UNDERSTANDING THE GENETIC MECHANISMS OF DEVELOPMENT RATE IN PETUNIA AND STEVIA By Prabhjot Kaur A DISSERTATION Submitted to Michigan State University in partial fulfillment of the requirements for the degree of Plant Breeding, Genetics, and Biotechnology – Horticulture – Doctor of Philosophy 2024 ABSTRACT Development rate, the rate at which plants produce new nodes/leaves, is crucial for determining crop readiness for harvest. Enhancing our genetic understanding and ability to manipulate this rate can lead to faster crop cycles, increased yield, and more efficient production, particularly for plants with short lifecycles or those harvested in the vegetative stage. Despite existing knowledge from other crops like Arabidopsis and rice, significant gaps remain in our understanding of development rate control. This dissertation investigates the genetic mechanisms controlling development rate in Petunia × hybrida and Stevia rebaudiana to improve crop timing and yield. Petunia, a popular annual bedding plant, often relies on heated greenhouse production in cooler climates. Identifying genetic factors regulating development rate in petunia is essential for reducing production costs and accelerating development rate at sub-optimal temperatures. The first objective was to evaluate the effect of candidate genes identified in previous studies by using virus-induced gene silencing (VIGS). Despite variable silencing efficiency and phenotypic data variability, the MEI2-like1 RNA binding protein emerged as a promising candidate, warranting further investigation with stable transformation methods. The second study employed an F7 recombinant inbred line (RIL) population developed from P. axillaris and P. exserta (AE population) to identify genes differentially expressed between fast- and slow-developing AE RILs. DEGs included genes related to auxin polar transport, gibberellin signaling, MATE efflux transporters, and the 2OG-Fe(II) dependent oxygenase superfamily, among others. Common DEGs between AE and a previous RIL study involved cell-wall mechanics related genes such as PECTINACETYLESTERASE FAMILY PROTEIN and L-ASCORBATE OXIDASE, providing crucial insights into genetic factors influencing development rate. Stevia, known for its zero- calorie sweetening compounds produced by leaf, benefits from breeding faster-developing varieties to enable multiple harvests per season. Stevia leaf yield depends on morphological traits such as leaf size, leaf production rate, plant canopy width and branch production. An F1 mapping population of 200 individuals was evaluated over two years at two field locations in Michigan for leaf-yield related traits. We generated a novel high-density SNP-based linkage map with eleven linkage groups encompassing eleven chromosomes. QTLs were identified at all four environments for traits such as maximum width, secondary branching, leaf length, and plant vigor, explaining 7-15% of phenotypic variation. Overlapping QTL regions were identified for traits including secondary branching, minimum canopy width, and leaf width explaining 7-15% of phenotypic variation. Differential expression analysis of F1 lines with contrasting development rates highlighted genes related to auxin efflux carrier proteins (PIN-like 2), auxin biosynthesis (YUC2), and cell wall loosening enzymes (EXPANSIN). Notably, CYP78A10, an ortholog of the Arabidopsis KLUH gene known to slow development rate, was upregulated in the slow development rate lines. Results from both species suggest that development rate is a complex process regulated by multiple factors, starting in the shoot apical meristem (SAM) through auxin polar transport, cell wall mechanics, and communication signals from emerging leaf primordia to the SAM. This research lays the foundation for breeding programs aimed at accelerating crop timing and increasing yield, enhancing overall crop production efficiency. This dissertation is dedicated to my Parents and Grandfather. Thank you for always believing in me!! iv ACKNOWLEDGEMENTS I would like to extend my heartfelt gratitude to my supervisor, Dr. Ryan Warner, for his exceptional mentoring and unwavering support throughout my PhD journey. His guidance has been invaluable, and I am deeply appreciative of his patience and wisdom!! I also want to express my sincere thanks to my lab members, Sue Hammar, Nate Durussel, Keivan Bahmani, and all the undergraduates I have worked with. Your support and contributions have significantly enriched my research experience! This journey would not have been possible without the support of my committee members, Dr. Corny Barry, Dr. Courtney Hollender, and Dr. Dechun Wang. Your insights and encouragement have been crucial in shaping my work! Additionally, I extend my gratitude to Dr. Joseph Hill, Joseph Coombs, and Mohit Mahey for their assistance and contributions! To the horticulture staff, your dedication and expertise have been instrumental in the success of my journey. Thank you for your unwavering support and hard work! To my office mates and friends, Andrea and Charity, your kindness, help, and camaraderie have made this journey so much more enjoyable! I am grateful for your presence and support!! I am deeply thankful to my parents for giving me this life. My mom’s unwavering support and sacrifices to provide us with quality education and a good life are beyond words. Thank you, Mom, for letting me live my dreams! I don’t say it enough, but I appreciate you and love so much!! Papa, I miss you every day and wish you were here to see this accomplishment! I wouldn’t be here today if it weren’t for you!! v To my Grandfather, thank you for raising me and my siblings with love and invaluable values!! I know you continue to be my biggest cheerleader in spirit!! To my wonderful husband, Parminder, you are my rock and my blessing! You make my life more beautiful and meaningful!! Thank you for loving me unconditionally, especially on the days I struggled to love myself. You celebrated my achievements and comforted me during my setbacks, always reminding me of my worth and potential. I am so grateful for your unwavering support and endless encouragement!! I promise to make more time for us now that this chapter is complete. You are my partner, my confidant, and my best friend, and I am forever thankful for your presence in my life!! To my siblings, Kamal and Navu, your love means everything to me! Kamal, you've been not just a brother but also a role model and father figure, and I deeply appreciate you. Thank you for your immense contributions to our family! Your guidance, encouragement, and unwavering belief in me have played a pivotal role in my success. Navu, my dear sister, your constant support, understanding, and love have been a constant source of comfort and strength for me! A heartfelt thanks also goes to my brother-in-law, Aman, for his unwavering support and kindness in numerous ways, and to my sister-in-law, Rupinder, for being an integral part of our family! To my second family, Mom, Dad, and sister-in-law (Jyoti), your love and support have not gone unnoticed. Thank you for welcoming me into the family and always being there for me!! To my adorable nephews, Arjan and Amarbir, and my beautiful niece Rehmat, your charm and innocence have brought so much joy to my life during this journey! Daler, our vi nephew, although we lost you too soon and couldn’t see you growing, you will always be remembered as God’s favorite child!! To my Uncle and Aunt, Nirmal and Ginni, you have been an essential part of this journey. When I decided to move away from home for grad school, your encouragement and support made this decision so much easier. Thank you for always being there for me and treating me like your own! To my dear friends Manjot, Nancy, and Karan, you were my second home during grad school. Your friendship and support have been invaluable. Manjot, your care, and kindness over the past four years will always stay with me!! To my other grad school friends, Harkirat, Mohit, and Sukhdeep, it was a pleasure meeting you and sharing this journey! Finally, to my childhood friends, Aman, Bhav, Taran, Ruby, and Rajwinder and my cousin Amolak, thank you for always believing in me and offering endless support. You keep me grounded, and your encouragement has been a powerful influence in my life! Above all, I am deeply grateful to God for His endless blessings and guidance. Your grace and strength have carried me through this journey, and I am forever thankful! This dissertation is a testament to the love, support, and encouragement I have received from all of you. Thank you!! vii TABLE OF CONTENTS CHAPTER 1 LITERATURE REVIEW ………………………………………………………....1 CHAPTER 2 EVALUATING THE EFFECT OF TRANSIENTLY SILENCED CANDIDATE GENE EXPRESSION ON PETUNIA DEVELOPMENT RATE BY USING VIRUS INDUCED GENE SILENCING (VIGS)……………………………………………… 13 CHAPTER 3 TRANSCRIPTOMIC ANALYSIS OF PETUNIA RECOMBINANT INBRED LINES (RILS) WITH CONTRASTING DEVELOPMENT RATES……………………………43 CHAPTER 4 IDENTIFICATION OF QUANTITATIVE TRAIT LOCI (QTL) RELATED TO STEVIA DEVELOPMENT RATE AND OTHER LEAF YIELD RELATED TRAITS……84 CHAPTER 5 TRANSCRIPTOMIC ANALYSIS OF STEVIA F1 LINES WITH CONTRASTING DEVELOPMENT RATES…………………………………………………..139 BIBLIOGRAPHY……………………………………………………………………………..189 APPENDIX…………………………………………………………………………….............206 viii CHAPTER 1 LITERATURE REVIEW 1 INTRODUCTION Crop plants grow at different speeds due to their different development rates, defined as the rate of nodes/leaves produced over time (Warner and Walworth, 2010). Development rate is an important biological phenomenon determining when crops are ready to harvest i.e. time to first crop yield. It indirectly influences seasonal biomass accumulation in forage and bioenergy crops by influencing how many leaves are available for photosynthetic carbon fixation. Since development rate varies between plant species and even within groups of plants, there is a potential to change it to improve how efficiently crops grow and produce. This could involve encouraging earlier flowering or fruiting, leading to quicker harvests and higher yields. Therefore, it is desirable to study the genetic control of development rate. The long-term goal of studying the genetics of development rate is to use this knowledge for speeding up crop timing even in less-than-ideal conditions and boosting overall crop production. This could mean more frequent harvests and increased yield, especially in plants with short lifecycles or those harvested in vegetative stage. My dissertation will focus on studying the genetic basis of development rate in two specific crops, Petunia  hybrida and Stevia rebaudiana. The insights gained from this research could pave the way for breeding new varieties of crops that develop faster and produce more efficiently. BACKGROUND During post-embryonic development, the shoot apical meristem (SAM) orchestrates the formation of nodes in a coordinated manner, influencing plant architecture through the regulation of two fundamental aspects: temporal (plastochron) and spatial (phyllotaxy) patterns (Stuurman et al., 2002) of node production. Plastochron, defined as the inverse of developmental rate, represents the time interval between the production of successive nodes (Wang et al., 2008; 2 Vallejo et al., 2015). Phyllotaxy, on the other hand, denotes the spatial arrangement of leaves along the stem (Giulini et al., 2004). Historically, two models have been proposed to elucidate leaf initiation. The first model posits the presence of a diffusible substance in the SAM and existing leaf primordia, inhibiting the formation of subsequent leaf primordia (Snow and Snow, 1932). Conversely, the second model suggests that physical forces within the SAM dictate phyllotactic arrangements (Selker et al., 1992; Green et al., 1996). However, the genetic underpinnings of these models remain elusive. Genetic and molecular investigations have provided insights into the mechanisms governing leaf initiation. Notably, polar auxin transport emerges as a critical determinant in specifying the site of leaf initiation at regular intervals (plastochron) (Reinhardt et al., 2000). Following the emergence of newly formed leaf primordia, they function as sites of auxin sink activity. Consequently, auxin is depleted from the immediate vicinity of these primordia, leading to its accumulation at a distinct distance where subsequent leaf primordia are initiated (Reinhardt et al., 2003). Furthermore, mutations in genes such as TERMINAL EAR 1 (TE1) in maize (Veit et al., 1998) and ALTERED MERISTEM PROGRAM 1 (AMP1) in Arabidopsis (Helliwell et al., 2001), encoding an MEI2-like RNA binding protein and glutamate carboxypeptidase, respectively, have been associated with alterations in both plastochron and phyllotaxy, accompanied by changes in cytokinin levels. Given the pleiotropic effects of these genes, a focused examination of the genetic basis of plastochron, distinct from spatial considerations, is critical. This approach holds promise for elucidating the molecular intricacies underlying temporal regulation of leaf initiation in plant development. Previous research has delved into plastochron-associated genes in several species. For instance, in rice, the gene PLASTOCHRON1 (PLA1), along with its Arabidopsis ortholog KLUH, which encodes the cytochrome P450 family protein CYP78A11, has been identified as a negative 3 regulator of development rate (Miyoshi et al., 2004). Similarly, loss-of-function mutants of the PLA2 gene, encoding an MEI2-like RNA binding protein and serving as the ortholog of maize TE1, have been associated with an accelerated development rate (Kawakatsu et al., 2006; Mimura et al., 2012). Additionally, PLA3, a rice ortholog of Arabidopsis AMP1, and maize VIVIPAROUS8, encoding a homolog of glutamate carboxypeptidase, has been implicated as a negative regulator of development rate (Kawakatsu et al., 2009). Loss-of-function mutations in all three rice PLA genes have led to an accelerated development rate, premature leaf maturation resulting in reduced leaf size, and the conversion of reproductive branches into vegetative shoots (Miyoshi et al., 2004; Kawakatsu et al., 2006; Kawakatsu et al., 2009; Mimura et al., 2012). Despite similar phenotypes observed in individual loss-of-function mutants of these genes, the phenotypic effects were exacerbated in double mutants, indicating that these plastochron genes operate via independent pathways (Kawakatsu et al., 2006). In barley, three MANY-NODED DWARF genes (MND1, MND4, and MND8), encoding a N-acetyltransferase-like protein, a CYP78A family protein (ortholog of rice PLA1), and a MATE transporter protein, respectively, have been identified as regulators of development rate (Hibara et al., 2021). This suggests that the control of development rate in barley involves transcriptional regulation of downstream genes by histone modulation (MND1), synthesis or metabolism of unknown substances (MND4), and transportation of unidentified cell molecules (MND8) (Hibara et al., 2021). Interestingly, MND4 and MND8 are orthologs of rice PLA1 and maize BIG EMBRYO 1 (BIGE1), respectively (Miyoshi et al., 2004; Suzuki et al., 2015). Double mutant analyses reveal independent regulation of development rate by MND1 and MND4 from MND8 (Hibara et al., 2021). Furthermore, these genes exhibit limited expression in the shoot apical meristem and leaf primordia, unlike MND8, which lacks specific expression around the shoot apex. 4 Moreover, negative regulation of a subset of genes within the SQUAMOSA PROMOTER BINDING PROTEIN-LIKE (SPL) plant-specific transcription factor family by miR156 leads to an augmented development rate. For instance, overexpression of miR156 downregulates the expression of SPL9 and SPL15 genes, consequently accelerating development rate, promoting branching, and altering inflorescence architecture (Schwarz et al., 2008). Conversely, the miR156-resistant form of SPL9 decreases development rate in Arabidopsis (Wang et al., 2008). Loss-of-function mutations in various SPL genes have been associated with accelerated development rates in rice and maize (Jiao et al., 2010; Chuck et al., 2014; Wang et al., 2015). Members of this gene family are also implicated in promoting vegetative and floral phase transitions (Schwarz et al., 2008). For instance, overexpression of SPL3 triggers early adult leaf trait emergence and flowering in Arabidopsis (Wu and Poethig, 2006). Alternatively, overexpression of miR156 downregulates SPL3 expression, resulting in an abundance of juvenile trait leaves and delayed flowering (Schwab et al., 2005; Wu and Poethig, 2006). Notably, mutations in SERRATE, an Arabidopsis zinc finger protein pivotal in miRNA biogenesis and primary miRNA processing, lead to a reduction in development rate (Prigge and Wagner, 2001; Grigg et al., 2005; Lobbes et al., 2006). Plant growth hormones, including auxins and cytokinins, are integral to the regulation of development rate in Arabidopsis and tobacco (Reinhardt et al., 2000; Werner et al., 2001). For instance, the overexpression of four cytokinin oxidase genes (AtCKX) in tobacco leads to a reduction in cytokinin concentration and a subsequent decrease in development rate (Werner et al., 2001). Similarly, the Arabidopsis slow motion (slomo) mutant, characterized by decreased free auxin levels, exhibits a reduced development rate (Lohmann et al., 2010). Additionally, in rice, genes PLA1 and PLA2 are known to operate downstream of the gibberellin signal transduction pathway (Mimura et al., 2012). Notably, pla1 and pla2 mutants display lower 5 concentrations of other phytohormones, such as cytokinin, abscisic acid, and auxin, compared to their wild-type counterparts (Kawakatsu et al., 2009). Despite our extensive knowledge of genes and mutants, there are still significant gaps in understanding how these components interconnect within a network and how their biochemical activities are coordinated. Investigating the genetic mechanisms underlying the development rate in various plant species is crucial to determine whether the regulation of this trait involves conserved pathways or novel mechanisms. Economics/Uses of Petunia Petunia  hybrida (Petunia), commonly known as garden petunia, originated from a cross between two wild species, P. axillaris and P. integrifolia, in the early 19th century (Gerats and Strommer, 2008). It has become one of the leading annual bedding crops in the United States, boasting a wholesale sales value of US$160 million recorded in 2020 (USDA, 2021). Renowned for its wide array of colorful flowers and diverse morphology, this plant belongs to the Solanaceae family, characterized by a base chromosome number of x=7, unlike the typical x=12 found in most other members of this family, such as tomato, potato, pepper, tobacco, and eggplant (Guo et al., 2017). The genus Petunia comprises 20 species native to South America (Stehmann et al., 2009; Guo et al., 2017) most of which can be readily hybridized with varying degrees of fertility (Ando et al., 2001; Anderson, 2006; Warner, 2010). Petunia is renowned for its ease of cultivation and short lifecycle, typically spanning approximately four months from seed to seed (Vandenbussche et al., 2016). It is classified as a facultative long-day plant, exhibiting accelerated flowering under extended daylight periods and relatively higher temperatures. 6 The development rate of petunia is intricately linked to temperature (Vandenbussche et al., 2016). Typically, plants exhibit faster development rate as temperatures rise up to a certain optimal range (18-24°C), and conversely, the rate slows as temperatures decrease towards the base temperature (Adams et al., 1998). In regions with northern latitudes across North America and Europe, greenhouse cultivation is common during colder periods of the year to ensure timely market readiness during spring (Guo et al., 2017). Consequently, greenhouse operators incur substantial energy costs to maintain optimal temperatures for petunia flowering. These expenses significantly impact profit margins within the greenhouse industry. Moreover, it is important to note that higher temperatures can compromise crop quality (Warner, 2009). Conversely, crops cultivated at lower temperatures tend to exhibit better quality due to an extended duration of exposure to harvest light (Personal communication by Erik Runkle). There exists an opportunity to mitigate these energy expenditures by developing petunia varieties capable of accelerated development rates at cooler temperatures. Consequently, current research endeavors focus on elucidating the genetic mechanisms governing the development rate of petunias, with the aim of reducing production time. If plants could produce the same number of nodes at an increased rate and lower temperatures, it would lead to a reduction in crop timing and production costs. Moreover, increasing the development rate of petunia plants can result in higher yields of cuttings for clonal propagation, which is widely practiced in petunia horticultural production (Santos et al., 2011; Toma et al., 2011; Vandenbussche et al., 2016). Ultimately, this would benefit both growers and customers. Genetic of development rate in Petunia Diverse development rates observed between wild petunia species and commercial cultivars at equivalent temperatures suggest the potential for breeding varieties with accelerated development rates (Warner and Walworth, 2010). Consequently, studies employing quantitative 7 trait loci (QTL) mapping were initiated to identify candidate genomic regions associated with petunia development rate. Initially, interspecific F2 populations, namely P. integrifolia × P. axillaris (the "IA" population) and between P. axillaris and the more recently diverged species P. exserta (the AE population), were utilized to assess variation in development rates at the genetic and genomic levels. These populations were chosen due to their demonstration of transgressive segregation for development rates (Warner and Walworth, 2010). QTL analysis revealed the presence of development rate-associated loci on chromosomes 1, 2, and 5, collectively explaining 34% of the observed variation (Vallejo et al., 2015). Moreover, reference transcriptomes for P. axillaris, P. exserta, and P. integrifolia, comprising mRNA libraries from various tissues including shoot apex, whole 3-week-old seedlings, mixed floral development stages, trichome, and callus tissues, have been established (Guo et al., 2015). These transcriptomes were mined to identify petunia transcripts homologous to genes associated with development rate, which were subsequently converted into molecular markers and mapped to the IA F2 population (Guo et al., 2015; Vallejo et al., 2015). However, except for one gene encoding an MEI2-like RNA binding protein homologous to the rice PLA2 gene (Kawakatsu et al., 2006), none of these development rate-associated gene homologs co-localized with the identified QTL for development rate in this population. Furthermore, the low marker density in the IA F2 population limits the efficacy of the development rate QTL in identifying candidate genes underlying this trait (Guo et al., 2017). Subsequently, F7 recombinant inbred lines (RILs) were established for the same species (the IA population and the AE population) (Guo et al., 2017). These RILs were phenotyped for development rate at three distinct temperatures (14, 17, and 20°C) to pinpoint QTL regions associated with the trait (Guo et al., 2017). Additionally, IA RILs exhibiting varying development rates were employed to identify 209 differentially expressed genes (DEGs) (Guo et 8 al., 2017). Subsequently, QTL and DEGs were mapped onto high-density single nucleotide polymorphism (SNP) bin-based linkage maps generated for both populations (Guo et al., 2017). Out of all DEGs, thirteen were found to map within 1 centimorgan (cM) of a development rate QTL, with notably large clusters of differentially expressed genes located proximate to IA development rate QTL on chromosomes 5 and 6 (Guo et al., 2017). Within these differentially expressed genes were transcripts associated with phytohormones (specifically auxin and cytokinin) synthesis or signaling pathways, as well as miRNA-mediated pathways, which have previously been implicated in the control of development rate as detailed in the background section above. This data represents a significant step forward in facilitating the identification and characterization of the genetic factors governing development rate. Economics/Uses of Stevia Stevia rebaudiana, commonly referred to as stevia, is a significant medicinal perennial plant within the Asteraceae family, with a chromosome count of 2n=22 (Goyal et al., 2010). Originating from northeast Paraguay (Shock, 1982; Ramesh et al., 2006), stevia leaves are renowned for producing a collection of zero-glycemic, low-calorie sweet-tasting compounds known as steviol glycosides (Brandle and Telmer, 2007; Ceunen and Geuns, 2013). These steviol glycosides (SGs), extracted from the leaves, can constitute up to 30% (on a dry mass basis) of these compounds, with their sweetness being 200-300 times greater than sucrose (Goyal et al., 2010; Yadav and Guleria, 2012; Ceunen and Geuns, 2013). Stevia has been utilized as a sweetener in various products in Japan since the 1970s, including seafood, soft drinks, and candies (Mizutani and Tanaka, 2001). Moreover, stevia has been explored as a weight control agent for obese individuals and as a natural diabetes control remedy in different regions worldwide (Gupta et al., 2013; Shivanna et al., 2013; Ahmad et al., 2020). Given their plant- based origin, steviol glycosides hold significant promise as alternatives to sugar and synthetic 9 sweeteners, appealing to those seeking natural ingredients in their diet. Consequently, there is a growing demand for high steviol glycoside-yielding cultivars, necessitating plant breeders to focus on their development. Stevia is characterized by high heterozygosity and obligate outcrossing due to self- incompatibility (Yadav et al., 2014; Attaya, 2017). Consequently, it is commonly propagated through stem cuttings and in-vitro methods (Ramesh et al., 2006; Sairkar et al., 2009). Harvesting above-ground tissue of stevia involves stripping off leaves for the extraction of steviol glycosides. To increase steviol glycoside yield, accelerating leaf production rate over time is crucial to facilitate multiple harvests per season. Therefore, understanding the genetic basis of stevia leaf production rate is essential for breeding high-yielding cultivars. However, genetic research on stevia is hindered by factors such as limited germplasm availability, molecular markers, and a high-resolution linkage map (Basharat et al., 2021; Huber and Wehner, 2023). As stevia is a relatively new crop in the genomics era, collaborative efforts are underway to develop diverse germplasm and generate genetic and genomic resources, facilitating breeding efforts for increased stevia biomass production (Kaur et al., 2015; Bahmani, 2021; Vallejo and Warner, 2021; Xu et al., 2021; Huber and Wehner, 2023). Potential implications of understanding the genetics of development rate The rate at which plants generate new nodes is a fundamental determinant of crop production timing or time to first yield in agricultural crops. Given that several significant fresh market vegetable crops, such as tomato, pepper, and eggplant, belong to the same family as petunia (Solanaceae), the findings from this research project could be promptly applied to enhance these crops. Similarly, the insights gained from this study could be extended to other crops with restricted growing seasons, particularly in regions of the United States characterized by long winters. For instance, the outcomes of this study could benefit numerous vegetable crops 10 cultivated within limited seasons for fresh consumption. By enhancing the developmental pace of these crops, production efficiency could be enhanced by enabling multiple harvests within a single season. This aligns with the increasing consumer demand for locally grown produce, a trend that has surged in recent years. Moreover, comprehending the genetics of vegetative development rate may shed light on the potential coupling of genes involved in shoot growth (vegetative development) with those governing reproductive phases (flowering/fruiting), thereby creating opportunities to adjust crop timing and enhance production efficiency through early flowering/fruiting. DISSERTATION OBJECTIVES Considering the significance of investigating the development rate trait, my dissertation aimed to explore the genetic underpinnings of development rate in both petunia and stevia. By studying these two distinct species, we sought to provide valuable insights into the regulation of this trait in both species and laying the foundation for future breeding efforts aimed at improving crop productivity and efficiency. The first objective in petunia was to functionally characterize potential candidate genes linked to development rate through the utilization of reverse genetics techniques. Secondly, to comprehensively examine the common and unique pathways involved in development rate, we conducted transcriptomic analyses of petunia RILs exhibiting varying development rates within a previously unexplored population. In the case of stevia, the dissertation objectives were formulated with the aim of expanding genetic resources and investigating the genetic basis of development rate in this species. Thus, the final two objectives involved open field trials of a stevia genetic mapping population to identify genomic regions associated with morphological traits related to stevia leaf 11 biomass, including development rate, and employing transcriptomic analyses to identify differentially expressed genes between genotypes with contrasting development rates. 12 CHAPTER 2 EVALUATING THE EFFECT OF TRANSIENTLY SILENCED CANDIDATE GENE EXPRESSION ON PETUNIA DEVELOPMENT RATE BY USING VIRUS INDUCED GENE SILENCING (VIGS) 13 INTRODUCTION Understanding the genetic architecture underlying development rate in crops like petunia is crucial for optimizing crop timing and improving production efficiency, especially in regions with sub-optimal temperature conditions. Development rate, characterized by the rate of vegetative node formation before floral initiation, directly impacts the timing of crop maturity and harvest (Warner and Walworth, 2010). Petunia  hybrida is a highly valuable bedding crop, with significant economic importance in the horticultural industry (USDA, 2021). In regions with cooler climates, such as the northeastern states of the U.S., petunia production often relies on heated greenhouses to maintain optimal temperatures for growth and development. However, this heating requirement increases production costs and reduces profit margins for growers (Guo et al., 2015). Therefore, there is a pressing need to identify genetic factors that regulate development rate in petunia, particularly those that could potentially accelerate development under sub-optimal temperature conditions. By studying the genetic architecture of development rate, researchers can identify key genes and pathways involved in regulating this trait. The knowledge gained from this can then be used to develop breeding strategies aimed at selecting faster-developing varieties of petunia that are better adapted to cooler temperatures. Accelerating development rate in petunia could reduce the reliance on heating in greenhouses, thereby lowering production costs and increasing profitability for growers. The genetic control of plastochron, which represents the time interval between two successive nodes and is the inverse of development rate, provides valuable insights into the regulation of development rate in plants (Wang et al., 2008; Guo et al., 2015). Previous research has identified several key genes and pathways involved in modulating plastochron. Loss-of- function mutants of PLASTOCHRON1 (PLA1) in rice and KLUH in Arabidopsis, both encoding 14 cytochrome P450 family proteins, have been associated with an increased development rate (Miyoshi et al., 2004, 2004; Wang et al., 2008). Similarly, loss-of-function mutants of PLASTOCHRON2 (PLA2) also result in accelerated development rates (Kawakatsu et al., 2006, 2006; Mimura and Itoh, 2014). Additionally, PLA3, a rice ortholog of Arabidopsis ALTERED MERISTEM PROGRAM 1 (AMP1) and maize VIVIPAROUS8, has been identified as a positive regulator of plastochron. Several genes, including MANY-NODED DWARF (MND) genes in barley (Hibara et al., 2021) and members of the SQUAMOSA PROMOTER BINDING PROTEIN- LIKE (SPL) transcription factor family, have also been implicated in regulating plastochron. Notably, miR156, a microRNA, negatively regulates genes belonging to the SPL transcription factor family. Overexpression of miR156 results in a shorter plastochron and accelerated development rate in various plant species (Xie et al., 2006; Xie et al., 2012), while silencing of specific SPL paralogs can either reduce or increase the development rate in petunia (Preston et al., 2016). Furthermore, plant growth hormones such as auxins, cytokinins, and gibberellins also play crucial roles in modulating development rate (Reinhardt et al., 2000; Werner et al., 2001). Despite these significant advances, there are still gaps in our understanding of the control of development rate. The identification of candidate genes for development rate control in petunia involved a multi-step approach combining genetic mapping, RNA sequencing, and literature analysis. Quantitative trait loci (QTL) associated with development rate were identified in two F7 recombinant inbred lines (RILs) derived from crosses between different wild progenitor species of petunia, P. integrifolia × P. axillaris (IA population) and P. axillaris × P. exserta (AE population), across multiple temperatures (Guo et al., 2017). Additionally, differentially expressed genes (DEGs) between IA RILs exhibiting slow and fast development rates were identified through RNA sequencing analysis in the same study (Guo et al., 2017). Candidate 15 genes potentially involved in development rate control were selected using five criterion, which included parameters such as significance of QTL association, differential expression levels, and functional relevance based on previous literature studies (Table 2-1) (Reinhardt et al., 2000; Prigge and Wagner, 2001; Miyoshi et al., 2004; Kawakatsu et al., 2006; Mimura and Itoh, 2014; Preston et al., 2016). Genes meeting at least two of these criteria were prioritized as candidate genes for further investigation. In total, 24 candidate genes were identified through this selection process, representing promising targets for functional validation studies (Table 2-2). Reverse genetics techniques, like virus-induced gene silencing (VIGS), offer a powerful means to rapidly evaluate the function of genes by suppressing their expression and observing resultant phenotypic changes. This approach bypasses the need for time-consuming plant regeneration steps, enabling quicker functional genomics studies (Benedito et al., 2004; Unver and Budak, 2009). VIGS relies on the RNA-silencing mechanism, where specific gene sequences are integrated into a viral vector, such as Tobacco rattle virus vector (TRV2), which is then introduced into the plant genome using Agrobacterium-mediated delivery (Reid et al., 2009; Zulfiqar et al., 2023). Once inside the plant cells, the viral vector triggers the degradation of mRNA molecules corresponding to the targeted genes, thereby silencing their expression. This process mimics the plant's natural defense mechanism against viruses. Researchers have successfully utilized VIGS to characterize phenotypes by silencing candidate genes associated with specific traits of interest. The versatility and effectiveness of VIGS have been demonstrated in various plant species, including petunia, Arabidopsis, tomato, tobacco, potato, barley, and more (Hein et al., 2005; Reid et al., 2009; Velásquez et al., 2009; Noor et al., 2014; Tomar et al., 2021; Singh et al., 2022). The objective of the current study was to assess the role of candidate genes in controlling development rate in petunia through the application of VIGS. By reducing or silencing the 16 expression of these candidate genes, the aim was to investigate whether alterations in gene expression levels would lead to observable changes in the development rate phenotype of petunia plants. MATERIALS AND METHODS Plant material and growth conditions P. axillaris (PI 667515) seeds were sown in a 72-cell tray with a cell volume of 16.4 cm3, under short day conditions (9-h light/ 22 C) in a growth chamber. Seedlings with 2-3 true leaves were moved to 50-cell trays with 75.4 cm3 per cell, a few days before Agroinfiltration. Plasmid construction and gene cloning The VIGS vectors used in this study were derived from Tobacco rattle virus and consisted of pTRV2-LIC (Dong et al., 2007) and pTRV1 (Liu et al., 2002). Plasmid miniprep was performed with EZ-10 Spin Column Plasmid DNA Miniprep Kit (Bio-Basic, Amherst, New York) to extract pTRV2-LIC vector DNA. This plasmid vector was linearized by digesting it with the PstI-HF®, restriction endonuclease (New England BioLabs (NEB), Ipswich, Massachusetts) and purified. Gene-of-interest target sequences of around 300 bp were designed (Table 2-3) by using the VIGS tool in the Sol Genomics Network (SGN) (Fernandez-Pozo et al., 2015). Forward and reverse primers were designed for each gene construct (Tm  60 C) using the primer design tool in the Benchling program (https://www.benchling.com) (Table 2-3). Two 15- bp adapter sequences, 5’-CGACGACAAGACCCT -3’and 5’- GAGGAGAAGAGCCCT- 3’, described previously (Dong et al., 2007), were included in the forward and reverse primer sequences of each gene, respectively. RNA was extracted from the leaves with the RNeasy Plant Mini kit (Qiagen, Germantown, Maryland). The gene segments were amplified with the iTaq™ Universal SYBR® Green One-Step Kit (Bio-Rad, Hercules, California) with cDNA as a 17 template. PCR products were purified with the EZ-10 Spin Column PCR Products Purification Kit (Bio-Basic, Amherst, New York) and run on a 1% agarose gel to visualize the ca. 300 bp bands. Ligation independent cloning (LIC) was performed to insert the gene of interest segments into the pTRV2-LIC vector (Dong et al., 2007). Briefly, both the vector and PCR products were treated with the T4 DNA polymerase (NEB) at 22 C for 30 min and 70 C for 20 min on a thermocycler. Following, the TRV2-LIC vector and PCR products were mixed in a 1:1 ratio and incubated at 65 C for 2 min and 22 C for 10 min to facilitate the covalent bonding. Then 6 L of the final LIC product were mixed with DH5 (E. coli) competent cells (NEB), incubated on ice for 30 min followed by a heat shock treatment at 42 C for 55 seconds and then back on ice for 2 min. The cells were then mixed with 600 L of the SOB (Super Optimal broth) medium and shaken at 200 r.p.m and 37 C for an hour. Finally, 80 l of the cells was spread on LB agar + Kanamycin plates and incubated overnight at 37 C. Transformants were tested by colony PCR using Phusion® High-Fidelity DNA polymerase (NEB). Primer sequences used for colony PCR were forward or reverse primer sequences specific to each gene and a TRV2- forward or reverse primer. Positive colonies were cultured overnight in a liquid LB + Kan medium on a shaker at 200 r.p.m and 37 C. Plasmid DNA was extracted, purified, and sent for Sanger sequencing at MSU’s genomics core facility. A total 6 L of the mixture containing 4 L of the plasmid DNA and 2 L of the TRV2- forward (5’-TGTTACTCAAGGAAGCACGATGAGCT -3’) or reverse primer (5’-AACTTCAGGCACGGATCTACTTA -3’) was used for sequencing. MEGAX was used for sequence alignment and verification of the constructs. Positive sequences (TRV2-LIC + gene of interest) were transformed into Agrobacterium tumefaciens GV3101 competent cells as previously described (Gelvin, 2012). 