiquitin ligase complex Gh_A06G0143 phospholipase A 2A lipid metabolic process Leucine-rich repeat receptor-like protein kinase family Gh_D02G0277 protein protein binding Gh_A07G0489 cytochrome P450, family 71, subfamily B, polypeptide 23 electron carrier activity Gh_A09G0174 Wall-associated kinase family protein protein tyrosine kinase activity Gh_D10G0276 glutamate receptor 2.7 membrane Gh_D09G2193 plant U-box 9 - Gh_A12G2449 ARM repeat superfamily protein ubiquitin ligase complex Gh_D06G1466 Leucine-rich repeat transmembrane protein kinase protein tyrosine kinase activity Gh_D09G0160 wall associated kinase-like 4 protein tyrosine kinase activity Gh_A04G0392 lipoxygenase 1 oxidoreductase activity Gh_D01G0361 protein serine/threonine kinases protein tyrosine kinase activity Gh_A02G1814 Rhamnogalacturonate lyase family protein -

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Appendix B. Description and ontology of differentially expressed genes identified in cluster B. Gene ID Gene description Gene ontology (GO) Gh_D05G1118 Major facilitator superfamily protein - Gh_A13G1628 protein serine/threonine kinases ATP binding Gh_A02G0724 wall-associated kinase 2 ATP binding Gh_D09G0677 protein serine/threonine kinases protein tyrosine kinase activity Gh_D05G0168 Lactoylglutathione lyase - Gh_D06G0311 Leucine-rich repeat protein kinase family protein protein tyrosine kinase activity Leucine-rich repeat receptor-like protein kinase Gh_A09G0196 family protein protein tyrosine kinase activity Gh_D06G1340 alternative NAD(P)H dehydrogenase 1 flavin adenine dinucleotide binding Gh_D01G0188 Armadillo/beta-catenin-like repeat family protein protein binding Gh_Sca004939G01 BON association protein 2 protein binding Gh_A11G0426 heat shock factor 4 nucleus Gh_A06G0135 Cupredoxin superfamily protein electron carrier activity Gh_D09G0676 protein serine/threonine kinases protein tyrosine kinase activity Gh_D01G2289 TOXICOS EN LEVADURA 2 protein binding Gh_D01G0927 SBP (S-ribonuclease binding protein) family protein - Gh_D07G0070 Phosphorylase superfamily protein nucleoside metabolic process Gh_A02G0727 wall-associated kinase 2 ATP binding Gh_D08G1980 Ankyrin repeat family protein protein binding Gh_D01G0263 protein serine/threonine kinases protein tyrosine kinase activity Gh_D09G1675 Protein kinase superfamily protein protein tyrosine kinase activity Gh_A05G2515 Major facilitator superfamily protein transmembrane transport 89

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Appendix B. Continued Gh_D04G1544 protein serine/threonine kinases protein tyrosine kinase activity Gh_A07G1744 cytochrome p450 79a2 electron carrier activity Gh_Sca084831G01 NAD(P)-binding Rossmann-fold superfamily protein - GroES-like zinc-binding dehydrogenase family Gh_A08G0960 protein zinc ion binding Gh_D06G0063 protein serine/threonine kinases protein tyrosine kinase activity NAC (No Apical Meristem) domain transcriptional Gh_D06G1546 regulator superfamily protein DNA binding Gh_D02G1555 multidrug resistance-associated protein 3 transport Gh_D05G1925 phospholipase A 2A lipid metabolic process Gh_D12G0126 Protein kinase superfamily protein protein tyrosine kinase activity mannosyl-glycoprotein endo-beta-N- Gh_D10G2391 Glycosyl hydrolase family 85 acetylglucosaminidase activity Gh_A05G1383 terpene synthase 04 magnesium ion binding Gh_D12G1050 BON association protein 2 protein binding Late embryogenesis abundant (LEA) hydroxyproline- Gh_A08G0979 rich glycoprotein family - Gh_A13G0955 O-acyltransferase (WSD1-like) family protein diacylglycerol O-acyltransferase activity Gh_D05G0318 UDP-Glycosyltransferase superfamily protein metabolic process Gh_A02G0472 cullin 1 ubiquitin-dependent protein catabolic process Gh_D04G0198 Kunitz family trypsin and protease inhibitor protein endopeptidase inhibitor activity Gh_D07G0088 Phosphoglycerate mutase family protein - Gh_A07G0078 Phosphoglycerate mutase family protein - Heavy metal transport/detoxification superfamily Gh_D05G1476 protein - 90

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Appendix B. Continued Gh_A06G1149 Calcium-binding EF-hand family protein calcium ion binding Gh_A12G2121 WRKY family transcription factor regulation of transcription Gh_D03G1589 RING/U-box superfamily protein - Gh_A07G1743 nudix hydrolase homolog 2 hydrolase activity Gh_D05G0630 Eukaryotic aspartyl protease family protein proteolysis Gh_A04G0581 UDP-glucosyl transferase 73D1 metabolic process basic helix-loop-helix (bHLH) DNA-binding Gh_A05G0019 superfamily protein Protein dimerization activity Gh_D13G1658 UDP-Glycosyltransferase superfamily protein metabolic process Gh_D05G1348 GDSL-motif lipase 2 hydrolase activity Gh_D01G1493 WRKY DNA-binding protein 4 regulation of transcription Gh_A05G3759 Phosphoglycerate mutase family protein metabolic process Gh_D06G1894 metacaspase 9 proteolysis Gh_A13G0427 protein serine/threonine kinases protein tyrosine kinase activity Gh_D10G1951 wall-associated kinase 2 ATP binding Gh_D07G2119 Ubiquitin-conjugating enzyme/RWD-like protein cellular protein modification process Disease resistance protein (CC-NBS-LRR class) Gh_D01G0350 family ADP binding Gh_A13G1207 terpene synthase 21 magnesium ion binding cytochrome P450, family 81, subfamily D, Gh_D12G0534 polypeptide 8 electron carrier activity Gh_A05G0484 WRKY DNA-binding protein 4 regulation of transcription Gh_A05G1332 ethylene responsive element binding factor 2 regulation of transcription Gh_D02G0213 FAD-binding Berberine family protein oxidoreductase activity

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Appendix B. Continued Gh_D09G1008 LOB domain-containing protein 1 - Gh_D06G2361 Subtilisin-like serine endopeptidase family protein serine-type endopeptidase activity Gh_D09G1021 protein kinase 2A protein tyrosine kinase activity Gh_D12G1207 WRKY family transcription factor regulation of transcription Gh_A12G1788 F-box family protein protein binding Gh_D09G1355 receptor serine/threonine kinase, putative - NAC (No Apical Meristem) domain transcriptional Gh_A06G1226 regulator superfamily protein DNA binding

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Appendix C. Description and ontology of differentially expressed genes identified in cluster C. Gene ID Gene description Gene ontology (GO) Calcium-dependent lipid-binding (CaLB domain) family Gh_A12G0518 protein protein binding Gh_A13G0497 Calcium-binding EF-hand family protein calcium ion binding Gh_A05G2928 NAC domain containing protein 83 DNA binding Gh_D08G0480 Leucine-rich repeat transmembrane protein kinase protein binding Gh_A12G2192 AP2/B3-like transcriptional factor family protein DNA binding Gh_D13G0644 B-box type zinc finger family protein zinc ion binding Gh_D04G1218 RING/FYVE/PHD zinc finger superfamily protein zinc ion binding Gh_D12G2372 AP2/B3-like transcriptional factor family protein DNA binding Gh_Sca069862G01 glutamine synthase clone R1 nitrogen compound metabolic process Gh_A12G2229 pseudo-response regulator 5 protein binding Gh_D08G1130 Transducin/WD40 repeat-like superfamily protein protein binding Gh_D03G0926 UDP-glucosyl transferase 89B1 metabolic process NAC (No Apical Meristem) domain transcriptional regulator Gh_D04G0293 superfamily protein DNA binding Gh_A02G0836 C2H2 type zinc finger transcription factor family metal ion binding Gh_A10G2163 Staphylococcal nuclease homologue nucleic acid binding Gh_D04G1136 exocyst subunit exo70 family protein H4 exocyst Gh_A12G0319 NAC domain containing protein 36 DNA binding

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Appendix C. Continued Gh_A05G3499 WRKY DNA-binding protein 70 regulation of transcription Gh_A11G2508 multidrug resistance-associated protein 3 transport Gh_A09G0177 wall associated kinase-like 7 polysaccharide binding 2-oxoglutarate (2OG) and Fe(II)-dependent oxygenase Gh_D13G2160 superfamily protein oxidoreductase activity Gh_D09G0471 Cytochrome P450 superfamily protein electron carrier activity Gh_A13G1348 UDP-Glycosyltransferase superfamily protein metabolic process Gh_A04G0742 RING/FYVE/PHD zinc finger superfamily protein zinc ion binding Gh_A11G0755 AGAMOUS-like 20 nucleus Gh_D12G0673 PLC-like phosphodiesterases superfamily protein - Gh_A11G2875 myb-like transcription factor family protein chromatin binding Gh_A05G3760 Phosphoglycerate mutase family protein - Gh_A07G0317 indole-3-acetic acid inducible 29 nucleus Gh_A09G1483 nodulin MtN21 /EamA-like transporter family protein membrane

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Appendix D. Description and ontology of differentially expressed genes identified in cluster D. Gene ID Gene description Gene ontology (GO) Gh_Sca004922G01 Plant basic secretory protein (BSP) family protein - Late embryogenesis abundant (LEA) hydroxyproline-rich Gh_D05G2611 glycoprotein family - Gh_D01G1963 terpene synthase 21 magnesium ion binding Gh_A11G2625 O-methyltransferase family protein O-methyltransferase activity Late embryogenesis abundant (LEA) hydroxyproline-rich Gh_A05G2344 glycoprotein family - Gh_D05G2793 Major facilitator superfamily protein transmembrane transport Gh_A05G3171 Calcium-binding EF-hand family protein calcium ion binding Gh_D03G0541 Glycosyl hydrolases family 32 protein hydrolase activity Gh_D08G1604 don-glucosyltransferase 1 metabolic process Gh_D08G2702 O-methyltransferase 1 O-methyltransferase activity Gh_A12G1558 NAD(P)-binding Rossmann-fold superfamily protein - Gh_A03G1439 B-box type zinc finger family protein zinc ion binding Gh_D09G2258 Ca2+-binding protein 1 calcium ion binding Gh_D02G1885 B-box type zinc finger family protein zinc ion binding Gh_D05G1668 plastocyanin-like domain-containing protein electron carrier activity Gh_D07G1197 Cytochrome P450 superfamily protein electron carrier activity Gh_A11G3105 Calcium-binding EF-hand family protein calcium ion binding