18 VIGS inoculation The VIGS protocol was followed as previously described (Velásquez et al., 2009). Day 1 of the protocol includes growing Agro-transformed pTRV1 vector, pTRV2-gene product, pTRV2-empty vector, and pTRV2-PDS on LB agar plates supplemented with 50 g/mL kanamycin and 100 g/mL rifampicin antibiotics for two days at room temperature. On day 3, the colonies are cultured on a liquid LB with the same antibiotics by shaking at 200 r.p.m. and 30 C for 16-18 hours. On day 4, the primary culture is diluted at 1:25 into the secondary induction medium (IM) (Velásquez et al., 2009) with the above-mentioned antibiotics plus 200 M acetosyringone and shaken at 200 r.p.m at 30 C for 20-24 hours. Finally, day 5 steps (Velasquez et al., 2009) are followed and pTRV1 and pTRV2 vectors are mixed 1:1. The inoculum finally becomes ready for the infiltration. Plants with 2-4 true leaves were used for inoculations (Figure 2-1). Two fully expanded leaves on each plant were scratched with a blade to poke a hole. Agroinfiltration was carried out by injecting the inoculum into the leaves to the point of cell saturation by using a 1 mL needless syringe. At least 15 plants were used per each construct. Plants for each construct were separated by a row and gloves were changed in between constructs to avoid cross-contamination. Plants were also not watered for at least 24 hours after the inoculations. pTRV2-E vector and wild type were used as negative controls and pTRV2-PDS was used as a positive control in each experiment. Silencing evaluation by qPCR analysis The onset of photobleaching symptoms on plants inoculated with pTRV2-PDS were used to schedule the tissue collection from all plants (Figure 2-1). The youngest fully expanded leaf tissues (~100 mg) were collected from each plant on a 96 well-plate and flash frozen with liquid 19 nitrogen and either used immediately or stored at -80 C for future use. RNA was extracted using the MagMAX™ Plant RNA Isolation Kit (Thermofisher Scientific, Waltham, Massachusetts), quantified using Nanodrop, and run on a 1% agarose gel. Gene-specific primers that amplify the region outside of the VIGS targeted region were designed with Net Primer https://www.premierbiosoft.com/netprimer/) and Benchling program tools (Table 2-4). Standard curves were made by serial dilutions of the TRV2-E RNA at five different concentrations (150, 100, 50, 25 and 12.5 ng). Primer efficiency was calculated as Efficiency (%) = (10(-1/slope of standard curve)-1) X 100 and primers with 80-110% efficiency were utilized. 10 L qPCR reactions were performed by using iTaq™ Universal SYBR® Green One-Step Kit (Bio-Rad) on a 384-well plate. The MIQE guidelines were followed for these qPCR experiments, ensuring standardization and reproducibility (Taylor et al., 2010). The qPCR conditions were 50 C for 15 min, 95 C for 1 min, 95 C for 20 sec, 55 C for 20 sec, 72 C for 1 min, repeat cycles 2-5 34 times and 72 C for 3 min. EF1 was used as a housekeeping gene (Mallona et al., 2010). Gene sequence of P. axillaris EF1 (Peaxi162Scf00389g00936.1) was extracted from the Jbrowse tool of SGN and primers were designed using Net Primer (https://www.premierbiosoft.com/netprimer/). The 2- ΔΔCT method (Livak et al., 2013)was used to calculate the relative expression of genes. Phenotyping Plants were moved to 15.24 cm diameter pots with a cell volume of 1420.76 cm3 in the greenhouse under short day conditions (9-hr light/ 22C) after tissue collection. Topmost fully expanded leaves on the main stem and two side branches per plant were marked with a white paint (Figure 2-1). The number of new nodes developed beyond that point were counted for each plant. We collected data at two time points, 4- and 6-week time intervals depending on the growth of the plant. We did not count the nodes on plants that were already flowering. 20 Data analysis Boxplots were generated to compare the node numbers of plants with relative gene expression thresholds of  0.4 for each gene construct separately by using R. One-way analysis of variance (ANOVA) was performed to test for significant differences between the boxplots and Tukey's HSD test was performed for pairwise comparisons between the boxplots in R (p  0.05). Phylogenetic analysis of MEI2-like genes Sequence data of Arabidopsis, maize, rice and yeast MEI2-like genes was retrieved from NCBI using the accession numbers previously described (Kaur et al., 2006). To identify MEI2 genes in petunia, the gene sequences from these species were blasted against the petunia genome (P. axillaris v1.6.2) in the Sol Genomics Network (Bombarely et al., 2016). The mRNA sequences of five MEI2 -like genes were found by BLAST search and used in this study: Peaxi162Scf00023g00929.1 ("MEI2-like protein 1"), Peaxi162Scf00128g01746.1 ("MEI2-like protein 1"), Peaxi162Scf00035g02717.1 ("MEI2-like protein 5"), Peaxi162Scf00214g00068.1 ("MEI2-like protein 5") and Peaxi162Scf00111g00117.1 ("MEI2-like protein 5"). Sequences were aligned using ClustaIW in MegaX (Kumar et al., 2018; Stecher et al., 2020). The evolutionary history was inferred by using the Maximum Likelihood method and Hasegawa-Kishino-Yano model (Hasegawa et al., 1985). The tree with the highest log likelihood (-91168.45) is shown. Initial tree(s) for the heuristic search were obtained automatically by applying Neighbor-Join and BioNJ algorithms to a matrix of pairwise distances estimated using the Maximum Composite Likelihood (MCL) approach, and then selecting the topology with superior log likelihood value. A discrete Gamma distribution was used to model evolutionary rate differences among sites (5 categories (+G, parameter = 4.5615)). The tree is drawn to scale, with branch lengths measured in the number of substitutions per site. This analysis involved 18 21 nucleotide sequences. There were a total of 9303 positions in the final dataset. Evolutionary analyses were conducted in MEGA X (Kumar et al., 2018; Stecher et al., 2020). RESULTS AND DISCUSSION We aimed to use VIGS as a high-throughput method to screen multiple candidate genes for their potential role in regulating development rate in plants. VIGS is a technique used to downregulate the expression of specific plant genes by introducing a virus that carries a fragment of the target gene's sequence. This leads to gene silencing and allows researchers to study the effects of gene knockdown on plant phenotypes. We selected 24 candidate genes based on their potential involvement in regulating development rate (Table 2-1). The designed gene fragments were cloned into the pTRV2 vector. Out of the 24 candidate genes, only 17 were successfully cloned into the pTRV2 vector and, for the remaining 7 genes, efficient VIGS target regions could not be designed. This was either because the genes were too small to yield suitable target regions or because there were too many off-target regions, making it difficult to design gene-specific VIGS constructs. The successful cloning of 17 genes into the VIGS vector allowed for the subsequent silencing of these genes in plants to assess their phenotypic effects. The first step in the phenotype evaluation process involved confirming whether the plants were effectively silenced using real-time quantitative PCR (qPCR). From qPCR analysis, genes Peaxi00008 and Peaxi00014 consistently showed high Cq values on the standard curve, indicating low expression levels. The large difference (~10 cycles) between the Cq values of these genes and the reference/housekeeping gene (EF1α) made it challenging to measure their expression levels accurately. Such genes with low mRNA abundance are reportedly known to be less susceptible to silencing by VIGS-like methods (Hu et al., 2004). Peaxi00012 presented multiple peaks on the melt curve with different qPCR primer sets, indicating the possibility of amplifying off-target products (Figure 2-2). However, the VIGS target region of Peaxi00012 was 22 designed with a target score of 100%. Plants inoculated with constructs targeting Peaxi00008.2, Peaxi00015, Peaxi00016, Peaxi00125, and Peaxi01115 genes exhibited photobleaching symptoms similar to those seen in Phytoene desaturase (PDS) inoculated plants (Figure 2-3). PDS is involved in carotenoid biosynthesis crucial for photosynthesis, and loss-of-function mutants display photobleached leaves due to chlorophyll disruption (Wang et al., 2009). PDS is often used as a positive control in VIGS experiments due to its easily observable phenotype (Fu et al., 2006). The reason for non-PDS inoculated plants displaying albino symptoms remains unknown particularly since precautions were taken to prevent cross-contamination, including changing gloves between constructs and spatially separating PDS-treated plants from others. We utilized the PDS phenotype as a visual indicator to guide tissue collection for qPCR analysis. Typically, PDS mutants exhibit symptoms within 10-14 days post-inoculation. However, we observed occasional delays in phenotype expression, possibly due to environmental factors such as cooler temperatures and lower humidity levels, as reported in previous studies on tomato (Fu et al., 2006). Upon evaluating relative gene expression levels in inoculated plants, we noted a wide range of expression values, consistent with observations in PDS-inoculated plants where the albino phenotype varied (Figure 2-4), suggesting that plants could still produce chlorophyll to some extent. To explore the correlation between PDS phenotype and relative expression levels, individual PDS leaves were subjected to qPCR analysis. Interestingly, leaves with both low (0.2) and moderate (0.5) relative expression levels exhibited weaker phenotypes, while a leaf with a relative expression level of 0.4 showed no phenotype (Figure 2-5). These findings suggest that qPCR expression levels reflect a general down-regulation of the PDS gene but may not precisely quantify the number of translationally active transcripts (Urso et al., 2013). This observation aligns with previous studies in petunia (Broderick and Jones, 2014), 23 highlighting the significance of thoroughly understanding mRNA degradation processes for effective gene suppression. The efficiency of VIGS in plants is heavily dependent on the systematic movement of the viral construct throughout the entire plant. Effective viral movement ensures the downregulation of endogenous genes, while ineffective movement hampers gene silencing. Factors such as the inoculation method, cultivar choice, and environmental conditions, particularly temperature, influence viral movement in plants. Previous studies (Caplan and Dinesh‐Kumar, 2006; Muruganantham et al., 2009; Zulfiqar et al., 2023) have highlighted the critical role of these factors in regulating viral movement and subsequent gene silencing. Optimal conditions for VIGS have been demonstrated in petunia, with the Picobella Blue cultivar identified as the most suitable for efficient gene silencing (Broderick and Jones, 2014). We conducted preliminary studies with Picobella Blue, but its compact growth habit made it difficult to evaluate the development rate phenotype, particularly in counting the number of nodes due to the dense growth. As a result, we decided to continue using the P. axillaris genotype for our experiments. Furthermore, factors contributing to variability in silencing efficiency are attributed to the viral method itself. VIGS operates through an RNA interference-based mechanism, where double- stranded RNAs (dsRNAs) produced in response to the virus are cleaved into short interfering RNAs (siRNAs) by the enzyme DICER. These siRNAs then guide the RNA-induced silencing complex (RISC) to degrade complementary target mRNAs (Zulfiqar et al., 2023). Various properties related to both siRNA sequences and target mRNAs influence the efficacy of siRNA- mediated gene silencing (Holen et al., 2002; Czauderna et al., 2003; Hu et al., 2004). For instance, the presence of secondary structures in the target mRNA or its specific subcellular localization makes it inaccessible to the siRNA-mediated degradation by the RISC complex (Holen et al., 2002). Additionally, structural variation and chemical properties of siRNAs also 24 impact their effectiveness in silencing. Moreover, a sequence complementarity of less than 11 nucleotides between siRNA and mRNA reduces the likelihood of successful gene silencing. These insights underscore the complexity of VIGS as a gene silencing tool and emphasize the importance of understanding the various factors influencing its efficiency for successful gene knockdown experiments. We focused on analyzing genes showing at least 60% silencing or relative expression values of ≤0.4, namely Peaxi00310, Peaxi00316, Peaxi00929, Peaxi00027, Peaxi00048, Peaxi00073, Peaxi00737, and Peaxi01330. This targeted approach allowed for a more focused and efficient analysis of gene function in relation to the highly quantitative trait under investigation. Node numbers of the plants were compared to the wild type and empty vector negative controls. The use of Peaxi162Scf00069g01624.1, a SQUAMOSA PROMOTOR BINDING-LIKE PROTEIN 12 (SPL12) gene, as a negative control did not yield significant results due to inefficient silencing. This gene is a homolog of Petunia × hybrida SPL gene (PhSBP2) known to negatively regulate development rate in petunia (Preston et al., 2016). There were no significant differences in node numbers between plants with gene expression levels ranging from 0.10 to 0.44 (equivalent to 90% to 56% silencing) and the empty vector wild type controls, both at the main stem and side branches (Figure 2-6). Similarly, comparing plants with gene expression levels of 0.1-0.4 to those with expression values of 0.5-0.9 revealed no significant differences in node numbers (Figure 2-7). Additionally, when evaluating the phenotype of inoculated plants at a relatively early developmental stage (four week-interval), no significant differences were observed in node numbers between plants with at least 80% silencing and wild type controls, both at side branches and the main stem (Figure 2-8). However, despite the lack of significant differences in phenotypic outcomes among effectively silenced plants, it is noted that the limited number of plants hindered robust conclusions, particularly 25 regarding Peaxi00929, which tended to produce higher node numbers compared to controls when relative expression was 0.4 or less. The gene Peaxi00929, functionally annotated as an MEI2-like protein 1 in petunia, is of particular interest due to evidence suggesting its involvement in development rate regulation (Guo et al., 2015; Guo et al., 2017). A previous study uncovered evidence indicating the presence of a CAPS marker co-localizing with a homolog of MEI2-like 1 within a development rate QTL region in an interspecific P. integrifolia x P. axillaris F2 population (unpublished results from a study conducted by Guo et al., 2015). Despite this QTL region likely containing other genes, this finding serves as an additional piece of evidence supporting the significance of this gene in the regulation of development rate. MEI2 like genes belong to a class of RNA binding protein genes, initially identified in the fission yeast Schizosaccharomyces pombe (Hirayama et al., 1997). The gene family is categorized into three functional clades based on sequence similarity, with two of these clades, Arabidopsis meiotic -like (AML), and terminal ear-like (TEL), identified as functional in plants (Jeffares et al., 2004). Genes belonging to these clades have been characterized in various plant species, including Arabidopsis, rice, maize, and barley (Hirayama et al., 1997; Jeffares et al., 2004; Mercier and Grelon, 2008; Wang et al., 2022). TEL clade genes, such as TERMINAL EAR1 (TE1) in maize and TEL1 and TEL2 in Arabidopsis, are known to be expressed in the shoot apical meristem and play roles in plant architecture. Loss-of-function mutants of TE1 in maize and its rice ortholog PLA2, have been shown to result in accelerated leaf initiation rates and dwarf phenotypes (Kawakatsu et al., 2006; Mimura and Itoh, 2014; Wang et al., 2022). AML genes, including AML1-5, are broadly expressed in both vegetative and reproductive tissues and are involved in meiosis and vegetative development (Anderson et al., 2004; Kaur et al., 2006). Loss-of-function mutants of AML1 and AML4 genes in Arabidopsis have been reported to lead to retarded seedling growth and seedling arrest (Kaur et al., 2006). 26 This evidence suggests that Peaxi00929, as a MEI2-like 1 gene, may play a significant role in regulating development rate in petunia, possibly through mechanisms involving RNA binding and meiotic functions, similar to its counterparts in other plant species. Our phylogenetic analysis of MEI2 genes from various species, including petunia, Arabidopsis, rice, maize, and yeast, provides valuable insights into the evolutionary relationships and classification of these genes into distinct clades (Figure 2-9). The phylogenetic tree reveals four main clades: two AML (AML 14 and AML 235) clades, a TEL clade, and a non-plant clade, consistent with previous studies (Anderson et al., 2004; Jeffares et al., 2004; Kaur et al., 2006). Interestingly, two petunia MEI2-like 1 genes including Peaxi00929.1 belong to the AML14 clade and three petunia MEI2-like 5 genes belong more broadly to AML235 clade, contrasting with the well-defined role of TEL clade genes in development rate regulation. This observation suggests a potentially novel role for AML14 clade genes in controlling vegetative growth traits. Therefore, the phylogenetic analysis supports the notion that MEI2-like genes, particularly those belonging to the AML14 clade, are promising candidate genes for the regulation of development rate- related traits. Further functional characterization of these MEI2-like genes in petunia could provide valuable insights into their specific roles in vegetative growth and development. This research avenue could lead to a deeper understanding of the unknown molecular mechanisms underlying vegetative development rate. We propose to functionally analyze the MEI2-like 1 gene using an efficient gene knockout method, particularly considering the need for consistent and widespread silencing to accurately evaluate the phenotype. Although the inefficient gene silencing is not uncommon in VIGS (Bennypaul et al., 2012), this method is more valuable for studies where the phenotype evaluation is straightforward and can be visually characterized. VIGS may not be ideal for studies where precise and consistent silencing is required, especially for quantitative traits or 27 complex phenotypes. In such cases, alternative gene editing techniques like CRISPR-Cas9 may offer better precision and control over gene manipulation. Clustered regularly interspaced short palindromic repeats (CRISPR) associated Cas9 has emerged as a popular gene editing tool for gene function analysis in various organisms, including plants (Noman et al., 2016). This method has been successfully employed in petunia for gene editing targeted towards various traits such as flower color, flower longevity, and disease resistance (Subburaj et al., 2016; Xu et al., 2021; Lin and Jones, 2022; Xu et al., 2023). However, it is important to acknowledge several key considerations while proposing this research. Firstly, the phenotyping method for development rate should be well-defined. While our initial approach involved counting nodes on two branches and a main stem of each plant, it may be beneficial to study the variation in node number across the entire branches to capture a more comprehensive understanding of development rate. Additionally, determining the critical time period for phenotyping plants for development rate is crucial. The shoot apical meristem undergoes morphological and genetic changes as plants progress through different stages of the vegetative phase, so identifying the optimal time window for phenotypic assessment will be essential for accurate evaluation. CONCLUSION In summary, our study on evaluating development rate candidate genes using the rapid evaluation VIGS method offers valuable insights into the challenges associated with this approach when investigating complex traits such as development rate. While we have identified varying levels of gene expression levels, indicative of inefficient silencing, we have also identified a promising candidate gene that warrants further investigation. Moving forward, further work is needed to test the promising candidate gene using stable and more reliable silencing method, such as CRISPR-Cas9, to accurately assess its effect on development rate. 28 Combining CRISPR-Cas9-mediated gene editing with thorough and well-defined phenotyping methods will enable a robust analysis of the role of MEI2-like 1 gene in controlling development rate in petunia. This approach holds promise for advancing our understanding of the genetic mechanisms underlying vegetative development rate and may inform breeding efforts aimed at improving crop timing in plants. 29 Tables & Figures Table 2-1: Description of the five criteria used to select candidate genes for development rate. The first two criteria were based on the transcriptomics studies in IA and AE RILs (Guo et al., 2017). Third criterion is based on the P. axillaris.v.162 genome (Bombarely et al., 2016). Fourth and fifth criteria are based on the literature studies of development rate and expression profile of genes. Criteria Differentially expressed genes between fast- and slow-developing IA RILs. Differentially expressed genes mapping close (<1 cM) to a development rate QTL. Located on genomic scaffolds harboring SNP markers underlying a QTL. Related to pathways previously implicated in the control of development rate. Preferentially expressed in a shoot apex tissue. 1. 2. 3. 4. 5. 30 Table 2-2: Summary of the candidate genes. GeneID and functional description are taken from P. axillaris. v1.6.2 genome in the Sol genomics network database. Third column lists the criteria (Table 2-1) related to each gene. Fourth column indicates the short form of the genes used in this study. GeneID Functional description Peaxi162Scf00572g00008.1 "Zinc finger CCCH domain-containing protein 32” Criteria matching 1, 2, 4 Peaxi162Scf00377g00012.1 “expansin B2” Peaxi162Scf01147g00014.1 “Homeobox protein knotted-1-like 6“ Peaxi162Scf00919g00310.1 “squamosa promoter-binding protein-like 1, 2, 4 3, 4, 5 3, 4 12” Short form Peaxi00008 Peaxi00012 Peaxi00014 Peaxi00310 Peaxi162Scf00953g00316.1 “carotenoid cleavage dioxygenase _1” Peaxi162Scf00023g00929.1 “MEI2-like protein 1” Peaxi162Scf00829g00016.1 "Regulator of chromosome condensation 1, 2, 4, 5 3, 4 1, 4, 5 Peaxi00316 Peaxi00929 Peaxi00016 (RCC1) family protein" Peaxi162Scf00316g00027.1 “Argonaute family protein” Peaxi162Scf00141g00048.1 "Auxin transporter-like protein 2" Peaxi162Scf00062g00073.1 "scarecrow-like 3" Peaxi162Scf01141g00125.1 "IAA-amino acid hydrolase ILR1-like 4" Peaxi162Scf00367g00737.1 "Leucine-rich repeat protein kinase family protein" Peaxi162Scf00023g01330.1 "zinc finger (C3HC4-type RING finger) family protein" Peaxi162Scf00461g00008.1 "CBS domain-containing protein" Peaxi162Scf01178g00015.1 "Gibberellin 20 oxidase 2" Peaxi162Scf00304g01115.1 "HAD superfamily subfamily IIIB acid phosphatase" Peaxi162Scf00069g01624.1 “squamosa promoter-binding protein-like 12” 3, 4 3, 4 3, 4, 5 1, 4 3, 4 1, 4 1, 2 1, 4 1, 4 Peaxi00027 Peaxi00048 Peaxi00073 Peaxi00125 Peaxi00737 Peaxi01330 Peaxi00008.2 Peaxi00015 Peaxi01115 Control Peaxi01624 31 Table 2-3: Summary of the primers used for amplifying virus induced gene silencing (VIGS) target regions of the candidate genes. Forward (F) and reverse (R) primers are up to 30 bp in length excluding 15bp adapter sequences (refer to Methods section). GeneID Peaxi162Scf00572g00008.1 Peaxi162Scf00377g00012.1 Peaxi162Scf01147g00014.1 Peaxi162Scf00919g00310.1 Peaxi162Scf00953g00316.1 Peaxi162Scf00023g00929.1 Peaxi162Scf00829g00016.1 Peaxi162Scf00316g00027.1 Peaxi162Scf00141g00048.1 Peaxi162Scf00062g00073.1 Peaxi162Scf01141g00125.1 Peaxi162Scf00367g00737.1 Peaxi162Scf00023g01330.1 Peaxi162Scf00461g00008.1 Peaxi162Scf01178g00015.1 Peaxi162Scf00304g01115.1 Peaxi162Scf00069g01624.1 gttccatgagctttccgaattt Forward (5’ – 3’) gcgacaagagaatgctcctacta caactatcccggagtatcactgg atggatcaacatgaaatgtatggtt tccatgaatggggataaaggc gaacaaatacaagaacaagccaa gtgctgcctctagttcctattttaa ttacaaaattcccatccatgtgctgca gacattttgaacagtttcttgtgcttttgg aatgcagcagagaaacctccatttttc aatggaaaacatgctatttggcgagg gatgagtgtttgaatccgttattga agggttggtttcacttttaaagctag attgcgactttgatggcctc aaacagacgaccaaccggaatatatc gaaggtgtaccaagattactgcaatgc tagcacgctttagtacccataaaatct Reverse (5’ – 3’) cgagtaagtgctccagttctgaa gatcgataagtttttccgggc aatacttgaaccaccttcatcaatat agttgtactttccctgcctggc taatttagccctgccgctatca tgattgcccaatattaatagttgaaga ccctgctccatcttttgacattccatc gccttgaacttggcctcataaagg gactcttgaaattctgcaacacc cacctcttgcatctccaagacga ctgaataggcaaagcatccatg tttaggtctgctatagactcaggtattc acttatgttttgaggtcactgcg tcagaatcaccttcttccacaacctt tcctccaataccatcttgatggaggat ctaattcaacttcatgatctttgtattctt gcttttacattttaggtttcactattaac 32 Table 2-4: Summary of the primers used for qPCR reactions. Primers are designed outside the VIGS target region to amplify the target gene. Gene ID Peaxi162Scf00572g00008.1 Peaxi162Scf01147g00014.1 Peaxi162Scf00919g00310.1 Peaxi162Scf00953g00316.1 Peaxi162Scf00023g00929.1 Peaxi162Scf00316g00027.1 Peaxi162Scf00141g00048.1 Peaxi162Scf00062g00073.1 Peaxi162Scf00367g00737.1 Peaxi162Scf00023g01330.1 Peaxi162Scf00389g00936.1 Peaxi162Scf00038g02444.1 Peaxi162Scf00069g01624.1 Forward (5’ -3’) cgctgcagttaggaggaagtgc ggagctgatcctgaactcgatgagt gcctggttgcgttgtgttaacg tgcaaacgattggcatgctgga taagaggtgccgtccgatcctc cgtgccaaagaccaagagatcg tcttgttggtagctggactgca agaaactgcgtgtgaaaacggg ctgatctttatccagatccttgtggt ctcaatccacacctgcaagctc tggtactgtccctgtcggtcgt gccagcaatgcttggaggacaa cagaggttctgccaacaatgca Reverse (5’-3’) ggtggtggaacttgcatgatgc tgtagtcgcttcattgaaaggcct tcaccagggacatccaaaagcc aggtggtgtctgtgagtagcca tgccaactcgacttttgctggt tccacggatgcttgcttttcac aaggcctgctgctttccagtat catgggcaagaagagtgtgcaa gggctgaattcaacatttggtgc cacttgtatcctggtccagcct cgagctccttaccagatcgcctgt ctgtcaccctatctggcacacc gccggcggcaactccttttagt 33 Figure 2-1: Overview of the VIGS protocol step-by-step starting from an inoculation stage to the data collection. Inoculations at 2-3 true leaf stage, ~20 plants/gene PDS start showing symptoms after 10-14 DAI, collect tissues for qPCR on ~20th day Moved to greenhouse under short days and mark the topmost fully opened leaves Count new number of nodes developed at 4–6- week time interval depending on the growing season and growth of the plant 34 Figure 2-2: Melt curve of Peaxi162Scf00377g00012.1 with two different primer sets. Y-axis represents the negative derivative of fluorescence and x-axis represents temperature. 35 Figure 2-3: P. axillaris PI667515 seedlings inoculated with Peaxi162Scf00829g00016.1 (A), Peaxi162Scf01141g00125.1 (B), and PHYTOENE DESATURASE (PDS) control construct (C). A C B 36 Figure 2-4: Two plants with PHYTOENE DESATURASE (PDS) gene silenced seven weeks post inoculations. PDS inoculated plants were used as positive controls for all VIGS experiments. 37 Figure 2-5: Leaves from three different PDS plants and their relative qPCR expression. PDS inoculated plants were used as positive controls for all VIGS experiments. PDS1 PDS2 PDS3 Leaf PDS1 PDS2 PDS3 Relative expression 0.2 0.52 0.40 38 Figure 2-6: Boxplots comparing the node number distribution of plants inoculated with different gene sets (qPCR relative expression threshold 0.4). Node numbers were counted after 6-week interval for this dataset. Letters on top of boxplots separate the boxplots based on their statistical difference (p  0.05). Y-axis represents average number of nodes on side branches (A) and number of nodes on the main stem (B). X-axis represents each gene construct. Numbers written after the dot on each gene construct indicate the number of plants for each gene construct. A B 39 Figure 2-7: Boxplots comparing the node number distribution of plants inoculated with different gene sets with qPCR relative expression 0.4 and 0.5-0.95. Node numbers were counted after 6- week interval for this dataset. Letters on top of boxplots separate the boxplots based on their statistical difference (p  0.05). Y-axis represents average number of nodes on side branches (A) and number of nodes on the main stem (B). X-axis represents each gene construct. Numbers written after the dot on each gene construct indicate the number of plants for each gene construct. Table (C) represents the range of relative expression values of plants evaluated in (A) and (B). A B ti C Plant 27_0.5 Relative expression range 0.47-0.93 27_0.4 0.23-0.44 73_0.5 0.48-0.68 73_0.4 0.10-0.32 737_0.5 0.53-0.60 737_0.4 0.15-0.36 929_0.5 0.49-0.83 929_0.4 0.17-0.36 1330_0.4 0.12-0.31 40 Figure 2-8: Boxplots comparing the node number distribution of plants inoculated with different gene sets (qPCR relative expression threshold 0.4). Node numbers were counted after 4- week interval for this dataset. Letters on top of boxplots separate the boxplots based on their statistical difference (p  0.02). Y-axis represents average number of nodes on side branches (A) and number of nodes on the main stem (B). X-axis represents each gene construct. Numbers written after the dot on each gene construct indicate the number of plants for each gene construct. Table (C) represents the range of relative expression values of plants evaluated in (A) and (B). A B C Plant Relative expression range 310 316 929 0.08-0.13 0.04-0.21 0.07-0.21 41 Figure 2-9: Phylogenetic tree of MEI2-like genes from Arabidoposis thaliana (AML1 -AML5, TEL1 and TEL2), Oryza sativa (OML1-OML5), Zea mays (TE1), Petunia axillaris (three MEI2- like protein 5 and two MEI2-like protein 1 genes) and a MEI2-like gene from Schizosaccharomyces pombe (Yeast Mei2). 42 CHAPTER 3 TRANSCRIPTOMIC ANALYSIS OF PETUNIA RECOMBINANT INBRED LINES (RILS) WITH CONTRASTING DEVELOPMENT RATES 43 INTRODUCTION Vegetative development rate, defined as the rate of nodes or leaves produced over time (expressed as nodes day⁻¹) (Warner and Walworth, 2010), is a crucial biological phenomenon in crop plants. This rate directly affects crop timing, such as the time to first yield, and indirectly influences seasonal biomass accumulation in forage and bioenergy crops by determining the number of leaves available for photosynthetic carbon fixation. Leaf production is initiated by the shoot apical meristem during post-embryonic vegetative shoot development (Stuurman et al., 2002), and it varies across species and within germplasm pools. This variability suggests that it is possible to alter the development rate to enhance production efficiency (e.g., early flowering or fruiting), yield, and biomass accumulation. Therefore, studying the genetic control of development rate is highly desirable. Petunia (Petunia  hybrida) ranks among the top annual bedding crops in the United States, with a wholesale sales value of approximately $160 million in 2020 (USDA, 2021). It is renowned for its diverse flower colors and is produced in greenhouses during the colder months in northern latitudes of North America and Europe (Guo et al., 2017). To meet spring production demands, greenhouses must be heated to ensure sufficient temperatures for flowering, resulting in significant energy costs and narrow profit margins for producers (Guo et al., 2015). Developing varieties with faster development rates at cooler temperatures could reduce these energy costs. Therefore, understanding the genetic mechanisms that control development rate is crucial. This knowledge can inform strategies to enhance development rates under sub-optimal temperatures, thereby optimizing crop timing and increasing the production efficiency of seasonal crops. 44 To investigate the genetics of development rate in petunia, F7 recombinant inbred lines (RILs) derived from the wild progenitor species P. integrifolia and P. axillaris (the IA population) were previously utilized (Guo et al., 2017). This study identified 209 differentially expressed genes (DEGs) between RILs exhibiting slow and fast development rates, as well as quantitative trait loci (QTL) associated with development rate. Some of these DEGs co-localized with QTL for development rate and/or had functions related to vegetative development as indicated in the literature, while many others had not previously been implicated in the control of development rate. Thus, these studies generated evidence for the genetic regulation of development rate in petunia, complementing the existing knowledge in the literature. However, despite these advancements, gaps in our understanding of this trait still exist. The current study aimed to understand the genetics of development rate by employing a second RIL population developed from a cross between P. axillaris and P. exserta (the AE population) (Guo et al., 2017). As P. axillaris and P. exserta diverged more recently than P. axillaris and P. integrifolia (Chen et al., 2007), this narrower cross was expected to identify novel genetic components. In 2014 and 2015, 171 AE RILs were grown under long-day conditions in the greenhouse (Guo et al., 2017). These RILs were phenotyped for development rate and other crop timing-related traits at three temperatures (14, 17, and 20°C). From this work, fast- and slow-developing RILs were carefully selected for the current study and further evaluated under greenhouse conditions to ensure robust identification of fast- and slow- development RILs. The primary objective was to explore the transcriptomics of these RILs, aiming to pinpoint genes that are consistently differentially expressed between fast- and slow- developing IA and AE RILs, and to leverage the narrower AE cross to uncover novel DEGs potentially involved in development rate control. Additionally, the study sought to identify a 45 subset of genes that co-expressed, shedding light on the pathways associated with development rate. MATERIALS AND METHODS Plant materials From the previous study by Guo et al. (2017), 12 and 13 RILs were consistently identified at the fast and slow ends of development rate, respectively, of the entire AE population across both years and three temperatures. To conduct the current study, seeds were sown in a 72- cell tray with each cell having a volume of 16.4 cm³ under short-day conditions (9 hours of light/22°C) in a growth chamber. Once the seedlings reached 4-6 nodes, they were transplanted into 15.24 cm round pots with a volume of 1420.76 cm³ and were grown in two replications following a randomized complete block design (RCBD) in a greenhouse under short-day conditions (9 hours of light/22°C). Each RIL was represented by at least five and maximum twenty-five plants. To track the number of new leaves produced over time, the edges of the two topmost fully expanded leaves on the main stem and two side shoots of each plant were marked with white paint. After four weeks from the date of marking, the number of new nodes/leaves produced were counted. Data analysis One-way ANOVA and Tukey’s HSD post-hoc tests were employed to identify the RILs with significant mean node number differences on both the main stem and side shoots (α=0.05). These statistical analyses were conducted using SPSS version 27 (IBM; Chicago, IL). RNA extraction and sequencing Shoot apex tissue samples of ca. 2 mm length from the tip were harvested and leaf primordia were removed as much as possible with forceps. These samples were collected from 46 all plants of the selected 13 RILs from both the replications, pooled for each RIL and stored at - 80°C. Total RNA was extracted by using MagMAXTM Plant RNA Isolation Kit (Catalog #A33784, Thermo Fisher Scientific). RNA samples were evaluated for Quality Control (QC) after running the samples on the TapeStation Analysis Software 3.2 at MSU’s Research Technology Support Facility (RTSF) Genomics core facility. RNA samples from ten RILs and their biological replicates (five each of fast and slow node production) with an RNA integrity (RIN) score ≥ 5.5 were selected for sequencing, resulting in a total of 20 samples. Libraries were prepared using the Illumina TruSeq Stranded mRNA Library Prep Kit with IDT for Illumina Unique Dual Index adapters following the manufacturer's recommendations. Completed libraries were QC’d and quantified using a combination of Qubit dsDNA HS and Agilent 4200 TapeStation HS DNA1000 assays. All 20 libraries were pooled in equimolar amounts and the pool was quantified using the Kapa Biosystems Illumina Library Quantification qPCR kit. This pool was loaded onto two lanes of an Illumina HiSeq 4000 Single Read flow cell. Sequencing was performed in a 1x50bp single end read format using HiSeq 4000 SBS reagents. Base calling was done by Illumina Real Time Analysis (RTA) v2.7.7 and output of RTA was demultiplexed and converted to FastQ format with Illumina Bcl2fastq v2.20.0. Quantification of transcripts The raw reads were trimmed for adaptor sequences and low quality using Trimmomatic version 0.39 (Bolger et al., 2014), and the quality of these reads was assessed using FastQC (Andrews, 2017). As sequencing for each sample was conducted on two separate lanes, reads from both lanes were merged into a single read. The reads were aligned to the P. axillaris genome v.1.6.2 (Bombarely et al., 2016) using STAR (Dobin et al., 2013). HTSeq (Putri et al., 47 2022) was then employed to count the number of reads for each gene, encompassing a total of 35,851 genes across all samples. Identification of differentially expressed genes (DEGs) DEGs analysis was conducted on the raw counts using the DESeq2 package in R with default parameters (Love et al., 2014). Before proceeding with the DEGs analysis, clustering of samples was performed using a Principal Component Analysis (PCA) plot and a heatmap of sample distances based on regularized logarithm (rlog) transformed counts. Outlier samples were removed from further analysis, resulting in three samples each of fast and slow categories. Subsequently, DEGs analysis was performed on three RILs each from the fast and slow node- producing categories. DEGs were identified using two approaches. In the first approach, the three RILs (and their biological replicates) from each fast and slow node-producing category were pooled into two conditions: "Fast" and "Slow", and DEGs between these two conditions were identified. In the second approach, each fast and slow genotype was considered as a separate condition, resulting in a total of six conditions: "Fast1", "Fast2", "Fast3", "Slow1", "Slow2", and "Slow3". Biological replicates were pooled for each condition. This approach aimed to identify DEGs that are robustly differentially expressed between RILs of fast and slow node-producing categories, resulting in a total of nine comparisons. DEGs were filtered using a p-value threshold of 0.05 and a log fold change threshold of |1.5|. Weighted gene co-expression network analysis (WGCNA) The WGCNA R package was employed to construct the coexpression network (Pei et al., 2017). Initially, samples were clustered using the 'hclust' function to detect and eliminate any outliers from further analysis. The R function 'pickSoftThreshold' was then utilized to calculate 48 the soft threshold power, employing "signed" networks and the "bicor" correlation function to build the adjacency matrix. A soft power of 22 was chosen based on an R2 fit of greater than or equal to 0.85. The Topological Overlap Matrix (TOM) was computed using the adjacency matrix, and gene dendrograms were plotted based on their dissimilarity. Hierarchical clustering and the dynamic tree cut function were subsequently employed to detect modules, with a tree cut height threshold of 0.25 used to cluster the module eigengenes. Gene significance (GS) and module membership (MM) were calculated to establish the relationship between modules and the development rate trait. Hub genes were identified from each module using MM >= 0.8 and GS >= 0.8 as thresholds. The corresponding module gene information was then extracted for further analysis. Gene ontology (GO) enrichment analysis Gene ontology (GO) enrichment analysis was conducted on both up- and down-regulated genes, as well as genes within significant modules, utilizing the GO enrichment tool in the PlantRegMap program (Tian et al., 2020). The analysis utilized topGO and Fisher’s exact tests to identify significantly over-represented GO terms (p-value < 0.05) within the input gene set, with all genes in P. axillaris serving as the background. Venn diagrams were generated using Venny 2.1.0 (Oliveros, 2016)to visualize the overlap of enriched GO terms between different gene sets. RESULTS Selection of slow and fast lines Selecting plants at the extremes of slow and fast development rates proved challenging due to the significant phenotypic variability in development rate. We focused on selecting RILs that were falling under one category (slow or fast) for at least three data points. Of the twenty- five evaluated RILs, thirteen RILs (highlighted in bold) falling under contrasting development 49 rate groups were selected for shoot apex tissue collection (Table 3-1). These thirteen RILs consisted of six fast (denoted with f; 45f, 85f, 110f, 173f, 193f and 319f) and seven slow developing RILs (denoted with s; 208s, 219s, 216s, 252s, 279s, 298s and 318s). Subsequently, RNA extraction was performed on these thirteen lines, each with two biological replicates. Specifically, five lines were selected from both the fast (45f, 85f, 110f, 173f and 319f) and slow (208s, 219s, 216s, 252s and 318s) development groups, ensuring that the chosen RNA samples had an RNA integrity (RIN) score of ≥ 5.5 (Table 3-2). Processing of reads RNA sequencing was performed to generate at least 25 million single-end 50 bp raw reads per sample. Quality control processes ensured that at least 99.7% of the reads survived filtering for adaptor sequences and low-quality reads. The percentage of uniquely mapped reads to the P. axillaris genome ranged from 84% to 91%, while 74% to 82% of the reads mapped to the exon regions of genes (Table 3-2). The number of reads for each gene in each sample was counted to identify differentially expressed genes. Differential gene expression analysis Pooled comparison PCA analysis revealed that the samples did not clearly group into distinct clusters based on slow and fast development rates, showing significant overlap instead (Fig. 3-1). Consequently, obvious outlier samples were removed, and samples from each category that formed close clusters were selected for further analysis. Specifically, three samples from the fast development group (AE319f, AE110f, and AE45f) and three from the slow development group (AE252s, AE219s, and AE216s) were chosen, with two biological replicates each, except for AE216s, which had one replicate. These samples were used for differential expression analysis (Fig. 3-1). 50 In the pooled comparison, 1834 genes were downregulated and 260 genes were upregulated in the slow development lines compared to the fast development lines (Fig. 3-2). Among the upregulated genes were those encoding GIBBERELLIN 2-OXIDASE (Peaxi162Scf00111g00920.1), an AUXIN EFFLUX CARRIER FAMILY PROTEIN (Peaxi162Scf00033g00711.1) and CAROTENOID CLEAVAGE DIOXYGENASE 8 (Peaxi162Scf00227g00714.1). Additionally, upregulated gene families included UDP- Glycosyltransferase superfamily protein, cytochrome P450 families 71, 76, 718, MATE efflux family protein, 2-oxoglutarate (2OG) and Fe(II)-dependent oxygenase superfamily protein (2OG-Fe(II)). Predominant gene families among the downregulated genes included the 2-oxoglutarate 2OG-Fe(II)-dependent oxygenase superfamily protein family (2OG-Fe(II)), basic helix-loop- helix (bHLH) DNA-binding superfamily protein, Pentatricopeptide repeat-containing protein (PPR), F-box/FBD/LRR-repeat protein, mitogen-activated protein kinase 17, 19, 20, 21 and MATE efflux family protein. Pairwise comparison Each individual slow and fast line was compared to identify robustly differentially expressed genes (Table 3-3 and Fig. 3-3). Generally, more genes were downregulated than upregulated in the slow development rate lines. Core DEGs were defined as genes that were commonly differentially expressed in at least six pairwise comparisons (Fig. 3-3). Only 14 core upregulated genes were identified, which were commonly upregulated in two slow lines (219s and 216s) (Fig. 3-3B). A total of 271 downregulated core DEGs were identified in all slow lines compared to the fast development rate lines (Fig. 3-3A). These genes included AUXIN RESPONSIVE FAMILY 51 PROTEIN (Peaxi162Scf00745g00810.1, Peaxi162Scf00006g00111.1, Peaxi162Scf00352g00718.1, Peaxi162Scf00945g00013.1), PPR CONTAINING PROTEIN (Peaxi162Scf00037g01030.1, Peaxi162Scf00604g00016.1), cytochrome P450 families 71 (Peaxi162Scf00032g01117.1, Peaxi162Scf00109g00064.1) and 718 (Peaxi162Scf00322g01519.1, Peaxi162Scf00474g00416.1), 2OG-Fe(II )-DEPENDENT OXYGENASE SUPERFAMILY PROTEIN (Peaxi162Scf00045g01928.1), DNA BINDING PROTEIN (Peaxi162Scf00064g00423.1, Peaxi162Scf00931g00117.1), RNA BINDING PROTEIN (Peaxi162Scf00036g00222.1), CELLULASE SYNTHASE LIKE C5 (Peaxi162Scf00160g00715.1), bHLH DNA-BINDING SUPERFAMILY PROTEIN (Peaxi162Scf00075g01418.1), MATE EFFLUX FAMILY PROTEIN (Peaxi162Scf00684g00553.1) and the meristem identity gene WUSCHEL (Peaxi162Scf00083g00516.1). WGCNA analysis Sample clustering dendrogram revealed three distinct clusters (Fig. 3-4A): one cluster containing the three fast samples (AE319f, AE110f, and AE45f), another with two slow samples (AE252s and AE219s), and a third cluster with sample AE216s-2, consistent with previous clustering in DEGs analysis. The first two clusters were selected for further analysis (Fig. 3-4B). After filtering genes with low counts or many missing values, 18,842 genes across 11 samples remained. Using a soft power threshold of 22, 48 modules were identified. Among these, six modules each were positively and negatively correlated with the development rate phenotype (p < 0.05 and R² > 0.6) (Fig. 3-5). The number of total genes per module ranged from 54 to 2,429 (Table 3-4), with hub genes ranging from 2 to 310 per module (Table APP-3-1). 52 Robust modules included bisque4, deepskyblue4, and orange1, which were negatively correlated with all slow line samples (Fig. 3-6A), and black, mistyrose2, sienna2, and tan2, which were positively correlated with all slow line samples (Fig. 3-6B). Other modules showed varying eigengene values within samples of the same development rate categories. Eigengene values represent a common gene expression matrix of the modules (Langfelder and Horvath, 2008). Genes in each module were compared to the core genes identified in pairwise comparisons. Module bisque4 had 54 genes, and orange1 had only 3 genes in common with the downregulated core genes, whereas module sienna2 had 6 genes in common with the core upregulated genes (Table 3-6). The common genes included bHLH DNA-BINDING SUPERFAMILY PROTEIN (Peaxi162Scf00075g01418.1), MADS-BOX TRANSCRIPTION FACTOR 3 (Peaxi162Scf00022g00098.1), WUSCHEL (Peaxi162Scf00083g00516.1), and AUXIN-RESPONSIVE GH3 FAMILY PROTEIN (Peaxi162Scf00945g00013.1), among others. Gene Ontology (GO) analysis Gene ontology (GO) enrichment analysis was performed on the differentially expressed genes and significant module genes to understand their roles in biological processes, cellular components, and molecular functions. This analysis identified 40 GO enriched terms for pooled upregulated genes and 110 for downregulated genes (Table APP-3-2). In the pairwise comparison, 69 GO terms were enriched for the downregulated core DEGs. Further GO analysis of the modules revealed 181 GO enriched terms for bisque4, 108 for deepskyblue4, 70 for orange1, 321 for black, 50 for mistyrose2, 348 for sienna2, and 35 for tan2 (Table APP-3-2). 53 DISCUSSION Petunia wild species demonstrate faster development rates compared to commercial cultivars at similar temperatures, implying the possibility of breeding varieties with accelerated development rates (Warner and Walworth, 2010). Given the significance of wild species as a rich resource for investigating the genetics of development rate and the potential for narrower crosses to introduce novel genetic variability, we conducted transcriptomic analysis. Specifically, we analyzed six AE RILs exhibiting divergent development rates (three fast and three slow), meticulously phenotyped for development rate prior to analysis. Nevertheless, the distinction between selected fast and slow lines based on their normalized read counts was not clear on the PCA plot. The indistinct grouping of slow and fast lines could stem from various factors. Firstly, the trait itself displays substantial natural variation, complicating the separation of genotypes. Moreover, interference from other inherent traits might interfere with the explicit clustering of genotypes with contrasting development rates. The identification of more downregulated genes suggests that the slow development rate phenotype results from the decreased expression of a large set of genes and the increased expression of a small set of genes. Previous studies have provided insights into the development rate, indicating that plastochron—the inverse of development rate, defined as the time interval between two successive nodes (Guo et al., 2015), is controlled by multiple independent pathways rather than a single unified model. For instance, in rice, genes such as PLA1, PLA2, and PLA3, which encode the cytochrome P450 family protein CYP78A11, MEI2-LIKE RNA BINDING PROTEIN, and GLUTAMATE CARBOXYPEPTIDASE, respectively, negatively regulate the development rate (Miyoshi et al., 2004; Kawakatsu et al., 2009). These genes exhibit pleiotropic effects, including 54 the regulation of plant height, changes in shoot apical meristem size, and the transition from vegetative to reproductive phases, underscoring the complex regulation of this trait. In our current study, while we observed differential expression of multiple genes related to various cytochrome P450 families, this does not directly imply their role in regulating development rate. However, we identified that a cytochrome P450 family protein (Peaxi162Scf01514g00021.1) is associated with gibberellin biosynthetic and metabolic processes, and another cytochrome P450 protein (Peaxi162Scf00814g00018.1) is linked to cytokinin biosynthetic processes, as indicated by GO terms. Given that the cytochrome P450 family is large and involved in diverse functions, with the possibility that members of the same families and sub-families participate in different pathways (Bak et al., 2011), it is crucial to understand the role of this gene family in development rate. This includes exploring their involvement in phytohormone synthesis and conducting in-depth studies on different sub- families to elucidate their specific functions. The differential expression of genes related to the MATE efflux family suggests that transport molecules may play a role in regulating development rate, as previously proposed. The Multidrug and Toxic Compound Extrusion (MATE) family, a large family involved in various pathways, including phytohormone transport and the movement of other substrates within a cell (Suzuki et al., 2015; Upadhyay et al., 2019), might have significant transport activity that impacts leaf initiation rate. For instance, the MND8 gene in barley and its Arabidopsis ortholog BIGE1, which encodes a MATE transporter, have been shown to negatively regulate development rate (Suzuki et al., 2015; Hibara et al., 2021). Additionally, BIGE1 is involved in the feedback regulation of the CYP78A pathway, indicating its role in development rate regulation through cell transport activities and/or CYP78A regulation (Wang et al., 2008). This 55 potential involvement necessitates further investigation to fully understand the role of MATE transporters in regulating development rate. The notable alteration in the expression of numerous genes associated with the 2OG- Fe(II) dependent oxygenase superfamily protein is intriguing. In a prior study, another gene from this family was identified as differentially expressed and closely mapped to a development rate QTL (Guo et al., 2017) in the petunia IA population. Members of the 2OG-Fe(II) dependent dioxygenase superfamily, including genes responsible for catalyzing gibberellin (GA) biosynthesis and inactivation reactions (such as GA2oxs, GA3oxs, and GA20oxs), play a pivotal role in maintaining the endogenous GA balance (Li et al., 2019; Kaur and Das, 2023). Gibberellin serves as a crucial hormone in regulating various aspects of plant development, encompassing stem elongation, meristem maintenance, phase transitions, flowering, seed maturation and germination (Peng and Harberd, 2002; Ogawa et al., 2003; Jasinski et al., 2005; Schwarz et al., 2008; Zhang et al., 2009; Bao et al., 2020). Additionally, its specific role in relation to development rate has also been explored. In rice, GA signaling positively influences PLA1 and PLA2 genes, thereby prolonging plastochron and reducing the development rate (Mimura et al., 2012). In our study, a gene encoding GIBBERELLIN OXIDASE 2 (GA2ox) was found to be upregulated in the slow lines, alongside other genes belonging to the 2OG-Fe(II) dependent oxygenase superfamily. It is conceivable that an endogenous gibberellin signal upregulates the expression of PLA1-like genes in petunia, thereby slowing down the development rate. Consequently, GA2ox is upregulated as a feedback mechanism to maintain the endogenous GA balance by inactivating the biologically active GAs, as observed in previous studies (Mimura et al., 2012). While this explanation appears plausible, the differential expression of GA-related 56 genes may or may not be directly associated with development rate in our study due to its pleiotropic effects on shoot apical meristem (SAM)-related functions. A future investigation specifically focusing on the impact of gibberellin signaling on regulating development rate while closely examining SAM would provide a more comprehensive understanding into the regulation of development rate. Two genes related to auxin polar transport, namely AUXIN EFFLUX CARRIER PROTEIN and CAROTENOID CLEAVAGE DIOXYGENASE 8, were upregulated in the slow lines. Polar auxin transport (PAT) is critical for leaf initiation at SAM, mediated by auxin efflux carrier proteins known as PIN proteins (Forestan and Varotto, 2012). In the shoot apical meristem (SAM), an auxin gradient is established as newly formed leaf primordia act as auxin sinks, depleting auxin in neighboring cells and creating an auxin maximum at distant sites where new leaf primordia can form (Reinhardt et al., 2000; Reinhardt et al., 2003). Polar auxin transport also regulates a class of cell wall-loosening enzymes called expansins, which facilitate the formation of bulges at sites in the SAM where new leaf primordia develop (Fleming et al., 1997; Reinhardt et al., 1998). In a previous petunia IA RILs study, CAROTENOID CLEAVAGE DIOXYGENASE 1 and EXPANSIN B2 (Peaxi162Scf00953g00316.1 and Peaxi162Scf00377g00012.1), were differentially expressed and mapped close to a development rate QTL (Guo et al., 2017). It is plausible that these proteins are overexpressed as part of a feedback mechanism in response to the slower development rate, signaling the SAM to maintain the integrity of leaf initiation events. The observation of fewer development rate-specific genes and a greater number of genes related to broader developmental pathways suggests that the SAM might be undergoing 57 structural and molecular changes, thereby regulating genes associated with phase transitions. Additionally, these differences could be influenced by both genotype and environmental factors. GO terms associated with basic helix-loop-helix (bHLH) proteins include regulation of transcription DNA-templated, transcription factor activity, red or far-red light signaling pathway, cellular response to red or far-red light, regulation of circadian rhythm, positive regulation of circadian rhythm, and entrainment of the circadian clock. The bHLH DNA-binding superfamily protein is a large family of transcription factors (TFs) characterized by a N-terminal basic DNA binding domain and a C-terminal protein interaction domain (Anderson et al., 1997). These proteins play pleiotropic regulatory roles in plant growth and development, including the regulation of phytohormone cross-talk, flowering time, and clock-derived signaling pathways (Anderson et al., 1997; Hao et al., 2021). Among the bHLH family, phytochrome-interacting factors (PIFs) are key transcription factors involved in light signaling pathways, including both phyA and phyB signaling in Arabidopsis (Huq and Quail, 2002; Jing and Lin, 2020). Beyond flowering time regulation, bHLH family members also participate in flower organ development and floral morphogenesis (Heisler et al., 2001; Groszmann et al., 2010). Although the GO terms and existing evidence point to their roles in reproductive phase regulation, it is plausible that bHLH proteins might also regulate other downstream genes related to development rate through their involvement in phytohormonal signaling. The overlap of genes involved in development rate and phase change remains an area of interest. Currently, we cannot definitively explain the role of bHLH transcription factors in regulating development rate based on the available evidence, but this family represents a critical area for further study. Similarly, several MADS-box transcription factors identified were associated with GO terms such as specification of organ identity, specification of floral organ identity, post- 58 embryonic organ morphogenesis, and floral organ formation, suggesting that SAM signals may activate genes related to the reproductive phase. The differential expression of genes related to mitogen-activated protein kinase (MAPK) elucidates the role of post-transcriptional modifications through phosphorylation of downstream signaling targets or transcription factors, leading to altered gene expression (Cristina et al., 2010; Zhang and Zhang, 2022). Specifically, the interplay between MAPK pathways and transcription factors, such as bHLH and MADS-box, highlights how MAPK cascades modulate the expression of genes regulated by these transcription factors through phosphorylation (Wei et al., 2018). Therefore, signal transduction pathways and transcription factors are critical for understanding the regulation of development rate and warrant further investigation. Similarly, several genes from the pentatricopeptide repeat (PPR) containing protein family, which are known to regulate genes involved in reproductive processes such as embryogenesis, gametogenesis, and seed development (Liu et al., 2013; Li et al., 2018), were differentially expressed in this study. The PPR family is involved in the post-transcriptional modification of organellar genes, relying on its RNA binding activity (Lurin et al., 2004; Barkan and Small, 2014). This finding aligns with previous research in IA petunia RILs, where a PPR family gene (Peaxi162Scf01021g00215.1) was found near genomic scaffolds harboring SNP markers associated with a development rate QTL (Guo et al., 2017). Although it is not definitively known whether this gene is directly related to development rate due to the presence of multiple genes within the QTL region, the evidence suggests that the PPR family is an important candidate for future studies on development rate. Based on the discussion above, a substantial proportion of differentially expressed genes are associated with reproductive phase functions, such as morphogenesis, pollen development, 59 and floral organ development. This finding is further supported by GO analysis of highly significant modules, which revealed functions related to meiosis, pollen development, and gametophyte development. Additionally, significant modules exhibited functions relevant to both vegetative and reproductive phases, including leaf morphogenesis, meristem development, post- embryonic development, meiotic chromosome segregation, shoot system development, regulation of flower development, and floral whorl development. These functional terms suggest a potential overlap between genes involved in vegetative phase processes, reproductive phase processes, and/or phase transition. For instance, MEI2 (meiotic inducer 2) gene family (Jeffares et al., 2004), consisting of three functional clades play role in both meiosis and development rate. AML (Arabidopsis- meiotic like) clade genes participate in both vegetative growth and meiosis and are expressed in both vegetative and reproductive tissues (Kaur et al., 2006). Similarly, loss-of-function mutants of the TEL (terminal ear-like clade) gene TE1 exhibit an accelerated development rate and dwarf architecture (Veit et al., 1998). Furthermore, the MND1 gene, which regulates plastochron, also influences phase transition (Hibara et al., 2021). Reports indicate that some transcription factors function in both vegetative and reproductive phases by interacting with different elements to regulate a set of target genes (Gregis et al., 2013). This evidence suggests that genes involved in development rate exhibit pleiotropic effects related to both vegetative and reproductive phases. Therefore, it is crucial to study in detail the gene families involved in the reproductive phase that were found to be differentially expressed in our study. To functionally characterize the roles of these genes, if any, in the development rate phenomenon, it is crucial to ensure that the shoot apical meristem (SAM) is structurally and molecularly in the vegetative phase. In our study, meristematic tissues, along with remnants of 60 surrounding leaf tissue, were macroscopically collected during the vegetative stage. However, to validate that the meristematic tissue indeed corresponds to the vegetative stage, visualization through sectioning under confocal microscopy could be employed (Lian et al., 2021). Understanding the structural and molecular changes occurring in the SAM will enhance our comprehension of the genetics underlying vegetative development rate, explaining the diverse set of gene families identified when comparing fast and slow developing plants. Several genes common between IA and AE RILs (Table 3-6) are associated with plant cell wall mechanics such as PECTINACETYLESTERASE FAMILY PROTEIN (PAE), PECTIN METHYLESTERASE INIHIBITOR SUPERFAMILY PROTEIN (PMEI), which play essential role during various stages of the plant life cycle, including cell division, elongation, and differentiation (Cosgrove, 2016; Houston et al., 2016). Pectinacetylesterases (PAEs) and pectin methylesterases (PMEs), including DUF proteins, play significant roles in cell wall pectin dynamics by modulating pectin acetylation and methyl esterification, respectively (de Souza et al., 2014; Salazar-Iribe et al., 2016; Coculo and Lionetti, 2022). This modulation regulates cell growth and shape by affecting the remodeling and physicochemical properties of cell wall polysaccharides, thereby influencing cell extensibility (Gholizadeh, 2020). Genes in these families are crucial for plant growth and development. For example, loss- of-function mutants of pae exhibit a significant increase in total cell wall acetate levels in Arabidopsis leaves and a decrease in inflorescence stem height. Furthermore, pectin deacetylation impairs cell elongation of floral organs and the germination and growth of pollen tubes in tobacco (Gou et al., 2012; de Souza et al., 2014; Houston et al., 2016). In addition to regulating growth, plant cell wall remodeling is integral to the heat response network (Wu et al., 2018; Ezquer et al., 2020). The differential expression of HEAT SHOCK PROTEIN 21 and cell 61 wall remodeling enzymes in both IA and AE studies is consistent with previous observations where PME-related genes were upregulated along with heat shock proteins in response to heat stress (Pineda-Hernández et al., 2022). This prompts an intriguing question regarding potential shared mechanisms connecting the development rate and the response to heat stress. Similarly, another gene related to the cell wall, L-ASCORBATE OXIDASE (AO), was commonly differentially expressed in both studies. AO is involved in rapid cell wall loosening and cell expansion mechanisms (Smirnoff and Wheeler, 2000). These mechanisms have been shown to accelerate plant development, as demonstrated by the overexpression of AO in tomato, leading to earlier flowering (Stevens et al., 2017). The differential expression of genes related to cell wall remodeling and loosening, coupled with existing evidence linking cell wall mechanisms to development rates, underscores the importance of further investigating these genes. Another common gene between both studies, LAG1, belongs to a gene family involved in the synthesis of ceramides, which are lipid second messengers crucial for various cellular processes, including the determination of cell polarity (Venkataraman and Futerman, 2002). Studies have shown that ceramide depletion leads to defective targeting of auxin polar carriers, AUX1 and PIN1, resulting in auxin-dependent inhibition of lateral root emergence (Markham et al., 2011). Given the role of PIN1 proteins in regulating development rates, LAG1 is a significant candidate for further study, particularly regarding its auxin-related activities and their impact on plant developmental processes. Furthermore, the ALPHA/BETA-HYDROLASE SUPERFAMILY PROTEIN (ABH), identified as commonly differentially expressed in both studies, is a member of a family that constitutes the core structure of phytohormone receptors in the gibberellin and other phytohormone pathways (Mindrebo et al., 2016). Specifically, in rice, the ABH protein 62 GIBBERELLIN INSENSITIVE DWARF 1 (GID1) serves as the gibberellin receptor, orchestrating GA signaling through GID1-mediated degradation of DELLA proteins (Ueguchi-Tanaka et al., 2005). As discussed above, GA signaling holds potential significance in governing development rate. CONCLUSION In summary, our discussion has shed light