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Appendix D. Continued Gh_D13G0168 high mobility group B1 - Gh_A01G0521 Dynein light chain type 1 family protein microtubule associated complex Gh_D04G1444 B-box type zinc finger family protein zinc ion binding Gh_A04G0927 B-box type zinc finger family protein zinc ion binding Gh_A09G2051 Ca2+-binding protein 1 calcium ion binding Gh_D06G2277 beta-1,3-glucanase 3 hydrolase activity

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Appendix E. Description and ontology of the 878 differentially expressed genes in SA-0718 and SA-3781 in response to cold stress. Gene ID Gene description Gene ontology AP2/B3-like transcriptional factor family Gh_D09G1287 protein DNA binding AP2/B3-like transcriptional factor family Gh_A13G0143 protein DNA binding AP2/B3-like transcriptional factor family Gh_A12G2192 protein DNA binding AP2/B3-like transcriptional factor family Gh_D12G2372 protein DNA binding Basic-leucine zipper (bZIP) transcription factor Gh_D09G1317 family protein regulation of transcription Basic-leucine zipper (bZIP) transcription factor Gh_A05G1580 family protein regulation of transcription Basic-leucine zipper (bZIP) transcription factor Gh_A01G0888 family protein regulation of transcription Basic-leucine zipper (bZIP) transcription factor Gh_D01G0926 family protein regulation of transcription C2H2 type zinc finger transcription factor Gh_Sca005232G01 family metal ion binding C2H2 type zinc finger transcription factor Gh_A02G0836 family metal ion binding Gh_Sca183484G01 E2F transcription factor 3 - GATA type zinc finger transcription factor Gh_D05G0434 family protein regulation of transcription Gh_D06G0976 global transcription factor group B1 -

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Appendix E. Continued Gh_A02G1062 heat shock transcription factor B3 nucleus K-box region and MADS-box transcription Gh_D02G1311 factor family protein nucleus K-box region and MADS-box transcription Gh_A10G2221 factor family protein nucleus Gh_D09G2413 myb-like transcription factor family protein chromatin binding Gh_A11G2875 myb-like transcription factor family protein chromatin binding Plant-specific transcription factor YABBY Gh_D01G1535 family protein - Plant-specific transcription factor YABBY Gh_D07G1125 family protein - Squamosa promoter-binding protein-like (SBP Gh_A12G1380 domain) transcription factor family protein nucleus Gh_D12G1207 WRKY family transcription factor regulation of transcription Gh_A12G2121 WRKY family transcription factor regulation of transcription Adenine nucleotide alpha hydrolases-like Gh_A06G1730 superfamily protein response to stress Gh_A06G1557 alpha/beta-Hydrolases superfamily protein - Gh_Sca008601G02 alpha/beta-Hydrolases superfamily protein triglyceride lipase activity Gh_A13G1831 alpha/beta-Hydrolases superfamily protein - Gh_A05G1054 alpha/beta-Hydrolases superfamily protein hydrolase activity Gh_A10G0681 alpha/beta-Hydrolases superfamily protein hydrolase activity Gh_D05G1929 alpha/beta-Hydrolases superfamily protein hydrolase activity

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Appendix E. Continued Gh_D10G0705 alpha/beta-Hydrolases superfamily protein hydrolase activity Gh_A13G1897 alpha/beta-Hydrolases superfamily protein catalytic activity Gh_D09G1068 alpha/beta-Hydrolases superfamily protein triglyceride lipase activity GDSL-like Lipase/Acylhydrolase superfamily Gh_D11G1558 protein hydrolase activity GDSL-like Lipase/Acylhydrolase superfamily Gh_D01G0673 protein hydrolase activity GDSL-like Lipase/Acylhydrolase superfamily Gh_D11G3414 protein hydrolase activity GDSL-like Lipase/Acylhydrolase superfamily Gh_D13G1086 protein hydrolase activity Gh_A05G0191 glycosyl hydrolase family 17 protein hydrolase activity mannosyl-glycoprotein endo-beta-N- Gh_D10G2391 Glycosyl hydrolase family 85 acetylglucosaminidase activity Gh_A07G1049 Glycosyl hydrolase family protein hydrolase activity Gh_D02G0831 Glycosyl hydrolase family protein hydrolase activity Gh_A05G0579 Glycosyl hydrolase superfamily protein hydrolase activity Gh_A09G0635 Glycosyl hydrolase superfamily protein hydrolase activity Gh_D03G0541 Glycosyl hydrolases family 32 protein hydrolase activity HAD-superfamily hydrolase, subfamily IG, 5\'- Gh_A01G1297 nucleotidase -

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Appendix E. Continued Haloacid dehalogenase-like hydrolase (HAD) Gh_D09G1741 superfamily protein trehalose biosynthetic proces Haloacid dehalogenase-like hydrolase (HAD) Gh_A12G1994 superfamily protein trehalose biosynthetic proces N-terminal nucleophile aminohydrolases (Ntn Gh_D02G0898 hydrolases) superfamily protein hydrolase activity Gh_D04G1377 nudix hydrolase homolog 13 hydrolase activity Gh_A07G1743 nudix hydrolase homolog 2 hydrolase activity P-loop containing nucleoside triphosphate Gh_D12G1282 hydrolases superfamily protein - P-loop containing nucleoside triphosphate Gh_Sca065421G01 hydrolases superfamily protein protein binding P-loop containing nucleoside triphosphate Gh_A10G0380 hydrolases superfamily protein ATP binding Gh_A05G1275 xyloglucan endotransglucosylase/hydrolase 16 apoplast 2-oxoglutarate (2OG) and Fe(II)-dependent Gh_A08G0954 oxygenase superfamily protein oxidoreductase activity 2-oxoglutarate (2OG) and Fe(II)-dependent Gh_D13G2160 oxygenase superfamily protein oxidoreductase activity 2-oxoglutarate (2OG) and Fe(II)-dependent Gh_A13G1789 oxygenase superfamily protein oxidoreductase activity 2-oxoglutarate (2OG) and Fe(II)-dependent Gh_A13G2342 oxygenase superfamily protein oxidoreductase activity Gh_D08G2223 lipoxygenase 1 oxidoreductase activity

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Appendix E. Continued Gh_A04G0392 lipoxygenase 1 oxidoreductase activity Gh_D06G2176 lipoxygenase 3 oxidoreductase activity Gh_D10G0402 nine-cis-epoxycarotenoid dioxygenase 5 - Gh_A10G0387 nine-cis-epoxycarotenoid dioxygenase 5 - Gh_D05G1407 nine-cis-epoxycarotenoid dioxygenase 5 - Gh_Sca038382G01 Oxoglutarate/iron-dependent oxygenase - PLAT/LH2 domain-containing lipoxygenase Gh_A08G0440 family protein oxidoreductase activity Gh_A01G1842 Putative lysine decarboxylase family protein - Gh_D05G1486 pyruvate decarboxylase-2 magnesium ion binding Basic helix-loop-helix (bHLH) DNA-binding Gh_A08G0962 family protein protein dimerization activity basic helix-loop-helix (bHLH) DNA-binding Gh_D13G0759 superfamily protein protein dimerization activity basic helix-loop-helix (bHLH) DNA-binding Gh_D04G1777 superfamily protein protein dimerization activity basic helix-loop-helix (bHLH) DNA-binding Gh_D05G1949 superfamily protein protein dimerization activity basic helix-loop-helix (bHLH) DNA-binding Gh_A05G0019 superfamily protein protein dimerization activity basic helix-loop-helix (bHLH) DNA-binding Gh_A12G1276 superfamily protein protein dimerization activity

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Appendix E. Continued basic helix-loop-helix (bHLH) DNA-binding Gh_A11G1120 superfamily protein protein dimerization activity basic helix-loop-helix (bHLH) DNA-binding Gh_D12G1398 superfamily protein protein dimerization activity basic helix-loop-helix (bHLH) DNA-binding Gh_D02G1245 superfamily protein protein dimerization activity basic helix-loop-helix (bHLH) DNA-binding Gh_A03G0864 superfamily protein protein dimerization activity basic helix-loop-helix (bHLH) DNA-binding Gh_D13G2183 superfamily protein protein dimerization activity basic helix-loop-helix (bHLH) DNA-binding Gh_D13G1892 superfamily protein protein dimerization activity Dof-type zinc finger DNA-binding family Gh_A10G0348 protein DNA binding Dof-type zinc finger DNA-binding family Gh_A02G1397 protein DNA binding Dof-type zinc finger DNA-binding family Gh_D02G0928 protein DNA binding HMG-box (high mobility group) DNA-binding Gh_A13G0042 family protein - Integrase-type DNA-binding superfamily Gh_A07G0221 protein regulation of transcription

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Appendix E. Continued Integrase-type DNA-binding superfamily Gh_A11G1288 protein regulation of transcription Integrase-type DNA-binding superfamily Gh_D12G1464 protein regulation of transcription Integrase-type DNA-binding superfamily Gh_A10G1108 protein regulation of transcription Integrase-type DNA-binding superfamily Gh_A07G0305 protein regulation of transcription Integrase-type DNA-binding superfamily Gh_A11G0697 protein regulation of transcription Integrase-type DNA-binding superfamily Gh_D07G0362 protein regulation of transcription Integrase-type DNA-binding superfamily Gh_D11G0813 protein regulation of transcription Integrase-type DNA-binding superfamily Gh_A07G1477 protein regulation of transcription Integrase-type DNA-binding superfamily Gh_A06G0838 protein regulation of transcription Predicted AT-hook DNA-binding family Gh_D09G1891 protein - Gh_A11G1811 WRKY DNA-binding protein 28 regulation of transcription Gh_A05G0484 WRKY DNA-binding protein 4 regulation of transcription Gh_D01G1493 WRKY DNA-binding protein 4 regulation of transcription Gh_A08G1540 WRKY DNA-binding protein 4 regulation of transcription