on the potential roles of auxin polar transport, gibberellin signaling, and MATE efflux transporters in regulating development rate in petunia. Furthermore, we delved into the pleiotropic effects of multiple gene families, indicating the necessity for additional studies to functionally characterize these gene families involved in both vegetative and reproductive phases. Additionally, we examined the shared genetic factors between IA and AE differentially expressed genes (DEGs), emphasizing their significance for further investigation. Such endeavors are crucial for achieving a comprehensive understanding of the genetic factors that influence development rate. 63 Tables & Figures Table 3-1: Mean and standard deviation of leaf number on the side branches over a four-week interval of the total twenty-five AE RILs phenotyped for development rate. N represents number of plants on which data was collected for each genotype. Genotypes marked bold were selected for shoot apex tissue collection and RNA extraction under slow and fast development rate categories. Genotype Mean  S.D (N) Mean  S.D (N) Mean  S.D (N) Mean  S.D (N) Side branch Main stem Side branch Main stem Rep 1 Rep 2 10.67  3.1 (3) 7.33  0.6 (3) 14  0 (1) 9.00  1.4 (2) 12.14  2.4 (7) 8.13  0.8 (8) 12.30  2.8 (10) 6.92  2.3 (12) 11.30  3.0 (10) 8.92  2.4 (13) 13.50  2.1 (8) 9.45  2.0 (11) 13.58  4.2 (12) 11.80  2.4 (10) 14.17  2.9 (12) 12.46  1.7 (13) 12.91  1.9 (11) 10.56  2.6 (9) 13.78  4.2 (9) 10.27  2.0 (11) 12.00  1.4 (4) 8.20  1.8 (5) 13.75  1.7 (8) 7.67  1.5 (6) 16.14  3.6 (7) 11.50  1.0 (6) 14.25  2.0 (8) 9.75  1.3 (8) 13.44  3.3 (9) 11.22  4.7 (9) 10.82  4.0 (11) 9.00  2.7 (11) 13.29  2.9 (14) 11.68  2.1 (19) 12.80  3.3 (10) 12.18  2.1 (17) 12.00  0.0 (1) 8.00  0.0 (1) 12.75  3.6 (4) 10.67  2.3 (3) 6.00  0.0 (1) 10.00  2.8 (2) 11.67  2.1 (3) 10.60  2.6 (5) 13.00  1.5 (6) 7.00  2.6 (4) 12.29  2.4 (7) 9.33  2.1 (6) 17.33  3.1 (3) 8.33  0.6 (3) 8.00  0.0 (1) 10.38  2.7 (8) 12.38  1.8 (8) 9.43  1.1 (7) 13.67  2.1 (9) 7.45  1.8 (11) 14.33  3.2 (3) 9.50  2.1 (2) 16.00  0 (2) 9.67  2.5 (3) 17 26 39 45 49 61 85 87 110 116 126 157 173 193 199 64 Table 3-1 (cont’d) Genotype Mean  S.D (N) Mean  S.D (N) Mean  S.D (N) Mean  S.D (N) Side branch Main stem Side branch Main stem Rep 1 Rep2 11.13  2.3 (8) 9.00  1.0 (4) 9.75  2.0 (8) 7.43  1.5 (7) 12.71  1.9 (7) 9.75  1.5 (8) 10.63  1.6 (8) 8.67  2.0 (9) 11.66  3.5 (3) 9.00  1.4 (2) 11.50  1.0 (4) 8.80  1.1 (5) 11.44  0.9 (9) 10.56  1.6 (9) 10.00  1.4 (5) 10.89 2.4 (9) 14.00  2.3 (8) 9.00  2.0 (4) 11.00  3.0 (12) 8.00  2.0 (12) 9.10  2.7 (10) 9.18  1.6 (11) 11.17  2.3 (12) 9.93  1.9 (14) 13.75  0.5 (4) 9.50  1.9 (4) 11.00  1.4 (2) 9.83  2.0 (6) 11.60  2.2 (10) 8.83  2.1 (12) 11.11  1.1 (9) 9.09  1.0 (11) 13.14  3.9 (7) 13.00  4.9 (9) 14.25  1.3 (4) 13.00  2.6 (8) 12.11  1.4 (9) 9.22  1.5 (9) 12.00  2.2 (9) 10.50  1.9 (12) 208 216 219 252 259 279 298 318 319 321 65 Table 3-2: Summary of twenty RNA samples and their biological replicates including the RNA integrity number (RIN), lane information, number of raw reads generated, number of reads after merging the two lanes, final number of reads that survived trimming, percentage of reads uniquely mapped to the P. axillaris genome and percentage of reads mapped to the exon regions. RIL Biological Replicate RIN score Lane Number of raw reads Number of merged reads Final number of reads after trimming Percent of uniquely mapped reads to the genome Percent of reads mapped to the exon AE318s 1 7.1 2 6.9 AE252s 1 6.6 2 5.6 AE219s 1 7.5 2 7.2 AE216s 1 7.7 2 7.1 AE208s 1 6.8 2 7.1 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 17,342,626 17296141 34853295 89.7 79 17,600,922 17557154 15,737,006 15693363 31781844 87.7 80 16,129,461 16088481 19,555,233 19503720 39394483 86.0 80 19,940,218 19890763 12,554,115 12520592 25358743 83.9 81 12,870,603 12838151 15,501,065 15456973 31177925 90.5 80 15,763,098 15720952 16,114,232 16069049 32542079 88.2 80 16,516,377 16473030 20,645,638 20589479 41610734 91.2 74 21,074,545 21021255 15,870,623 15827345 32004906 89.1 78 16,219,001 16177561 19,501,537 19448910 39194271 88.9 79 19,794,733 19745361 19,967,546 19914789 40398108 85.5 80 66 Table 3-2 (cont’d) RIL Biological Replicate RIN score Lane Number of raw reads Final number of reads after trimming Number of merged reads AE319f 1 6.9 2 7.3 AE173f 1 6.9 2 6.5 AE110f 1 6.8 2 7.0 AE85f 1 7.3 2 6.5 2 1 2 1 2 2 1 2 1 2 1 2 1 2 1 2 1 2 Percent of uniquely mapped reads to the genome 85.5 Percent of reads mapped to the exon 80 78 20,533,515 20483319 40398108 16,204,948 16159045 32762137 89.6 16,647,313 16603092 16,627,647 16582737 33549819 87.7 79 17,009,885 16967082 14,954,491 14914420 14,628,426 14586530 29500950 90.3 80 14,954,491 14914420 17,150,853 17104292 34641052 88.5 80 17,581,035 17536760 16,568,767 16520738 33394712 86.6 82 16,919,750 16873974 19,528,317 19474938 39497021 87.4 81 20,072,463 20022083 19,496,034 19440662 39260523 89.6 81 19,872,300 19819861 18,986,587 18933799 38265044 90.1 80 19,381,081 19331245 67 Table 3-2 (cont’d) RIL Biological Replicate RIN score Lane Number of raw reads Final number of reads after trimming Number of merged reads Percent of uniquely mapped reads to the genome 89.4 Percent of reads mapped to the exon 80 AE45f 1 7.4 2 7.3 1 2 1 2 23,295,629 23230884 46860056 23,689,962 23629172 19,409,346 19354918 39194168 90.3 80 19,891,452 19839250 68 Figure 3-1: Principal component analysis conducted based on normalized gene expression count of samples. Round circle represents three red- colored samples (two biological replicates of each sample) from the fast and oval circle represents three blue-colored samples (at least one biological replicate per sample) from slow development rate categories, respectively. The x-axis represents the PC1 and the percentage of variance explained and y-axis represents PC2 and the percentage of variance explained. 69 Figure 3-2: Heatmaps of differentially expressed genes in slow lines as compared to the fast lines. 1834 downregulated genes in the pooled comparisons (A), 210 upregulated genes in the pooled comparisons (B), and 271 core downregulated genes in the pairwise comparisons (C). A) B) 70 Figure 3-2 (cont’d) C) 71 Table 3-3: Pairwise comparisons of differentially expressed genes. D and U indicates number of down-regulated and up-regulated genes, respectively, in each of the comparisons. AE319f AE110f AE45f D U D U D U AE252s 1298 (66%) 672 (34%) 1170 D (70%) 495 (30%) 1349 (68%) 634 (32%) AE219s 2442 (80%) 628 (20%) 2271 (82%), 493 (18%) 2505 (79%) 662 (21%) AE216s 2333 (73%) 855 (27%) 1965 (82%), 446 (18%) 2262 (74%) 788 (26%) 72 Figure 3-3: Venn diagrams representing individual pairwise comparisons of differentially expressed genes. First three Venn diagrams are comparisons of down-regulated (A) and up- regulated genes (B) between each slow line with all three fast lines and the fourth diagram draws comparisons between results of first three comparisons. A) 73 Figure 3-3 (cont’d) B) 74 Figure 3-4: Sample clustering dendrogram of all samples (A) and only 11 samples used for the WGCNA analysis (B). A) B) 75 Figure 3-5:Heatmap displaying the significantly correlated modules with the development rate phenotype. Numbers inside each box indicate Pearson correlation coefficient between the module and the phenotype and a p-value in bracket. Red color indicates positive correlation whereas blue color indicates a negative correlation. 76 Table 3-4: Summary of significantly correlated modules. Hub genes are defined as genes with module membership & gene significance values greater than equal to 0.8. Module Mistyrose2 Black Tan2 Aquamarine Darkorchid Sienna2 Bisque4 Deepskyblue4 Darkred Antiquewhite3 Darkseagreen2 Orange1 Number of total genes 189 Number of hub genes 75 847 54 2429 565 967 1438 326 689 139 72 301 105 2 45 24 58 310 63 63 10 2 11 77 Figure 3-6: Bar plots representing eigenvalues of the negatively (A) and positively correlated modules (B) in each of the samples. Phenotypes 1-3 are the fast lines samples; 1- AE319f, 2- AE110f and 3- AE45f and phenotypes 4-6 are the slow line samples; 4- AE252s, 5- AE219s, and 6- AE216s. Eigengene value represents the average expression profile of all genes within the module for each sample, serving as a representative expression value for the entire module. A) 78 Figure 3-6 (cont’d) B) 79 Table 3-5: Functional description of genes common in modules identified in WGCNA and core DEGs. Core DEGs are identified as genes commonly differentially expressed in at least six pairwise comparisons of slow and fast lines. Module bisque4 GeneID Peaxi162Scf00001g00481.1 SPX (SYG1/Pho81/XPR1) domain- Functional description containing protein Peaxi162Scf00002g00332.1 calcium ATPase 2 Peaxi162Scf00003g02039.1 conserved hypothetical protein [Ricinus communis] gb|EEF43357.1| conserved hypothetical protein [Ricinus communis] Peaxi162Scf00003g05227.1 nodulin MtN21 /EamA-like transporter family protein Peaxi162Scf00011g00077.1 SRF-type transcription factor family protein [Solanum lycopersicum] Peaxi162Scf00016g02234.1 cysteine proteinase1 Peaxi162Scf00022g00098.1 MADS-box transcription factor 3 Peaxi162Scf00037g01116.1 40S ribosomal protein S10-3 Peaxi162Scf00045g00142.1 NAD(P)-binding Rossmann-fold superfamily protein Peaxi162Scf00069g01326.1 Unknown protein Peaxi162Scf00073g00173.1 Unknown protein Peaxi162Scf00075g01418.1 basic helix-loop-helix (bHLH) DNA- Peaxi162Scf00079g00129.1 glucose-6-phosphate/phosphate binding superfamily protein translocator 2 Peaxi162Scf00083g00516.1 Protein WUSCHEL Peaxi162Scf00089g01235.1 Quinolinate synthase, chloroplastic Peaxi162Scf00089g01857.1 UDP-N-acetylglucosamine--N- acetylmuramyl- pyrophosphoryl- undecaprenol N-acetylglucosamine transferase isoform 1 Peaxi162Scf00111g00125.1 NADH-ubiquinone oxidoreductase chain 5 Peaxi162Scf00128g01233.1 HORMA domain-containing protein 1 Peaxi162Scf00140g00212.1 Mannan endo-1,4-beta-mannosidase 7 Peaxi162Scf00152g00612.1 squalene monooxygenase 2 Peaxi162Scf00152g01220.1 actin-11 Peaxi162Scf00155g00096.1 cationic amino acid transporter 5 Peaxi162Scf00160g00117.1 RING/U-box superfamily protein Peaxi162Scf00171g00044.1 Ornithine decarboxylase Peaxi162Scf00174g00101.1 phospholipase D alpha 1 Peaxi162Scf00198g00148.1 MLP-like protein 28 Peaxi162Scf00199g01019.1 Pectin lyase-like superfamily protein Peaxi162Scf00253g01317.1 Unknown protein Peaxi162Scf00253g01317.1 Unknown protein 80 Table 3-5 (cont’d) Module Sienna2 Functional description GeneID Peaxi162Scf00270g00089.1 Unknown protein Peaxi162Scf00284g00022.1 Peroxidase superfamily protein Peaxi162Scf00288g00815.1 Galactosyltransferase family protein Peaxi162Scf00349g00711.1 calmodulin-binding family protein Peaxi162Scf00406g00134.1 Glutelin type-A 1 [Morus notabilis] Peaxi162Scf00409g00516.1 RmlC-like cupins superfamily protein Peaxi162Scf00434g00334.1 GDSL esterase/lipase Peaxi162Scf00451g00723.1 Transmembrane amino acid transporter family protein Peaxi162Scf00481g00063.1 Zinc transporter 2 Peaxi162Scf00486g00310.1 alpha/beta-Hydrolases superfamily protein Peaxi162Scf00516g00671.1 NAD(P)-binding Rossmann-fold superfamily protein Peaxi162Scf00560g00223.1 Aldehyde dehydrogenase family 2 member C4 Peaxi162Scf00570g00049.1 PIG93, partial [Petunia x hybrida] Peaxi162Scf00619g00113.1 Protein kinase superfamily protein Peaxi162Scf00620g00814.1 Glucose-methanol-choline (GMC) oxidoreductase family protein Peaxi162Scf00623g00041.1 O-fucosyltransferase family protein Peaxi162Scf00717g00215.1 Calcium-dependent phosphotriesterase Peaxi162Scf00739g00426.1 Peaxi162Scf00907g00120.1 Major facilitator superfamily protein Peaxi162Scf00931g00117.1 DNA binding protein, putative [Ricinus superfamily protein response regulator 17 communis] gb|EEF52579.1| DNA binding protein, putative [Ricinus communis] Peaxi162Scf00943g00003.1 conserved hypothetical protein [Ricinus communis] gb|EEF30394.1| conserved hypothetical protein [Ricinus communis] Peaxi162Scf00945g00013.1 Auxin-responsive GH3 family protein Peaxi162Scf01002g00114.1 Pollen Ole e 1 allergen and extensin family protein [Theobroma cacao] gb|EOY03810.1| Pollen Ole e 1 allergen and extensin family protein [Theobroma cacao] Peaxi162Scf01694g00018.1 K(+)-insensitive pyrophosphate-energized proton pump Peaxi162Scf02085g00005.1 DNAse I-like superfamily protein Peaxi162Scf00129g01043.1 Homeobox-leucine zipper protein HAT5 Peaxi162Scf00140g01136.1 beta glucosidase 11 Peaxi162Scf00351g00526.1 Unknown protein Peaxi162Scf00355g00111.1 Lupus la ribonucleoprotein, putative isoform 2 [Theobroma cacao] gb|EOY34314.1| 81 Table 3-5 (cont’d) Module Orange1 GeneID Peaxi162Scf00818g00014.1 Disease resistance protein (CC-NBS-LRR class) family Functional description Peaxi162Scf01039g00236.1 Protein kinase superfamily protein Peaxi162Scf00129g00543.1 Peaxi162Scf00169g00182.1 arogenate dehydrogenase Peaxi162Scf00287g01112.1 Plasma membrane ATPase 3 thioredoxin 2 82 Table 3-6: Summary of genes commonly differentially expressed between AE and IA RILs. Gene Functional description Peaxi162Scf00049g01720.1 Peaxi162Scf00306g00022.1 Peaxi162Scf00118g00042.1 Peaxi162Scf00204g01613.1 Peaxi162Scf00204g00116.1 Peaxi162Scf00549g00015.1 Peaxi162Scf00241g00053.1 Peaxi162Scf00002g00191.1 Peaxi162Scf00734g00066.1 Peaxi162Scf00666g00042.1 alpha/beta-Hydrolases superfamily protein Protein of unknown function, DUF584 Pectinacetylesterase family protein L-ascorbate oxidase LAG1 longevity assurance homolog 3 HXXXD-type acyl-transferase family protein Plant invertase/pectin methylesterase inhibitor superfamily protein heat shock protein 21 "Protein kinase family protein" “Protein LIGHT-DEPENDENT SHORT HYPOCOTYLS 10” 83 CHAPTER 4 IDENTIFICATION OF QUANTITATIVE TRAIT LOCI (QTL) RELATED TO STEVIA DEVELOPMENT RATE AND OTHER LEAF YIELD RELATED TRAITS 84 INTRODUCTION Stevia rebaudiana, commonly known as stevia (2n=22), is an important medicinal perennial plant belonging to the Asteraceae family (Goyal et al., 2010). Native to northeast Paraguay (Shock, 1982; Ramesh et al., 2006), stevia leaves produce a group of zero-glycemic, low-calorie sweet-tasting compounds called steviol glycosides (Brandle and Telmer, 2007; Ceunen and Geuns, 2013). These steviol glycosides (SGs) are extracted from the leaves, which can contain up to 30% of these compounds on a dry mass basis (Goyal et al., 2010; Yadav and Guleria, 2012; Ceunen and Geuns, 2013), and are 200-300 times sweeter than sucrose. In Japan, steviol glycosides have been used as a sweetener in seafoods, soft drinks, and candies since the 1970s (Mizutani and Tanaka, 2001). Beyond their use as sweeteners, stevia has been employed as a weight control agent in obese individuals (Gupta et al., 2013) and as a natural treatment for diabetes in various parts of the world (Shivanna et al., 2013). Stevia products appeal to consumers seeking natural ingredients in their diet. Due to their plant-based origin, steviol glycosides hold great potential as alternatives to sugar and synthetic sweeteners. To meet the growing demand for stevia, it is crucial for plant breeders to develop high SG- yielding cultivars. This will ensure a consistent supply of these beneficial compounds, supporting both consumer health and the food industry’s need for natural sweetening agents. Improvement of stevia through traditional breeding approaches is hampered by its self- incompatibility and low seed germination rate (Yadav et al., 2014; Ucar et al., 2016; Attaya, 2017; Simlat et al., 2018). As an alternative, stevia is clonally propagated by stem cuttings and in-vitro methods to produce genetically uniform plant populations (Goettemoeller and Ching, 1999; Ramesh et al., 2006; Smitha and Umesha, 2012). The above-ground tissue of stevia is harvested, and the leaves are stripped off for the extraction of steviol glycosides. A key strategy 85 to increase the yield of steviol glycosides is to enhance the rate of leaf production over time, enabling multiple harvests per season. Therefore, understanding the genetic architecture underlying stevia leaf production rate is crucial for breeding high-yielding cultivars. This knowledge will facilitate the development of stevia varieties that can produce more leaves and, consequently, higher amounts of steviol glycosides, meeting the increasing demand for this natural sweetener. Since stevia is a relatively novel crop, genetic research has predominantly focused on studying the biosynthesis of steviol glycosides (SGs), leaving a significant knowledge gap regarding the genetic mechanisms underlying a broader range of traits related to biomass production. Stevia leaf yield, which corresponds to overall biomass, depends on various morphological traits including leaf size (length and width), rate of leaf production (number of new nodes/leaves produced over time), branch production (primary and secondary branches), maximum and minimum plant canopy width, flowering time, and overall plant vigor. Increased biomass production in stevia would help meet the growing consumer demand for natural sugar alternatives. Thus, investigating the genetic regulation of traits related to biomass production is essential for advancing stevia breeding programs (Hastoy et al., 2019). However, stevia research at the genetic level is constrained by several factors, including the limited availability of germplasm, molecular markers, and a high-resolution linkage map. Overcoming these limitations is crucial for the development of high-yielding stevia cultivars. The initial genetic linkage map for Stevia rebaudiana was constructed using random amplified polymorphic DNA (RAPD) markers (Yao et al., 1999). Due to the limited efficiency of RAPD markers, we developed an improved linkage map using simple sequence repeat (SSR) markers, leveraging RNA sequencing data from young fully expanded leaves, shoot apices, 86 flowers, and callus tissues (Vallejo and Warner, 2021). This enhanced map enabled the identification of the first quantitative trait loci (QTL) for steviol glycosides, such as Reb D and Reb A, as well as for plant height and vigor, based on phenotypic data from an F1 mapping population of 161 individuals (Vallejo and Warner, 2021). However, for more precise QTL mapping, a high-density linkage map comprising 11 linkage groups corresponding to the 11 chromosomes of stevia is essential. Genotyping-by-sequencing (GBS)-based single nucleotide polymorphism (SNP) markers are considered optimal for developing such a linkage map (Kho et al., 2021). These markers can be detected in large quantities through automated processes, are relatively abundant, and offer greater genetic stability compared to SSR markers (Delourme et al., 2013; Tsykun et al., 2017). With the availability of a chromosome-level genome assembly, it is now possible to compare the genetic positions of markers with their physical locations on the stevia chromosomes (Xu et al., 2021). This study aims to overcome the limitations in stevia genetics research and deepen our comprehension of the genetic determinants governing biomass production in stevia. The primary objectives are to construct a high-density linkage map based on SNP markers, evaluate the performance of an F1 mapping population concerning biomass-related traits across diverse environmental conditions and to pinpoint genomic regions (QTL) linked with these traits. The findings from this investigation will offer valuable insights into the genetic regulation of traits influencing leaf yield in stevia. MATERIALS AND METHODS Plant materials A stevia F1 mapping population (designated as MSU18-02) was established through a cross between two distinct lines from the MSU stevia program, namely 10-RJR and 10-19, which 87 exhibit variability in steviol glycoside production (Bahmani, 2021). Subsequently, individuals from this population were propagated clonally. The population, comprising 200 individuals, was planted in a randomized complete block design with three replications at two field trial locations in June 2020 and repeated in June 2021. These trial sites were the MSU Horticultural Teaching and Research Center (HTRC) in Holt, MI, and the MSU Southwest Michigan Research and Education Center (SWMREC) in Benton Harbor, MI. Planting was carried out using raised, plastic-covered rows with drip irrigation installed and utilized as required. Data collection At the time of planting, two consecutive newly fully expanded leaves were marked using white paint. Subsequently, after a ten-week period in the field, various morphological parameters were assessed. These included the maximum and minimum width of the plant (in cm), as well as the length and width at the widest point of a young, fully expanded leaf (mm). Additionally, the stem caliper (mm) was recorded at the point of the young, fully expanded leaf. The number of new nodes formed above the marked leaves and the phyllotaxy pattern were also documented: "Opposite" (O) when two leaves arose from a single node, "Alternate" (A) when leaves arose from individual nodes and “Hybrid” (H) when a plant displayed a mix of both O and A phyllotaxy pattern. Furthermore, the number of primary branches (> 3 cm) extending from the main stem was counted. Subjective indices were established to quantify secondary branching, flowering stage, and plant vigor. Secondary branching was rated on a scale ranging from 1 (low) to 5 (high), with 1 indicating no lateral shoots emanating from the primary branches and 5 representing strong secondary branching. Similarly, the flowering stage of the plant was assessed on a scale from 1 to 5, with 1 indicating the absence of visible flower buds and only vegetative growth, and 5 indicating a plant that has been flowering for some time with numerous open 88 flowers. Plant vigor was rated from 1 to 5 based on the overall volume of the plant canopy, with plants rated as 1 exhibiting minimal branching and consequently very low biomass, while those rated as 5 displayed strong primary and secondary branching, resulting in high overall biomass. Data analysis Descriptive statistics, population distributions, and Pearson correlation coefficients were computed and analyzed using SPSS version 27 (IBM; Chicago, IL). Broad-sense heritability (H2) was calculated as follows: H2 = σ2(genotype) / [(σ2 (genotype) + (σ2 (genotype: location)/2) + (σ2 (genotype: year)/2 + (RESIDUAL/3*2*2)]. This calculation was performed using a linear mixed model (lmerMod) in a two-stage model approach, as described previously (Schmidt et al., 2019), in the R programming environment. Genotyping and linkage map generation DNA was extracted from leaf samples collected from 238 individuals of the MSU18-02 (F1) population, and these samples were subsequently sent to the University of Minnesota Genomics Center (UMGC) for genotyping using the genotyping-by-sequencing (GBS) approach. The variant calling and genotyping processes were conducted following standard procedures outlined in the Genome Analysis ToolKit (GATK) software suite (DePristo et al., 2011). Prior to variant calling, GBS sequence reads underwent alignment to a reference assembly of the stevia genome (Xu et al., 2021)using the BWA-MEM alignment tool (Li, 2013). The reference genome comprised 6978 contigs, with 6358 of these contigs assembled into 11 chromosomes. The finalized chromosome-level genome encompassed 3708 scaffolds, boasting a scaffold N50 value of 106.55 Mb and a cumulative length of 1416 Mb. Following BWA-MEM alignment, reads were subjected to filtering to exclude alignments with a mapping quality (MAPQ) score of less 89 than 20 or those designated as non-primary alignments, thereby eliminating reads that mapped to multiple locations within the genome. The resulting alignments were further processed using GATK, and variants were called utilizing its HaplotypeCaller utility. Notably, variant calling was restricted to the 11 chromosomes, despite the reads being mapped to the entire reference assembly, inclusive of unassembled contigs. Variants spanning all F1 individuals and the parental lines were combined, and genotypes were assigned to each individual. Subsequently, variants were subjected to filtering, retaining those with a mean sequence depth ≥ 5, variant quality ≥ 50, genotype quality ≥ 10, and missing rate ≤ 0.25. Variants with a minor allele frequency (MAF) < 1% or those harboring more than two alleles were excluded from the dataset, yielding final genotype data for all 238 individuals. The genotypes, called in Variant Call Format (VCF), were converted to a format suitable for JoinMap, wherein alleles in the F1 individuals were labeled as missing if they were absent in the parental lines or exhibited Mendelian inheritance errors. Variant sites with an allele missing rate > 0.01 underwent filtration, resulting in the generation of a final set of loci for constructing the linkage map in JoinMap5 (Ooijen, 2018). Markers displaying segregation distortion (p ≤ 0.05) and redundant markers were eliminated from subsequent analysis. Linkage groups were delineated using the 'Independence LOD' function, and marker order was determined utilizing the maximum likelihood method. Map distances were calculated employing the Kosambi mapping function (Kosambi, 2016). QTL identification MapQTL6.0 (Ooijen, 2009) was used as the tool for identifying QTL positions in the genome linked to all phenotypic traits. QTL were discerned via the interval mapping method employing the regression algorithm with default settings. A genome wide significant LOD score 90 was established at the relative cumulative value of 0.95 (corresponding to a 95% probability level), determined through 1000 permutation tests. The proportion of total phenotypic variation explained by each QTL (VE%) was one of the outcomes of interval mapping. Subsequently, significant QTL, defined as those exhibiting overlapping regions across at least two environments on the same linkage group, were visualized using MapChart v2.32 (Voorrips, 2002). RESULTS SNP marker development The initial number of raw reads (150 bp paired-end) spanned from 6 million to 36 million pairs across the samples, with a median of 10 million pairs. Post-mapping to the stevia reference genome and subsequent retention of uniquely mapped reads, the count of remaining reads (properly mapped reads) ranged from 6 million to 33 million, with a median of 10 million (individual reads were counted instead of read pairs, considering some reads lacked properly mapped mates) (Table 4-1). Our finalized genotype data for 238 individuals encompassed 181,614 variant sites, exhibiting an average genotyping rate of 0.74. Subsequently, 181,614 variants were filtered to eliminate markers with an allele missing rate of 0.01 or higher, resulting in a total of 11,575 SNPs for linkage map development. Linkage map generation A total of 1452 non-redundant markers were assigned to eleven linkage groups, corresponding to the eleven chromosomes. Upon comparing the genetic positions of these markers with their physical chromosome positions, 130 markers with conflicting positions were identified and subsequently removed (Figure 4-1). This curation yielded 1322 markers distributed across 11 linkage groups (Table 4-2), covering a cumulative distance of 2001.8 cM. 91 Although the average marker density across the map was 6.62 cM, it was notably inflated by linkage group 8, which comprised only 18 markers but spanned 1044.1 cM. This group exhibited the lowest marker density at 58.0 cM (Table 4-2). Notably, approximately 91% of the 841 markers mapped to chromosome 8 displayed segregation distortion and were consequently excluded from linkage group 8 analysis, resulting in only 18 markers on this linkage group in the final map. Excluding linkage group 8, the remaining ten linkage groups collectively spanned 1947.7 cM, with individual linkage groups ranging from 77.1 cM for linkage group 4 to 325.5 cM for linkage group 2 (Table 4-2). The number of SNPs varied from 67 on linkage group 4 to 209 on linkage group 2. The average marker density across the map was 1.48 cM, with individual linkage group marker densities, excluding linkage group 8, ranging from 1.15 cM on linkage group 4 to 1.84 cM on linkage group 7. Phenotyping The MSU18-02 population exhibited transgressive segregation across the majority of traits at both locations and in both years (HTRC and SWMREC 2020, and HTRC and SWMREC 2021), with the exception of flowering stage across all environments and secondary branching in 2021, which displayed skewed distributions (Figures 4-2, 4-3, 4-4, and 4-5). Generally, mean values for most traits were higher in 2020 compared to 2021 (Table 4-3). Additionally, the maximum values for minimum and maximum width were higher in 2020 than in 2021. Mean trait values of at least one parent (10-RJR) exceeded the population means for leaf width, primary branching, minimum width, and maximum width. Moreover, the maximum values of primary branching ranged from 1 to 6 in 2020. Across most traits, the mean values were higher for 10-RJR compared to 10-19 and the population means, while the mean of leaves was higher for 10-19 compared to 10-RJR. 92 Plants exhibited varying phyllotaxy patterns both within and between genotypes across all four locations. Phyllotaxy, the arrangement of leaves on the main stem, is typically categorized as alternate (one leaf per node) or opposite (two leaves per node) (Fleming, 2005; Lee et al., 2009). The percentage of plants displaying opposite phyllotaxy was the highest in all environments, followed by those with an alternate pattern. The occurrence of hybrid (mix of both opposite and alternate on a same plant) phyllotaxy was the least frequent (Table 4-4). In 2021, the percentage of plants with alternate and opposite phyllotaxy were comparable. However, in 2020, the proportion of plants with opposite phyllotaxy was 16% higher at HTRC and 23% higher at SWMREC compared to those with alternate phyllotaxy (Table 4-4). Plant height displayed significant positive correlations with all traits, except for flowering stage, which did not exhibit a significant correlation with plant height (Table 4-5). Leaf length showed positive correlations with all other traits, except for flowering stage across both environments, and with primary and secondary branching in one of the locations in each year (SWMREC 2020 and HTRC 2021). Similarly, leaf width demonstrated positive correlations with all traits except primary branching and flowering stage in HTRC 2020 and SWMREC 2021. In SWMREC 2020 and HTRC 2021, leaf width exhibited positive correlations with leaves, plant vigor, and stem caliper (stem diameter), while it displayed a negative correlation with flowering stage. Primary branching exhibited positive correlations with secondary branching, minimum and maximum width, and vigor at both locations in 2020. Secondary branching was positively correlated with minimum and maximum canopy width and vigor across both environments and negatively correlated with leaves at both locations in 2020, but positively correlated at SWMREC 2021. Leaves showed a negative correlation with minimum width, maximum width, and plant vigor in HTRC 2020, while the correlation with these traits was positive in SWMREC 93 2021. Moreover, minimum canopy width demonstrated positive correlations with maximum width, vigor, stem caliper, and flowering stage in at least three environments. Additionally, maximum width displayed positive correlations with vigor and stem caliper across both environments, and vigor was positively correlated with stem caliper in both environments. Broad-sense heritability estimates were predominantly high (> 0.5) for most traits, except for primary branching, stem caliper, and leaf count, which displayed heritability estimates of 0.33, 0.23, and 0.39, respectively (Table 4-6). QTL identification QTL analysis across four environments revealed the presence of at least one QTL for key traits such as maximum width, secondary branching, leaf length, vigor, and flowering stage (Table 4-7). Minimum canopy width and leaf width exhibited QTL in three environments, while leaves and stem caliper displayed QTL in two environments. Single environments showed QTL for plant height and primary branching. Significant QTL were identified for secondary branching on linkage groups 2, 7, and 11, accounting for 8.4% to 15.3% of phenotypic variation (VE%) (Figure 4-6). Another significant QTL was observed for minimum canopy width on linkage group 9, explaining 6.8% to 9.8% of VE%. Leaf width analysis revealed three distinct QTL positions (7.1, 10.6, and 10.4 cM) on linkage group 2 across various environments. Plant vigor analysis identified significant QTL on linkage groups 7 (explaining 10.7% to 11.6% VE%) and 11 (explaining 7.8% to 9.2% VE%). Furthermore, QTL for flowering stage were detected on linkage groups 3, 5, and 8. Notably, a QTL hotspot spanning the genetic interval of 160.90– 161.90 cM on linkage group 7 was associated with multiple traits, including secondary branching, minimum width, maximum width, and plant vigor. For leaf count, no significant QTL 94 were detected except three single QTL at linkage groups 1, 2 and 4 which were identified at both sites in 2021. DISCUSSION The breeding of stevia for higher leaf yield is imperative to enhance the production of desirable steviol glycosides, which vary in composition (Ahmad et al., 2020). To achieve this goal, a high-density genetic linkage map is essential for precisely mapping these traits and facilitating marker-assisted selection breeding strategies. Previous efforts utilized linkage maps based on random amplified polymorphic DNA (RAPD) and inter simple sequence repeat (ISSR) markers to assess genetic diversity in stevia germplasm panels (Yao et al., 1999; Heikal et al., 2008; Chester et al., 2013). However, due to their limited reproducibility, these markers were found to be suboptimal. A more recent linkage map based on co-dominant simple sequence repeat (SSR) markers covered a distance of 582 cM across 13 linkage groups (Vallejo and Warner, 2021). Nonetheless, the efficiency and small marker numbers of SSR markers pose constraints on constructing a high-resolution genetic map (Kho et al., 2021). The advent of next- generation sequencing technologies has revolutionized marker development, particularly SNPs, offering a promising avenue for advancing stevia breeding efforts (Tam et al., 2019). Here, we present the novel stevia linkage map constructed from 1322 SNP markers condensed into 11 linkage groups, corresponding to the 11 chromosomes of the stevia genome. Notably, this map exhibits a markedly higher average marker density compared to previously published maps, with an average spacing of 1.48 cM, in contrast to 6.0 cM and 7.6 cM in earlier studies (Yao et al., 1999; Vallejo and Warner, 2021). Despite this improvement, linkage group 8 stands out for its low marker density, considerable gaps, and a markedly non-linear marker order relative to physical chromosomal positions (refer to Figure 4-1). The presence of these large gaps 95 could not be rectified by reintegrating segregating distorted markers onto this group. The underlying causes for these gaps remain elusive; they may stem from genotyping errors or insufficient marker segregation between the parental lines within this linkage group. Further investigations are warranted to elucidate the precise factors contributing to this phenomenon. One potential avenue for enhancing the quality of this linkage group could involve the development of an integrated linkage map through the incorporation of SNP and SSR markers previously established. Stevia ideotype breeding primarily aims to enhance the yield of steviol glycosides (SGs) and dry leaf yield, while also optimizing plant architecture for mechanized harvest, improving regrowth after winter, and enhancing tolerance to pathogens like Septoria leaf spot and weeds (Angelini et al., 2018; Tavarini et al., 2018; Hastoy et al., 2019; Huber and Wehner, 2023). Leaf yield in stevia is influenced by various factors including leaf size, leaf number, branching pattern, canopy dimensions, and leaf-to-stem ratios (Benhmimou et al., 2017; Abdulameer et al., 2018). Beyond leaf yield, the branching pattern and canopy size also play pivotal roles in determining plant architecture and overall vigor, which in turn affect the plant's competitive ability against weeds. The MSU18-02 F1 population exhibited a normal distribution across most of the leaf yield traits, implying polygenic control with varying genetic effects. Given its biparental nature, uncovering QTL associated with these traits would significantly augment our understanding of their genetic regulation. Through the identification of SNP markers linked to these QTL regions, marker-assisted selection breeding strategies can be employed to enhance desirable traits in stevia cultivars (Al-Taweel et al., 2021). Moreover, these QTL regions serve as valuable genomic 96 regions for identifying candidate genes underlying these traits, further advancing our knowledge of stevia genetics and facilitating targeted breeding efforts (Yang et al., 2021). Overall, a positive correlation was noted among the leaf yield-related traits examined in this study, although some variations in correlation were observed across different environments. Notably, certain traits such as showed positive correlations in one environment but lacked correlation in others. Additionally, there were instances where the correlation between leaf count and other traits reversed across different environments. Nevertheless, a more extensive F1 population and conducting trials across multiple environments could provide a more comprehensive understanding of the interplay between these traits. The heritability estimates for most traits in our study ranged from moderate to high, indicating that genetic factors play a significant role in determining the variation observed in these traits. However, it's noteworthy that the heritability estimates for stem caliper were lower in our study compared to a previous investigation where it was reported as 0.75 (Vallejo and Warner, 2021). Similarly, the heritability estimates for traits like plant height, secondary branching, plant vigor, and leaf area were also relatively lower compared to the previous study (Vallejo and Warner, 2021). Conducting further investigations to refine the heritability estimates for these morphological traits would provide valuable insights into their true genetic potential and aid in optimizing breeding strategies (Huber and Wehner, 2023). Leaf shape and size, encompassing parameters such as length, width, and angle, play pivotal roles in determining key physiological processes such as photosynthetic rate and canopy architecture, ultimately influencing overall plant biomass (Khuluq et al., 2022; Zhou et al., 2024). Consequently, the selection for optimal leaf size is paramount in breeding endeavors aimed at developing high-yielding varieties. However, it is equally crucial to investigate the 97 correlation between leaf size and steviol glycosides (SGs) content to inform strategic breeding decisions. Stevioside (ST) and rebuadioside (Reb) A are among the most prevalent types of SGs utilized as sugar substitutes, albeit accompanied by a bitter aftertaste (Gupta et al., 2013). In contrast, Reb D and Reb M, although present in lower concentrations compared to ST and Reb A, offer a similar sweetness profile to Reb A while mitigating bitterness and enhancing taste (Prakash et al., 2014; Vallejo and Warner, 2021). The MSU18-02 population was also phenotyped for various SGs, including Reb A, D, and M, as well as ST and total SGs content across four distinct environments (Bahmani, 2021; Warner et al., unpublished). Analyzing trait correlations revealed intriguing insights, particularly regarding the relationship between leaf size and SGs content. Notably, leaf dimensions, including length and width, exhibited a negative correlation (p < 0.01) with Reb M in two of the four environments (HTRC 2020 and SWMREC 2020) (Table 4-8). Similarly, Reb D displayed a negative correlation with leaf size parameters in these environments, although statistical significance was not observed. The absence of correlation between leaf size and both Reb A and ST suggests that other factors may influence SGs concentration, independent of leaf size. It is plausible that SGs content and leaf size are not directly correlated but instead influenced by shared underlying traits related to biomass production. Therefore, careful consideration is warranted when selecting for both traits simultaneously in breeding programs to ensure optimal outcomes. Leaves exhibit remarkable plasticity in shape and size, a characteristic influenced by diverse environmental factors (Tsukaya, 2005). Among the various determinants of leaf size, phytohormones such as auxins, cytokinins, gibberellins, and brassinosteroids are known to play pivotal roles by modulating cellular processes like cell proliferation and expansion (Wang et al., 98 2021). Additionally, the TCP (TEOSINTE BRANCHED, CYCLOIDEA, and PCF1/2) transcription factor family, microRNAs including miR319 and miR396, and regulators of transcription factors orchestrate leaf size regulation through intricate and coordinated pathways (Kessler and Sinha, 2004; Wang et al., 2021). Despite the well-established roles of these factors in leaf development across various plant species, the genetic control mechanisms governing these traits remain largely unexplored in stevia. Consequently, there is a critical need for comprehensive investigations to elucidate the genetic underpinnings of leaf size regulation in this economically important crop. Previous studies have primarily focused on identifying QTL associated with steviol glycoside compounds, with limited attention to leaf yield-related traits, except for overall plant vigor (Vallejo and Warner, 2021). In this study, we present the first set of QTL associated with several key agronomic traits related to leaf yield. These QTL exhibit minor to moderate effects (7-15% VE%) and are characterized by large intervals. Further refinement of these QTL regions through fine mapping approaches could potentially narrow down the genomic regions of interest (Su et al., 2010; Zhang et al., 2019). Interestingly, we observed QTL at different positions on the same linkage group across different environments, suggesting the possibility of genomic regions shifting positions across environments. To enhance the resolution of such QTL regions, future studies may benefit from employing a larger F1 population and/or adopting a more refined composite interval mapping approach. Additionally, QTL associated with several traits were identified around 137 cM on linkage group 8. However, the utilization of these QTL warrants further validation due to the inconsistencies observed within this linkage group, as discussed above. 99 The absence of significant QTL for leaf count (development rate), a crucial trait related to stevia biomass, is noteworthy, especially considering its importance in biomass estimation (Benhmimou et al., 2017). This trait exhibited lower broad-sense heritability and the highest level of inconsistency in correlation studies, indicating potential complexity in its genetic regulation. A plausible explanation for this complexity could lie in the inconsistent phyllotaxy patterns observed in stevia. Phyllotaxy, the arrangement of leaves on the main stem, is typically categorized as alternate (one leaf per node) or opposite (two leaves per node) (Fleming, 2005; Lee et al., 2009). While stevia typically exhibits an opposite phyllotactic pattern (Rossi et al., 2018), our study observed instances of both opposite and alternate phyllotaxy patterns, as well as irregular phyllotaxy patterns where the arrangement shifted during plant development, particularly during the transition to the reproductive stage. The change in phyllotaxy patterns, from opposite to alternate, could be attributed to changes in plastochron ratio and meristem characteristics, phenomena observed in other plant species (Jackson and Hake, 1999; Rutishauser and Peisl, 2001). Plastochron ratio, which measures the radial distances between successive leaf primordia emergence, influences leaf arrangement (Jean and Barab, 1998). Soybean serves as a notable example of a crop undergoing significant shoot architecture changes associated with phyllotaxy alteration during the transition from opposite to alternate patterns in the vegetative phase (Yoshikawa et al., 2013). The expression levels of microRNAs (miR156 and miR172) and their target genes, known to regulate phase changes, play a role in determining phyllotaxy in soybean (Wang et al., 2008; Preston et al., 2016). Additionally, mutants of cytokinin-related genes in maize and rice also exhibit altered phyllotaxy patterns (Giulini et al., 2004), further highlighting the polygenic nature of phyllotaxy regulation (Reinhardt and Kuhlemeier, 2002). 100 Given these complexities, understanding the genetic control of phyllotaxy in stevia warrants further investigation. Transcriptomic analysis of meristem-related tissues in plants with varying phyllotaxy patterns could provide valuable insights into the underlying genetic mechanisms. CONCLUSION In summary, this study marks a significant advancement in stevia breeding efforts by providing essential resources for pre-breeding initiatives. It represents the inaugural establishment of a high-density SNP-based linkage map in stevia, a pivotal tool for pinpointing QTL associated with traits essential for the stevia ideotype. The availability of molecular markers offers a promising avenue for the identification of closely linked candidate genes responsible for regulating stevia leaf yield-related traits. Additionally, the identification of QTL associated with these traits represents a pioneering accomplishment. Moving forward, this research sets the stage for further exploration of genomic regions housing narrower QTL, thereby facilitating the identification of potential candidate genes governing these morphological traits. 101 Tables & Figures Table 4-1: Summary statistics of mapping of reads after applying the filtering criteria (MAPQ > 20 and retaining only primary alignment). Sample Total reads Properly mapped reads 10-19 10-19TC 10-RJR 10-RJRTC 18-02-001 18-02-002 18-02-003 18-02-004 18-02-005 18-02-006 18-02-007 18-02-008 18-02-009 18-02-010 18-02-011 18-02-012 18-02-013 18-02-014 18-02-015 18-02-016 18-02-017 18-02-018 18-02-019 18-02-020 18-02-021 18-02-022 18-02-023 18-02-024 18-02-025 18-02-026 18-02-027 18-02-028 18-02-029 17894754 38120846 17965456 18407756 51688736 33176164 19124020 32836254 21429010 17236578 35122182 36034264 38723628 28429672 25643738 33743096 32054420 22032996 34709156 21123628 17579632 20493308 20883766 25693526 23500376 34172458 20578526 23454370 23834752 22830316 14314336 15354616 14130078 8714343 18881283 8458058 8968565 24749681 16298230 9950417 16049878 11199114 8237025 17678803 17754030 19034414 13625845 13338078 17020049 15859122 11044525 17463025 10790673 9037168 10468966 10100630 12630201 11888442 17276411 10620452 11354929 11527986 10931919 7131934 7487088 6952402 Proportion of properly mapped reads 0.486978 0.495301 0.470796 0.487217 0.478822 0.491263 0.52031 0.488785 0.522615 0.477881 0.503352 0.492699 0.491545 0.479283 0.52013 0.504401 0.494756 0.501272 0.503124 0.510834 0.51407 0.510848 0.483659 0.491571 0.505883 0.505565 0.516094 0.484129 0.483663 0.478833 0.498237 0.487612 0.492029 102 Table 4-1 (cont’d) Sample Total reads 18-02-030 18-02-031 18-02-032 18-02-033 18-02-034 18-02-035 18-02-036 18-02-037 18-02-038 18-02-039 18-02-040 18-02-041 18-02-042 18-02-043 18-02-044 18-02-045 18-02-046 18-02-047 18-02-048 18-02-049 18-02-050 18-02-051 18-02-052 18-02-053 18-02-054 18-02-055 18-02-056 18-02-057 18-02-058 18-02-059 18-02-060 18-02-061 18-02-062 18-02-063 18-02-064 15901236 17331626 18313612 23895294 26777886 17955358 18281698 46412048 22610060 14771850 21996370 17032786 20681132 72608284 26985428 29287560 28447400 24149284 29573470 16199286 13752170 15007832 13625120 16311636 19042886 18283850 20644226 21339260 29206686 17898950 17011100 42896590 37240392 21371388 35876226 Properly mapped reads (%) 0.513473 0.483333 0.484281 0.521795 0.495913 0.510371 0.460901 0.466014 0.470347 0.518162 0.477967 0.485429 0.47269 0.464882 0.48783 0.478072 0.456768 0.465286 0.45718 0.486358 0.476993 0.496095 0.515845 0.492458 0.553154 0.461411 0.477677 0.500384 0.493591 0.526636 0.490774 0.494286 0.502231 0.489282 0.483773 Properly mapped reads 8164853 8376943 8868941 12468451 13279503 9163890 8426050 21628661 10634568 7654207 10513528 8268206 9775769 33754267 13164310 14001553 12993874 11236333 13520396 7878658 6559691 7445304 7028449 8032800 10533658 8436376 9861265 10677828 14416165 9426232 8348598 21203176 18703279 10456629 17355958 103 Table 4-1 (cont’d) Sample Total reads 18-02-065 18-02-066 18-02-067 18-02-068 18-02-069 18-02-071 18-02-072 18-02-073 18-02-074 18-02-075 18-02-076 18-02-077 18-02-078 18-02-079 18-02-080 18-02-081 18-02-082 18-02-083 18-02-084 18-02-085 18-02-086 18-02-087 18-02-088 18-02-089 18-02-090 18-02-091 18-02-092 18-02-093 18-02-094 18-02-095 18-02-096 18-02-097 18-02-098 18-02-099 18-02-100 18-02-101 24911928 27340836 33010450 31037118 41081398 34479996 36400154 21738430 15603084 12553990 15647526 12800006 13736464 19364898 18031568 19779374 19839408 20243128 18006824 19757408 15994378 13034978 15826766 13241156 15873488 16972652 17349306 26989048 20463422 34433376 25762974 30716412 28087438 21513786 28428720 28062480 Properly mapped reads (%) 0.470485 0.463432 0.493591 0.477431 0.509822 0.499666 0.458582 0.468241 0.476134 0.480525 0.445032 0.493689 0.470682 0.4498 0.466773 0.520896 0.478042 0.505673 0.440976 0.475846 0.488339 0.476881 0.494189 0.517599 0.45695 0.473046 0.487898 0.500167 0.482917 0.467966 0.487226 0.525164 0.484756 0.524477 0.494226 0.476668 Properly mapped reads 11720683 12670605 16293668 14818089 20944203 17228490 16692446 10178822 7429163 6032512 6963657 6319222 6465501 8710329 8416653 10302999 9484067 10236395 7940583 9401491 7810672 6216129 7821408 6853609 7253398 8028839 8464687 13499028 9882142 16113654 12552389 16131167 13615557 11283479 14050205 13376498 104 Table 4-1 (cont’d) Sample Total reads 18-02-102 18-02-103 18-02-104 18-02-105 18-02-106 18-02-107 18-02-108 18-02-109 18-02-110 18-02-111 18-02-112 18-02-113 18-02-114 18-02-115 18-02-116 18-02-117 18-02-118 18-02-119 18-02-120 18-02-121 18-02-122 18-02-123 18-02-124 18-02-125 18-02-126 18-02-127 18-02-128 18-02-129 18-02-130 18-02-131 18-02-132 18-02-133 18-02-134 18-02-135 18-02-136 32419994 27825660 16040760 37241666 54248968 22087854 17941924 21254196 20765546 18791592 18203110 20499406 20727494 20245166 21872490 29961626 20869092 15534608 14478364 16599590 15869356 14725508 13615646 17736472 15445700 15996438 17732046 26615682 27142988 30780444 27228004 15278890 31039374 27344180 25106052 Properly mapped reads (%) 0.496204 0.528415 0.489484 0.537113 0.496864 0.505609 0.515743 0.514815 0.539249 0.492473 0.506932 0.527516 0.528413 0.512617 0.518494 0.505724 0.497051 0.49698 0.491408 0.508508 0.534279 0.486034 0.494475 0.479936 0.520988 0.505206 0.530881 0.486583 0.474736 0.490731 0.481764 0.482326 0.538243 0.47587 0.481793 Properly mapped reads 16086930 14703494 7851702 20002982 26954344 11167821 9253429 10941981 11197792 9254349 9227745 10813774 10952674 10378017 11340750 15152325 10373005 7720385 7114785 8441025 8478670 7157103 6732593 8512380 8047021 8081489 9413602 12950734 12885760 15104906 13117474 7369399 16706741 13012263 12095922 105 Table 4-1 (cont’d) Sample Total reads 18-02-137 18-02-138 18-02-139 18-02-140 18-02-141 18-02-142 18-02-143 18-02-144 18-02-145 18-02-146 18-02-147 18-02-148 18-02-149 18-02-150 18-02-151 18-02-152 18-02-153 18-02-155 18-02-156 18-02-157 18-02-158 18-02-159 18-02-160 18-02-161 18-02-162 18-02-163 18-02-164 18-02-165 18-02-166 18-02-167 18-02-168 18-02-169 18-02-170 18-02-171 18-02-172 18-02-173 30790504 28980968 19000716 29342312 56823002 21072820 17424240 15681938 14199954 15971086 16327300 14438942 17844458 16664160 20326154 17915848 33924524 33556346 27717682 28508732 39874304 28330004 40373934 29089152 22426042 35912798 59892434 19668322 16045916 14335940 15374218 15464558 14817680 16173442 23243100 15072942 Properly mapped reads (%) 0.485801 0.519733 0.470624 0.506843 0.474303 0.486553 0.496256 0.539203 0.495636 0.549829 0.485696 0.486147 0.486269 0.526576 0.494521 0.51981 0.472006 0.500425 0.495375 0.52071 0.493022 0.493033 0.487375 0.526597 0.487969 0.5154 0.488623 0.485577 0.475467 0.483768 0.48922 0.511145 0.485796 0.468186 0.458688 0.511884 Properly mapped reads 14958053 15062367 8942189 14871952 26951301 10253037 8646881 8455751 7038002 8781363 7930104 7019450 8677207 8774952 10051714 9312839 16012574 16792429 13730650 14844781 19658908 13967619 19677266 15318263 10943211 18509443 29264833 9550478 7629311 6935264 7521375 7904625 7198364 7572186 10661327 7715600 106 Table 4-1 (cont’d) Sample Total reads 18-02-174 18-02-175 18-02-176 18-02-177 18-02-178 18-02-179 18-02-180 18-02-181 18-02-182 18-02-183 18-02-185 18-02-186 18-02-187 18-02-188 18-02-189 18-02-190 18-02-191 18-02-192 18-02-193 18-02-194 18-02-195 18-02-196 18-02-197 18-02-198 18-02-199 18-02-200 18-02-201 18-02-202 18-02-203 18-02-204 18-02-205 18-02-206 18-02-208 18-02-209 18-02-210 18-02-211 18-02-212 12709162 16917618 29207724 18654478 18626800 14509252 17471908 15595416 13935442 15085828 16591612 12062060 16961964 24602578 26694398 33126406 20572670 35718184 23731464 37497754 23781060 26766274 19985882 21390124 20164132 18756874 19731400 20355152 21712920 14150408 16032844 14176806 18373890 24446600 33335772 19329716 25829732 Properly mapped reads (%) 0.477852 0.505104 0.47044 0.496087 0.497327 0.512233 0.498874 0.52836 0.495324 0.482722 0.509446 0.482598 0.538371 0.474447 0.492228 0.515759 0.503665 0.494217 0.490714 0.477502 0.512978 0.502546 0.534656 0.509941 0.530063 0.507367 0.494492 0.500676 0.514672 0.514505 0.502082 0.528085 0.495666 0.483618 0.495626 0.504832 0.490399 Properly mapped reads 6073096 8545150 13740488 9254241 9263619 7432111 8716281 8239997 6902556 7282258 8452528 5821126 9131832 11672623 13139722 17085242 10361734 17652547 11645364 17905247 12199166 13451279 10685577 10907710 10688256 9516616 9757015 10191345 11175023 7280458 8049795 7486561 9107310 11822804 16522077 9758251 12666874 107 Table 4-1 (cont’d) Sample Total reads 18-02-213 18-02-214 18-02-215 18-02-216 18-02-217 18-02-218 18-02-219 18-02-220 18-02-221 18-02-222 18-02-223 18-02-224 18-02-225 18-02-226 18-02-227 18-02-228 18-02-229 18-02-230 18-02-231 18-02-232 18-02-233 18-02-234 18-02-235 18-02-236 18-02-237 18-02-238 20250972 23376876 56464212 22602792 23710262 14911332 16898080 14693290 16331716 17002306 34840306 27331988 25864884 31844550 26103800 24175818 13654834 19916222 23785374 13910288 17892948 13548406 15376700 21074016 20751294 13809302 Properly mapped reads 10511927 11654153 27140322 10922735 11990451 7607645 8406836 7442571 8117625 8403388 17050064 14099171 13277392 16018020 13900143 12166127 7144398 9349335 11750317 6770834 8522394 7302682 7370677 10289962 10522899 6916828 Properly mapped reads (%) 0.519083 0.498533 0.480664 0.483247 0.505707 0.510192 0.497502 0.506529 0.497047 0.49425 0.489378 0.515849 0.513337 0.503007 0.532495 0.503235 0.523214 0.469433 0.494014 0.48675 0.476299 0.539007 0.479341 0.488277 0.507096 0.500882 108 Table 4-2. Summary of linkage map generated by genotyping 234 individuals from stevia MSU 18-02 F1 population. Linkage group Length (cM) Number of Average marker density (cM) 1 2 3 4 5 6 7 8 9 10 11 Total 201.6 325.5 168.5 77.1 151.3 240.7 266.7 1044.1 172.2 159.1 185.0 2991.8 markers 152 209 122 67 89 171 145 18 127 108 114 1322 1.33 1.56 1.38 1.15 1.7 1.41 1.84 58.0 1.35 1.47 1.62 6.62 109 Figure 4-1: Comparison of 11 linkage groups with eleven chromosomes (Xu et al., 2021) by using AllMaps. Figure on left represents comparison of genetic positions (cM) of each linkage group with the physical positions (Mb) of corresponding chromosomes by straight lines. Figure on right represents the same comparison by dotted plot. 110 Figure 4-1 (cont’d) 111 Figure 4-1 (cont’d) 112 Figure 4-1 (cont’d) 113 Figure 4-1 (cont’d) 114 Figure 4-1 (cont’d) 115 Figure 4-2: Population distribution of MSU18-02 F1 population for all traits at Horticulture Teaching Research Center (HTRC), Holt, MI in 2020. Each panel represents the population distribution of a single trait. Panels (a) through (k) represent, plant height (a), number of leaves (b), maximum canopy width (c), minimum canopy width (d), leaf length (e), leaf width (f), primary branching (g), secondary branching (h), stem caliper (i), plant vigor (j) and flowering stage (k). Arrows represent parental mean values. 10-RJR 10-19 a 10-19 b 10-RJR 10-19 10-RJR c d 10-19 10-RJR 10-19 10-RJR e f 10-RJR 10-19 116 Figure 4-2 (cont’d) 10-RJR 10-19 g i 10-RJR 10-19 k 10-RJR 10-19 10-19 h 10-RJR j 10-19 and 10-RJR 117 Figure 4-3: Population distribution of MSU18-02 F1 population for all traits at Southwest Michigan Research and Education Center (SWMREC), Benton Harbor, MI in 2020. Each panel represents the population distribution of a single trait. Panels (a) through (k) represent, plant height (a), number of leaves (b), maximum canopy width (c), minimum canopy width (d), leaf length (e), leaf width (f), primary branching (g), secondary branching (h), stem caliper (i), plant vigor (j) and flowering stage (k). Arrows represent parental mean values. 10-19 a 10-RJR 10-RJR 10-19 b 10-19 c 10-RJR 10-RJR 10-19 e d 10-19 10-RJR 10-19 f 10-RJR 118 Figure 4-3 (cont’d) 10-RJR g 10-19 10-RJR i 10-19 k 10-19 10-RJR 10-19 10-RJR h j 10-19 and 10-RJR 119 Figure 4-4: Population distribution of MSU18-02 F1 population for all traits at Horticulture Teaching Research Center (HTRC), Holt, MI in 2021. Each panel represents the population distribution of a single trait. Panels (a) through (i) represent, maximum canopy width (a), minimum canopy width (b), leaf length (c), leaf width (d) number of leaves (e), secondary branching (f), stem caliper (g), plant vigor (h) and flowering stage (i). Arrows represent parental mean values. a 10-19 10-RJR c 10-RJR 10-19 e 10-RJR 10-19 10-19 10-RJR 10-RJR 10-19 10-19 and 10-RJR b d f 120 Figure 4-4 (cont’d) 10-RJR g 10-19 h 10-RJR 10-19 10-RJR i 10-19 121 Figure 4-5: Population distribution of MSU18-02 F1 population for all traits at Southwest Michigan Research and Education Center, Benton Harbor (SWMREC), MI 2021. Each panel represents the population distribution of a single trait. Panels (a) through (i) represent, maximum canopy width (a), minimum canopy width (b), leaf length (c), leaf width (d) number of leaves (e), secondary branching (f), stem caliper (g), plant vigor (h) and flowering stage (i). Arrows represent parental mean values. 10-19 10-RJR a c e 10-RJR 10-19 10-RJR 10-19 b d f 10-19 10-RJR 10-RJR 10-19 10-19 10-RJR 122 10-19 h 10-RJR Figure 4-5 (cont’d) 10-RJR g 10-19 i 10-RJR 10-19 123 Table 4-3: Descriptive statistics of MSU18-02 F1 mapping population for 11 leaf yield traits: Leaf length (LeafLen), Leaf width (LeafWid), Primary branching (PriBr), Secondary branching (SecBr), Minimum canopy width (MinWid), Maximum Canopy Width (MaxWid), Number of leaves (Leaves), Stem caliper (StemCal), Plant Vigor (Vig), Flowering stage (FStage) and Plant height (Hght) at two locations (HTRC, Holt, Michigan and SWMREC, Benton Harbor, MI) and two years (2020 and 2021). N represents the total number of progeny individuals for which data is available (across at least two replications to a maximum of three replications at all environments). For the parental lines, N = 3 for all traits. SD represents standard deviation of mean. MSU 18-02 population Parental means Trait N Minimum Maximum Mean SD 10-19 10-RJR HRTC 2020 LeafLen (mm) 564 17 LeafWid (mm) 564 PriBr SecBr (1-5) 571 571 MinWid (cm) 565 MaxWid (cm) 565 Leaves 398 5 1 5 5 8 4 82 21 6 5 60 80 54 46.12 9.70 48.00 52.33 12.13 2.80 14.33 14.67 2.71 0.99 2.33 2.67 2.79 1.03 2.00 4.00 29.15 9.86 25.67 41.33 39.44 11.50 35.00 52.00 21.06 5.56 20.67 19.67 StemCal (mm) 564 1.0 10.0 4.44 1.37 3.10 3.63 Vig (1-5) 570 FStage (1-5) 572 1 1 5 5 1.53 1.16 1.00 1.00 3.01 0.96 2.00 4.00 Hght (cm) 565 16.00 74.00 46.82 10.58 48.00 53.00 SWMREC 2020 LeafLen (mm) 534 14.0 LeafWid (mm) 534 5.4 PriBr SecBr (1-5) 535 535 1 1 83.0 21.0 6 5 40.23 8.17 45.17 42.63 10.86 2.40 11.33 12.33 2.31 0.97 1.67 2.33 2.94 1.15 2.33 3.67 124 Table 4-3 (cont’d) MSU 18-02 population Parental means Trait N Minimum Maximum Mean SD 10-19 10-RJR MaxWid (cm) 533 7.5 MinWid (cm) 532 4.0 Leaves 352 4 StemCal (mm) 534 1.0 Vig (1-5) 535 FStage (1-5) 535 1 1 SWMREC 2020 79.0 61.5 56.0 9.3 5 5 35.15 10.11 33.83 45.33 25.48 8.86 27.17 38.83 23.94 7.56 30.00 27.33 3.59 1.31 4.60 2.73 1.48 1.06 1.00 1.00 2.86 1.08 3.00 4.00 Hght (cm) 534 10.5 70.0 39.29 11.21 41.40 48.83 LeafLen (mm) 501 11 LeafWid (mm) 499 3.3 SecBr (1-5) 508 MinWid (cm) 505 MaxWid (cm) 505 Leaves 363 StemCal (mm) 504 Vig (1-5) 516 FStage (1-5) 514 1 6 8 2 1 1 1 HTRC 2021 66 19.6 5 50 60 52 8 5 5 38.04 8.88 46.33 32.13 10.02 2.48 11.00 9.53 2.31 1.07 2.33 2.33 24.84 8.58 28.33 35.67 30.18 9.89 33.00 38.97 22.01 8.35 31.33 18.00 2.84 1.15 3.83 2.33 2.87 1.04 4.00 3.67 2.30 1.48 1.67 1.00 SWMREC 2021 LeafLen (mm) 498 17 LeafWid (mm) 496 4.5 73 17.9 37.52 8.66 43.53 38.17 9.83 2.22 10.37 9.07 125 Table 4-3 (cont’d) MSU 18-02 population Parental means Trait N Minimum Maximum Mean SD 10-19 10-RJR SecBr (1-5) 512 MinWid (cm) 507 MaxWid (cm) 507 Leaves 342 StemCal (mm) 507 Vig (1-5) 522 FStage (1-5) 520 1 3 4 3 1 1 1 5 2.24 1.12 1.67 3.67 51 57 62 9 5 5 25.20 8.73 29.67 37.00 30.30 9.89 36.00 44.33 23.95 10.26 28.67 23.33 2.64 1.04 3.07 2.47 2.80 1.11 3.00 4.67 2.06 1.39 2.00 1.67 126 Table 4-4: Summary of phyllotaxy patterns of MSU18-02 population at all four environments (years and locations). Numbers represent percentage of plants showing each phyllotaxy type among the total number of plants for which phyllotaxy was recorded. Environment HTRC 2020 SWMREC 2020 HTRC 2021 SWMREC 2021 Opposite (%) 55.39 51.02 Alternate (%) 39.13 27.54 49.70 42.54 48.23 45.67 Hybrid (%) 5.48 21.44 2.03 11.79 127 Table 4-5. Pearson correlation coefficients for 11 traits in stevia biparental cross population MSU18-02 at two locations (HTRC and SWMREC) over two years (2020 and 2021). ** indicates correlation is significant at a p-value of 0.01 and * indicates significant correlation at a p-value of 0.05. Hght LeafLen LeafWid PriBr SecBr Leaves MinWid MaxWid Vig StemCal HTRC 2020 LeafLen .313** LeafWid .271** .572** PriBr .300** 0.096 0.072 SecBr .230** .246** .236** .184* Leaves .194* -0.082 0.075 0.041 -.333** MinWid .377** .282** .295** .298** .764** -.184* MaxWid .418** .309** .343** .313** .722** -.217** .839** Vig .377** .291** .304** .277** .859** -.183* .764** .746** StemCal .436** .527** .530** -0.065 0.113 0.107 .168* .200** .187** FStage -0.107 -0.068 -0.135 -0.111 0.104 -.208* 0.064 0.041 0.024 -0.137 LeafLen .352** SWMREC 2020 128 Table 4-5 (cont’d) Hght LeafLen LeafWid PriBr SecBr Leaves MinWid MaxWid Vig StemCal LeafWid .270** .546** PriBr .149* -0.007 -0.019 SecBr .243** 0.103 0.03 .142* Leaves .324** .190* 0.132 -.164* -.183* MinWid .425** .182* 0.055 .290** .720** -0.076 MaxWid Vig .505** .235** .178* .265** .823** -0.006 .814** .807** StemCal .489** .483** .589** 0.009 0.009 .332** 0.138 .246** .261** FStage -0.049 -0.074 -.225** -.151* .225** -0.053 .181* 0.097 0.096 -.195** HTRC 2021 LeafWid .482** SecBr Leaves 0.106 0.112 .614** .228** .210* 0.14 0.059 MinWid .220** 0.114 .683** .731** 0.152 129 Table 4-5 (cont’d) Hght LeafLen LeafWid PriBr SecBr Leaves MinWid MaxWid Vig StemCal MaxWid .216** 0.129 .651** .694** 0.119 .893** Vig StemCal FStage .304** .244** .692** .792** 0.152 .797** .781** .554** .453** 0.107 0.063 .314** .187* .172* .321** -0.048 -.203** 0.028 .239** -0.015 .267** .229** .211** -0.007 SWMREC 2021 LeafWid .517** SecBr Leaves MinWid MaxWid Vig StemCal FStage .250** .167* .648** .183* .239** .199* .208* .306** .170* .712** .651** .246** .355** .249** .699** .647** .328** .921** .360** .252** .752** .703** .277** .784** .775** .472** .434** .208** .156* .319** .295** .327** .408** 0.041 -0.103 -0.098 0.017 -0.021 .150* 0.145 0.01 -0.041 130 Table 4-6. Broad sense heritability (H2) estimates of leaf-yield traits by utilizing data across all four environments (HTRC 2020, SWMREC 2020, HTRC 2021 and SWMREC 2021). Trait (unit/index) Hght (cm) LeafWid (mm) LeafLen (mm) MinWid (cm) MaxWid (cm) Leaves (count) StemCal (mm) PriBr (count) SecBr (1-5) Vig (1-5) FStage (1-5) H2 0.50 0.64 0.63 0.58 0.52 0.39 0.23 0.33 0.60 0.62 0.55 131 Table 4-7. QTL summary of 11 leaf yield traits for MSU18-02 F1 mapping population phenotyped at two locations (HTRC, Holt, Michigan and SWMREC, Benton Harbor, MI) and two years (2020 and 2021): Trait abbreviations used for all traits: Leaf length (LL), Leaf width (LW), Primary branching (PBr), Secondary branching (SBr), Minimum canopy width (MiW), Maximum canopy width (MW), Number of leaves (L), Stem caliper (SC), Flowering stage (FS), Plant Vigor (V) and Plant height (H). QTL names start with a q followed by a trait abbreviation, middle part represents the location and year combination HTRC and SWMREC (h and s) and two years, 2020 and 2021 (20 and 21) and the last two digits represent the linkage group and the QTL number on each linkage group. QTL in bold indicate significant QTL (overlapping peaks or regions) at more than one environment for each trait. Trait Environment QTL PriBr SecBr HTRC 2020 HTRC 2020 HTRC 2020 HTRC 2020 HTRC 2020 SWMREC 2021 HTRC 2020 SWMREC 2020 SWMREC 2020 HTRC 2021 SWMREC 2021 HTRC 2020 SWMREC 2020 SWMREC 2021 HTRC 2020 SWMREC 2020 SWMREC 2020 HTRC 2021 SWMREC 2021 MinWid HTRC 2020 SWMREC 2021 LOD VE % LG Marker 4.05 2.95 5.12 3.91 3.4 3.08 93.37 3.67 93.37 4.62 93.32 3.28 Position (cM) Chr2_45671067 84.13 Chr5_21303512 71.61 Chr8_76558323 357.29 Chr9_29289415 78.03 Chr10_70858836 107.29 Chr1_19664284 47.19 qPBrh20.2.1 2 qPBr.h20.5.1 5 qPBrh20.8.1 8 qPBrh20.9.1 9 10 qPBrh20.10.1 1 qSBr.s21.1.1 2 Chr2_88859523 qSbr.h20.2.1 Chr2_88859523 2 qSbr.s20.2.1 qSBr.s20.5.1 Chr5_70413501 5 qSBr.h21.5.1 5 Chr5_8875199 31.53 4.89 3.0 48.03 qSBR.s21.5.1 2.9 5 qSbr.h20.7.1 7 161.90 4.52 3.0 qSBr.s20.7.1 7 qSBrs.s21.7.1 7 qSbr.h20.8.1 8 qSBr.s20.8.1 8 qSBr.s20.11.1 11 qSBr.h21.11.1 11 qSBr.s21.11.1 11 qMiW.h20.1.1 1 1 qMiW.s21.1.1 Chr5_14226909 Chr7_67368224 Chr7_67368224 161.90 Chr7_67368224 160.90 3.97 2.9 Chr8_69370654 134.20 4.1 3.0 Chr8_69370654 136.20 5.31 2.9 Chr11_101141677 145.92 4.08 2.9 Chr11_98216818 3.0 3.57 Chr11_101141677 146.14 3.55 2.9 2.94 Chr1_121840368 149.71 3.0 Chr1_26652684 72.61 LOD threshold 9.3 2.95 13.6 2.95 11.6 2.95 8.9 2.95 7.8 2.95 2.9 8.2 10.3 3.0 10.5 2.9 7.6 2.9 12.1 7.8 9.4 15.3 9.9 8.4 12.0 9.3 9 8.9 7.2 11.5 2.9 142.13 3.06 4.64 3.26 6.90 132 Table 4-7 (cont’d) Trait Environment HTRC 2020 SWMREC 2021 HTRC 2020 SWMREC 2020 SWMREC 2020 HTRC 2020 SWMREC 2020 SWMREC 2021 MaxWid HTRC 2020 SWMREC 2020 HTRC 2021 SWMREC 2021 HTRC 2020 SWMREC 2020 HTRC 2020 SWMREC 2020 SWMREC 2020 SWMREC 2021 LeafLen HTRC 2020 HTRC 2021 SWMREC 2020 SWMREC 2021 SWMREC 2021 LeafWid HTRC 2021 HTRC 2020 HTRC 2021 SWMREC 2021 SWMREC 2021 Leaves QTL LOD VE % LG Marker Position (cM) 3.51 3.58 4.71 3.31 3.86 4.27 2.91 3.08 Chr1_23732853 65.59 34.40 Chr1_12529469 Chr2_129991039 220.57 Chr5_88787733 133.16 Chr7_29729896 130.62 Chr8_69370654 137.20 Chr7_67368224 160.90 Chr9_103237598 156.57 Chr9_103237598 156.57 Chr9_99875743 169.87 LOD threshold 7.2 2.94 qMiW.h20.2.1 2 9.0 3.0 qMiW.s21.5.1 5 10.8 2.94 qMiW.h20.7.1 7 7.7 2.95 qMiW.s20.8.1 8 8.9 2.95 qMiW.s20.7.1 7 9.8 2.94 qMiW.h20.9.1 9 6.8 2.95 qMiW.s20.9.1 9 11.5 qMiW.s20.9.1 3.0 9 13.0 qMW.h20.1.1 1 Chr1_118883873 144.90 5.73 2.9 7.0 2.9 qMW.s20.1.1 1 qMW.h21.1.1 3 9.2 1 qMW.s21.1.1 1 Chr1_26332160 67.29 130.62 3.85 2.9 Chr7_29729896 qMW.h20.7.1 7 2.9 4.16 Chr7_67368224 161.90 qMW.s20.7.1 7 2.9 3.67 Chr8_69370654 140.20 qMW.h20.8.1 8 3.7 Chr8_69370654 137.20 2.9 qMW.s20.8.1 8 3.23 2.9 Chr9_103971984 154.71 qMW.s20.9.1 9 2.9 4.26 Chr9_99875743 169.55 9 qMW.s21.1.1 3.1 9.4 4.08 235.39 Chr2_137266803 2 qLL.h20.2.1 3.1 8.4 3.3 223.34 Chr8_32893096 8 qLL.h21.8.1 6.0 5.8 91.59 Chr9_30645963 13.1 qLL.s20.9.1 9 3.0 12.8 5.13 154.51 Chr9_103971984 qLL.s21.9.1 9 3.0 12.8 5.13 154.51 Chr9_103971984 qLL.s21.9.1 9 3.1 8.0 3.13 0 qLW.h21.1.1 1 Chr1_192607 2 Chr2_1373203 qLW.h20.2.1 3.0 7.1 3.05 2.36 Chr2_73382612 106.93 4.22 3.1 2 qLW.h21.2.1 76.25 Chr2_41296939 2 qLW.s21.2.1 Chr1_6937018 20.96 qL.s21.1.1 1 3.0 3.68 3.15 2.9 7.9 10.6 3.1 10.4 14.4 3.0 8.9 9.6 8.5 8.6 7.5 10.6 4.11 4.43 133 Table 4-7 (cont’d) Trait Environment QTL HTRC 2021 SWMREC 2021 StemCal HTRC 2021 Vig FStage Hght SWMREC 2020 SWMREC 2021 HTRC 2020 HTRC 2021 HTRC 2021 HTRC 2020 SWMREC 2020 HTRC 2020 SWMREC 2020 HTRC 2020 SWMREC 2020 SWMREC 2020 SWMREC 2020 HTRC 2021 SWMREC 2021 HTRC 2021 HTRC 2020 SWMREC 2020 HTRC 2021 SWMREC 2021 HTRC 2021 HTRC 2020 LOD VE % LG Marker LOD threshold 11 3.0 3.0 10.5 2.9 8.2 3.0 6.9 3 .0 7.8 9.3 3.0 8.0 3.0 9.4 3.0 10.7 3.0 11.6 2.9 9.5 3.0 8.3 2.9 9.2 3.0 2.9 7.5 3.1 8.8 9.9 3.1 Position (cM) Chr2_40037011 83.83 Chr4_81753662 2.31 Chr6_69726411 223.94 Chr11_40295935 49.46 Chr1_122391298 149.26 Chr2_90038864 98.11 93.20 Chr2_88859523 Chr5_12177923 44.99 Chr7_67368224 160.90 Chr7_67368224 160.90 Chr8_69370654 136.20 111.41 Chr11_93951106 133.74 Chr11_101141677 122.28 1 Chr1_133317152 175.04 3.31 2 qL.h21.2.1 3.16 4 qL.s21.4.1 3.23 6 qSC.h21.6.1 2.96 11 qSC.s20.11.1 3.12 qV.s21.1.1 1 4.08 qV.h20.2.1 2 3.24 qV.h21.2.1 2 3.83 qV.h21.5.1 5 4.70 qV.h20.7.1 7 5.15 qV.s20.7.1 7 4.14 qV.h20.8.1 8 3.61 qV.s20.8.1 8 4.02 11 qV.h20.11.1 3.25 qV.s20.11.1 11 3.86 qFS.s20.1.1 qFS.s20.3.1 3 Chr3_18803890 60.54 4.37 qFS.h21.3.1 3 Chr3_45920040 134.38 5.01 3 12.2 qFS.s21.3.1 3 qFS.h21.5.1 5 8 qFS.h20.8.1 qFS.s20.8.1 8 qFS.h21.8.1 8 8 qFS.s21.8.1 11 qFS.h21.11.1 3 qH.h20.3.1 2.9 11.8 Chr3_45920040 134.81 4.81 3.0 8.7 Chr5_61449999 89.50 3.0 11.6 227.34 Chr8_32893096 15.4 3.1 Chr8_32893096 243.34 3.0 Chr8_32893096 304.02 4.07 10 2.9 8.7 Chr8_76558323 11.1 Chr11_117450739 185.01 2.98 7.2 81.37 Chr3_22852840 4.51 3.0 3.09 3.51 5.15 6.97 319.02 3.49 134 Figure 4-6: Visualization of QTL on linkage groups 7 and 11 for secondary branching (2br) a) and linkage group 1 for maximum canopy width (MaxWid) (b). QTL names include an acronym for each trait followed by the environment from which data was taken. 'H’ and ‘S’ represents our two locations (HTRC, Holt, Michigan and SWMREC, Benton Harbor, MI, respectively. 