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Appendix E. Continued Gh_A12G1488 WRKY DNA-binding protein 57 regulation of transcription Gh_D12G1253 WRKY DNA-binding protein 65 regulation of transcription Gh_A05G3499 WRKY DNA-binding protein 70 regulation of transcription Gh_A09G0501 5\'-3\' exonuclease family protein DNA binding Gh_Sca060164G01 AAA-type ATPase family protein - Gh_A05G3331 ABC transporter family protein ATP binding Gh_A09G1496 ABC-2 type transporter family protein membrane Gh_Sca094925G01 ABC-2 type transporter family protein membrane Gh_D11G0974 ACC synthase 1 biosynthetic process Gh_Sca102885G01 ACD1-like - Gh_D08G2763 ACD1-like 2 iron, 2 sulfur cluster binding Gh_D13G1687 acetyl CoA:(Z)-3-hexen-1-ol acetyltransferase transferase activity Gh_A05G2040 ACT domain repeat 3 amino acid binding Gh_A05G1098 actin binding protein family - Gh_A09G0405 actin depolymerizing factor 7 actin binding Gh_D05G3180 activator of spomin::LUC2 protein binding Gh_D10G1422 acyl activating enzyme 5 metabolic process Gh_D06G0858 acyl carrier protein 5 phosphopantetheine Acyl-CoA N-acyltransferases (NAT) Gh_D02G0384 superfamily protein N-acetyltransferas Gh_D08G0922 ADP-ribosylation factor 3 intracellular prot Gh_D11G0883 AGAMOUS-like 20 nucleus

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Appendix E. Continued Gh_A11G0755 AGAMOUS-like 20 nucleus Gh_D07G0780 AGAMOUS-like 8 nucleus Gh_D13G1912 AGAMOUS-like 80 DNA binding Gh_D13G1541 AGAMOUS-like 80 DNA binding Gh_D02G1989 AGD2-like defense response protein 1 lysine biosynthetic process via diaminopimelate Gh_D06G1340 alternative NAD(P)H dehydrogenase 1 flavin adenine dinucleotide binding Aluminium induced protein with YGL and Gh_A08G0626 LRDR motifs - Gh_A05G2016 amino acid permease 3 - Gh_D11G1444 aminoalcoholphosphotransferase 1 membrane Gh_A11G1238 ammonium transporter 1;4 membrane AMP-dependent synthetase and ligase family Gh_D11G3478 protein metabolic process AMP-dependent synthetase and ligase family Gh_A09G1371 protein metabolic process Gh_A08G2234 Ankyrin repeat family protein protein binding Gh_D06G1554 Ankyrin repeat family protein protein binding Gh_D08G1980 Ankyrin repeat family protein protein binding Gh_D12G0977 Ankyrin repeat family protein - Gh_D04G0670 Ankyrin repeat family protein protein binding

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Appendix E. Continued Gh_D12G0974 Ankyrin repeat family protein - ankyrin repeat family protein / regulator of chromosome condensation (RCC1) family Gh_A13G1459 protein - Ankyrin repeat family protein with DHHC zinc Gh_A08G1648 finger domain protein binding Gh_D10G1854 appr-1-p processing enzyme family protein Gh_D01G1559 ARABIDILLO-2 ubiquitin-dependent protein catabolic process Gh_A13G2120 Arabidopsis Hop2 homolog - Arabidopsis thaliana protein of unknown Gh_D11G0926 function (DUF821) - Gh_D07G1394 arabinogalactan protein 20 - Gh_A12G2449 ARM repeat superfamily protein ubiquitin ligase complex Armadillo/beta-catenin-like repeat family Gh_D01G0188 protein protein binding Gh_D08G2094 ascorbate peroxidase 1 response to oxidative stress Gh_D02G0659 aspartic proteinase A1 proteolysis Gh_A09G2445 AT-hook motif nuclear-localized protein 1 - Gh_D11G3002 ATP synthase D chain, mitochondrial - Gh_D05G2765 Auxin efflux carrier family protein transmembrane transport Gh_A05G3374 Auxin efflux carrier family protein transmembrane transport Gh_A04G0280 Auxin-responsive GH3 family protein -

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Appendix E. Continued Gh_A12G1233 B-box type zinc finger family protein zinc ion binding Gh_D13G0644 B-box type zinc finger family protein zinc ion binding Gh_A04G0927 B-box type zinc finger family protein zinc ion binding Gh_D04G1444 B-box type zinc finger family protein zinc ion binding Gh_A03G1439 B-box type zinc finger family protein zinc ion binding Gh_D02G1885 B-box type zinc finger family protein zinc ion binding Gh_D02G1898 B-box type zinc finger family protein zinc ion binding Gh_D11G1798 B-box zinc finger family protein zinc ion binding BED zinc finger ;hAT family dimerisation Gh_D02G0141 domain DNA binding Gh_D06G2277 beta-1,3-glucanase 3 hydrolase activity Bifunctional inhibitor/lipid-transfer protein/seed Gh_A05G2906 storage 2S albumin superfamily protein lipid binding Bifunctional inhibitor/lipid-transfer protein/seed Gh_A11G1143 storage 2S albumin superfamily protein lipid binding Gh_D05G1888 Biotin/lipoate A/B protein ligase family octanoyltransferase activity Gh_D05G0140 blue-copper-binding protein electron carrier activity Gh_Sca004939G01 BON association protein 2 protein binding Gh_A11G0781 BON association protein 2 protein binding Gh_D12G1050 BON association protein 2 protein binding Gh_D07G1822 brassinosteroid-6-oxidase 2 electron carrier activity

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Appendix E. Continued Gh_A06G0671 BREVIS RADIX-like 4 - C2H2 and C2HC zinc fingers superfamily Gh_D12G1435 protein metal ion binding C2H2 and C2HC zinc fingers superfamily Gh_D03G1634 protein metal ion binding Gh_A12G0859 C2H2-like zinc finger protein metal ion binding Gh_A09G2051 Ca2+-binding protein 1 calcium ion binding Gh_D09G2258 Ca2+-binding protein 1 calcium ion binding Gh_A12G2144 calcineurin B-like protein 8 calcium ion binding Gh_A11G2050 calcium exchanger 7 transmembrane transport Gh_D08G0675 Calcium-binding EF hand family protein calcium ion binding Gh_A05G2960 Calcium-binding EF hand family protein calcium ion binding Gh_D03G1652 Calcium-binding EF hand family protein calcium ion binding Gh_D06G1321 Calcium-binding EF-hand family protein calcium ion binding Gh_A01G0532 Calcium-binding EF-hand family protein calcium ion binding Gh_A06G1149 Calcium-binding EF-hand family protein calcium ion binding Gh_D01G0545 Calcium-binding EF-hand family protein calcium ion binding Gh_D08G0253 Calcium-binding EF-hand family protein calcium ion binding Gh_A05G3171 Calcium-binding EF-hand family protein calcium ion binding Gh_A05G2022 Calcium-binding EF-hand family protein calcium ion binding Gh_A13G0497 Calcium-binding EF-hand family protein calcium ion binding

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Appendix E. Continued Gh_A11G3105 Calcium-binding EF-hand family protein calcium ion binding Calcium-dependent ARF-type GTPase Gh_A05G2413 activating protein family regulation of ARFGTPase activity Calcium-dependent lipid-binding (CaLB Gh_A12G0518 domain) family protein protein binding Calcium-dependent lipid-binding (CaLB Gh_A01G0076 domain) family protein protein binding Gh_A09G0574 Caleosin-related family protein Gh_D06G0077 calmodulin-like 41 calcium ion binding Gh_D03G0651 calreticulin 3 protein folding cAMP-regulated phosphoprotein 19-related Gh_A10G1515 protein - CAP (Cysteine-rich secretory proteins, Antigen 5, and Pathogenesis-related 1 protein) Gh_D01G1757 superfamily protein extracellular region CAP (Cysteine-rich secretory proteins, Antigen 5, and Pathogenesis-related 1 protein) Gh_D08G0917 superfamily protein extracellular region Carbohydrate-binding X8 domain superfamily Gh_A10G1095 protein - Carbohydrate-binding X8 domain superfamily Gh_A09G0754 protein - Gh_A05G3597 CCT motif family protein protein binding Gh_D12G1384 cell wall protein precursor, putative -

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Appendix E. Continued Gh_A12G1255 cell wall protein precursor, putative - Gh_A11G2103 cellulose synthase like E1 cellulose synthase (UDP-forming) activity Gh_D12G0299 Chalcone and stilbene synthase family protein transferase activity Gh_Sca006253G01 Chalcone and stilbene synthase family protein transferase activity Gh_D04G1926 Chalcone-flavanone isomerase family protein intramolecular lyase activity Gh_A01G1665 Chaperone DnaJ-domain superfamily protein - Gh_A04G0402 chaperonin 60 beta cellular protein metabolic process Gh_D08G2015 chlorophyllase 1 chlorophyllase activity Gh_Sca005798G01 chlororespiratory reduction 3 - Gh_D06G1084 chorismate mutase 2 chorismate metabolic process Gh_D08G0851 chromomethylase 1 - CHY-type/CTCHY-type/RING-type Zinc finger Gh_D03G1332 protein protein binding Gh_Sca069039G01 cinnamate-4-hydroxylase electron carrier activity Class I glutamine amidotransferase-like Gh_D08G0958 superfamily protein - Gh_Sca065531G01 CLIP-associated protein - COBRA-like extracellular glycosyl- Gh_D12G1819 phosphatidyl inositol-anchored protein family cellulose microfibril organization Gh_D12G0298 COBRA-like protein 2 precursor cellulose microfibril organization Concanavalin A-like lectin protein kinase Gh_A10G1990 family protein carbohydrate binding

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Appendix E. Continued Concanavalin A-like lectin protein kinase Gh_D04G1538 family protein carbohydrate binding Gh_A06G1351 copper ion binding;electron carriers electron carrier activity Gh_D06G1685 copper ion binding;electron carriers electron carrier activity Gh_A12G2080 Copper transport protein family - Gh_D12G2254 Copper transport protein family metal ion binding Gh_D11G0857 Cornichon family protein membrane crooked neck protein, putative / cell cycle Gh_D07G1683 protein, putative RNA processing Gh_A09G1941 CSL zinc finger domain-containing protein - Gh_A02G0472 cullin 1 ubiquitin-dependent protein catabolic proces Gh_A06G0135 Cupredoxin superfamily protein electron carrier activity Gh_D02G0457 cyanase cyanate metabolicprocess Gh_A05G1890 Cyclin D2;1 nucleus Gh_D13G0652 Cyclin D6;1 nucleus Gh_A13G0493 Cyclin D6;1 nucleus Gh_A06G1232 Cyclin/Brf1-like TBP-binding protein nucleus Cyclin-dependent kinase inhibitor family Gh_A03G1676 protein nucleus Cyclin-dependent kinase inhibitor family Gh_D13G0384 protein nucleus Cyclin-dependent kinase inhibitor family Gh_D10G0930 protein nucleus

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Appendix E. Continued Gh_D04G1904 cycling DOF factor 3 DNA binding Gh_D01G0235 Cyclopropane-fatty-acyl-phospholipid synthase lipid biosynthetic process Gh_A01G0189 Cyclopropane-fatty-acyl-phospholipid synthase lipid biosynthetic process Gh_A11G3170 CYS, MET, PRO, and GLY protein 2 ubiquitin ligase complex Cystathionine beta-synthase (CBS) family Gh_A06G1116 protein - Cystathionine beta-synthase (CBS) family Gh_D05G1543 protein protein binding Gh_D05G3275 Cysteine proteinases superfamily protein cysteine-type peptidase activity Gh_D05G3284 Cysteine proteinases superfamily protein cysteine-type peptidase activity Gh_A04G0364 Cysteine proteinases superfamily protein cysteine-type peptidase activity Gh_A04G0366 Cysteine proteinases superfamily protein cysteine-type peptidase activity Gh_D05G3279 Cysteine proteinases superfamily protein cysteine-type peptidase activity Gh_D10G1467 Cysteine proteinases superfamily protein cysteine-type peptidase activity Gh_D12G0080 Cysteine proteinases superfamily protein - Cysteine/Histidine-rich C1 domain family Gh_A12G0102 protein protein-disulfidereductase activity Cytidine/deoxycytidylate deaminase family Gh_A11G2314 protein hydrolase activity Gh_D02G0891 cytochrome B5 isoform D heme binding Gh_A02G0841 cytochrome B5 isoform D heme binding Cytochrome b561/ferric reductase Gh_D07G1854 transmembrane protein family integral to membrane

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Appendix E. Continued Gh_A01G1156 cytochrome C biogenesis 452 - Gh_A07G1744 cytochrome p450 79a2 electron carrier activity Gh_D09G0471 Cytochrome P450 superfamily protein electron carrier activity Gh_D07G1197 Cytochrome P450 superfamily protein electron carrier activity cytochrome P450, family 706, subfamily A, Gh_D05G3537 polypeptide 7 electron carrier activity cytochrome P450, family 71, subfamily A, Gh_A05G2517 polypeptide 21 electron carrier activity cytochrome P450, family 71, subfamily B, Gh_A07G0489 polypeptide 23 electron carrier activity cytochrome P450, family 76, subfamily C, Gh_D12G2035 polypeptide 1 electron carrier activity cytochrome P450, family 76, subfamily C, Gh_A09G1730 polypeptide 2 electron carrier activity cytochrome P450, family 76, subfamily C, Gh_D12G2034 polypeptide 4 electron carrier activity cytochrome P450, family 76, subfamily C, Gh_A12G1863 polypeptide 4 electron carrier activity cytochrome P450, family 81, subfamily D, Gh_A02G0844 polypeptide 8 electron carrier activity cytochrome P450, family 81, subfamily D, Gh_D12G0534 polypeptide 8 electron carrier activity

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Appendix E. Continued cytochrome P450, family 82, subfamily C, Gh_A05G1708 polypeptide 4 electron carrier activity cytochrome P450, family 82, subfamily C, Gh_D05G1895 polypeptide 4 electron carrier activity cytochrome P450, family 82, subfamily G, Gh_A07G0841 polypeptide 1 electron carrier activity cytochrome P450, family 86, subfamily A, Gh_A01G0715 polypeptide 1 protein kinase regulator activity cytochrome P450, family 87, subfamily A, Gh_D13G0938 polypeptide 6 electron carrier activity cytochrome P450, family 94, subfamily C, Gh_A09G0894 polypeptide 1 electron carrier activity Gh_D09G1132 cytokinin oxidase/dehydrogenase 1 cytokinin dehydrogenase activity Gh_D04G1300 Cytosol aminopeptidase family protein proteolysis Gh_A12G1789 DERLIN-1 - Gh_A07G1275 dihydroflavonol 4-reductase cellular metabolic process Gh_D06G0041 dihydroflavonol 4-reductase cellular metabolic process Disease resistance protein (CC-NBS-LRR class) Gh_D01G0350 family ADP binding Disease resistance-responsive (dirigent-like Gh_D03G0709 protein) family protein - Gh_A12G2509 DNA glycosylase superfamily protein base-excision repair Gh_D03G0666 DNA-directed DNA polymerases -

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Appendix E. Continued DNAJ heat shock N-terminal domain- Gh_A04G1062 containing protein - Gh_D04G1149 DNAse I-like superfamily protein phosphatidylinositol dephosphorylation Gh_A04G0683 DNAse I-like superfamily protein phosphatidylinositol dephosphorylation Gh_D08G1604 don-glucosyltransferase 1 metabolic process Gh_D08G1450 downstream target of AGL15 2 - Gh_A08G1167 downstream target of AGL15 2 - Gh_D10G0629 downstream target of AGL15-4 - Gh_D08G0661 DREB2A-interacting protein 2 metal ion binding Gh_D07G1136 dsRNA-binding protein 2 double-stranded RNA binding dual specificity protein phosphatase (DsPTP1) Gh_A05G2393 family protein protein tyrosine Duplicated homeodomain-like superfamily Gh_Sca005143G02 protein chromatin binding Gh_D11G3275 DVL family protein Gh_A13G0045 Dynamin related protein 4C GTP binding Gh_Sca005608G01 Dynamin related protein 4C GTP binding Gh_A11G2563 Dynein light chain type 1 family protein microtubule associated complex Gh_D01G2155 Dynein light chain type 1 family protein microtubule associated complex Gh_D05G3487 Dynein light chain type 1 family protein microtubule associated complex

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Appendix E. Continued Gh_A01G0521 Dynein light chain type 1 family protein microtubule associated complex Gh_D01G0534 Dynein light chain type 1 family protein microtubule associated complex DZC (Disease resistance/zinc finger/chromosome condensation-like region) Gh_A05G3520 domain containing protein - Gh_A10G1384 EF hand calcium-binding protein family calcium ion binding Gh_A09G0681 endonuclease 4 DNA catabolic process Gh_A10G0254 ERF domain protein 12 regulation of transcription Gh_A05G1332 ethylene responsive element binding factor 2 regulation of transcription Gh_D08G1801 ethylene responsive element binding factor 5 regulation of transcription Gh_D11G0803 ethylene-responsive element binding factor 13 regulation of transcription Gh_D05G0627 Eukaryotic aspartyl protease family protein proteolysis Gh_D05G0630 Eukaryotic aspartyl protease family protein proteolysis Eukaryotic protein of unknown function Gh_A04G0525 (DUF914) transport Gh_D11G2780 eukaryotic translation initiation factor 3A protein binding Gh_A02G1327 eukaryotic translation initiation factor 3A eukaryotic translation initiation factor 3 complex Gh_D04G1136 exocyst subunit exo70 family protein H4 exocyst Gh_D05G1397 EXORDIUM like 1 - Gh_D13G0786 expansin A10 plant-type cell wall organization Gh_D02G0213 FAD-binding Berberine family protein oxidoreductase activity Gh_D10G0067 FAD-binding Berberine family protein oxidoreductase activity

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Appendix E. Continued Gh_D05G3655 FAD-linked oxidases family protein UDP-N-acetylmuramate dehydrogenase activity Gh_D12G2165 Family of unknown function (DUF577) - Gh_D08G0544 FASCICLIN-like arabinoogalactan 7 - Gh_A08G1739 fatA acyl-ACP thioesterase thiolester hydrolase activity Gh_A01G0611 fatty acid desaturase A - Gh_D01G0627 fatty acid desaturase A - Gh_D09G0904 fatty acid reductase 4 fatty-acyl-CoA reductase (alcohol-forming) activity Gh_A12G1306 fatty acyl-ACP thioesterases B thiolester hydrolase activity Gh_A02G1654 F-box family protein protein binding Gh_A05G1443 F-box family protein - Gh_A12G1788 F-box family protein protein binding Gh_A07G1728 F-box family protein protein binding F-box family protein with a domain of unknown Gh_A02G0544 function (DUF295) - Gh_A02G1541 F-box protein-related - Gh_D06G0299 F-BOX WITH WD-40 2 - FKBP-like peptidyl-prolyl cis-trans isomerase Gh_A01G0742 family protein protein folding FKBP-like peptidyl-prolyl cis-trans isomerase Gh_D01G0762 family protein protein folding Gh_D04G1957 flavonol synthase 1 oxidoreductase activity, acting on paired donors