20 and 21 represent the years 2020 and 2021. a) 135 Figure 4-6 (cont’d) b) 136 Table 4-8: Pearson correlation coefficients for stevioside (ST), rebaudiosides A, D and M, leaf length and leaf width for stevia biparental MSU18-02 population at two locations (HTRC and SWMREC) over two years (2020 and 2021). This population was also phenotyped for steviol glycosides along with other agronomic traits at two locations (HTRC and SWMREC) and two years (2020 and 2021). ** indicates correlation is significant at a p-value of 0.01 and * indicates significant correlation at a p-value of 0.05. ST Reb A Reb D Reb M LeafLen HTRC 2021 Reb A .404** Reb D 0.098 -.351** Reb M -.603** -.294** .375** LeafLen 0.094 0.143 -0.111 -.215** LeafWid 0.121 0.138 -0.013 -.213** .556** SWMREC 2020 Reb A 0.295** Reb D -0.013 -0.493** Reb M -0.697** -0.286** 0.342** LeafLen 0.103 0.03 -0.035 -.0195** LeafWid 0.108 0.014 0.04 -0.214** 0.523** HTRC 2021 Reb A -0.123 Reb D -0.05 -.238** Reb M -.682** -0.1 .488** LeafLen -0.07 -0.115 0.087 0.088 LeafWid -0.114 0.006 .195* 0.159 .458** SWMREC 2021 Reb A 0.042 137 Table 4-8 (cont’d) ST Reb A Reb D Reb M LeafLen Reb D -0.034 -.430** Reb M -.746** -.186* .373** LeafLen 0.068 -0.128 0.097 -0.087 LeafWid 0.053 -0.078 0.08 -0.1 .504** 138 CHAPTER 5 TRANSCRIPTOMIC ANALYSIS OF STEVIA F1 LINES WITH CONTRASTING DEVELOPMENT RATES 139 INTRODUCTION Stevia rebaudiana (stevia) is a perennial shrub prized for its production of zero-calorie sweeteners called steviol glycosides in the leaves (Sharma et al., 2016). These compounds are incredibly potent, up to 300 times sweeter than sucrose (Gupta et al., 2013) yet they are metabolized safely by the human body without affecting blood sugar levels (Carakostas et al., 2012). Consequently, steviol glycosides are hailed as a healthy alternative to sugar, offering additional benefits such as antioxidant, antihyperglycemic, anti-inflammatory, and anti-cancer properties (Basharat et al., 2021). While native to Paraguay, the majority of commercial stevia production is centered in China (Madan et al., 2010; Ijaz et al., 2015) posing a challenge in meeting global demand for low-calorie foods and beverages. This demand is particularly pressing in countries like the United States, where obesity-related conditions such as diabetes and cardiovascular diseases are prevalent among adults (Moraes et al., 2013; Skinner et al., 2018). As a result, collaborative efforts among researchers aim to establish a sustainable stevia industry within the United States, potentially offering a local solution to a global health challenge. Stevia thrives in warm climates with well-drained soil and ample sunlight, making regions with such conditions ideal for its cultivation (Ramesh et al., 2006; Libik-Konieczny et al., 2021). Due to challenges with seed germination, stevia is commonly propagated vegetatively through stem cuttings or in vitro methods (Ramesh et al., 2006). During the vegetative stage, above-ground tissue is harvested, and leaves are stripped for glycoside extraction. Since only the above-ground tissue is harvested and the plants are cut while still vegetative, they can continue to grow, allowing multiple harvests during a single growing season. Thus, if the development rate, defined as the rate at which plants produce new nodes or leaves over time before flowering, were 140 increased, it could allow for more frequent harvests in a growing season, boosting overall yield. Development rate plays a crucial role in determining crop timing and the timing to first yield (Guo et al., 2015; Guo et al., 2017). By breeding for faster-developing varieties, it is possible to achieve an advanced first harvest of stevia. However, to effectively breed for faster development, it is essential to first understand the underlying genetic mechanisms governing development rate. Plastochron, the time interval between two successive nodes (Lee et al., 2009), is the inverse of development rate. It is controlled by a diverse set of genes encoding various proteins. These include cytochrome P450 (CYP78A11 and CYP78A5) enzymes, RNA-binding protein, MATE cell transporter, glutamate carboxypeptidase, and N-acetyltransferase-like protein (Veit et al., 1998; Miyoshi et al., 2004; Kawakatsu et al., 2009; Griffiths et al., 2011; Mimura et al., 2012; Suzuki et al., 2015; Hibara et al., 2021). Additionally, interaction of miR156/SPL genes, where SPL potentially acts as a leaf-derived signal to suppress the formation of young leaf primordia, as elucidated by Wang et al. (2008). Overexpression of miR156 in leaf primordia suppresses the function of SPL genes, resulting in an accelerated development rate (Wang et al., 2008). Furthermore, phytohormones such as auxins, cytokinin, and gibberellins influence development rate, as discussed previously (Reinhardt et al., 2000; Werner et al., 2003; Mimura et al., 2012). Despite this knowledge, gaps persist in our understanding of development rate regulation due to the pleiotropic effects of genes identified to impact development rate and the diverse nature of encoded products. The evidence suggests that development rate is regulated by a complex mechanism(s), and further research is needed to clarify potential interactions between these pathways and determine whether they are conserved among different species. The current study was structured to uncover the genetic basis of development rate in stevia by utilizing F1 lines exhibiting contrasting development rates. The MSU18-02 (10-19 10- 141 RJR) F1 population was phenotyped for development rate alongside other morphological traits in an open field setting (Chapter 4-4). Subsequently, lines falling within the top and bottom twenty- five percentiles of the development rate spectrum were selected for further assessment in a controlled greenhouse environment. The primary objective of this investigation was to phenotype these selected lines under controlled environmental conditions and analyze the transcriptomes of selected lines to identify genes with differential expression between lines with fast and slow development rates. This approach seeks to deepen our understanding of the genes or gene families involved in regulating development rate in stevia, thus contributing to broader insights into stevia cultivation and breeding efforts. MATERIALS AND METHODS Plant materials For this study, twenty-four lines from the MSU18-02 (10-19 X 10-RJR) F1 population exhibiting varying development rates (Chapter 4) were chosen. Each line was represented by at least 20 clonally propagated plants, which were transferred to 15.24 cm, 1420.76 cm3 pots. The plants were arranged in a randomized complete block design with two replications and subjected to long-day conditions (16 hours of light per day) at a temperature of 22°C in two greenhouse compartments. Upon transplantation, the topmost fully expanded leaves on the main stem and two side shoots of each plant were marked with white paint. The number of new nodes produced by each plant was then recorded over a six-week period starting from the date of marking. Data was collected from only eighteen lines, as the remaining six lines had started flowering. Data analysis One-way ANOVA and Tukey’s Honestly Significant Difference (HSD) post-hoc tests were utilized to examine significant differences in node numbers among the F1 lines, with a 142 significance level of α=0.05. These statistical analyses were performed using SPSS v. 27 (IBM; Chicago, IL). RNA extraction and sequencing Shoot apex tissue samples of ca. 2 mm length from the tip were harvested and leaf primordia were removed as much as possible with forceps. Samples for each line (within a replication) were pooled, flash frozen with liquid nitrogen, and stored at -80°C. Total RNA extraction was performed using the MagMAXTM Plant RNA Isolation Kit (Catalog #A33784). Subsequently, the RNA samples underwent Quality Control (QC) assessment by running them on TapeStation Analysis Software 3.2 at MSU’s Research Technology Support Facility (RTSF) Genomics core facility. Samples with an RNA integrity (RIN) score of ≥ 6.0 were selected for sequencing. Library preparation was carried out using the Illumina Stranded mRNA Prep Ligation kit, incorporating IDT for Illumina TruSeq RNA Unique Dual Indexes, following the manufacturer’s protocols. Completed libraries underwent QC and quantification using a combination of Qubit dsDNA HS and Agilent 4200 TapeStation HS DNA1000 assays. The libraries were then pooled in equimolar amounts, and the pool was quantified using the Invitrogen Collibri Quantification qPCR kit. This combined pool was loaded onto one lane of an Illumina S4 flow cell, and sequencing was performed in a 2x150 bp paired-end format using a NovaSeq v1.5, 300 cycle reagent kit. Base calling was conducted using Illumina Real Time Analysis (RTA) v3.4.4, and the output of RTA was demultiplexed and converted to FastQ format using Illumina Bcl2fastq v2.20.0. 143 Quantification of transcripts The raw reads were trimmed to remove adaptor sequences and low-quality bases using Trimmomatic version 0.39 (Bolger et al., 2014). Subsequently, the quality of these processed reads was assessed using FastQC (Andrews, 2017). As sequencing for each sample was conducted on two separate lanes, reads from both lanes were merged into a single read. Stevia transcriptome index was constructed using STAR (Dobin et al., 2013), and the reads were aligned to the stevia transcriptome (Vallejo and Warner, 2021) using this index. Samtools feature idxstats was then employed to enumerate the number of reads mapped to each transcript (Danecek et al., 2021). Identification of differentially expressed genes (transcripts) (DEGs) Quality control of the samples was conducted through the generation of a heatmap and principal component analysis (PCA) plot. These analyses utilized regularized logarithm (rlog) transformed counts to visualize the relationships between samples based on their gene expression profiles. Any outlier samples detected through these analyses were removed from further analysis to prevent confounding effects. Subsequently, the identification of Differentially Expressed Genes (DEGs) was performed using the DESeq2 package (Love et al., 2014) with default parameters. Weighted Gene Co-expression Network Analysis (WGCNA) In the analysis, the WGCNA R package was employed to construct a co-expression network (Pei et al., 2017). Initially, samples were clustered using the 'hclust' function to identify and eliminate any outliers from subsequent analysis, ensuring the robustness of the results. The R function 'pickSoftThreshold' was then utilized to determine the soft threshold power. This was achieved by employing "signed" networks and the "bicor" correlation function to construct the 144 adjacency matrix. A soft power of 22 was chosen based on a threshold of R2 fit greater than or equal to 0.85. Next, the Topological Overlap Matrix (TOM) was calculated using the adjacency matrix, and gene dendrograms were generated based on their dissimilarity. Hierarchical clustering and the dynamic tree cut function were applied to detect modules within the coexpression network, with a tree cut height threshold of 0.25 used to cluster the module eigengenes. To relate modules to the development rate trait, gene significance (GS) and module membership (MM) were calculated. Hub genes within each module were identified by applying thresholds of MM >= 0.8 and GS >= 0.8. The corresponding module gene information was then extracted for further analysis, providing valuable insights into the genetic mechanisms underlying the development rate trait in stevia. Venn diagrams were created to visualize the intersection of enriched GO terms between different gene sets using Venny 2.1.0 (Oliveros, 2016). RESULTS Selection of slow and fast lines Of the eighteen evaluated F1 lines, fourteen, consisting of seven slow and seven fast development rate lines (highlighted in bold), exhibited variation in development rate over a six- week period (Table 5-1). Selecting plants at the extremes of slow and fast development rates proved challenging due to the significant phenotypic variability in development rate. The number of leaves produced by lines 32, 60, and 80 varied between replicates. Notably, lines 30, 71, 75, 165, and 238 consistently produced fewer than 29 leaves, while lines 61, 64, 81, 139, and 171 consistently produced more than 31 leaves at both replications, categorizing them into the slow and fast development rate groups, respectively (Table 5-1). Additionally, lines 103 and 107 were chosen as backup lines in the slow development group, and lines 50 and 58 served as backups in 145 the fast development group. These backup lines were selected to account for the possibility of losing RNA samples or encountering insufficient RNA quality. Subsequently, RNA extraction was performed on these fourteen lines, each with two biological replicates. Specifically, five lines were selected from both the fast (denoted with f, e.g. 61f, 64f, 81f, 139f and 171f) and slow (denoted with s, e.g. 30s, 71s, 75s, 165s and 238s) development groups, ensuring that the chosen RNA samples had an RNA integrity (RIN) score of ≥ 6.0 (Table 5-2). Processing of raw reads Each sample yielded a minimum of 35 million read pairs, up to a maximum of 65 million read pairs (Table 5-2). Following quality control, at least 99.5% of reads were retained after trimming for adaptor sequences and low-quality reads. The percentage of reads that uniquely mapped to the stevia transcriptome ranged from 70.3% to 76.8%, while mapping to the stevia genome ranged from 39.6% to 71.34% (refer to Table 5-2). Due to better mapping performance, we proceeded with the transcriptome-aligned reads. The number of reads corresponding to each gene in every sample was tallied to identify differentially expressed transcripts. Differential gene expression analysis Pooled comparison After conducting clustering analysis, we identified specific samples (30s, 75s, 238s, 61f, 64f, 81f, and 139f where biological replicates clustered together. Subsequently, principal component analysis (PCA) was performed on these samples (Fig. 5-1A). However, the PCA results indicated that the samples did not distinctly segregate into two clusters based on slow and fast development rates; instead, they exhibited overlapping patterns (Fig. 5-1B). Samples were chosen based on the clustering of biological replicates, leading to the exclusion of sample 61f 146 from further analysis due to the biological replicates being non-clustered. For the subsequent differential expression analysis, three samples from both the fast (64f, 81f, and 139f) and slow (30s, 75s, and 238s) development rate categories, each with two biological replicates, were selected. In the pooled comparison, 57 transcripts were found to be downregulated, while 114 transcripts were upregulated, in the slow lines compared to fast lines (Fig. 5-2A and 2B). Notably, among the upregulated transcripts in the slow development rate lines were the Cytochrome P450 family gene CYP78A10 (Locus_2519), EXPANSIN-LIKE 1 (EXPL1) (Locus_18240), YUC2 (Locus_52753), CELLULOSE SYNTHASE LIKE D (Locus_50384), PPR CONTAINING PROTEIN (Locus_28383) and F-box transcripts (Locus_32597, Locus_11780 and Locus_33084). Among the downregulated transcripts were those encoding PIN-LIKE PROTEIN 3 (Locus_41089) and transcripts related to FAR1-RELATED_SEQUENCE_5-like protein (Locus_33270, Locus_33271 and Locus_42243). Pairwise comparison Individual comparisons were conducted between each slow and fast line to pinpoint robustly differentially expressed transcripts (DEGs) (Table 5-3). In each comparison, the number of downregulated transcripts was similar to the number of upregulated transcripts. Venn diagrams were utilized to identify core DEGs present in all pairwise comparisons (Fig. 5-3). Core DEGs were defined as transcripts that were consistently differentially expressed in at least six pairwise comparisons, meaning in at least two slow line comparisons to all three fast lines. Among the core DEGs, 82 downregulated transcripts were identified in the comparisons between slow lines 30s and 75s, while 31 core DEGs were found in the 75s and 238s lines. Interestingly, only one core gene was shared between the 238s and 30s lines compared to all fast 147 development rate lines (Fig. 5-3A). Additionally, a single core DEG was observed to be upregulated in all slow lines (Fig. 5-3B). This transcript was functionally annotated as one of the UDP-GLYCOSYLTRANSFERASE like genes. Among the other upregulated core DEGs, Locus_21363, which encodes a HAD superfamily subfamily IIIB acid phosphatase, was found. A gene from this gene family was previously reported as differentially expressed in petunia genotypes with differing development rates. (Guo et al., 2017). Other core upregulated DEGs (Locus_33084 and Locus_11780) encoded F-box domains with FBD/LRR-REPEAT PROTEIN and F-BOX LIKE PROTEIN. Among core downregulated DEGs, transcripts encoding putative PHYTOHORMONE BINDING PROTEIN-LIKE (Locus_29612), FRS5-like (Locus_42243), GENERAL TRANSCRIPTION FACTOR 2-RELATED ZINC FINGER PROTEIN (Locus_40595), and PUTATIVE LEUCINE-RICH REPEAT DOMAIN L- LIKE PROTEIN (Locus_28113) were found. WGCNA The sample clustering analysis focused on selecting samples with contrasting development rates for further study, while removing outliers. In line with the sample clustering in Figure 5-1A, samples with different development rates did not form two distinct clusters (Fig. 5- 4A). For further analysis, samples that grouped with their biological replicates were chosen. Sample 61f was excluded from this analysis because it clustered too far from the other fast development samples. Final samples included three fast development samples (64f, 81f, and 139f) and three slow development samples (30s, 75s, and 238s) for weighted gene co-expression network analysis (Figure 5-B). After filtering out transcripts with low expression levels or excessive missing data, 31,219 transcripts from a total of 12 samples were retained for further analysis. At a soft power 148 threshold of 9, 127 co-expression modules were identified. Among these modules, only one, designated "royalblue1", was significantly correlated with the development rate phenotype, showing a negative correlation of -0.85 with this phenotype (p < 0.01, Figure 5-5). This indicates that slow development rate has an inverse relationship with the eigengene expression of this module (Figure 5-6), suggesting that as the eigengene expression of this module increases, the likelihood of slow development decreases. The "royalblue1" module contained 47 transcripts, including nine hub transcripts (Table 5-4). One interesting transcript co-expressing in this module was identified as Locus_2494, which corresponds to MEI2-LIKE PROTEIN 1, an RNA- binding protein family known to influence development rates in rice and other species (Veit et al., 1998; Miyoshi et al., 2004). The fact that this transcript co-expresses with other transcripts in this module suggests that these genes might share common regulatory mechanisms, participate in similar functions, or respond to the same signaling pathway. DISCUSSION The molecular mechanisms underlying leaf initiation have been investigated, particularly concerning the rate of leaf initiation (development rate) and leaf arrangement (phyllotaxy). Existing information highlights the involvement of both biophysical and genetic factors in orchestrating these processes (Reinhardt and Kuhlemeier, 2002; Fleming, 2005; Mimura et al., 2012). However, due to the pleiotropic effects of genes previously implicated in regulation of these traits, significant gaps remain in our understanding of their biochemical activities, interrelationships and conservation of gene functions among species. To address this, our study focused exclusively on unraveling the genetic regulation of development rate in stevia. We aimed to elucidate the specific genetic factors governing this trait and determine if any previously identified genes in other species also regulate development rate in stevia. 149 Principal component analysis (PCA) analysis revealed that the genotypes selected for fast and slow development rates overlap on the PCA plot, indicating additional natural variation beyond the trait under study. Analysis of gene expression patterns reveals that slow-developing lines exhibit a substantial upregulation of transcripts, with nearly double the number compared to fast-developing lines. This implies that slow development in stevia plants may be attributed to the upregulation of a diverse set of genes, potentially influencing pathways that slow down the rate of leaf emergence or redirect resources to alternative pathways. The observed differential expression of a PIN-like putative auxin efflux carrier family protein (Locus_41089) aligns with previous findings (Reinhardt et al., 2003). PIN (pin-formed) proteins are recognized as carriers facilitating the polar transport of auxin, thereby establishing auxin gradients essential for organ initiation (Forestan and Varotto, 2012). Examination of PIN1 expression suggests that its subcellular polarization leads to localized auxin accumulation at sites of incipient primordia (Adamowski and Friml, 2015), thereby becoming a site for new leaf primordium formation at each plastochron (Reinhardt et al., 2003). The observed downregulation of this transcript in slowly developing plants implies a reduction in polar auxin transport, leading to the creation of auxin minima, consequently delaying leaf initiation. This suggests a prolonged plastochron and a slower development rate. These findings underscore the intertwined genetics governing leaf development rate and arrangement. Notably, genes such as TE1 and AMP1 regulate both plastochron and phyllotaxy in maize and Arabidopsis, respectively (Veit et al., 1998; Helliwell et al., 2001). The observed upregulation of a transcript encoding cytochrome P450 protein CYP78A10, identified as a homolog of Arabidopsis KLUH encoding CYP78A5/7 (Wang et al., 2008), aligns with previous research findings. Notably, this gene shares orthology with rice PLA1, known for 150 its negative regulation of development rate (Miyoshi et al., 2004). PLA1 encodes a member of the plant-specific cytochrome P450 monooxygenases subfamily, CYP78A11, with expression predominantly observed in young leaf primordia rather than the shoot apical meristem (Miyoshi et al., 2004). Importantly, the study suggests that signals mediated by PLA1 operate non-cell- autonomously, transmitting from leaf primordia to modulate leaf initiation in the shoot apical meristem (Miyoshi et al., 2004). These findings underscore the conservation of the role of the cytochrome P450 family 78 in regulating development rate, thereby implying a potential similar regulatory mechanism in stevia. The upregulation of YUC2, a key enzyme in the auxin biosynthesis pathway, in the slow development rate lines presents an intriguing observation. YUC2, belonging to the flavin-binding monooxygenase family protein, catalyzes the conversion of indole-3-pyruvate (IPA) to indole-3- acetic acid (IAA), a pivotal step in auxin biosynthesis (Dai et al., 2013). This upregulation seemingly contradicts previous evidence suggesting that increased auxin biosynthesis or increased free auxin levels correlate with faster development rates. For instance, mutants of the Arabidopsis F-box protein SLOMO exhibit reduced auxin levels in the shoot apical meristem (SAM), delaying the formation of an auxin maxima critical for the initiation of subsequent leaf events (Lohmann et al., 2010). Logically, increased auxin biosynthesis, as seen in the upregulation of a biosynthetic gene like YUC2, would be expected to accelerate leaf initiation. However, the observation of upregulated auxin biosynthesis genes in slowly developing lines suggests a more nuanced regulatory mechanism. One plausible explanation is that the upregulation of YUC2 could serve to compensate for the reduced auxin levels in the SAM, thereby maintaining the integrity of leaf initiation events despite the overall slower development 151 rate. These findings, in conjunction with previous research, underscore the pivotal role of auxin in modulating development rate. Previous research underscores the role of localized growth modulation, achieved through the modulation of cell wall extensibility, as a critical event in leaf initiation (Pien et al., 2001). Expansins are a family of extracellular proteins that participate in cell wall loosening, consequently altering the physical stress patterns in the meristem. This alteration leads to the acquisition of a new leaf primordium identity as a result of tissue bulging (Fleming et al., 1997). The upregulation of expansin-related genes at the site of a new leaf primordium in tomato further supports this notion (Reinhardt et al., 1998). Additionally, it has been proposed that polar auxin transport activity relies on the cell wall extensibility of expansin proteins (Cosgrove, 2000) or auxin may act as the driver of expansin activity or may regulate expansin-related cells, thereby contributing to leaf initiation events (Kessler and Sinha, 2004). These findings are substantiated by the concurrent upregulation of both auxin biosynthesis and expansin-like transcripts observed in the current study. Additionally, cell wall synthase enzymes such as CELLULOSE SYNTHASE LIKE D (CSLD5) play a crucial role in maintaining cell proliferation and wall integrity within the SAM (Yang et al., 2016). The increased expression of CSLD5, coupled with the upregulation of a gene involved in cell wall loosening, may indicate a compensatory mechanism aimed at regulating SAM cell wall mechanics. In a prior investigation, a gene from the HAD (haloacid dehalogenase) superfamily subfamily IIIB acid phosphatase family exhibited distinct expression patterns among petunia genotypes characterized by contrasting rates of development (Guo et al., 2017). Although direct evidence linking this gene family to plant development rate is lacking, its observed differential expression in the current study (Locus_21363) provides additional support for its putative role. 152 This differential expression suggests plausible role in cell signaling pathways, possibly through post-transcriptional dephosphorylation mechanisms (Sanyal et al., 2020). The inferred function of post-transcriptional dephosphorylation by members of the HAD superfamily subfamily IIIB acid phosphatase family may implicate regulatory roles in pivotal signaling molecules or proteins governing plant developmental processes. While further research is needed to elucidate the precise mechanisms by which this gene family impacts development rate, it emerges as a potential candidate gene family for future functional studies. Several genes belonging to the pentatricopeptide repeat (PPR) containing protein family exhibited differential expression. Notably, a transcript from this family was also identified in a highly correlated module, royalblue1, through WGCNA. This finding is consistent with previous research in petunia, where a gene from the PPR family (Peaxi162Scf01021g00215.1 – PPR superfamily protein) was found to be located near genomic scaffolds harboring SNP markers associated with a development rate QTL (Guo et al., 2017). While QTL regions may contain numerous genes, the proximity of this PPR gene to the identified QTL provides a compelling rationale for further investigation into this gene family. Additionally, our latest study on petunia AE Recombinant Inbred Lines (RILs) with varying development rates corroborated the differential expression of this gene (Chapter 3). The PPR family comprises RNA-binding proteins characterized by repeated RNA motifs, instrumental in RNA binding and metabolism (Barkan and Small, 2014). These proteins are primarily localized to organelles such as mitochondria and chloroplasts (Lurin et al., 2004). They modulate gene expression post- transcriptionally and are implicated in embryogenesis, gametogenesis, and seed development, crucial processes governed by cell division and hormonal signals (Liu et al., 2013). Disruption in the expression of PPR proteins can impair plant metabolism, impacting energy balance and 153 hormonal signaling pathways (Liu et al., 2010; Barkan and Small, 2014). Mutations in PPR genes have led to lethal or defective embryos and albino seedlings, via disruption of cell proliferation or primary metabolites production in plastids (Tzafrir et al., 2004; Lu et al., 2011; Li et al., 2018). The available evidence suggests that the PPR gene family regulates genes involved in reproductive processes, demanding a detailed exploration of the physical and structural changes occurring in the shoot apical meristem preceding flowering initiation. The pleiotropic effects of genes related to the vegetative phase may contribute to the signaling that activates these genes. However, the specific mechanism of action of this gene family in our trait of interest remains elusive and warrants further investigation. Transcripts related to phytochrome A (phyA) signaling, such as FAR-RELATED SEQUENCE 5 (Ma and Li, 2018), were found to be differentially expressed. Studies have highlighted their involvement in light signal transduction, photomorphogenesis, circadian clock regulation, flowering time control, shoot meristem maintenance, and floral development (Wang and Deng, 2002; Lin et al., 2007; Li et al., 2011; Li et al., 2016). These findings align with the gene ontology terms identified in our petunia AE RILs exhibiting contrasting development rates, as described in Chapter 3. The enrichment of gene ontology terms associated with flowering initiation suggests the activation of flowering signals, prompting further investigation into the molecular changes occurring in the SAM. Elucidating the intricate interplay of phyA signaling and other regulatory pathways in the SAM is crucial for understanding the mechanisms governing the transition to flowering and the broader regulation of processes related to plant development rate. Additionally, the observed differential expression of transcripts associated with the putative F-box domain containing leucine-rich repeat regions, alongside the upregulation of 154 CDPK (calcium-dependent protein kinase) involved in phytohormone signaling, suggests that protein degradation and calcium-dependent signaling cascades (Xu and Huang, 2017; Matsushima et al., 2019) might play a crucial role in regulating development rate. WGCNA was utilized to identify a group of co-expressed genes potentially associated with the regulation of development rate. Although the correlation between the identified module and the phenotype was robust (0.85), the precise relationship between all genes within the module and development rate control remain challenging to interpret. This difficulty likely stemmed from the presence of noise generated by other pathways, influenced by the inherent variability among genotypes. Significantly, a transcript belonging to the MEI2-like 1 protein family (Locus_46362), recognized as an ortholog of Arabidopsis AML clade gene (AML1), was discovered within the royalblue 1 module. The MEI2-like gene family encodes RNA-binding protein characterized by a highly conserved RNA-binding motif that was initially identified in the MEI2 gene of the fission yeast S. pombe (Hirayama et al., 1997). Previous studies have identified associated members of this gene family, such as PLA2 and TE1, with the negative regulation of development rate in rice and maize (Veit et al., 1998; Mimura et al., 2012). AML1, falling into the AML14 clade, one of two sister clades within the MEI2-like gene family (Kaur et al., 2006), further supports the significance of our results outlined in Chapter 2, where a potential candidate gene from petunia also belonged to the AML14 clade (refer to Figure 2-9). These findings underscore the importance of investigating the AML14 clade for its potential role in regulating development rate. CONCLUSION In summary, our results indicate that leaf initiation is a complex process orchestrated by several factors, starting in the SAM through auxin polar transport, cell wall mechanics, and 155 communication signals from emerging leaf primordia to the SAM. Auxin, in particular, plays a pivotal role in this process, encompassing its biosynthesis and signaling pathways. Furthermore, we found evidence suggesting that the regulation of development rate in stevia is likely influenced by the cytochrome P450 subfamily 78 (CYP78A), potentially through the metabolism of an unidentified substrate. Our identification of a differentially expressed transcript belonging to an AML clade underscores the need for further investigation into the potential role of this clade in leaf initiation and development rate control. Overall, the regulation of development rate involves a complex interplay of genetic, endogenous (hormonal and signaling peptides), and environmental factors. Understanding these intricate mechanisms is essential for elucidating the molecular basis of leaf development rate in plants like stevia. 