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Appendix E. Continued Gh_A07G0685 formyltransferase, putative biosynthetic process Gh_D09G0190 Galactose mutarotase-like superfamily protein isomerase activity Galactose oxidase/kelch repeat superfamily Gh_D11G2030 protein - Gh_A08G0649 gamma histone variant H2AX nucleus Gh_A13G2093 G-box binding factor 6 regulation of transcription Gh_D05G1348 GDSL-motif lipase 2 hydrolase activity Gh_D05G1349 GDSL-motif lipase 5 hydrolase activity Gh_D01G0642 geranylgeranyl reductase isoprenoid biosynthetic process Gh_A01G0586 germin-like protein 5 nutrient reservoir activity Gh_D12G1147 GHMP kinase family protein isoprenoid biosynthetic process Gh_D13G0261 gibberellin 2-oxidase 8 oxidoreductase activity Gh_D02G1357 gibberellin 2-oxidase 8 oxidoreductase activity Gh_D03G0239 Gibberellin-regulated family protein - Gh_Sca092001G01 glucan synthase-like 12 1,3-beta-D-glucan synthase activity Gh_A05G2796 glucosyl transferase family 8 transferase activity Gh_D10G0276 glutamate receptor 2.7 membrane Gh_D07G0774 glutamine dumper 4 - Gh_A03G0645 glutamine dumper 4 - Gh_Sca069862G01 glutamine synthase clone R1 nitrogen compound metabolic process Gh_D08G1174 Glutaredoxin family protein protein disulfide oxidoreductase activity Gh_D04G1332 glutathione S-transferase TAU 18 protein binding

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Appendix E. Continued Gh_D07G1780 glutathione S-transferase TAU 28 protein binding Gh_D02G0323 glutathione S-transferase tau 7 protein binding Gh_D05G1799 glycine-rich protein - Gh_D04G0711 golgin candidate 1 - Gh_A11G0470 GRAM domain family protein - GroES-like zinc-binding dehydrogenase family Gh_A08G0960 protein zinc ion binding Gh_A04G0542 growth-regulating factor 3 Gh_A11G0426 heat shock factor 4 nucleus Heavy metal transport/detoxification Gh_D07G1640 superfamily protein metal ion binding Heavy metal transport/detoxification Gh_A05G1306 superfamily protein metal ion binding Heavy metal transport/detoxification Gh_A13G2272 superfamily protein metal ion binding Heavy metal transport/detoxification Gh_D05G1476 superfamily protein - Heavy metal transport/detoxification Gh_A03G2159 superfamily protein metal ion binding Heavy metal transport/detoxification Gh_A11G1367 superfamily protein metal ion binding

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Appendix E. Continued Heavy metal transport/detoxification Gh_D07G0769 superfamily protein metal ion binding Heavy metal transport/detoxification Gh_D11G1515 superfamily protein metal ion binding Heavy metal transport/detoxification Gh_D05G1685 superfamily protein metal ion binding Heavy metal transport/detoxification Gh_D09G1375 superfamily protein metal ion binding Gh_D12G1248 heme binding heme binding Gh_A09G0778 hexokinase 2 ATP binding Gh_A07G2041 high affinity nitrate transporter 2.7 transmembrane transport Gh_D13G0168 high mobility group B1 - Gh_D12G1336 high-mobility group box 6 - Gh_A11G0396 histidine kinase 1 membrane Gh_D05G0831 histidine-containing phosphotransfer factor 5 - Gh_A09G1290 histidine-containing phosphotransfer factor 5 phosphorelay signal transduction system Gh_D06G1234 histone acetyltransferase of the CBP family 1 nucleus Gh_D10G1475 histone acetyltransferase of the MYST family 1 nucleus Gh_D11G3254 histone deacetylase 2C metal ion binding Gh_A08G2543 histone H2A 12 nucleus Gh_D08G0035 histone H2A 12 nucleus Gh_D08G0034 histone H2A 12 nucleus

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Appendix E. Continued Gh_D10G0466 Histone superfamily protein nucleus Gh_D10G0981 Histone superfamily protein DNA binding Gh_D13G2384 Histone superfamily protein nucleus Gh_A09G2488 Histone superfamily protein nucleus Gh_A13G1985 Histone superfamily protein nucleus Gh_A09G2489 Histone superfamily protein nucleus Gh_A08G2548 Histone superfamily protein nucleus Gh_D08G0037 Histone superfamily protein nucleus Gh_D03G1234 Histone superfamily protein nucleus Gh_D10G0770 homeobox protein 5 nucleus Gh_A08G1369 Homeodomain-like superfamily protein chromatin binding Gh_D01G1355 Homeodomain-like superfamily protein - Gh_D10G1313 Homeodomain-like superfamily protein - Gh_D13G0783 Homeodomain-like superfamily protein chromatin binding Gh_A11G0869 Homeodomain-like superfamily protein chromatin binding Gh_A10G1269 homolog of carrot EP3-3 chitinase chitin binding Gh_A09G2323 homolog of carrot EP3-3 chitinase chitin binding Gh_D09G2020 homolog of carrot EP3-3 chitinase chitin binding Gh_A07G0859 HSP20-like chaperones superfamily protein - Gh_D05G2717 HSP20-like chaperones superfamily protein Gh_D07G2347 HXXXD-type acyl-transferase family protein transferase activity

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Appendix E. Continued Gh_A05G0991 HXXXD-type acyl-transferase family protein transferase activity Gh_Sca033040G01 HXXXD-type acyl-transferase family protein transferase activity Gh_D05G1108 HXXXD-type acyl-transferase family protein transferase activity Gh_D13G1689 HXXXD-type acyl-transferase family protein transferase activity Gh_A05G3763 hydroxyproline-rich glycoprotein family protein - Gh_D02G0228 indole-3-acetate beta-D-glucosyltransferase metabolic process Gh_D07G0374 indole-3-acetic acid inducible 29 nucleus Gh_D05G0138 indole-3-acetic acid inducible 29 nucleus Gh_A07G0317 indole-3-acetic acid inducible 29 nucleus Gh_D11G1104 indole-3-acetic acid inducible 30 nucleus Gh_D08G1355 indole-3-acetic acid inducible 30 nucleus Gh_A09G2330 Integrin-linked protein kinase family protein tyrosine kinase activity Gh_D09G1129 IQ-domain 12 protein binding iron-sulfur cluster binding;electron carriers;4 Gh_A09G0449 iron, 4 sulfur cluster binding iron-sulfur cluster binding Gh_D08G0730 isopentenyltransferase 3 ATP binding Gh_A09G0741 jasmonate-zim-domain protein 8 protein binding Gh_A05G1642 jasmonic acid carboxyl methyltransferase methyltransferase activity Gh_D07G0446 KAR-UP oxidoreductase 1 oxidoreductase activity Gh_A06G1067 kinases;protein kinases protein tyrosine kinase activity

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Appendix E. Continued Kunitz family trypsin and protease inhibitor Gh_D04G0198 protein endopeptidase inhibitor activity Gh_A13G2102 laccase 11 copper ion binding Lactoylglutathione lyase / glyoxalase I family Gh_D05G0168 protein - Late embryogenesis abundant (LEA) Gh_D12G1562 hydroxyproline-rich glycoprotein family - Late embryogenesis abundant (LEA) Gh_D03G0692 hydroxyproline-rich glycoprotein family - Late embryogenesis abundant (LEA) Gh_A08G0979 hydroxyproline-rich glycoprotein family - Late embryogenesis abundant (LEA) Gh_D11G0979 hydroxyproline-rich glycoprotein family - Late embryogenesis abundant (LEA) Gh_A05G2344 hydroxyproline-rich glycoprotein family - Late embryogenesis abundant (LEA) Gh_D05G2611 hydroxyproline-rich glycoprotein family - Late embryogenesis abundant (LEA) Gh_D01G0583 hydroxyproline-rich glycoprotein family - Gh_D12G0668 Late embryogenesis abundant protein, group 2 - Gh_D08G0138 Lateral root primordium (LRP) protein-related -

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Appendix E. Continued LEM3 (ligand-effect modulator 3) family Gh_D10G0417 protein / CDC50 family protein membrane Leucine-rich receptor-like protein kinase family Gh_A03G1651 protein protein binding Leucine-rich receptor-like protein kinase family Gh_A01G0074 protein - Leucine-rich receptor-like protein kinase family Gh_D13G0421 protein protein binding Gh_D01G1011 Leucine-rich repeat (LRR) family protein Leucine-rich repeat protein kinase family Gh_D06G0311 protein protein tyrosine kinase activity Leucine-rich repeat receptor-like protein kinase Gh_A09G0182 family protein protein tyrosine kinase activity Leucine-rich repeat receptor-like protein kinase Gh_A09G0196 family protein protein tyrosine kinase activity Leucine-rich repeat receptor-like protein kinase Gh_D02G0277 family protein protein binding Leucine-rich repeat receptor-like protein kinase Gh_D01G2332 family protein protein binding Leucine-rich repeat receptor-like protein kinase Gh_D09G0167 family protein protein tyrosine kinase activity Leucine-rich repeat transmembrane protein Gh_A12G0746 kinase protein tyrosine kinase activity

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Appendix E. Continued Leucine-rich repeat transmembrane protein Gh_D06G1466 kinase protein tyrosine kinase activity Leucine-rich repeat transmembrane protein Gh_D06G2019 kinase protein binding Leucine-rich repeat transmembrane protein Gh_A01G0330 kinase protein tyrosine kinase activity Leucine-rich repeat transmembrane protein Gh_D08G0480 kinase protein binding Leucine-rich repeat transmembrane protein Gh_A05G0354 kinase protein binding Leucine-rich repeat transmembrane protein Gh_D10G0462 kinase family protein protein tyrosine kinase activity Gh_D09G2050 lipid transfer protein 1 lipid binding Gh_A10G2324 lipid transfer protein 3 lipid binding Gh_D07G0141 LJRHL1-like 3 regulation of transcription Gh_D12G0149 LMBR1-like membrane protein - Gh_D09G1008 LOB domain-containing protein 1 - Gh_A11G0741 LOB domain-containing protein 19 - Gh_D09G0477 LOB domain-containing protein 42 - LORELEI-LIKE-GPI ANCHORED PROTEIN Gh_D02G1833 3 - Low temperature and salt responsive protein Gh_A07G1199 family integral to membrane

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Appendix E. Continued Gh_A05G3248 low-molecular-weight cysteine-rich 68 defense response LRR and NB-ARC domains-containing disease Gh_D11G2949 resistance protein ADP binding LRR and NB-ARC domains-containing disease Gh_D08G1575 resistance protein protein binding Gh_Sca032584G01 Major facilitator superfamily protein membrane Gh_A10G1661 Major facilitator superfamily protein membrane Gh_A05G2515 Major facilitator superfamily protein transmembrane transport Gh_D07G0431 Major facilitator superfamily protein membrane Gh_D10G2073 Major facilitator superfamily protein transmembrane transport Gh_D05G2793 Major facilitator superfamily protein transmembrane transport Gh_D05G1118 Major facilitator superfamily protein - Gh_A09G1006 Major facilitator superfamily protein membrane Gh_A05G0377 Major facilitator superfamily protein membrane Gh_A03G1877 maternal effect embryo arrest 9 MBOAT (membrane bound O-acyl transferase) Gh_A07G0001 family protein Gh_D06G1894 metacaspase 9 proteolysis Gh_D06G2394 metacaspase 9 proteolysis Gh_A03G0578 microtubule end binding protein EB1A protein binding Gh_D05G3618 microtubule-associated proteins 65-1 microtubule cytoskeleton organization

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Appendix E. Continued Gh_D09G1669 Mitochondrial substrate carrier family protein - mitogen-activated protein kinase kinase kinase Gh_D03G0213 19 protein tyrosine kinase activity mitogen-activated protein kinase kinase kinase Gh_D02G0745 3 protein tyrosine kinase activity mitogen-activated protein kinase kinase kinase Gh_A02G0700 3 protein tyrosine kinase activity mitogen-activated protein kinase kinase kinase Gh_D04G0447 7 ATP binding Gh_D05G0090 mitotic-like cyclin 3B from Arabidopsis nucleus Gh_D01G1269 MLP-like protein 423 defense response Gh_D05G0124 monogalactosyldiacylglycerol synthase 2 carbohydrate binding Gh_D02G1555 multidrug resistance-associated protein 3 transport Gh_A11G2508 multidrug resistance-associated protein 3 transport Gh_A12G1504 myb domain protein 106 chromatin binding Gh_A09G0645 myb domain protein 108 chromatin binding Gh_D08G1187 myb domain protein 111 chromatin binding Gh_A08G2408 myb domain protein 111 chromatin binding Gh_D12G1237 myb domain protein 112 chromatin binding Gh_A10G1002 myb domain protein 14 chromatin binding Gh_A12G2615 myb domain protein 2 chromatin binding Gh_A08G1059 myb domain protein 2 chromatin binding

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Appendix E. Continued Gh_D05G1297 myb domain protein 26 chromatin binding Gh_A07G0140 myb domain protein 3 chromatin binding Gh_A08G1106 myb domain protein 3 chromatin binding Gh_A13G2030 myb domain protein 36 chromatin binding Gh_D02G2244 myb domain protein 4 chromatin binding Gh_D07G0168 myb domain protein 4 chromatin binding Gh_A01G1265 myb domain protein 4 chromatin binding Gh_A13G1994 myb domain protein 48 chromatin binding Gh_A05G3650 myb domain protein 4r1 chromatin binding Gh_Sca008577G01 myb domain protein 4r1 chromatin binding Gh_A05G1167 myb domain protein 4r1 chromatin binding Gh_A08G1517 myb domain protein 4r1 chromatin binding Gh_D08G1815 myb domain protein 4r1 chromatin binding Gh_D13G1045 myb domain protein 5 chromatin binding Gh_A11G1492 myb domain protein 6 chromatin binding Gh_A11G1913 myb domain protein 84 chromatin binding myb-like HTH transcriptional regulator family Gh_D10G2397 protein chromatin binding myb-like HTH transcriptional regulator family Gh_A06G1158 protein -

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Appendix E. Continued NAC (No Apical Meristem) domain Gh_A09G0159 transcriptional regulator superfamily protein DNA binding NAC (No Apical Meristem) domain Gh_D06G1546 transcriptional regulator superfamily protein DNA binding NAC (No Apical Meristem) domain Gh_A06G1226 transcriptional regulator superfamily protein DNA binding NAC (No Apical Meristem) domain Gh_D04G0293 transcriptional regulator superfamily protein DNA binding Gh_A12G0319 NAC domain containing protein 36 DNA binding Gh_A08G2057 NAC domain containing protein 42 DNA binding Gh_A05G2928 NAC domain containing protein 83 DNA binding Gh_D04G0712 NAC domain containing protein 83 DNA binding Gh_D09G0950 NAC domain containing protein 89 DNA binding NAD(P)-binding Rossmann-fold superfamily Gh_D07G1435 protein metabolic process NAD(P)-binding Rossmann-fold superfamily Gh_A11G0970 protein metabolic process NAD(P)-binding Rossmann-fold superfamily Gh_D11G1235 protein metabolic process NAD(P)-binding Rossmann-fold superfamily Gh_D01G1989 protein metabolic process

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Appendix E. Continued NAD(P)-binding Rossmann-fold superfamily Gh_Sca084831G01 protein - NAD(P)-binding Rossmann-fold superfamily Gh_A02G0842 protein - NAD(P)-binding Rossmann-fold superfamily Gh_D07G1434 protein metabolic process NAD(P)-binding Rossmann-fold superfamily Gh_A09G1330 protein metabolic process NAD(P)-binding Rossmann-fold superfamily Gh_D06G2263 protein - NAD(P)-binding Rossmann-fold superfamily Gh_D05G3906 protein metabolic process NAD(P)-binding Rossmann-fold superfamily Gh_D07G1291 protein metabolic process NAD(P)-binding Rossmann-fold superfamily Gh_A01G1739 protein metabolic process NAD(P)-binding Rossmann-fold superfamily Gh_D10G1194 protein metabolic process NAD(P)-binding Rossmann-fold superfamily Gh_A12G1558 protein - malate dehydrogenase (oxaloacetate-decarboxylating) Gh_D07G1855 NAD-dependent malic enzyme 2 activity Gh_D04G0142 NADPH:quinone oxidoreductase -

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Appendix E. Continued NB-ARC domain-containing disease resistance Gh_D01G0348 protein - Gh_D10G0641 NDH-dependent cyclic electron flow 1 electron carrier activity Gh_D05G0038 NFU domain protein 3 iron-sulfur cluster assembly Gh_D11G2812 nicotianamine synthase 3 nicotianamine synthase activity Gh_A12G2640 nitrate transporter 1:2 membrane Gh_A12G1331 nitrate transporter2.5 transmembrane transport Gh_D12G1457 nitrate transporter2.5 transmembrane transport Gh_A04G0489 nitrilase 4 nitrogen compound Nodulin MtN21 /EamA-like transporter family Gh_A12G1977 protein membrane nodulin MtN21 /EamA-like transporter family Gh_A05G3059 protein membrane nodulin MtN21 /EamA-like transporter family Gh_A05G3738 protein membrane nodulin MtN21 /EamA-like transporter family Gh_D05G2226 protein membrane nodulin MtN21 /EamA-like transporter family Gh_D02G1676 protein membrane nodulin MtN21 /EamA-like transporter family Gh_A09G1483 protein membrane Gh_D07G2390 nuclear factor Y, subunit A5 nucleus

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Appendix E. Continued Gh_A08G1630 nuclear factor Y, subunit C3 CCAAT-binding factor complex Gh_A12G1223 nucleolin like 2 nucleic acid binding O-acetylserine (thiol) lyase (OAS-TL) isoform Gh_D09G2091 A1 cysteine biosynthetic process from serine Gh_A13G0955 O-acyltransferase (WSD1-like) family protein diacylglycerol O-acyltransferase activity octicosapeptide/Phox/Bem1p (PB1) domain- Gh_A12G1336 containing protein protein binding Gh_A06G1787 Octicosapeptide/Phox/Bem1p family protein protein binding Gh_D09G0456 Oleosin family protein integral to membrane Gh_D06G2250 oligopeptide transporter 5 transmembrane transport Gh_A08G2432 oligopeptide transporter 7 transmembrane transport Gh_D08G2702 O-methyltransferase 1 O-methyltransferase activity Gh_D07G1626 O-methyltransferase family protein O-methyltransferase activity Gh_A11G2625 O-methyltransferase family protein O-methyltransferase activity OSBP(oxysterol binding protein)-related Gh_A02G0767 protein 1C protein binding Gh_D01G0727 Outward rectifying potassium channel protein membrane Gh_A01G2099 ovate family protein 6 - Gh_D12G2457 PAR1 protein - Gh_D11G1278 PATATIN-like protein 9 lipid metabolic process Gh_A11G2284 pathogenesis-related 4 defense response to fungus

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Appendix E. Continued Gh_D11G2592 pathogenesis-related 4 defense response to fungus Pathogenesis-related thaumatin superfamily Gh_A03G1871 protein - Gh_A13G1191 PDI-like 5-3 cell redox homeostasis PEBP (phosphatidylethanolamine-binding Gh_A08G2015 protein) family protein - PEBP (phosphatidylethanolamine-binding Gh_D04G1296 protein) family protein - Gh_A10G0667 Pectin lyase-like superfamily protein carbohydrate metabolic process Gh_A08G0241 Pectin lyase-like superfamily protein carbohydrate metabolic process Gh_D06G0668 Pectin lyase-like superfamily protein cell wall Gh_D01G2061 Pectin lyase-like superfamily protein - Gh_A03G1717 pectinesterase 11 cell wall Gh_D03G1029 pectinesterase family protein enzyme inhibitor activity Pentatricopeptide repeat (PPR) superfamily Gh_D10G1307 protein - Pentatricopeptide repeat (PPR) superfamily Gh_D12G1765 protein - Pentatricopeptide repeat (PPR) superfamily Gh_D09G0450 protein - Gh_D10G1222 PEP1 receptor 2 protein binding Gh_D04G1467 PEP1 receptor 2 protein binding