156 Tables & Figures Table 5-1: Average leaf number and standard deviation of F1 genotypes collected after six weeks of marking the leaves in both replications. N represents number of plants on which data was collected for each genotype. Blank cells indicate that we could not record the leaf number data of certain genotypes as they were already flowering. Genotypes marked bold were selected for shoot apex tissue collection and RNA extraction under slow and fast development rate categories. Genotype Mean  S.D (N) Side branch Rep 1 25.07  2.1 (15) 26.82  6.2 (17) 31.46  3.7 (13) 30.00  5.1 (11) 30.46  3.4 (13) 31.09  6.3 (11) 34.13  2.3 (8) 28.00  5.2 (5) 23.50  4.1 (8) 30.25  8.4 (4) 32.17  2.4 (6) 28.20  5.4 (10) 28.50  3.9 (10) 34.35  6.6 (17) 21.20  3.0 (5) 31.71  3.9 (7) 29.33  3.1 (3) 25.33  3.2 (9) 30 32 50 58 60 61 64 71 75 80 81 103 107 139 165 171 219 238 Mean  S.D (N) Main stem 28.31  3.4 (13) 32.25  4.1 (4) 33.75  5.9 (11) 36.40  4.0 (5) 36.33  5.9 (15) 35.20  3.6 (5) 28.71  6.1 (7) 32.29  2.4 (7) 40.70  5.2 (10) 34.75  1.7 (4) 40.40  5.2 (5) 24.83  5.0 (6) Mean  S.D (N) Side branch Rep 2 25.25  3.7 (16) 32.38  3.5 (13) 32.24  3.8 (21) 33.33  5.3 (18) 31.86  6.5 (14) 35.33  5.8 (12) 33.31  6.1 (13) 27.20  6.4 (5) 23.83  4.5 (12) 33.67  9.2 (6) 33.29  5.3 (7) 27.31  4.7 (13) 27.92  4.6 (12) 32.68  6.8 (19) 24.63  3.2 (8) 34.71  4.1 (7) Mean  S.D (N) Main stem 28.33  4.9 (12) 32.33  3.4 (6) 32.33  5.7 (3) 34.08  5.4 (12) 29.67  3.2 (6) 40.43  4.6 (7) 33.90  8.0 (10) 26.00  3.6 (4) 24.63  3.2 (8) 31.50  6.4 (4) 38.40  3.3 (5) 31.67  4.6 (6) 32.50  1.5 (6) 36.50  7.5 (12) 29.33  3.1 (3) 28.88  6.2 (8) 31.00  6.3 (5) 157 Table 5-2: Summary of twenty RNA samples and their biological replicates including the RNA integrity number (RIN), lane information, number of raw reads generated, number of reads after merging the two lanes, final number of reads that survived trimming, percentage of reads uniquely mapped to the Stevia rebaudiana genome and Stevia transcriptome. F1 genotype Biological Replicate RIN score Lane Number of raw read pairs Number of merged read pairs Percent of uniquely mapped read pairs to the genome (%) 67.55 Final number of read pairs after trimming (%) 40815717 (99.47%) Percent of uniquely mapped read pairs to the transcriptome (%) 71.33 61f 64f 81f 1 2 1 2 1 2 7.3 8.1 7.3 7.6 7.2 7.7 1 2 1 2 1 2 1 2 1 2 1 2 10,574,631 41031869 30,457,238 16,517,612 65009431 64793036 39.66 75.59 48,491,819 (99.67%) 8,744,782 34860527 34730355 67.33 71.23 26,115,745 (99.63%) 12,063,850 46564894 46358515 68.62 71.50 34,501,044 (99.56%) 10,729,327 42152834 41952732 67.48 70.98 31,423,507 (99.53%) 10,329,115 40186180 39984073 67.95 72.79 29,857,065 (99.50%) 158 Percent of uniquely mapped read pairs to the genome (%) 67.18 Final number of read pairs after trimming (%) 44403710 (99.55%) Percent of uniquely mapped read pairs to the transcriptome (%) 71.51 Table 5-2 (cont’d) F1 genotype Biological Replicate RIN score Lane Number of raw read pairs Number of merged read pairs 139f 171f 30s 71s 1 2 1 2 1 2 1 7.1 7.9 7.5 9.2 7.2 8.0 7.6 1 2 1 2 1 2 1 2 1 2 1 2 1 11,683,019 44606161 32,923,142 12,618,943 48764149 48596877 69.6 73.56 36,145,206 (99.66%) 11,925,397 46654498 46495489 69.93 72.64 34,729,101 (99.66%) 11,694,108 44682079 44521028 69.94 73.05 32,987,971 (99.64%) 12,834,101 48413335 48221756 68 71.57 35,579,234 (99.60%) 11,398,749 43857584 43699250 69.06 72.44 32,458,835 (99.64%) 10,990,446 42488550 42305578 68.07 70.35 (99.57%) 159 Table 5-2 (cont’d) F1 genotype Biological Replicate RIN score Lane Number of raw read pairs Number of merged read pairs Final number of read pairs after trimming (%) Percent of uniquely mapped read pairs to the genome (%) Percent of uniquely mapped read pairs to the transcriptome (%) 2 1 2 1 2 1 75s 165s 238s 2 1 2 1 2 1 2 1 2 1 2 1 7.9 9.2 7.3 8.6 7.6 7.9 31,498,104 12,505,036 47565928 47411705 70.40 74.12 35,060,892 (99.68%) 12,887,895 49261849 49070087 68.23 70.90 36,373,954 (99.61%) 11,509,199 45270477 44995816 68.04 71.16 33,761,278 (99.39%) 13,502,595 53700177 53516286 68.63 71.49 40,197,582 (99.66%) 13,482,300 50105277 49911293 71.34 76.83 36,622,977 (99.61%) 11,114,876 42820728 42674709 69.06 72.54 (99.66%) 160 Table 5-2 (cont’d) F1 genotype Biological Replicate RIN score Lane Number of raw read pairs Number of merged read pairs Final number of read pairs after trimming (%) Percent of uniquely mapped read pairs to the genome (%) Percent of uniquely mapped read pairs to the transcriptome (%) 2 7.7 2 1 2 31,705,852 10,779,307 41390735 41190954 68.29 72.35 30,611,428 (99.55%) 161 Figure 5-1: Quality control of samples based on gene expression profiles by using regularized logarithm normalization counts of samples in a heatmap (A) and on a principal component analysis plot (B). The x-axis represents the PC1 and the percentage of variance explained and y- axis represents PC2 and the percentage of variance explained. A) 162 Figure 5-1 (cont’d) B) 163 Figure 5-2: Heatmaps of transcripts upregulating (A) and downregulating (B) in the slow lines as compared to the fast lines. Y-axis represents regularized logarithm normalization counts of genes in each of the samples represented on X-axis. A) B) 164 Table 5-3: Pairwise comparisons of differentially expressed transcripts. D and U indicates number of down-regulated and up-regulated transcripts, respectively, in each of the comparisons. Slow/Fast 64f D U 81f D U 139f D U 30s 75s 754 (48%) 822 (52%) 866 (46%) 1001 (54%) 751 (58%) 534 (42%) 886 (54%) 744 (46%) 859 (52%) 778 (48%) 1041 (64%) 597 (36%) 238s 664 (43%) 890 (57%) 503 (40%) 745 (60%) 432 (54%) 364 (46%) 165 Figure 5-3: Venn diagrams representing individual pairwise comparisons of differentially expressed transcripts. First three Venn diagrams are comparisons of down-regulated (A) and up- regulated transcripts. (B) between each slow line with all three fast lines and the fourth diagram draws comparisons between results of first three comparisons. A) 166 Figure 5-3 (cont’d) B) 167 Table 5-4: Summary of differentially expressed transcripts identified in the pooled comparison of slow vs fast development rate lines (LFC  1.5 and  =0.05) . Positive and negative values of log2Fold change indicate that the transcripts are upregulated and downregulated, respectively, in the slow lines as compared to the fast lines. Only the annotated genes are listed in the table. Transcript ID Functional description Log2foldchange Locus_21363_Transcript_1/1_Confide nce_1.000_Length_542 |_Symbols:_|_HAD_superfamily,_subfamily_IIIB_acid_ phosphatase_|_chr5:17712433- 17714046_FORWARD_LENGTH=272_AT5G44020.1 3.8478 Locus_14053_Transcript_1/1_Confide nce_1.000_Length_1332 |_Symbols:_|_NAD(P)- linked_oxidoreductase_superfamily_protein_|_chr1:220714 10-22073067_REVERSE_LENGTH=326_AT1G59960.1 2.3480 Locus_24596_Transcript_1/1_Confide nce_1.000_Length_1555 |_Symbols:_|_PLC- like_phosphodiesterases_superfamily_protein_|_chr5:2575 152-2576770_REVERSE_LENGTH=372_AT5G08030.1 Locus_23473_Transcript_2/2_Confide nce_0.750_Length_2101 |_Symbols:_|_Transmembrane_amino_acid_transporter_fa mily_protein_|_chr2:17167561- 17170145_REVERSE_LENGTH=536_AT2G41190.1 Locus_26073_Transcript_1/1_Confide nce_1.000_Length_1651 |_Symbols:_|_Transmembrane_amino_acid_transporter_fa mily_protein_|_chr4:17935533- 17936843_FORWARD_LENGTH=436_AT4G38250.1 Locus_10697_Transcript_1/1_Confide nce_1.000_Length_426 |_Symbols:_|_unknown_protein_BEST_Arabidopsis_thalia na_protein_match_is:_unknown_protein_(TAIR:AT5G019 70.1)_Has_246_Blast_hits_to_244_proteins_in_61_species :_Archae_-_0_Bacteria_-_8_Metazoa_-_78_Fungi_- _10_Plants_-_117_Viruses_-_0_Other_Eukaryotes_- _33_(source:_NCBI_BLink)._|_chr1:10543177- 10544418_FORWARD_LENGTH=389_AT1G30050.1 1.6173 1.6205 2.7352 2.8999 168 Table 5-4 (cont’d) Transcript ID Functional description Log2foldchange Locus_38764_Transcript_1/1_Confide nce_1.000_Length_1703 |_Symbols:_ATCDPK1,_CPK10,_CDPK1,_AtCPK10_|_ca lcium-dependent_protein_kinase_1_|_chr1:6523468- 6525736_REVERSE_LENGTH=545_AT1G18890.1 1.7637 Locus_17315_Transcript_1/2_Confide nce_0.667_Length_1616 |_Symbols:_ATCEL2,_CEL2_|_cellulase_2_|_chr1:613386 -616103_REVERSE_LENGTH=501_AT1G02800.1 3.2695 Locus_18240_Transcript_1/1_Confide nce_1.000_Length_828 |_Symbols:_ATEXLA1,_EXPL1,_ATEXPL1,_ATHEXP _BETA_2.1,_EXLA1_|_expansin- like_A1_|_chr3:16896238- 16897189_FORWARD_LENGTH=265_AT3G45970.1 Locus_20009_Transcript_1/1_Confide nce_1.000_Length_1091 |_Symbols:_AtHSD5,_HSD5_|_hydroxysteroid_dehydroge nase_5_|_chr4:6268363- 6270179_FORWARD_LENGTH=389_AT4G10020.1 Locus_27883_Transcript_1/1_Confide nce_1.000_Length_213 |_Symbols:_ATPAP29,_PAP29_|_purple_acid_phosphatas e_29_|_chr5:25328237- 25329616_FORWARD_LENGTH=389_AT5G63140.1 Locus_43193_Transcript_1/1_Confide nce_1.000_Length_1271 |_Symbols:_ATSPO11- 1_|_Spo11/DNA_topoisomerase_VI,_subunit_A_protein_| _chr3:4231560- 4234192_REVERSE_LENGTH=362_AT3G13170.1 1.5918 1.9269 2.7988 1.5077 Locus_2519_Transcript_5/5_Confiden ce_0.667_Length_1790 |_Symbols:_CYP78A10_|_cytochrome_P450,_family_78, _subfamily_A,_polypeptide_10_|_chr1:27866667- 27868368_REVERSE_LENGTH=537_AT1G74110.1 2.1893 169 Table 5-4 (cont’d) Transcript ID Functional description Log2foldchange Locus_52753_Transcript_1/1_Confide nce_1.000_Length_216 |_Symbols:_YUC2_|_Flavin- binding_monooxygenase_family_protein_|_chr4:772184 0-7723616_REVERSE_LENGTH=415_AT4G13260.1 Locus_52753_Transcript_1/1_Confide nce_1.000_Length_216 |_Symbols:_YUC2_|_Flavin- binding_monooxygenase_family_protein_|_chr4:772184 0-7723616_REVERSE_LENGTH=415_AT4G13260.1 Locus_20121_Transcript_2/2_Confide nce_0.667_Length_551 Barwin- like_endoglucanase_[Artemisia_annua]_>_gb|PWA9631 2.1|_Barwin- like_endoglucanase_[Artemisia_annua]_PWA47986 4.3116 4.3116 6.4189 Locus_19379_Transcript_1/1_Confide nce_1.000_Length_1318 Glycosyl_hydrolase_family_100_[Artemisia_annua]_PWA 59137 2.5538 Locus_50384_Transcript_1/1_Confide nce_1.000_Length_205 Locus_11780_Transcript_1/1_Confide nce_1.000_Length_1268 cellulose synthase like_protein_D5_[Lactuca_sativa]_>_gb|PLY89076.1| _hypothetical_protein_LSAT_9X27001_[Lactuca_sativ a]_XP_023759090 F-box_protein_At5g07610- like_[Helianthus_annuus]_>_gb|OTG05796.1|_hypot hetical_protein_HannXRQ_Chr12g0377621_[Helianthu s_annuus]_XP_021996051 7.5023 2.3391 Locus_30118_Transcript_1/1_Confide nce_1.000_Length_629 heat_shock_70_kDa_protein_18- like_isoform_X2_[Cynara_cardunculus_var._scolymus]_X P_024981473 8.2274 170 Table 5-4 (cont’d) Transcript ID Functional description Log2foldchange Locus_47762_Transcript_1/1_Confide nce_1.000_Length_222 hypothetical_protein_E3N88_23486_[Mikania_micrantha] _KAD4585885 1.7247 Locus_44072_Transcript_1/1_Confide nce_1.000_Length_252 hypothetical_protein_E3N88_44369_[Mikania_micrantha] _KAD0371268 5.4773 Locus_48349_Transcript_1/1_Confide nce_1.000_Length_292 hypothetical_protein_E3N88_45324_[Mikania_micrantha] _KAC9735356 6.1228 Locus_11266_Transcript_1/1_Confide nce_1.000_Length_465 hypothetical_protein_LSAT_7X88080_[Lactuca_sativa]_P LY63425 2.0246 Locus_49632_Transcript_1/1_Confide nce_1.000_Length_580 hypothetical_protein_LSAT_9X34960_[Lactuca_sativa]_P LY73047 6.1875 Locus_21578_Transcript_2/2_Confide nce_0.800_Length_741 Locus_53753_Transcript_1/1_Confide nce_1.000_Length_270 LEAF_RUST_10_DISEASE- RESISTANCE_LOCUS_RECEPTOR- LIKE_PROTEIN_KINASE- like_2.1_isoform_X3_[Lactuca_sativa]_XP_023756050 immediate_early_response_3-interacting_protein_1- like_[Helianthus_annuus]_>_ref|XP_021973880.1|_imm ediate_early_response_3-interacting_protein_1- like_[Helianthus_annuus]_XP_021973878 Locus_7732_Transcript_3/8_Confiden ce_0.583_Length_1372 PAZ_domain- containing_protein_[Artemisia_annua]_PWA66503 Locus_28383_Transcript_1/1_Confide nce_1.000_Length_282 Pentatricopeptide_repeat- containing_protein_[Artemisia_annua]_PWA38618 4.5235 1.6363 1.5456 1.5498 171 Table 5-4 (cont’d) Transcript ID Functional description Log2foldchange Locus_77_Transcript_1/1_Confidence _1.000_Length_509 phylloplanin-like_[Helianthus_annuus]_XP_022029707 1.6521 Locus_15669_Transcript_1/2_Confide nce_0.667_Length_800 phylloplanin- like_isoform_X2_[Helianthus_annuus]_XP_022029705 1.6565 Locus_40853_Transcript_1/1_Confide nce_1.000_Length_372 probable_disease_resistance_protein_At5g66900_[Helianth us_annuus]_XP_022012802 1.6984 Locus_19579_Transcript_3/3_Confide nce_0.778_Length_3869 proteasome_activator_subunit_4- like_[Helianthus_annuus]_>_gb|OTG31764.1|_putative_ proteasome_activating_protein_[Helianthus_annuus]_XP_ 022028770 Locus_3307_Transcript_3/3_Confiden ce_0.750_Length_1575 protein_kinase-like_domain- containing_protein_[Artemisia_annua]_PWA88905 Locus_28314_Transcript_2/3_Confide nce_0.714_Length_794 Locus_20080_Transcript_1/1_Confide nce_1.000_Length_1136 protein_NLP3- like_isoform_X2_[Helianthus_annuus]_>_gb|OTF85241 .1|_hypothetical_protein_HannXRQ_Chr17g0537831_[Hel ianthus_annuus]_XP_022025386 protein_STRICTOSIDINE_SYNTHASE-LIKE_6- like_[Helianthus_annuus]_>_gb|OTG23966.1|_putative_ strictosidine_synthase_[Helianthus_annuus]_XP_02203703 1 1.7375 2.0272 2.2288 2.2684 Locus_40881_Transcript_1/1_Confide nce_1.000_Length_433 putative_bulb- type_lectin_domain,_Thaumatin_[Helianthus_annuus]_OT G31983 2.2909 172 Table 5-4 (cont’d) Transcript ID Functional description Log2foldchange Locus_32597_Transcript_1/1_Confide nce_1.000_Length_325 putative_F-box_domain,_Leucine- rich_repeat_domain,_L_domain- like_protein_[Helianthus_annuus]_OTF85159 Locus_33084_Transcript_1/1_Confide nce_1.000_Length_1805 putative_F-box/FBD/LRR- repeat_protein_At4g13965_[Helianthus_annuus]_XP_0 22023416 Locus_51430_Transcript_1/1_Confide nce_1.000_Length_231 putative_germin-like_protein_2- 1_[Helianthus_annuus]_>_gb|OTG15939.1|_putative_ge rmin,_RmlC- like_cupin_domain_protein_[Helianthus_annuus]_XP_021 978932 Locus_23135_Transcript_2/2_Confide nce_0.667_Length_1075 putative_isoprenoid_synthase_domain- containing_protein_[Helianthus_annuus]_OTG07247 Locus_56584_Transcript_1/1_Confide nce_1.000_Length_219 putative_PGG_domain- containing_protein_[Helianthus_annuus]_OTG30422 Locus_10843_Transcript_1/1_Confide nce_1.000_Length_1130 putative_protein_kinase-like_domain,_Concanavalin_A- like_lectin/glucanase_domain_protein_[Helianthus_annuus ]_OTG17372 Locus_47898_Transcript_1/1_Confide nce_1.000_Length_302 putative_spo11/DNA_topoisomerase_VI_subunit_A,_Heav y_metal- associated_domain,_HMA_[Helianthus_annuus]_OTG267 27 2.3680 2.4412 2.7419 2.8896 2.9092 2.9925 3.0356 Locus_13762_Transcript_1/1_Confide nce_1.000_Length_733 putative_ubiquitin_[Helianthus_annuus]_OTG34721 3.6878 173 Table 5-4 (cont’d) Transcript ID Functional description Log2foldchange Locus_28600_Transcript_1/1_Confide nce_1.000_Length_1246 putative_zinc_finger,_CCHC- type_[Helianthus_annuus]_OTG34013 Locus_14198_Transcript_1/1_Confide nce_1.000_Length_460 RecName:_Full=2S_seed_storage_protein__AltName:_Ful l=2S_albumin_storage_protein__Flags:_Precursor_[Heliant hus_annuus]_>_emb|CAA29699.1|_HaG5_protein_[Heli anthus_annuus]_P15461 4.1550 4.1987 Locus_14348_Transcript_1/1_Confide nce_1.000_Length_320 ubiquitin_carboxyl-terminal_hydrolase_22- like_[Cynara_cardunculus_var._scolymus]_XP_024966816 4.2396 Locus_47420_Transcript_1/1_Confide nce_1.000_Length_1760 UDP-glycosyltransferase_84B1- like_[Helianthus_annuus]_XP_021985591 4.2600 Locus_23909_Transcript_1/1_Confide nce_1.000_Length_616 uncharacterized_protein_LOC110866443_[Helianthus_ann uus]_XP_021971282 4.4133 Locus_42111_Transcript_1/1_Confide nce_1.000_Length_246 uncharacterized_protein_LOC110871979_isoform_X3_[H elianthus_annuus]_XP_021976431 4.4608 Locus_34412_Transcript_1/1_Confide nce_1.000_Length_611 uncharacterized_protein_LOC110882522_[Helianthus_ann uus]_>_ref|XP_021986896.1|_uncharacterized_protein_ LOC110883463_[Helianthus_annuus]_XP_021986212 Locus_27532_Transcript_1/1_Confide nce_1.000_Length_894 uncharacterized_protein_LOC110886255_[Helianthus_ann uus]_>_gb|OTG12461.1|_putative_ulp1_protease_family ,_C-terminal_catalytic_domain- containing_protein_[Helianthus_annuus]_XP_021989724 5.0361 5.0380 174 Table 5-4 (cont’d) Transcript ID Functional description Log2foldchange Locus_33515_Transcript_1/1_Confide nce_1.000_Length_1330 uncharacterized_protein_LOC110907935_isoform_X1_[H elianthus_annuus]_XP_022008540 5.2078 Locus_38817_Transcript_1/1_Confiden ce_1.000_Length_220 uncharacterized_protein_LOC110902245_[Helianthus_ann uus]_XP_022004644 5.0755 Locus_21382_Transcript_2/3_Confide nce_0.700_Length_360 uncharacterized_protein_LOC110911583_[Helianthus_ann uus]_XP_022011896 5.2413 Locus_40940_Transcript_1/1_Confide nce_1.000_Length_231 uncharacterized_protein_LOC111891235_[Lactuca_sativa] _XP_023743072 5.7445 Locus_51810_Transcript_1/1_Confide nce_1.000_Length_317 UniRef100_B9SCJ0_Xyloglucan_endotransglucosylase/hy drolase_protein_2,_putative_n=1_Tax=Ricinus_communis _RepID=B9SCJ0_RICCO_4756433 5.8174 Locus_8028_Transcript_1/1_Confiden ce_1.000_Length_1020 UniRef100_C6TBS7_Putative_uncharacterized_protein_n= 1_Tax=Glycine_max_RepID=C6TBS7_SOYBN_4857141 5.9515 Locus_6852_Transcript_1/1_Confiden ce_1.000_Length_1778 UniRef100_F6H021_Putative_uncharacterized_protein_n= 1_Tax=Vitis_vinifera_RepID=F6H021_VITVI_5117528 5.9980 Locus_7881_Transcript_1/1_Confiden ce_1.000_Length_1555 UniRef100_F6H064_Putative_uncharacterized_protein_n= 1_Tax=Vitis_vinifera_RepID=F6H064_VITVI_5117569 6.1610 Locus_21514_Transcript_1/1_Confide nce_1.000_Length_975 UniRef100_F6H5H6_Putative_uncharacterized_protein_n= 1_Tax=Vitis_vinifera_RepID=F6H5H6_VITVI_5119328 6.2466 Locus_51498_Transcript_1/1_Confide nce_1.000_Length_265 UniRef100_F8S1H8_Cytochrome_P450_n=1_Tax=Heli anthus_annuus_RepID=F8S1H8_HELAN_5134371 6.3407 175 Table 5-4 (cont’d) Transcript ID Functional description Log2foldchange Locus_4983_Transcript_1/1_Confiden ce_1.000_Length_218 UniRef100_G7LEY4_Auxilin- like_protein_n=1_Tax=Medicago_truncatula_RepID=G7L EY4_MEDTR_5210753 Locus_30838_Transcript_2/2_Confide nce_0.727_Length_541 UniRef100_Q6Y0Z7_RGC2- like_protein_(Fragment)_n=1_Tax=Helianthus_annuus_Re pID=Q6Y0Z7_HELAN_5543929 Locus_17846_Transcript_1/1_Confide nce_1.000_Length_1512 vinorine_synthase- like_[Lactuca_sativa]_>_gb|PLY78746.1|_hypothetical_ protein_LSAT_9X45161_[Lactuca_sativa]_XP_02377265 9 Locus_21409_Transcript_2/3_Confide nce_0.667_Length_909 zinc_finger_BED_domain- containing_protein_RICESLEEPER_2- like_[Helianthus_annuus]_XP_021995786 Locus_30431_Transcript_1/1_Confide nce_1.000_Length_399 |_Symbols:_|_GDSL- like_Lipase/Acylhydrolase_superfamily_protein_|_chr1:10 044603- 10046379_REVERSE_LENGTH=390_AT1G28580.1 Locus_40595_Transcript_1/1_Confide nce_1.000_Length_304 |_Symbols:_|_General_transcription_factor_2- related_zinc_finger_protein_|_chr3:11593924- 11595441_REVERSE_LENGTH=505_AT3G29763.1 Locus_50799_Transcript_1/1_Confide nce_1.000_Length_277 |_Symbols:_|_Plant_protein_1589_of_unknown_function_| _chr3:20473876- 20474705_REVERSE_LENGTH=95_AT3G55240.1 6.4058 6.5441 6.6833 6.7179 -1.6098 -2.4695 -1.6467 176 Table 5-4 (cont’d) Transcript ID Functional description Log2foldchange Locus_27393_Transcript_1/1_Confide nce_1.000_Length_279 ankyrin_repeat- containing_domain,_PGG_domain_protein_[Artemisia_an nua]_PWA40442 Locus_33463_Transcript_1/1_Confide nce_1.000_Length_210 |_Symbols:_CYP72A14_|_cytochrome_P450,_family_72,_ subfamily_A,_polypeptide_14_|_chr3:4934478- 4936462_FORWARD_LENGTH=512_AT3G14680.1 Locus_55032_Transcript_1/1_Confide nce_1.000_Length_219 ankyrin_repeat-containing_protein_ITN1- like_[Helianthus_annuus]_XP_021980816 -1.5130 -1.5720 -3.4371 Locus_9395_Transcript_9/10_Confide nce_0.156_Length_2191 CALMODULIN- BINDING_PROTEIN60_[Artemisia_annua]_PWA94216 -2.0728 Locus_40373_Transcript_1/1_Confide nce_1.000_Length_239 hypothetical_protein_C1H46_032516_[Malus_baccata]_T QD81913 -4.1233 Locus_46394_Transcript_1/1_Confide nce_1.000_Length_269 hypothetical_protein_E3N88_34488_[Mikania_micrantha] _KAD3066608 -2.4523 Locus_44884_Transcript_1/1_Confide nce_1.000_Length_655 hypothetical_protein_E3N88_40340_[Mikania_micrantha] _KAD2393363 -1.6421 Locus_2225_Transcript_1/1_Confiden ce_1.000_Length_1153 nodulin-related_protein_1- like_[Helianthus_annuus]_>_gb|OTG12295.1|_putative_ protein_involved_in_response_to_salt_stress_[Helianthus_ annuus]_XP_021989583 Locus_33271_Transcript_1/1_Confide nce_1.000_Length_603 protein_FAR1-RELATED_SEQUENCE_5- like_[Helianthus_annuus]_XP_021973751 -1.6812 -3.1070 177 Table 5-4 (cont’d) Transcript ID Functional description Log2foldchange Locus_33271_Transcript_1/1_Confide nce_1.000_Length_603 protein_FAR1-RELATED_SEQUENCE_5- like_[Helianthus_annuus]_XP_021973751 Locus_6353_Transcript_1/1_Confiden ce_1.000_Length_599 probable_glutathione_S- transferase_isoform_X1_[Helianthus_annuus]_XP_021988 109 Locus_33270_Transcript_1/1_Confide nce_1.000_Length_574 protein_FAR1-RELATED_SEQUENCE_5- like_[Helianthus_annuus]_XP_021971663 Locus_42243_Transcript_1/1_Confide nce_1.000_Length_214 protein_FAR1-RELATED_SEQUENCE_5- like_[Helianthus_annuus]_XP_022030249 Locus_41089_Transcript_1/1_Confide nce_1.000_Length_269 protein_PIN-LIKES_3- like_[Helianthus_annuus]_>_gb|OTF98280.1|_putati ve_auxin_efflux_carrier_family_protein_[Helianthus_a nnuus]_XP_022009931 -3.1070 -4.2768 -3.2480 -3.1375 -2.4274 Locus_43619_Transcript_1/1_Confide nce_1.000_Length_204 putative_AMP- dependent_synthetase/ligase_[Helianthus_annuus]_OTG20 787 -4.2277 Locus_41902_Transcript_1/1_Confide nce_1.000_Length_597 putative_RNA- directed_DNA_polymerase,_eukaryota_[Helianthus_annuu s]_OTG18874 -1.6221 Locus_50854_Transcript_1/1_Confide nce_1.000_Length_265 putative_RNA- directed_DNA_polymerase,_eukaryota_[Helianthus_annuu s]_OTG24384 -5.2285 Locus_4587_Transcript_3/3_Confiden ce_0.625_Length_764 uncharacterized_protein_LOC110889668_isoform_X2_[H elianthus_annuus]_XP_021992924 -2.5417 178 Table 5-4 (cont’d) Transcript ID Functional description Log2foldchange Locus_20620_Transcript_1/1_Confide nce_1.000_Length_701 uncharacterized_protein_LOC110937409_[Helianthus_ann uus]_XP_022035518 -3.5405 Locus_50636_Transcript_1/1_Confide nce_1.000_Length_275 uncharacterized_protein_LOC110890546_[Helianthus_ann uus]_XP_021993858 -5.5652 Locus_37706_Transcript_1/1_Confide nce_1.000_Length_246 UniRef100_C6ZLB7_NBS-LRR_resistance- like_protein_RGC260_n=3_Tax=Helianthus_annuus_RepI D=C6ZLB7_HELAN_4861248 -1.8742 Locus_8515_Transcript_1/1_Confiden ce_1.000_Length_226 UniRef100_F6I0B3_Putative_uncharacterized_protein_n= 1_Tax=Vitis_vinifera_RepID=F6I0B3_VITVI_5129176 -1.6925 Locus_27976_Transcript_1/1_Confide nce_1.000_Length_1553 UniRef100_Q309D0_P450_mono- oxygenase_n=1_Tax=Stevia_rebaudiana_RepID=Q309 D0_STERE_5487509 Locus_3755_Transcript_7/7_Confiden ce_0.500_Length_1714 UniRef100_Q6VAB0_UDP- glycosyltransferase_85C2_n=1_Tax=Stevia_rebaudiana_R epID=Q6VAB0_STERE_5542241 -2.3982 -1.5443 179 Figure 5-4: Sample clustering dendrogram of all samples (A) and only 12 samples used for the WGCNA analysis (B). A) B) *Samples falling under the red spectrum of phenotype are slow development rate lines. 180 75−175−230−130−2238−1238−264−164−281−181−2139−1139−23540455055Sample dendrogram and trait heatmaphclust (*, "average")dist(gene_count)HeightPhenotype Figure 5-5: Heatmap displaying the modules correlated with the development rate phenotype. Numbers inside each box indicate Pearson correlation coefficient between the module and the phenotype and a p-value in bracket. Red color indicates positive correlation whereas blue color indicates a negative correlation. 181 Figure 5-6: Eigenvalues of the royalblue1 module in all samples. Samples 30s, 75s and 238s are the slow development rate lines and 64f, 81f and 139f are the fast development rate lines. Each sample has two biological replicates. Bar plots represent eigenvalues of royalblue1 module individually in all samples. 182 Table 5-5: Functional description of 47 transcripts in module Royalblue1. Hub genes (module membership and gene significance greater than 0.8) for this module are highlighted in bold. Transcript ID Functional description Locus_179_Transcript_1/1_Confidence_1.0 00_Length_1051 |_Symbols:_|_Protein_of_unknown_function_(DUF579)_|_chr5:26819019- 26819972_FORWARD_LENGTH=317_AT5G67210.1 Locus_596_Transcript_2/2_Confidence_0.7 50_Length_2117 |_Symbols:_emb2004_|_RNI-like_superfamily_protein_|_chr1:3461771- 3465590_FORWARD_LENGTH=605_AT1G10510.1 Locus_1168_Transcript_2/2_Confidence_0. 667_Length_706 |_Symbols:_|_Bacterial_sec- independent_translocation_protein_mttA/Hcf106_|_chr5:10784142- 10785677_REVERSE_LENGTH=147_AT5G28750.1 Locus_1340_Transcript_3/3_Confidence_0. 571_Length_1646 |_Symbols:_|_Sulfite_exporter_TauE/SafE_family_protein_|_chr2:10977174- 10979677_FORWARD_LENGTH=476_AT2G25737.1 Locus_1603_Transcript_1/1_Confidence_ 1.000_Length_1694 |_Symbols:_ENO1_|_enolase_1_|_chr1:27839465- 27841901_REVERSE_LENGTH=477_AT1G74030.1 Locus_1838_Transcript_4/4_Confidence_0. 556_Length_1418 |_Symbols:_OASC,_ATCS-C_|_O- acetylserine_(thiol)_lyase_isoform_C_|_chr3:22072668- 22075345_REVERSE_LENGTH=430_AT3G59760.3 Locus_1988_Transcript_4/4_Confidence_0. 375_Length_1634 UniRef100_Q6VAB0_UDP- glycosyltransferase_85C2_n=1_Tax=Stevia_rebaudiana_RepID=Q6VAB0_ST ERE_5542241 Locus_2494_Transcript_1/2_Confidence_0. 857_Length_2237 protein_MEI2- like_1_[Helianthus_annuus]_>_ref|XP_021984220.1|_protein_MEI2- like_1_[Helianthus_annuus]_>_gb|OTG16663.1|_putative_RNA_recognition _motif_2,_Nucleotide-binding_alpha- beta_plait_domain_protein_[Helianthus_annuus]_XP_021984219 183 Table 5-5 (cont’d) Transcript ID Functional description Locus_3314_Transcript_1/1_Confidence_1. 000_Length_1121 uncharacterized_protein_LOC110885730_[Helianthus_annuus]_>_gb|OTG1 1807.1|_putative_UBA-like_protein_[Helianthus_annuus]_XP_021989131 Locus_3690_Transcript_1/2_Confidence_0. 750_Length_1082 |_Symbols:_|_INVOLVED_IN:_biological_process_unknown_LOCATED_IN: _endomembrane_system_EXPRESSED_IN:_22_plant_structures_EXPRESSE D_DURING:_13_growth_stages_CONTAINS_InterPro_DOMAIN/s:_Mannos e-6- phosphate_receptor,_binding_(InterPro:IPR009011),_Glucosidase_II_beta_sub unit- like_(InterPro:IPR012913)_Has_30201_Blast_hits_to_17322_proteins_in_780_ species:_Archae_-_12_Bacteria_-_1396_Metazoa_-_17338_Fungi_- _3422_Plants_-_5037_Viruses_-_0_Other_Eukaryotes_- _2996_(source:_NCBI_BLink)._|_chr5:13354552- 13356725_REVERSE_LENGTH=282_AT5G35080.1 Locus_4490_Transcript_1/1_Confidence_1. 000_Length_1057 |_Symbols:_|_Ribosomal_protein_L21_|_chr1:13208777- 13210246_FORWARD_LENGTH=220_AT1G35680.1 Locus_5097_Transcript_3/3_Confidence_0. 714_Length_1148 |_Symbols:_ADK1_|_adenylate_kinase_1_|_chr5:25393274- 25394817_REVERSE_LENGTH=246_AT5G63400.1 Locus_5976_Transcript_4/5_Confidence_0. 182_Length_1793 |_Symbols:_|_Aldolase-type_TIM_barrel_family_protein_|_chr5:4302080- 4304212_REVERSE_LENGTH=438_AT5G13420.1 Locus_6029_Transcript_1/2_Confidence_0. 750_Length_827 |_Symbols:_HYD1_|_C-8,7_sterol_isomerase_|_chr1:6949160- 6950135_FORWARD_LENGTH=223_AT1G20050.1 Locus_6275_Transcript_2/2_Confidence_ 0.667_Length_1041 |_Symbols:_CLPP4,_NCLPP4_|_CLP_protease_P4_|_chr5:18396351- 18397586_FORWARD_LENGTH=292_AT5G45390.1 184 Table 5-5 (cont’d) Transcript ID Functional description Locus_6391_Transcript_1/1_Confidence_1. 000_Length_829 |_Symbols:_|_Ribosomal_protein_L17_family_protein_|_chr3:20067672- 20068385_REVERSE_LENGTH=211_AT3G54210.1 Locus_7502_Transcript_2/2_Confidence_0. 833_Length_2135 |_Symbols:_ATIMD2,_IMD2_|_isopropylmalate_dehydrogenase_2_|_chr1:302 87833-30290126_FORWARD_LENGTH=405_AT1G80560.1 Locus_8431_Transcript_1/1_Confidence_ 1.000_Length_689 |_Symbols:_|_CONTAINS_InterPro_DOMAIN/s:_Ribosomal_protein_L53 ,_mitochondrial_(InterPro:IPR019716)_Has_50_Blast_hits_to_50_proteins _in_19_species:_Archae_-_0_Bacteria_-_0_Metazoa_-_6_Fungi_- _0_Plants_-_42_Viruses_-_0_Other_Eukaryotes_- _2_(source:_NCBI_BLink)._|_chr5:15854188- 15854771_REVERSE_LENGTH=127_AT5G39600.1 Locus_8522_Transcript_2/2_Confidence_0. 750_Length_1332 |_Symbols:_RSZ22a,_At-RSZ22a_|_RNA_recognition_motif_and_CCHC- type_zinc_finger_domains_containing_protein_|_chr2:10449837- 10450860_FORWARD_LENGTH=196_AT2G24590.1 Locus_9055_Transcript_1/2_Confidence_0. 800_Length_2067 NAC_domain-containing_protein_53- like_[Helianthus_annuus]_>_gb|OTF99367.1|_putative_NAC_domain_conta ining_protein_53_[Helianthus_annuus]_XP_022006101 Locus_9661_Transcript_4/4_Confidence_ 0.500_Length_1620 |_Symbols:_TBL39_|_TRICHOME_BIREFRINGENCE- LIKE_39_|_chr2:17717498- 17719921_REVERSE_LENGTH=367_AT2G42570.1 Locus_10701_Transcript_1/1_Confidence_ 1.000_Length_677 PREDICTED:_probable_CCR4- associated_factor_1_homolog_6_isoform_X1_[Gossypium_hirsutum]_XP_016 738488 Locus_10748_Transcript_1/1_Confidence _1.000_Length_1665 |_Symbols:_|_Ypt/Rab- GAP_domain_of_gyp1p_superfamily_protein_|_chr2:13086147- 13088991_REVERSE_LENGTH=440_AT2G30710.1 185 Table 5-5 (cont’d) Transcript ID Functional description Locus_11433_Transcript_3/3_Confidence_ 0.714_Length_1930 |_Symbols:ATLCB1,_LCB1,_EMB2779,_FBR11_|_long- chain_base1_|_chr4:17218598- 17221124_FORWARD_LENGTH=482_AT4G36480.2 Locus_11858_Transcript_2/2_Confidence_ 0.750_Length_418 transketolase_family_protein_[Artemisia_annua]_PWA81515 Locus_11872_Transcript_1/1_Confidence _1.000_Length_906 |_Symbols:_PANC,_PTS,_ATPTS_|_homolog_of_bacterial_PANC_|_chr5: 19803823-19805041_REVERSE_LENGTH=310_AT5G48840.1 Locus_12634_Transcript_1/1_Confidence_ 1.000_Length_933 |_Symbols:_|_unknown_protein_FUNCTIONS_IN:_molecular_function_unkno wn_INVOLVED_IN:_biological_process_unknown_LOCATED_IN:_chloropl ast_thylakoid_membrane,_chloroplast_EXPRESSED_IN:_22_plant_structures_ EXPRESSED_DURING:_13_growth_stages_Has_42_Blast_hits_to_42_protei ns_in_19_species:_Archae_-_0_Bacteria_-_0_Metazoa_-_0_Fungi_- _0_Plants_-_40_Viruses_-_0_Other_Eukaryotes_- _2_(source:_NCBI_BLink)._|_chr3:19109118- 19109842_FORWARD_LENGTH=181_AT3G51510.1 Locus_13598_Transcript_1/1_Confidence_ 1.000_Length_1164 |_Symbols:_LysoPL2_|_lysophospholipase_2_|_chr1:19651378- 19652576_FORWARD_LENGTH=332_AT1G52760.1 Locus_15440_Transcript_3/4_Confidence_ 0.667_Length_1685 protein_indeterminate-domain_9- like_[Cynara_cardunculus_var._scolymus]_XP_024983804 Locus_16062_Transcript_2/2_Confidence_ 0.750_Length_1369 UniRef100_D8WUJ0_WRKY_transcription_factor_n=1_Tax=Artemisia_annu a_RepID=D8WUJ0_ARTAN_5024140 Locus_16381_Transcript_1/2_Confidence_ 0.750_Length_339 |_Symbols:_|_Ankyrin_repeat_family_protein_|_chr2:1036192- 1037536_REVERSE_LENGTH=240_AT2G03430.1 186 Table 5-5 (cont’d) Transcript ID Functional description Locus_16799_Transcript_3/3_Confidence_ 0.667_Length_1258 |_Symbols:_|_phytanoyl- CoA_dioxygenase_(PhyH)_family_protein_|_chr2:221316- 223187_FORWARD_LENGTH=283_AT2G01490.1 Locus_18115_Transcript_1/2_Confidence _0.667_Length_954 |_Symbols:_|_HSP20- like_chaperones_superfamily_protein_|_chr5:23725936- 23727528_REVERSE_LENGTH=158_AT5G58740.1 Locus_21076_Transcript_1/1_Confidence_ 1.000_Length_234 hypothetical_protein_HannXRQ_Chr17g0560531_[Helianthus_annuus]_OTF8 7324 Locus_21780_Transcript_1/1_Confidence _1.000_Length_428 Armadillo_[Artemisia_annua]_PWA78229 Locus_22670_Transcript_1/1_Confidence_ 1.000_Length_504 protein_NUCLEAR_FUSION_DEFECTIVE_6,_chloroplastic/mitochondrial- like_[Cynara_cardunculus_var._scolymus]_XP_024995105 Locus_22833_Transcript_1/3_Confidence_ 0.714_Length_826 Locus_28230_Transcript_3/3_Confidence_ 0.571_Length_544 Armadillo-like_helical_[Cynara_cardunculus_var._scolymus]_KVH93747 putative_mitogen- activated_protein_(MAP)_kinase_kinase_kinase_Ste11,_Cryptococcus_[Helian thus_annuus]_OTF97831 Locus_30719_Transcript_3/3_Confidence_ 0.667_Length_852 |_Symbols:_|_Pyridoxal_phosphate_phosphatase- related_protein_|_chr4:14496164- 14497310_FORWARD_LENGTH=245_AT4G29530.1 Locus_36329_Transcript_1/1_Confidence_ 1.000_Length_202 F-box/FBD/LRR-repeat_protein_At1g13570- like_[Helianthus_annuus]_XP_022020378 187 Table 5-5 (cont’d) Transcript ID Functional description Locus_37894_Transcript_1/1_Confidence_ 1.000_Length_284 UniRef100_B9N178_Predicted_protein_n=1_Tax=Populus_trichocarpa_RepID =B9N178_POPTR_4733137 Locus_44863_Transcript_1/2_Confidence_ 0.667_Length_254 transcription_initiation_factor_TFIID_subunit_6_isoform_X1_[Cynara_cardun culus_var._scolymus]_>_ref|XP_024980057.1|_transcription_initiation_facto r_TFIID_subunit_6_isoform_X1_[Cynara_cardunculus_var._scolymus]_>_r ef|XP_024980058.1|_transcription_initiation_factor_TFIID_subunit_6_isoform _X1_[Cynara_cardunculus_var._scolymus]_>_ref|XP_024980059.1|_transcri ption_initiation_factor_TFIID_subunit_6_isoform_X1_[Cynara_cardunculus_v ar._scolymus]_>_ref|XP_024980060.1|_transcription_initiation_factor_TFII D_subunit_6_isoform_X1_[Cynara_cardunculus_var._scolymus]_>_ref|XP_ 024980061.1|_transcription_initiation_factor_TFIID_subunit_6_isoform_X1_[ Cynara_cardunculus_var._scolymus]_>_ref|XP_024980062.1|_transcription_ initiation_factor_TFIID_subunit_6_isoform_X1_[Cynara_cardunculus_var._sc olymus]_XP_024980056 Locus_45167_Transcript_1/1_Confidence_ 1.000_Length_231 putative_quinoprotein_alcohol_dehydrogenase like superfamily_[Helianthus_annuus]_OTG27617 Locus_46362_Transcript_1/1_Confidence_ 1.000_Length_332 |_Symbols:_TPPF_|_Haloacid_dehalogenase like_hydrolase_(HAD)_superfamily_protein_|_chr4:7365480- 7367346_REVERSE_LENGTH=368_AT4G12430.1 Locus_48727_Transcript_1/1_Confidence_ 1.000_Length_231 putative_pentatricopeptide_repeat_protein_[Helianthus_annuus]_OTG02960 188 BIBLIOGRAPHY Abdulameer DA, Osman MB, Sulaiman Z, Yusop MR, Abdullah S, Azizi P, Muttaleb QA (2018) Assessment of Stevia rebaudiana Bertoni genotypes via morpho-agronomic traits under two light conditions. 