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Appendix E. Continued Gh_A05G0759 peptide transporter 1 membrane Gh_D02G0459 peptide transporter 3 membrane peptidoglycan-binding LysM domain- Gh_A11G0765 containing protein - Gh_D11G0607 Peroxidase superfamily protein response to oxidative stress Gh_A05G1328 Peroxidase superfamily protein response to oxidative stress Gh_A12G2370 Peroxidase superfamily protein response to oxidative stress Gh_A07G2195 peroxin 13 - Gh_D05G3728 peroxin 5 - Gh_D02G0140 PETER PAN-like protein - Gh_D12G0761 phosphoenolpyruvate carboxykinase 1 ATP binding Gh_A05G3759 Phosphoglycerate mutase family protein metabolic process Gh_A07G0078 Phosphoglycerate mutase family protein - Gh_D07G0088 Phosphoglycerate mutase family protein - Gh_A06G0310 Phosphoglycerate mutase family protein metabolic process Gh_A05G3760 Phosphoglycerate mutase family protein - Gh_D05G1925 phospholipase A 2A lipid metabolic process Gh_A06G0143 phospholipase A 2A lipid metabolic process Gh_D08G1933 phosphoprotein phosphatase inhibitors protein phosphatase inhibitor activity Gh_D07G0070 Phosphorylase superfamily protein nucleoside metabolic process Gh_A01G1208 Phototropic-responsive NPH3 family protein protein binding Gh_D02G1256 phytochrome kinase substrate 1 -

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Appendix E. Continued Gh_A03G0607 phytochrome-interacting factor7 regulation of transcription Gh_A08G1366 pinoresinol reductase 1 - Plant basic secretory protein (BSP) family Gh_Sca004922G01 protein - Plant basic secretory protein (BSP) family Gh_A05G3279 protein - Plant basic secretory protein (BSP) family Gh_A13G0387 protein - Plant invertase/pectin methylesterase inhibitor Gh_D03G1024 superfamily enzyme inhibitor activity Plant invertase/pectin methylesterase inhibitor Gh_D08G0454 superfamily protein enzyme inhibitor activity Gh_D05G0087 plant natriuretic peptide A - Gh_A04G1173 Plant protein 1589 of unknown function - Gh_A09G2059 Plant protein of unknown function (DUF247) - Gh_A03G1200 Plant protein of unknown function (DUF828) - Gh_A09G0885 Plant protein of unknown function (DUF868) - Gh_Sca045973G01 Plant regulator RWP-RK family protein - Plant stearoyl-acyl-carrier-protein desaturase Gh_A05G2312 family protein acyl-[acyl-carrier-protein] desaturase activity Gh_D09G1261 plant U-box 23 ubiquitin ligase complex Gh_A09G0943 plant U-box 24 ubiquitin ligase complex Gh_A10G0746 plant U-box 29 protein binding

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Appendix E. Continued Gh_D09G2193 plant U-box 9 - Gh_D07G1395 plastid division2 - Gh_D05G1668 plastocyanin-like domain-containing protein electron carrier activity PLC-like phosphodiesterases superfamily Gh_D12G0673 protein - Pleckstrin homology (PH) and lipid-binding Gh_A03G0271 START domains-containing protein - Pollen Ole e 1 allergen and extensin family Gh_D12G0918 protein - Pollen Ole e 1 allergen and extensin family Gh_D01G1607 protein - Pollen Ole e 1 allergen and extensin family Gh_A07G1611 protein - Pollen Ole e 1 allergen and extensin family Gh_D07G2484 protein - Polyketide cyclase/dehydrase and lipid transport Gh_D10G2388 superfamily protein - Polyketide cyclase/dehydrase and lipid transport Gh_A12G2230 superfamily protein - Polynucleotidyl transferase, ribonuclease H-like Gh_A04G0438 superfamily protein - Gh_Sca004812G06 polyol/monosaccharide transporter 5 membrane Gh_D06G0677 profilin 4 actin binding Gh_D03G0557 profilin 5 actin binding

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Appendix E. Continued Gh_A11G0746 protein binding - Gh_D12G1021 protein binding - Gh_D03G1530 protein binding - Gh_D09G1021 protein kinase 2A protein tyrosine kinase activity Protein kinase family protein with leucine-rich Gh_Sca004803G06 repeat domain protein tyrosine kinase activity Gh_D12G0321 Protein kinase superfamily protein protein tyrosine kinase activity Gh_A13G1391 Protein kinase superfamily protein - Gh_D08G0016 Protein kinase superfamily protein protein tyrosine kinase activity Gh_D09G1675 Protein kinase superfamily protein protein tyrosine kinase activity Gh_A08G0245 Protein kinase superfamily protein protein tyrosine kinase activity Gh_D08G0334 Protein kinase superfamily protein protein tyrosine kinase activity Gh_D12G0126 Protein kinase superfamily protein protein tyrosine kinase activity Gh_A12G0808 Protein kinase superfamily protein - Gh_D06G1972 Protein kinase superfamily protein protein tyrosine kinase activity Gh_D09G0393 Protein kinase superfamily protein protein tyrosine kinase activity Gh_A11G2389 Protein kinase superfamily protein protein tyrosine kinase activity Gh_D09G1591 protein phosphatase 2A-4 - Gh_A09G2224 Protein phosphatase 2C family protein catalytic activity Gh_D11G2986 protein serine/threonine kinases protein tyrosine kinase activity Gh_D09G0677 protein serine/threonine kinases protein tyrosine kinase activity Gh_D01G0361 protein serine/threonine kinases protein tyrosine kinase activity

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Appendix E. Continued Gh_A13G1628 protein serine/threonine kinases ATP binding Gh_D01G0306 protein serine/threonine kinases protein tyrosine kinase activity Gh_D09G0676 protein serine/threonine kinases protein tyrosine kinase activity Gh_D06G0063 protein serine/threonine kinases protein tyrosine kinase activity Gh_A11G2552 protein serine/threonine kinases protein tyrosine kinase activity Gh_Sca005862G01 protein serine/threonine kinases protein tyrosine kinase activity Gh_D01G0263 protein serine/threonine kinases protein tyrosine kinase activity Gh_D04G1544 protein serine/threonine kinases protein tyrosine kinase activity Gh_A13G0427 protein serine/threonine kinases protein tyrosine kinase activity Gh_D04G0244 protein serine/threonine kinases ATP binding Gh_D04G0241 protein serine/threonine kinases protein tyrosine kinase activity Gh_Sca134425G01 proton gradient regulation 3 - Gh_D05G1606 proton gradient regulation 3 - Gh_A10G0975 proton gradient regulation 5 - Gh_A12G2229 pseudo-response regulator 5 protein binding Gh_D08G2186 Pseudouridine synthase family protein RNA binding Gh_D02G1200 purple acid phosphatase 3 hydrolase activity Putative integral membrane protein conserved Gh_D06G1952 region (DUF2404) - Gh_Sca004991G02 Putative membrane lipoprotein - Gh_A02G1579 Putative methyltransferase family protein -

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Appendix E. Continued Pyridoxal phosphate (PLP)-dependent Gh_D13G1769 transferases superfamily protein carboxylic acid me Gh_D11G0553 RAB GTPase homolog A4A membrane Gh_A07G1793 RAB GTPase homolog A5E membrane Gh_Sca005357G01 RAB GTPase homolog C2A membrane Gh_D05G1847 RAD-like 1 - Gh_D13G0332 RAD-like 1 chromatin binding Gh_A11G0405 RAD-like 6 chromatin binding Gh_A11G1690 RAD-like 6 chromatin binding Gh_D11G1091 RAD-like 6 - Gh_D06G2022 receptor like protein 15 protein binding Gh_D11G2899 receptor like protein 50 - Gh_D09G1355 receptor serine/threonine kinase, putative - Gh_A11G1608 receptor serine/threonine kinase, putative - Gh_D07G1778 receptor-like kinase 902 protein tyrosine kinase activity Gh_A12G1638 Remorin family protein - Gh_A13G0029 response regulator 3 phosphorelay signal transduction system Gh_A06G0382 response regulator 9 phosphorelay signal transduction system Gh_D10G1909 response regulator 9 phosphorelay signal transduction system Gh_A02G1814 Rhamnogalacturonate lyase family protein - Gh_Sca061303G01 Riboflavin synthase-like superfamily protein oxidation-reduction process

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Appendix E. Continued Gh_D12G1636 ribonuclease 3 RNA binding Gh_A10G0983 Ribosomal L28 family ribosome Ribosomal protein L12/ ATP-dependent Clp Gh_D12G2610 protease adaptor protein ClpS family protein ribosome Gh_D02G1836 Ribosomal protein L2 family ribosome Gh_A13G2230 ribosomal protein L22 ribosome Gh_A09G0965 ribosomal protein L33 - Gh_A12G0252 Ribosomal protein L9/RNase H1 ribosome Ribosomal protein S5 domain 2-like Gh_Sca175099G01 superfamily protein ribosome RING/FYVE/PHD zinc finger superfamily Gh_D04G1218 protein zinc ion binding RING/FYVE/PHD zinc finger superfamily Gh_A04G0742 protein zinc ion binding Gh_D09G0249 RING/U-box superfamily protein protein binding Gh_A12G1006 RING/U-box superfamily protein protein binding Gh_D01G0785 RING/U-box superfamily protein protein binding Gh_A11G2994 RING/U-box superfamily protein protein binding Gh_D03G1589 RING/U-box superfamily protein - Gh_D10G0482 RING/U-box superfamily protein protein binding Gh_A04G0701 RING/U-box superfamily protein protein binding Gh_D04G1166 RING/U-box superfamily protein protein binding

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Appendix E. Continued Gh_D11G0297 RING/U-box superfamily protein protein binding Gh_D07G2392 RING/U-box superfamily protein protein binding Gh_D07G2017 RING/U-box superfamily protein - Gh_D05G3072 RING/U-box superfamily protein protein binding Gh_Sca008057G01 RNA helicase family protein - RNA polymerase transcriptional regulation regulation of transcription from RNA polymerase II Gh_D10G0601 mediator-related promoter RNA-directed DNA polymerase (reverse Gh_D01G1408 transcriptase)-related family protein - Gh_A02G1560 RUB1 conjugating enzyme 1 - Gh_A12G1764 Rubredoxin-like superfamily protein iron ion binding S-adenosyl-L-methionine-dependent Gh_A13G0250 methyltransferases superfamily protein methyltransferase activity S-adenosyl-L-methionine-dependent Gh_A07G1231 methyltransferases superfamily protein - Gh_D13G0303 salt tolerance homolog2 zinc ion binding Gh_A12G0376 SAUR-like auxin-responsive protein family - Gh_D01G1280 SAUR-like auxin-responsive protein family - Gh_D06G0011 SAUR-like auxin-responsive protein family - Gh_A05G1606 SAUR-like auxin-responsive protein family - Gh_D13G2428 SAUR-like auxin-responsive protein family - Gh_D02G2199 SAUR-like auxin-responsive protein family -

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Appendix E. Continued Gh_A11G0725 SAUR-like auxin-responsive protein family - Gh_D11G0844 SAUR-like auxin-responsive protein family - SBP (S-ribonuclease binding protein) family Gh_D01G0927 protein - SBP (S-ribonuclease binding protein) family Gh_D06G1526 protein protein binding SBP (S-ribonuclease binding protein) family Gh_D05G2435 protein - SBP (S-ribonuclease binding protein) family Gh_D12G2109 protein protein binding SBP (S-ribonuclease binding protein) family Gh_A05G1210 protein protein binding Gh_A09G1222 SC35-like splicing factor 33 nucleic acid binding Gh_A05G0802 SCARECROW-like 14 - Sec14p-like phosphatidylinositol transfer family Gh_D09G1634 protein - Gh_D08G0698 Seed maturation protein - Gh_A12G1429 semialdehyde dehydrogenase family protein oxidoreductase activity Gh_D08G0465 senescence-associated gene 21 response to stress Gh_A03G1331 senescence-related gene 3 glycerol metabolic process Serine protease inhibitor, potato inhibitor I-type Gh_A11G1179 family protein response to wounding

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Appendix E. Continued Serine protease inhibitor, potato inhibitor I-type Gh_D05G2818 family protein response to wounding Serine/threonine-protein kinase WNK (With No Gh_D05G1972 Lysine)-related - Gh_A05G2513 Shugoshin C terminus nucleus Gh_Sca083015G01 Single hybrid motif superfamily protein glycine cleavage complex Gh_A06G1320 SLAC1 homologue 1 transmembrane transport Gh_A08G1741 SMAD/FHA domain-containing protein protein binding Gh_D08G0402 small acidic protein 1 Gh_A10G1617 SOS3-interacting protein 1 signal transduction SPFH/Band 7/PHB domain-containing Gh_A10G0904 membrane-associated protein family membrane SPFH/Band 7/PHB domain-containing Gh_A12G2217 membrane-associated protein family membrane Gh_A03G0870 sphingoid base hydroxylase 2 iron ion binding Gh_D08G1273 SPIRAL1-like5 - Gh_Sca111437G01 spliceosomal protein U1A mRNA splicing Gh_A13G1913 splicing endonuclease 1 tRNA splicing Gh_A07G0303 SPX domain gene 2 - Gh_A10G2217 squamosa promoter binding protein-like 3 nucleus Gh_D10G0251 squamosa promoter binding protein-like 3 nucleus Gh_D13G1551 squamosa promoter binding protein-like 5 nucleus

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Appendix E. Continued Gh_D10G2413 Staphylococcal nuclease homologue nuclease activity Gh_A10G2163 Staphylococcal nuclease homologue nucleic acid binding Gh_A09G0145 starch synthase 2 biosynthetic process structural molecules;transmembrane Gh_D08G1511 receptors;structural molecules structural molecule activity Gh_A02G0947 Subtilase family protein serine-type endopeptidase activity Subtilisin-like serine endopeptidase family Gh_D06G2361 protein serine-type endopeptidase activity Gh_Sca044223G01 Sugar isomerase (SIS) family protein protein binding Gh_A05G2433 sulfotransferase 2B sulfotransferase activity Gh_D11G1682 suppressor of npr1-1 constitutive 4 protein tyrosine kinase activity Gh_D02G1344 syntaxin of plants 131 membrane Gh_D08G2267 syntaxin of plants 52 - Gh_D09G0210 TBP-associated factor 5 protein binding Gh_D05G1551 terpene synthase 04 magnesium ion binding Gh_A05G1383 terpene synthase 04 magnesium ion binding Gh_A13G1207 terpene synthase 21 magnesium ion binding Gh_D01G1963 terpene synthase 21 magnesium ion binding Tetratricopeptide repeat (TPR)-like superfamily Gh_Sca146625G01 protein -

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Appendix E. Continued Tetratricopeptide repeat (TPR)-like superfamily Gh_A05G2268 protein - Tetratricopeptide repeat (TPR)-like superfamily Gh_Sca084155G01 protein - Tetratricopeptide repeat (TPR)-like superfamily Gh_A08G0024 protein protein binding Tetratricopeptide repeat (TPR)-like superfamily Gh_D08G0063 protein protein binding Thioesterase/thiol ester dehydrase-isomerase Gh_A11G1335 superfamily protein - Gh_D06G1943 thioredoxin H-type 9 protein disulfide oxidoreductase activity Gh_A10G0397 Thioredoxin superfamily protein protein disulfide oxidoreductase activity Gh_A09G0345 Thioredoxin superfamily protein - Gh_Sca157066G01 Thioredoxin superfamily protein - Gh_A06G0776 Thioredoxin superfamily protein protein disulfide oxidoreductase activity Gh_A03G1529 tobamovirus multiplication 1 - Gh_D03G1568 tonoplast intrinsic protein 2;3 membrane Gh_D01G2289 TOXICOS EN LEVADURA 2 protein binding Gh_A03G0231 TOXICOS EN LEVADURA 63 protein binding Gh_A08G0552 TRAF-like family protein protein binding Gh_A02G0574 TRAF-like family protein protein binding

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Appendix E. Continued Gh_A13G0546 Transcription initiation Spt4-like protein - Transducin family protein / WD-40 repeat Gh_A07G0393 family protein protein binding Transducin/WD40 repeat-like superfamily Gh_D02G1397 protein protein binding Transducin/WD40 repeat-like superfamily Gh_D02G1212 protein protein binding Transducin/WD40 repeat-like superfamily Gh_A08G1361 protein - Transducin/WD40 repeat-like superfamily Gh_A03G0794 protein protein binding Transducin/WD40 repeat-like superfamily Gh_D08G1130 protein protein binding Gh_A10G0966 translocase of outer membrane 22-V - Gh_A05G3139 TRICHOME BIREFRINGENCE-LIKE 22 - Gh_D13G0104 TRICHOME BIREFRINGENCE-LIKE 36 - Gh_D13G1596 TRICHOME BIREFRINGENCE-LIKE 41 - Gh_A01G0324 TRICHOME BIREFRINGENCE-LIKE 42 - Gh_D05G3778 TRICHOME BIREFRINGENCE-LIKE 7 - Gh_D11G2323 tRNA (guanine-N-7) methyltransferase tRNA modification Gh_A05G3275 Tropomyosin-related - Gh_D05G1116 tubby like protein 8 -

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Appendix E. Continued Gh_A05G0998 tubby like protein 8 - Ubiquitin-conjugating enzyme/RWD-like Gh_D07G2119 protein cellular protein modification process Gh_D12G0482 ubiquiting-conjugating enzyme 2 - UDP-D-glucose/UDP-D-galactose 4-epimerase Gh_A01G1809 5 cellular metabolic process Gh_A04G0581 UDP-glucosyl transferase 73D1 metabolic process Gh_D03G0926 UDP-glucosyl transferase 89B1 metabolic process Gh_A07G2052 UDP-Glycosyltransferase superfamily protein metabolic process Gh_A10G2039 UDP-Glycosyltransferase superfamily protein metabolic process Gh_A12G2646 UDP-Glycosyltransferase superfamily protein metabolic process Gh_D13G1658 UDP-Glycosyltransferase superfamily protein metabolic process Gh_A13G1348 UDP-Glycosyltransferase superfamily protein metabolic process Gh_A13G0173 UDP-Glycosyltransferase superfamily protein metabolic process Gh_D05G0318 UDP-Glycosyltransferase superfamily protein metabolic process Gh_A01G1073 UDP-Glycosyltransferase superfamily protein metabolic process Gh_A05G0993 unknown protein 6 - Gh_D05G1111 unknown protein 6 - Gh_Sca015851G01 urease nitrogen compound metabolic process Gh_A13G0727 ureide permease 1 - Gh_A04G1441 Vacuolar iron transporter (VIT) family protein - Gh_D09G2317 vesicle-associated membrane protein 724 integral to membrane

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Appendix E. Continued Gh_A11G2750 VQ motif-containing protein - Gh_D09G0160 wall associated kinase-like 4 protein tyrosine kinase activity Gh_A09G0177 wall associated kinase-like 7 polysaccharide binding Gh_A02G0724 wall-associated kinase 2 ATP binding Gh_D10G1951 wall-associated kinase 2 ATP binding Gh_A02G0727 wall-associated kinase 2 ATP binding Gh_A09G0174 Wall-associated kinase family protein protein tyrosine kinase activity Gh_A03G1537 white-brown complex homolog protein 11 membrane Gh_A07G0882 WUSCHEL related homeobox 2 regulation of transcription Gh_D12G0540 XH/XS domain-containing protein - Gh_A09G1534 Yippee family putative zinc-binding protein - Ypt/Rab-GAP domain of gyp1p superfamily Gh_D10G0924 protein - Gh_Sca080137G01 zinc finger (C2H2 type) family protein - Gh_A01G1196 zinc finger (Ran-binding) family protein - Gh_A01G0984 zinc finger of Arabidopsis thaliana 6 metal ion binding Gh_D11G1168 zinc finger protein 6 metal ion binding Gh_A06G0194 zinc finger protein 7 metal ion binding Gh_D06G0189 zinc finger protein 7 metal ion binding Gh_Sca045498G01 zinc finger protein 8 metal ion binding Gh_D01G1208 zinc finger protein 8 metal ion binding Gh_A05G2741 zinc finger protein 8 metal ion binding

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Appendix E. Continued Gh_Sca012128G01 zinc ion binding - Gh_A01G1253 zinc ion binding;nucleic acid binding - Gh_D06G0766 zinc knuckle (CCHC-type) family protein - Gh_A02G0058 Zinc-binding dehydrogenase family protein zinc ion binding

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