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GeneID Functional description Peaxi162Scf00274g00850.1 Peaxi162Scf00120g00723.1 conserved hypothetical protein [Ricinus communis] gb|EEF44747.1| conserved hypothetical protein [Ricinus communis] Phosphoribosylaminoimidazole carboxylase Peaxi162Scf00192g01018.1 cleavage and polyadenylation specificity factor 160 Peaxi162Scf00100g00717.1 RNA-binding KH domain-containing protein Peaxi162Scf01115g00013.1 Zinc finger CCCH domain-containing protein 23 Peaxi162Scf00020g00421.1 Peaxi162Scf00129g00828.1 U-box domain-containing protein kinase family protein CRM family member 2 Peaxi162Scf00372g00127.1 S-norcoclaurine synthase [Morus notabilis] Peaxi162Scf00777g00213.1 Elongation factor Tu Peaxi162Scf01204g00001.1 Unknown protein Peaxi162Scf00362g00145.1 tonoplast monosaccharide transporter2 Peaxi162Scf01123g00025.1 multidrug resistance-associated protein 10 Peaxi162Scf00065g01016.1 Peaxi162Scf00632g00022.1 transducin family protein / WD-40 repeat family protein binding Peaxi162Scf00071g00020.1 GTPase Der Peaxi162Scf00539g00210.1 novel interactor of JAZ Peaxi162Scf00320g00212.1 auxin response factor 19 Peaxi162Scf00394g00815.1 Peaxi162Scf01622g00017.1 Peaxi162Scf00128g00134.1 Chloroplastic group IIA intron splicing facilitator CRS1, chloroplastic Protein translocase subunit SecY Cyclin-related, putative isoform 2 [Theobroma cacao] gb|EOY00626.1| Cyclin-related, putative isoform 2 [Theobroma cacao] Peaxi162Scf00081g02729.1 Transducin family protein / WD-40 repeat family protein Peaxi162Scf00548g00537.1 Pentatricopeptide repeat-containing protein Peaxi162Scf00275g00114.1 Pentatricopeptide repeat (PPR) superfamily protein Peaxi162Scf00214g00068.1 MEI2-like protein 5 Peaxi162Scf00139g01315.1 Poly(A)-specific ribonuclease PARN Peaxi162Scf00371g00319.1 Transducin/WD40 repeat-like superfamily protein Peaxi162Scf00033g00415.1 SEUSS-like 2 Peaxi162Scf00301g01111.1 Decapping nuclease rai1 Peaxi162Scf00069g00185.1 Pentatricopeptide repeat-containing protein Peaxi162Scf00991g00016.1 Peaxi162Scf00389g00932.1 Peaxi162Scf00177g00619.1 Peaxi162Scf00066g01518.1 Haloacid dehalogenase-like hydrolase (HAD) superfamily protein RNAligase ARID/BRIGHT DNA-binding domain-containing protein ATP-dependent zinc metalloprotease FtsH Module black black black black black black black black black black black black black black black black black black black black black black black black black black black black black black black black black 206 Table APP-3-1 (cont’d) GeneID Peaxi162Scf00161g00018.1 Functional description Module N6-adenosine-methyltransferase subunit METTL14 black Peaxi162Scf00074g01827.1 anthranilate synthase 2 Peaxi162Scf00732g00248.1 Peaxi162Scf00486g00096.1 Peaxi162Scf00929g00519.1 Peaxi162Scf00038g01649.1 ATPase E1-E2 type family protein / haloacid dehalogenase-like hydrolase family protein Copper-exporting P-type ATPase A Acylamino-acid-releasing enzyme, putative isoform 1 [Theobroma cacao] ref|XP_007017351.1| Acylamino-acid-releasing enzyme, putative isoform 1 [Theobroma cacao] gb|EOY14575.1| Acylamino- acid-releasing enzyme, putative isoform 1 [Theobroma cacao] gb|EOY14576.1| Acylamino- acid-releasing enzyme, putative isoform 1 [Theobroma cacao] FAR1-related sequence 4 Peaxi162Scf00072g00510.1 Carbamoyl-phosphate synthase large chain Peaxi162Scf00119g00520.1 Serine/threonine-protein kinase Rio1 Peaxi162Scf00527g00065.1 TatD related DNase Peaxi162Scf00008g03517.1 ATPase family AAA domain-containing protein 3 Peaxi162Scf00740g00424.1 Peaxi162Scf00526g00520.1 Peaxi162Scf00166g01048.1 Peaxi162Scf00045g00731.1 P-loop containing nucleoside triphosphate hydrolases superfamily protein Duplicated homeodomain-like superfamily protein ERD (early-responsive to dehydration stress) family protein Unknown protein Peaxi162Scf01061g00130.1 PENTATRICOPEPTIDE REPEAT 596 Peaxi162Scf01006g00315.1 Protein CPR-5 Peaxi162Scf00503g00220.1 Protein kinase superfamily protein Peaxi162Scf00332g00222.1 Major facilitator superfamily protein Peaxi162Scf01015g00113.1 Transcription initiation factor TFIID subunit 12 Peaxi162Scf00078g00548.1 KH domain-containing protein Peaxi162Scf00071g00022.1 Peaxi162Scf00351g00625.1 Pyridoxal phosphate (PLP)-dependent transferases superfamily protein DEAD-box ATP-dependent RNA helicase 53 Peaxi162Scf00877g00127.1 Unknown protein Peaxi162Scf00357g00734.1 Peaxi162Scf00005g04810.1 Peaxi162Scf00037g00123.1 Peaxi162Scf00113g00317.1 Thiosulfate sulfurtransferase/rhodanese-like domain- containing protein 2 Lariat debranching enzyme Heavy metal transport/detoxification superfamily protein geranylgeranyl pyrophosphate synthase 1 Peaxi162Scf00685g00122.1 actin-related protein 5 Peaxi162Scf00045g00223.1 RNA binding Peaxi162Scf00106g00514.1 Unknown protein Peaxi162Scf00171g00246.1 Plastid division protein CDP1, chloroplastic Peaxi162Scf00276g00133.1 Peaxi162Scf00128g01776.1 Peaxi162Scf01276g00039.1 Nucleotide-diphospho-sugar transferases superfamily protein Arf-GAP with GTPase, ANK repeat and PH domain-containing protein 3 2-oxoglutarate (2OG) and Fe(II)-dependent oxygenase superfamily protein black black black black black black black black black black black black black black black black black black black black black black black black black black black black black black black black black 207 Table APP-3-1 (cont’d) GeneID Functional description Module Peaxi162Scf00660g00118.1 UPF0505 protein C16orf62 homolog Peaxi162Scf00015g00521.1 ADP,ATP carrier protein 1 Peaxi162Scf00437g00632.1 alpha/beta-Hydrolases superfamily protein Peaxi162Scf00084g00818.1 Peaxi162Scf00225g00014.1 zinc finger family protein [Populus trichocarpa] gb|ERP50285.1| zinc finger family protein [Populus trichocarpa] cysteine synthase D1 Peaxi162Scf00650g00229.1 Unknown protein Peaxi162Scf00062g00128.1 eukaryotic translation initiation factor 4G Peaxi162Scf00945g00216.1 heavy metal atpase 1 Peaxi162Scf01123g00232.1 multidrug resistance-associated protein 4 Peaxi162Scf00071g00942.1 alpha/beta-Hydrolases superfamily protein Peaxi162Scf00074g00928.1 Mitochondrial inner membrane protein OXA1 Peaxi162Scf00189g00020.1 prohibitin 1 Peaxi162Scf00102g01053.1 Peaxi162Scf00444g00851.1 Peaxi162Scf00081g00218.1 Peaxi162Scf00007g00122.1 Phosphatidylinositol N- acetylglucosaminyltransferase subunit P VASCULAR-RELATED NAC-DOMAIN 6 Emsy N Terminus (ENT)/ plant Tudor-like domains- containing protein allene oxide synthase Peaxi162Scf00413g00085.1 LL-diaminopimelate aminotransferase Peaxi162Scf00078g00736.1 Peaxi162Scf00878g00327.1 Bifunctional aspartokinase/homoserine dehydrogenase 1 DNA-binding bromodomain-containing protein Peaxi162Scf00875g00226.1 50S ribosomal protein L14 Peaxi162Scf00598g00071.1 Unknown protein Peaxi162Scf00257g01624.1 S-locus lectin protein kinase family protein Peaxi162Scf00074g01728.1 Unknown protein Peaxi162Scf00038g02668.1 Peaxi162Scf00003g04426.1 Peaxi162Scf00854g00219.1 Tetratricopeptide repeat (TPR)-like superfamily protein 2-oxoglutarate (2OG) and Fe(II)-dependent oxygenase superfamily protein Protein of unknown function, DUF647 Peaxi162Scf00110g00912.1 Threonine dehydratase biosynthetic Peaxi162Scf00193g00726.1 nudix hydrolase homolog 3 Peaxi162Scf00714g00217.1 Proteinase inhibitor I-B Peaxi162Scf00714g00528.1 GPI transamidase subunit PIG-U Peaxi162Scf00595g00039.1 Peaxi162Scf00003g01335.1 Mediator of RNA polymerase II transcription subunit 21 MATE efflux family protein Peaxi162Scf00009g01231.1 Kelch-like protein 8 Peaxi162Scf00170g00624.1 Peaxi162Scf00036g00320.1 Signal recognition particle 43 kDa protein, chloroplastic Protein kinase superfamily protein Peaxi162Scf00014g02119.1 transposase-like protein [Arabidopsis thaliana] Peaxi162Scf00488g00925.1 Cytosolic Fe-S cluster assembly factor NARFL Peaxi162Scf00331g00003.1 ATP-dependent RNA helicase ddx23 black black black black black black black black black black black black black black black black black black black mistyrose2 mistyrose2 mistyrose2 mistyrose2 mistyrose2 mistyrose2 mistyrose2 mistyrose2 mistyrose2 mistyrose2 mistyrose2 mistyrose2 mistyrose2 mistyrose2 mistyrose2 mistyrose2 mistyrose2 mistyrose2 mistyrose2 208 Table APP-3-1 (cont’d) GeneID Functional description Peaxi162Scf00129g01648.1 Protein kinase superfamily protein Peaxi162Scf00031g00054.1 Peaxi162Scf00110g01622.1 Peaxi162Scf00827g00010.1 -- 26S proteasome non-ATPase regulatory subunit 13 homolog B phospholipase D alpha 1 Peaxi162Scf00408g00833.1 50S ribosomal protein L24 Peaxi162Scf00064g00421.1 -- Peaxi162Scf00264g00857.1 Unknown protein Peaxi162Scf00003g05025.1 Peaxi162Scf00014g02020.1 2-oxoglutarate (2OG) and Fe(II)-dependent oxygenase superfamily protein AC transp product [Oryza sativa Japonica Group] Module mistyrose2 mistyrose2 mistyrose2 mistyrose2 mistyrose2 mistyrose2 mistyrose2 mistyrose2 mistyrose2 Peaxi162Scf00959g00124.1 tRNA N6-adenosine threonylcarbamoyltransferase mistyrose2 Peaxi162Scf00194g00931.1 Peaxi162Scf00153g01323.1 Peaxi162Scf00153g01322.1 Peaxi162Scf00393g00052.1 S-adenosyl-L-methionine-dependent methyltransferases superfamily protein -- protein MATERNAL EFFECT EMBRYO ARREST 50 [Arabidopsis thaliana] gb|AAC19301.1| far-red elongated hypocotyls 3 Peaxi162Scf00655g00328.1 Protein NRT1/ PTR FAMILY 8.3 Peaxi162Scf00285g00428.1 Cytochrome P450 superfamily protein mistyrose2 mistyrose2 mistyrose2 mistyrose2 mistyrose2 mistyrose2 Peaxi162Scf00165g01722.1 Dof-type zinc finger DNA-binding family protein mistyrose2 Peaxi162Scf00096g01719.1 Branched-chain-amino-acid aminotransferase 6 Peaxi162Scf00263g01019.1 Peaxi162Scf00009g02530.1 Regulator of chromosome condensation (RCC1) family with FYVE zinc finger domain F-box family protein Peaxi162Scf00074g01332.1 Mitochondrial ribosomal protein L27 Peaxi162Scf00618g00115.1 6-phosphogluconolactonase 5 Peaxi162Scf00765g00049.1 exocyst complex component sec5 mistyrose2 mistyrose2 mistyrose2 mistyrose2 mistyrose2 mistyrose2 Peaxi162Scf00227g00611.1 RNA recognition motif (RRM)-containing protein mistyrose2 Peaxi162Scf00312g00017.1 ent-kaurenoic acid hydroxylase 2 Peaxi162Scf00110g01617.1 Unknown protein Peaxi162Scf00174g00026.1 Pectin lyase-like superfamily protein Peaxi162Scf00407g00632.1 Peaxi162Scf00071g00837.1 Peaxi162Scf00152g01537.1 Peaxi162Scf00014g00082.1 Peaxi162Scf00342g00073.1 ATP binding microtubule motor family protein isoform 1 [Theobroma cacao] gb|EOY07615.1| ATP binding microtubule motor family protein isoform 1 [Theobroma cacao] Chlorophyll(ide) b reductase NOL, chloroplastic FAD/NAD(P)-binding oxidoreductase family protein conserved hypothetical protein [Ricinus communis] gb|EEF32658.1| conserved hypothetical protein [Ricinus communis] Ethylene insensitive 3 family protein Peaxi162Scf00073g00057.1 Elongation factor 2 Peaxi162Scf00684g00224.1 Diaminopimelate decarboxylase mistyrose2 mistyrose2 mistyrose2 mistyrose2 mistyrose2 mistyrose2 mistyrose2 mistyrose2 mistyrose2 mistyrose2 209 Table APP-3-1 (cont’d) GeneID Peaxi162Scf00476g00222.1 Peaxi162Scf00059g00128.1 Peaxi162Scf00272g00224.1 Functional description translocon at the inner envelope membrane of chloroplasts 20 methyltransferase [Arabidopsis thaliana] dbj|BAB02862.1| unnamed protein product [Arabidopsis thaliana] gb|AAL67056.1| unknown protein [Arabidopsis thaliana] gb|AAN13150.1| unknown protein [Arabidopsis thaliana] gb|AEE77448.1| methyltransferase [Arabidopsis thaliana] MLO-like protein 6 Peaxi162Scf00152g00329.1 Protein of unknown function (DUF620) Peaxi162Scf00016g01745.1 Peaxi162Scf00130g00620.1 P-loop containing nucleoside triphosphate hydrolases superfamily protein UDP-Glycosyltransferase superfamily protein Peaxi162Scf00102g01433.1 Protein kinase superfamily protein Peaxi162Scf00311g01419.1 Peaxi162Scf00038g00043.1 Peaxi162Scf00078g00824.1 Peaxi162Scf00272g00828.1 Peaxi162Scf00003g02311.1 Peaxi162Scf00009g01923.1 Peaxi162Scf00431g00311.1 Cyclophilin-like peptidyl-prolyl cis-trans isomerase family protein S-adenosylmethionine decarboxylase proenzyme Protein kinase family protein with ARM repeat domain rhodanese-like domain-containing protein / PPIC- type PPIASE domain-containing protein Origin recognition complex subunit 2 8-amino-7-oxononanoate synthase [Theobroma cacao] gb|EOY07291.1| 8-amino-7-oxononanoate synthase [Theobroma cacao] Protein transport protein Sec24-like Peaxi162Scf00248g01414.1 NAC domain containing protein 73 Peaxi162Scf00257g00111.1 formin homology5 Peaxi162Scf00140g01221.1 beta glucosidase 11 Peaxi162Scf00025g03227.1 DHHC-type zinc finger family protein Peaxi162Scf00673g00002.1 trigalactosyldiacylglycerol 1 Peaxi162Scf00789g00035.1 lysine histidine transporter 1 Peaxi162Scf00822g00313.1 Peaxi162Scf00658g00065.1 Plastid-lipid associated protein PAP / fibrillin family protein expressed protein [Oryza sativa Japonica Group] Peaxi162Scf00954g00314.1 auxin response factor 19 Peaxi162Scf00270g00419.1 Peaxi162Scf00188g00545.1 Homeobox-leucine zipper family protein / lipid- binding START domain-containing protein Uridine kinase Peaxi162Scf00078g00424.1 WD repeat-containing protein 74 Peaxi162Scf00458g00318.1 Remorin family protein Peaxi162Scf00380g00098.1 Peaxi162Scf00264g00004.1 NRC1 [Solanum lycopersicum] gb|ABC26878.1| NRC1 [Solanum lycopersicum] Phototropic-responsive NPH3 family protein Peaxi162Scf00818g00118.1 uracil dna glycosylase Peaxi162Scf00363g00082.1 zinc finger protein 8 Peaxi162Scf00015g90043.1 Peaxi162Scf00271g00518.1 Protein of unknown function (DUF630 and DUF632) Cysteine protease ATG4A Module mistyrose2 mistyrose2 mistyrose2 mistyrose2 mistyrose2 mistyrose2 mistyrose2 mistyrose2 mistyrose2 mistyrose2 mistyrose2 mistyrose2 mistyrose2 mistyrose2 mistyrose2 mistyrose2 mistyrose2 mistyrose2 mistyrose2 mistyrose2 mistyrose2 mistyrose2 sienna2 sienna2 sienna2 sienna2 sienna2 sienna2 sienna2 sienna2 sienna2 sienna2 sienna2 210 Table APP-3-1 (cont’d) GeneID Functional description Peaxi162Scf00450g00317.1 Remorin family protein Peaxi162Scf00189g00610.1 Peaxi162Scf00441g00624.1 Peaxi162Scf00241g00059.1 Peaxi162Scf00269g01519.1 Leucine-rich repeat receptor-like protein kinase family protein Histone-lysine N-methyltransferase, H3 lysine-36 specific Plant invertase/pectin methylesterase inhibitor superfamily protein Omega-amidase NIT2 Peaxi162Scf00044g00112.1 tRNA (cytosine(34)-C(5))-methyltransferase Peaxi162Scf00351g00526.1 Unknown protein Peaxi162Scf00140g01136.1 beta glucosidase 11 Peaxi162Scf00103g01617.1 Ras-related protein Rab-6.1 Peaxi162Scf00351g00412.1 Elongation factor 1-alpha Peaxi162Scf00349g00059.1 -- Peaxi162Scf00008g04010.1 Splicing factor U2af large subunit B Peaxi162Scf01003g00017.1 Protein kinase superfamily protein Peaxi162Scf00341g00714.1 Polyadenylation factor subunit 2 Peaxi162Scf00493g00012.1 Peaxi162Scf01276g00047.1 Peaxi162Scf01159g00023.1 Peaxi162Scf00892g00120.1 Peaxi162Scf00377g00029.1 Peaxi162Scf00531g00610.1 Disease resistance protein (CC-NBS-LRR class) family Cytochrome P450 superfamily protein Auxin-induced protein-like protein [Medicago truncatula] gb|AES85188.1| Auxin-induced protein-like protein [Medicago truncatula] S-adenosyl-L-methionine-dependent methyltransferases superfamily protein Basic helix-loop-helix DNA-binding superfamily protein isoform 1 [Theobroma cacao] gb|EOX96336.1| DNA mismatch repair protein MutS Peaxi162Scf01159g00226.1 ABC-2 type transporter family protein Peaxi162Scf00525g00079.1 4-hydroxy-tetrahydrodipicolinate synthase Peaxi162Scf00695g00009.1 Peaxi162Scf00258g00925.1 cytochrome P450, family 88, subfamily A, polypeptide 3 F-box/LRR-repeat protein 20 Peaxi162Scf00039g00093.1 diaminopimelate epimerase family protein Peaxi162Scf00650g00226.1 exostosin family protein Peaxi162Scf00130g00071.1 UDP-Glycosyltransferase superfamily protein Peaxi162Scf00180g00519.1 Peaxi162Scf00915g00249.1 DNA double-strand break repair rad50 ATPase, putative isoform 3 [Theobroma cacao] gb|EOY04772.1| F-box family protein Peaxi162Scf00258g00314.1 DNA-binding bromodomain-containing protein Peaxi162Scf00825g00416.1 Peaxi162Scf00140g01352.1 Double Clp-N motif-containing P-loop nucleoside triphosphate hydrolases superfamily protein Protein of unknown function (DUF1624) Peaxi162Scf00038g02247.1 CCAAT/enhancer-binding protein zeta Peaxi162Scf00403g00113.1 Peaxi162Scf00130g00932.1 Enoyl-[acyl-carrier-protein] reductase [NADH], chloroplastic Transmembrane proteins 14C Module sienna2 sienna2 sienna2 sienna2 sienna2 sienna2 sienna2 sienna2 sienna2 sienna2 sienna2 sienna2 sienna2 sienna2 sienna2 sienna2 sienna2 sienna2 sienna2 sienna2 sienna2 sienna2 sienna2 sienna2 sienna2 sienna2 sienna2 sienna2 sienna2 sienna2 sienna2 sienna2 sienna2 sienna2 sienna2 211 Table APP-3-1 (cont’d) GeneID Functional description Peaxi162Scf00071g00119.1 PAR1 protein Peaxi162Scf01336g00122.1 Peaxi162Scf01010g00217.1 OB-fold-like isoform 1 [Theobroma cacao] gb|EOY05603.1| OB-fold-like isoform 1 [Theobroma cacao] Uridine 5'-monophosphate synthase Peaxi162Scf00608g00317.1 embryo defective 1923 Peaxi162Scf01139g00002.1 Peaxi162Scf00004g02625.1 Serine/threonine-protein kinase irlC isoform 1 [Theobroma cacao] gb|EOY08470.1| sulfite reductase Peaxi162Scf00915g00250.1 Protein of unknown function (DUF2930) Peaxi162Scf00175g00525.1 Squamosa promoter-binding-like protein 6 Peaxi162Scf00444g00034.1 NADH-ubiquinone oxidoreductase-related Peaxi162Scf00097g00108.1 Eukaryotic translation initiation factor 5A-4 Peaxi162Scf00073g00115.1 Peaxi162Scf00254g00215.1 Plasma membrane, myosin-like, Tubulin/FtsZ, N- terminal, putative isoform 4 [Theobroma cacao] gb|EOX91323.1| Acetolactate synthase small subunit Peaxi162Scf01333g00136.1 BTB/POZ domain-containing protein Peaxi162Scf00408g00320.1 COBW domain-containing protein 1 Peaxi162Scf00451g00723.1 Peaxi162Scf00911g00028.1 Peaxi162Scf00042g02412.1 Transmembrane amino acid transporter family protein cytochrome P450, family 704, subfamily B, polypeptide 1 SBP (S-ribonuclease binding protein) family protein Peaxi162Scf00052g00210.1 xyloglucan endotransglucosylase/hydrolase 30 Peaxi162Scf00253g01317.1 Unknown protein Peaxi162Scf00288g00815.1 Galactosyltransferase family protein Peaxi162Scf00022g00098.1 MADS-box transcription factor 3 Peaxi162Scf00344g01720.1 YELLOW STRIPE like 7 Peaxi162Scf01039g00134.1 Peaxi162Scf00038g01035.1 dehydroquinate dehydratase, putative / shikimate dehydrogenase, putative pleiotropic drug resistance 7 Peaxi162Scf00152g00612.1 squalene monooxygenase 2 Peaxi162Scf00166g00529.1 4-coumarate--CoA ligase-like 1 Peaxi162Scf01056g90029.1 Endo-1 3(4)-beta-glucanase 1 Peaxi162Scf00683g00448.1 Peaxi162Scf00516g00671.1 Calcium-dependent lipid-binding (CaLB domain) family protein NAD(P)-binding Rossmann-fold superfamily protein Peaxi162Scf00139g00015.1 Ycf1 protein Peaxi162Scf00717g00215.1 Peaxi162Scf01022g00329.1 Calcium-dependent phosphotriesterase superfamily protein C2H2-like zinc finger protein Peaxi162Scf00658g00634.1 Pectin lyase-like superfamily protein Peaxi162Scf00019g03125.1 magnesium transporter 9 Peaxi162Scf00002g02826.1 Transcription initiation factor IIB-2 Peaxi162Scf00241g00053.1 Peaxi162Scf00153g00416.1 Plant invertase/pectin methylesterase inhibitor superfamily protein zinc induced facilitator-like 1 Module sienna2 sienna2 sienna2 sienna2 sienna2 sienna2 sienna2 sienna2 sienna2 sienna2 sienna2 sienna2 tan2 tan2 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 212 Table APP-3-1 (cont’d) GeneID Functional description Peaxi162Scf00128g01233.1 HORMA domain-containing protein 1 Peaxi162Scf01240g00005.1 Pectin lyase-like superfamily protein Peaxi162Scf00131g00027.1 Peaxi162Scf00326g00711.1 Peaxi162Scf00200g00418.1 Peaxi162Scf00422g00413.1 Peaxi162Scf00003g02039.1 Peaxi162Scf00074g01411.1 P-loop containing nucleoside triphosphate hydrolases superfamily protein Leucine-rich repeat receptor-like protein kinase family protein RNA binding protein, putative [Ricinus communis] gb|EEF38985.1| RNA binding protein, putative [Ricinus communis] Acyl-coenzyme A oxidase 2, peroxisomal conserved hypothetical protein [Ricinus communis] gb|EEF43357.1| conserved hypothetical protein [Ricinus communis] Cation/H(+) antiporter 18 Peaxi162Scf00073g00173.1 Unknown protein Peaxi162Scf00177g00415.1 Peaxi162Scf00922g00002.1 Peaxi162Scf00104g00017.1 Peaxi162Scf01108g00332.1 Peaxi162Scf00132g01516.1 Peaxi162Scf00069g00810.1 Peaxi162Scf00003g05227.1 Peaxi162Scf00174g00101.1 ATPase E1-E2 type family protein / haloacid dehalogenase-like hydrolase family protein E3 ubiquitin-protein ligase AIP2 ERD (early-responsive to dehydration stress) family protein conserved hypothetical protein 16 [Hevea brasiliensis] S-adenosyl-L-methionine-dependent methyltransferases superfamily protein beta-fructofuranosidase, insoluble isoenzyme 1-like [Solanum tuberosum] gb|AEV46310.1| apoplastic invertase [Solanum tuberosum] nodulin MtN21 /EamA-like transporter family protein phospholipase D alpha 1 Peaxi162Scf00149g00116.1 Pyrroline-5-carboxylate reductase Peaxi162Scf00449g00512.1 squalene monooxygenase 2 Peaxi162Scf00029g02515.1 early nodulin-like protein 18 Peaxi162Scf00362g00634.1 Peaxi162Scf00329g00213.1 Peaxi162Scf00931g00032.1 Peaxi162Scf00943g00003.1 Peaxi162Scf00560g00223.1 SBP family protein, putative [Theobroma cacao] gb|EOX97652.1| SBP family protein, putative [Theobroma cacao] dihydroflavonol 4-reductase-like1 conserved hypothetical protein [Ricinus communis] gb|EEF43976.1| conserved hypothetical protein [Ricinus communis] conserved hypothetical protein [Ricinus communis] gb|EEF30394.1| conserved hypothetical protein [Ricinus communis] Aldehyde dehydrogenase family 2 member C4 Peaxi162Scf00459g00839.1 RING/U-box superfamily protein Peaxi162Scf00525g00614.1 Serine/threonine-protein kinase SAPK7 Peaxi162Scf00199g01019.1 Pectin lyase-like superfamily protein Peaxi162Scf00004g04219.1 Ribosome production factor 1 Peaxi162Scf00680g00410.1 RING/U-box superfamily protein Peaxi162Scf00945g00013.1 Auxin-responsive GH3 family protein Peaxi162Scf00155g00096.1 cationic amino acid transporter 5 Module bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 213 Table APP-3-1 (cont’d) GeneID Functional description Peaxi162Scf00032g00821.1 NAD kinase 1 Peaxi162Scf01160g00149.1 poly(A) polymerase 3 Peaxi162Scf02085g00005.1 DNAse I-like superfamily protein Peaxi162Scf01290g00241.1 actin 4 Module bisque4 bisque4 bisque4 bisque4 Peaxi162Scf00715g00117.1 DNA repair and recombination protein RAD54-like bisque4 Peaxi162Scf00091g00176.1 Cytochrome P450 superfamily protein Peaxi162Scf00002g00332.1 calcium ATPase 2 Peaxi162Scf00129g00085.1 zinc finger (AN1-like) family protein Peaxi162Scf00177g01228.1 Tetraspanin family protein Peaxi162Scf00069g01326.1 Unknown protein Peaxi162Scf00045g01440.1 Peaxi162Scf00107g00910.1 Peaxi162Scf01204g00123.1 Keratin-associated protein 10-6 isoform 1 [Theobroma cacao] ref|XP_007011486.1| Keratin- associated protein 10-6 isoform 1 [Theobroma cacao] gb|EOY29104.1| Keratin-associated protein 10-6 isoform 1 [Theobroma cacao] gb|EOY29105.1| Keratin-associated protein 10-6 isoform 1 [Theobroma cacao] 2-oxoglutarate (2OG) and Fe(II)-dependent oxygenase superfamily protein Unknown protein Peaxi162Scf00128g00314.1 Protein of unknown function (DUF707) Peaxi162Scf00284g00022.1 Peroxidase superfamily protein Peaxi162Scf00570g00049.1 PIG93, partial [Petunia x hybrida] Peaxi162Scf00535g00320.1 DA1 Peaxi162Scf00186g00133.1 Unknown protein Peaxi162Scf00186g01211.1 Pyruvate kinase family protein Peaxi162Scf00005g00017.1 -- Peaxi162Scf01343g00154.1 N-alpha-acetyltransferase 50 Peaxi162Scf00103g00071.1 Blue copper protein Peaxi162Scf00396g00134.1 Peaxi162Scf00046g00168.1 6-phosphogluconate dehydrogenase, decarboxylating 3 Zinc-binding dehydrogenase family protein Peaxi162Scf00486g00310.1 alpha/beta-Hydrolases superfamily protein Peaxi162Scf00983g00045.1 cinnamyl alcohol dehydrogenase 9 Peaxi162Scf00134g02026.1 Unknown protein Peaxi162Scf00038g00519.1 Oxysterol-binding protein-related protein 1C Peaxi162Scf00075g01418.1 Peaxi162Scf00045g00142.1 basic helix-loop-helix (bHLH) DNA-binding superfamily protein NAD(P)-binding Rossmann-fold superfamily protein Peaxi162Scf00913g00019.1 cinnamyl alcohol dehydrogenase 6 Peaxi162Scf00035g00412.1 Nuclear pore complex protein Nup96 homolog Peaxi162Scf00592g00446.1 Peaxi162Scf00117g01515.1 basic helix-loop-helix (bHLH) DNA-binding superfamily protein Cation/H(+) antiporter 18 Peaxi162Scf00498g00523.1 serine/threonine protein kinase 2 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 214 Table APP-3-1 (cont’d) GeneID Peaxi162Scf00953g00514.1 Peaxi162Scf00089g01857.1 Peaxi162Scf00372g01015.1 Peaxi162Scf00268g00613.1 Peaxi162Scf00137g00107.1 Functional description conserved hypothetical protein [Ricinus communis] gb|EEF44617.1| conserved hypothetical protein [Ricinus communis] UDP-N-acetylglucosamine--N-acetylmuramyl- pyrophosphoryl-undecaprenol N-acetylglucosamine transferase isoform 1 [Theobroma cacao] serine carboxypeptidase-like 48 conserved hypothetical protein [Ricinus communis] ref|XP_004144901.1| PREDICTED: uncharacterized protein LOC101221471 [Cucumis sativus] ref|XP_004153271.1| PREDICTED: uncharacterized protein LOC101206100 [Cucumis sativus] ref|XP_004161015.1| PREDICTED: uncharacterized protein LOC101226661 [Cucumis sativus] gb|EEF48006.1| conserved hypothetical protein [Ricinus communis] ARF-GAP domain 5 Peaxi162Scf00011g00157.1 Protein kinase superfamily protein Peaxi162Scf00523g00015.1 aspartate aminotransferase 3 Peaxi162Scf00415g00526.1 Peaxi162Scf00763g00428.1 Peaxi162Scf00086g00615.1 Peaxi162Scf00179g00097.1 RNA-binding (RRM/RBD/RNP motifs) family protein Copper amine oxidase family protein conserved hypothetical protein [Ricinus communis] gb|EEF33266.1| conserved hypothetical protein [Ricinus communis] copper transporter 1 Peaxi162Scf01223g00009.1 RHOMBOID-like protein 12 Peaxi162Scf00026g02614.1 Peaxi162Scf00638g00210.1 Peaxi162Scf00215g00053.1 Peaxi162Scf00157g00417.1 Peaxi162Scf00083g01118.1 Peaxi162Scf00253g00321.1 Isocitrate dehydrogenase [NAD] subunit 2, mitochondrial Heavy metal transport/detoxification superfamily protein Alpha-1,4-glucan-protein synthase family protein PHD type transcription factor with transmembrane domain protein [Arabidopsis thaliana] gb|AED93945.1| DNA binding and zinc-finger domain-containing protein [Arabidopsis thaliana] Lactoylglutathione lyase / glyoxalase I family protein NAC domain protein, Peaxi162Scf00745g00839.1 Major facilitator superfamily protein Peaxi162Scf00748g00111.1 COP1-interacting protein 7 Peaxi162Scf00102g00108.1 C2H2-like zinc finger protein Peaxi162Scf00198g00148.1 MLP-like protein 28 Peaxi162Scf00053g00524.1 Alternative oxidase 3, mitochondrial Peaxi162Scf00028g00206.1 Peroxidase superfamily protein Peaxi162Scf02113g00007.1 Calcium-binding EF-hand family protein Peaxi162Scf00406g00134.1 Glutelin type-A 1 [Morus notabilis] Peaxi162Scf00765g00133.1 actin-11 Peaxi162Scf00102g01416.1 IQ-domain 22 Peaxi162Scf00373g00139.1 Plant protein of unknown function (DUF946) Peaxi162Scf00212g00310.1 Peroxidase superfamily protein Module bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 215 Table APP-3-1 (cont’d) GeneID Functional description Module Peaxi162Scf00039g00429.1 Lung seven transmembrane receptor family protein bisque4 Peaxi162Scf00527g00516.1 Coatomer, alpha subunit Peaxi162Scf00002g01611.1 Peaxi162Scf00078g01015.1 conserved hypothetical protein [Ricinus communis] gb|EEF33467.1| conserved hypothetical protein [Ricinus communis] Sucrose-phosphate synthase family protein Peaxi162Scf00668g00445.1 Protein kinase superfamily protein Peaxi162Scf00959g00046.1 sulfotransferase 16 Peaxi162Scf00015g00327.1 sulfate transporter 3 Peaxi162Scf00153g01319.1 Peaxi162Scf00084g00027.1 Peaxi162Scf00270g00009.1 Zinc finger, C3HC4 type (RING finger) family protein SBP family protein [Theobroma cacao] gb|EOY24121.1| SBP family protein [Theobroma cacao] Protein of unknown function (DUF1666) Peaxi162Scf00141g00334.1 Yippee family putative zinc-binding protein Peaxi162Scf00619g00113.1 Protein kinase superfamily protein Peaxi162Scf00342g00113.1 myb domain protein 33 Peaxi162Scf00209g00122.1 Oxidoreductase family protein Peaxi162Scf00175g00122.1 ureide permease 2 Peaxi162Scf00222g00823.1 RING/FYVE/PHD zinc finger superfamily protein Peaxi162Scf01068g00019.1 Peaxi162Scf00126g01127.1 Late cornified envelope protein 1E [Theobroma cacao] gb|EOX96360.1| Late cornified envelope protein 1E [Theobroma cacao] Calmodulin-binding transcription activator 2 Peaxi162Scf00658g00419.1 Mannose-1-phosphate guanyltransferase Peaxi162Scf00579g00049.1 Protein SUPPRESSOR OF GENE SILENCING 3 Peaxi162Scf00406g00236.1 UDP-glucuronic acid decarboxylase 3 Peaxi162Scf00944g00145.1 Inositol oxygenase 2 Peaxi162Scf00129g00823.1 GPI ethanolamine phosphate transferase 1 Peaxi162Scf00235g00085.1 Unknown protein Peaxi162Scf00232g00517.1 ubiquitin-conjugating enzyme 28 Peaxi162Scf00033g01724.1 Acyl carrier protein 1, chloroplastic Peaxi162Scf00015g00623.1 Peaxi162Scf00349g00711.1 Binding-like protein isoform 4 [Theobroma cacao] gb|EOY20913.1| Binding-like protein isoform 4 [Theobroma cacao] calmodulin-binding family protein Peaxi162Scf00004g04126.1 Hydroxymethylglutaryl-CoA lyase, mitochondrial Peaxi162Scf00833g00531.1 Cysteine proteinases superfamily protein Peaxi162Scf00485g00037.1 Developmental regulator, ULTRAPETALA Peaxi162Scf00517g90028.1 Protein of Unknown Function (DUF239) Peaxi162Scf00493g00219.1 Peaxi162Scf00076g01055.1 Peaxi162Scf00016g02427.1 conserved hypothetical protein [Ricinus communis] gb|EEF44617.1| conserved hypothetical protein [Ricinus communis] galactose-1-phosphate uridyl transferase-like protein [Arabidopsis thaliana] 1,2-alpha-L-fucosidases Peaxi162Scf00111g00125.1 NADH-ubiquinone oxidoreductase chain 5 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 216 Table APP-3-1 (cont’d) GeneID Functional description Peaxi162Scf00479g00005.1 Transcription factor CYCLOIDEA Peaxi162Scf00223g01515.1 LisH dimerisation motif Peaxi162Scf00152g01526.1 Peaxi162Scf00122g02417.1 Peaxi162Scf00945g00123.1 Peaxi162Scf00119g00722.1 Lactoylglutathione lyase / glyoxalase I family protein RB1-inducible coiled-coil protein 1, putative isoform 2 [Theobroma cacao] gb|EOX90658.1| RB1- inducible coiled-coil protein 1, putative isoform 2 [Theobroma cacao] Maternal effect embryo arrest 59, putative isoform 1 [Theobroma cacao] gb|EOX91570.1| Maternal effect embryo arrest 59, putative isoform 1 [Theobroma cacao] Unknown protein Peaxi162Scf00081g01411.1 LOB domain-containing protein 38 Peaxi162Scf00006g00529.1 RING/U-box superfamily protein Peaxi162Scf00899g00310.1 Nodulin MtN3 family protein Peaxi162Scf01650g00038.1 Peaxi162Scf00234g00713.1 Peaxi162Scf01650g00057.1 conserved hypothetical protein [Ricinus communis] gb|EEF42326.1| conserved hypothetical protein [Ricinus communis] SPFH/Band 7/PHB domain-containing membrane- associated protein family hydrogen peroxide-induced 1 [Nicotiana tabacum] Peaxi162Scf00159g00912.1 nudix hydrolase homolog 25 Peaxi162Scf00235g00842.1 Peaxi162Scf00006g00811.1 Actin binding Calponin homology (CH) domain- containing protein ubiquitin-conjugating enzyme 28 Peaxi162Scf00746g00039.1 -- Peaxi162Scf00003g05367.1 Peaxi162Scf00258g00045.1 Peaxi162Scf00001g00481.1 Peaxi162Scf00084g00923.1 Basic-leucine zipper (bZIP) transcription factor family protein phosphatidylinositol-4-phosphate 5-kinase family protein SPX (SYG1/Pho81/XPR1) domain-containing protein Vacuolar protein sorting 55 (VPS55) family protein Peaxi162Scf00089g01235.1 Quinolinate synthase, chloroplastic Peaxi162Scf00739g00426.1 response regulator 17 Peaxi162Scf00284g00317.1 -- Peaxi162Scf00146g00319.1 Polyadenylate-binding protein 7 Peaxi162Scf00574g00117.1 ABC transporter A family member 7 Peaxi162Scf00944g00140.1 myb domain protein 68 Peaxi162Scf00873g00113.1 D-aminoacyl-tRNA deacylases Peaxi162Scf00006g00534.1 Nucleotide/sugar transporter family protein Peaxi162Scf00175g00523.1 cytochrome B5 isoform A Peaxi162Scf00907g00120.1 Major facilitator superfamily protein Peaxi162Scf00013g01413.1 TRICHOME BIREFRINGENCE-LIKE 8 Peaxi162Scf00915g00120.1 RHOMBOID-like protein 3 Peaxi162Scf00635g00021.1 armadillo repeat only 2 Peaxi162Scf00133g01414.1 Defence response isoform 1 [Theobroma cacao] gb|EOX92285.1| Defence response isoform 1 [Theobroma cacao] Module bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 217 Table APP-3-1 (cont’d) GeneID Peaxi162Scf00450g00124.1 Peaxi162Scf00117g00315.1 Peaxi162Scf00105g01426.1 Peaxi162Scf00218g00312.1 Functional description Basic helix-loop-helix DNA-binding superfamily protein, putative isoform 1 [Theobroma cacao] gb|EOX91461.1| Basic helix-loop-helix DNA- binding superfamily protein, putative isoform 1 [Theobroma cacao] Inorganic pyrophosphatase conserved hypothetical protein [Ricinus communis] gb|EEF45949.1| conserved hypothetical protein [Ricinus communis] -- Peaxi162Scf00037g00424.1 long-chain acyl-CoA synthetase 7 Peaxi162Scf00156g01314.1 auxin response factor 11 Peaxi162Scf00341g00036.1 Eukaryotic aspartyl protease family protein Peaxi162Scf00885g00320.1 -- Peaxi162Scf01002g00114.1 Peaxi162Scf00740g00554.1 Pollen Ole e 1 allergen and extensin family protein [Theobroma cacao] gb|EOY03810.1| Pollen Ole e 1 allergen and extensin family protein [Theobroma cacao] SAUR-like auxin-responsive protein family Peaxi162Scf00091g00621.1 ferredoxin 3 Peaxi162Scf00067g00626.1 sodium hydrogen exchanger 2 Peaxi162Scf00081g00089.1 Peaxi162Scf00166g01042.1 Peaxi162Scf00753g00333.1 Peaxi162Scf00481g00623.1 Peaxi162Scf00228g00514.1 RNA binding protein, putative [Ricinus communis] gb|EEF52315.1| RNA binding protein, putative [Ricinus communis] Protein kinase superfamily protein ubiquitin-associated (UBA)/TS-N domain- containing protein conserved hypothetical protein [Ricinus communis] gb|EEF41898.1| conserved hypothetical protein [Ricinus communis] Peroxidase superfamily protein Peaxi162Scf00344g00159.1 Unknown protein Peaxi162Scf01112g00011.1 Peaxi162Scf00016g03013.1 Inositol monophosphatase - like protein [Arabidopsis thaliana] Unknown protein Peaxi162Scf00100g00094.1 Unknown protein Peaxi162Scf00017g02910.1 Peaxi162Scf00553g00216.1 conserved hypothetical protein [Ricinus communis] gb|EEF52000.1| conserved hypothetical protein [Ricinus communis] TFIIB zinc-binding protein Peaxi162Scf00595g00425.1 ATP-dependent Clp protease proteolytic subunit 1 Peaxi162Scf00269g01526.1 methyl esterase 12 Peaxi162Scf00316g00340.1 aspartic proteinase A1 Peaxi162Scf00579g00211.1 spermidine synthase 1 Peaxi162Scf00015g00049.1 Peaxi162Scf00345g01028.1 Regulator of Vps4 activity in the MVB pathway protein F-box family protein Peaxi162Scf00020g01714.1 Bifunctional pinoresinol-lariciresinol reductase Peaxi162Scf00620g00621.1 Protein STAY-GREEN, chloroplastic Peaxi162Scf00421g00323.1 nodulin MtN21 /EamA-like transporter family protein Module bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 218 Table APP-3-1 (cont’d) GeneID Functional description Peaxi162Scf00297g00712.1 Fatty acid hydroxylase superfamily Peaxi162Scf00550g00448.1 Protein kinase superfamily protein Peaxi162Scf00819g00027.1 Peaxi162Scf00585g01018.1 Peptidyl-prolyl cis-trans isomerase G isoform 3 [Theobroma cacao] gb|EOY14010.1 Eukaryotic translation initiation factor 1A Peaxi162Scf00071g00528.1 translationally controlled tumor protein Peaxi162Scf00620g00814.1 Peaxi162Scf00332g00444.1 Peaxi162Scf01011g00005.1 Glucose-methanol-choline (GMC) oxidoreductase family protein Polynucleotidyl transferase, ribonuclease H-like superfamily protein phosphoglucosamine mutase family protein Peaxi162Scf00548g00650.1 Mitochondrial outer membrane protein porin 2 Peaxi162Scf01012g00233.1 Eukaryotic translation initiation factor 1A Peaxi162Scf00314g00922.1 Protein kinase superfamily protein Peaxi162Scf00046g00161.1 Zinc-binding dehydrogenase family protein Peaxi162Scf00385g00068.1 bZIP transcription factor 60 Peaxi162Scf00029g00077.1 Peaxi162Scf00089g00119.1 Peaxi162Scf00051g00621.1 Peaxi162Scf00128g01750.1 Cytokinin riboside 5'-monophosphate phosphoribohydrolase LOG7 Copine family protein 2 Transmembrane emp24 domain-containing protein p24delta3 GRAM domain family protein Peaxi162Scf00003g02440.1 caffeoyl-CoA 3-O-methyltransferase Peaxi162Scf00083g00022.1 Peaxi162Scf00073g01420.1 Heterogeneous nuclear ribonucleoprotein U-like protein 1 RNA-dependent RNA polymerase 6 Peaxi162Scf00276g00211.1 ubiquitin conjugating enzyme 9 Peaxi162Scf01312g00069.1 Peaxi162Scf00919g00313.1 P-loop containing nucleoside triphosphate hydrolases superfamily protein NAD(P)-binding Rossmann-fold superfamily protein Peaxi162Scf00037g01117.1 Rubber elongation factor protein (REF) Peaxi162Scf00323g00620.1 Peaxi162Scf00064g01131.1 Peaxi162Scf00367g00410.1 tyrosine-rich hydroxyproline-rich glycoprotein, partial [Petroselinum crispum] conserved hypothetical protein [Ricinus communis] gb|EEF52842.1| conserved hypothetical protein [Ricinus communis] Glycine cleavage system H protein 2, mitochondrial Peaxi162Scf01133g00024.1 E3 ubiquitin-protein ligase CHIP Peaxi162Scf01160g00021.1 ATP-citrate synthase Peaxi162Scf00140g00212.1 Mannan endo-1,4-beta-mannosidase 7 Module bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 Peaxi162Scf00420g00642.1 Pollen Ole e 1 allergen and extensin family protein bisque4 Peaxi162Scf00016g02234.1 cysteine proteinase1 Peaxi162Scf00328g00219.1 Eukaryotic aspartyl protease family protein Peaxi162Scf00230g00067.1 Bax inhibitor-1 family protein Peaxi162Scf00241g00022.1 Peaxi162Scf00469g00218.1 Ribosomal protein L12/ ATP-dependent Clp protease adaptor protein ClpS family protein glycerol-3-phosphatase 1 bisque4 bisque4 bisque4 bisque4 bisque4 219 Table APP-3-1 (cont’d) GeneID Peaxi162Scf00772g00022.1 Peaxi162Scf00073g02123.1 Functional description vacuolar protein sorting 55 [Populus trichocarpa] gb|ERP63405.1| vacuolar protein sorting 55 [Populus trichocarpa] Protein kinase superfamily protein Peaxi162Scf00130g00832.1 Unknown protein Peaxi162Scf00276g00413.1 Sulfite exporter TauE/SafE family protein Peaxi162Scf00041g01016.1 tetratricopetide-repeat thioredoxin-like 3 Peaxi162Scf00074g00435.1 trehalase 1 Peaxi162Scf00321g00514.1 RING/U-box superfamily protein Peaxi162Scf00753g00338.1 myb domain protein 62 Module bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 Peaxi162Scf00342g00929.1 Pentatricopeptide repeat (PPR) superfamily protein bisque4 Peaxi162Scf00304g00091.1 glutamate dehydrogenase 2 Peaxi162Scf00903g00213.1 Peaxi162Scf00096g01718.1 Haloacid dehalogenase-like hydrolase (HAD) superfamily protein myb domain protein 68 Peaxi162Scf00114g00053.1 Histone H2B.10 Peaxi162Scf00021g01218.1 Peaxi162Scf00035g00711.1 thioesterase family protein [Populus trichocarpa] gb|EEE81840.1| thioesterase family protein [Populus trichocarpa] SNARE associated Golgi protein family Peaxi162Scf00856g00014.1 Nuclear pore complex protein Nup96 homolog Peaxi162Scf00016g00933.1 NAD(P)H dehydrogenase (quinone) Peaxi162Scf00110g01814.1 Enolase Peaxi162Scf00145g00144.1 U-box domain-containing protein 14 Peaxi162Scf00003g01333.1 Receptor expression-enhancing protein 5 Peaxi162Scf00003g05340.1 Peaxi162Scf00689g00221.1 Peaxi162Scf00818g00536.1 Peaxi162Scf00146g00068.1 conserved hypothetical protein [Ricinus communis] gb|EEF28605.1| conserved hypothetical protein [Ricinus communis] HCO3- transporter family conserved hypothetical protein [Ricinus communis] gb|EEF36870.1| conserved hypothetical protein [Ricinus communis] Ubiquitin-conjugating enzyme/RWD-like protein Peaxi162Scf00821g00039.1 Rhamnogalacturonate lyase family protein Peaxi162Scf00961g00116.1 -- Peaxi162Scf00257g01610.1 Calcium-binding EF-hand family protein Peaxi162Scf00037g01116.1 40S ribosomal protein S10-3 Peaxi162Scf00020g02022.1 Unknown protein Peaxi162Scf00016g02023.1 caffeoyl-CoA 3-O-methyltransferase Peaxi162Scf00481g00063.1 Zinc transporter 2 Peaxi162Scf00041g01316.1 pumilio 7 Peaxi162Scf01183g00136.1 Peaxi162Scf02113g00020.1 Isocitrate dehydrogenase [NAD] subunit 1, mitochondrial Protein kinase superfamily protein Peaxi162Scf00276g00065.1 DNA repair protein XRCC3 homolog Peaxi162Scf00722g00412.1 Cyclophilin-like peptidyl-prolyl cis-trans isomerase family protein bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 220 Table APP-3-1 (cont’d) GeneID Peaxi162Scf00170g00717.1 Peaxi162Scf00601g00035.1 Peaxi162Scf00338g00422.1 Functional description Phospho-N-acetylmuramoyl-pentapeptide- transferase homolog nodulin MtN21 /EamA-like transporter family protein carbonic anhydrase 2 Peaxi162Scf00327g00052.1 Glutaredoxin-C6 Peaxi162Scf00164g00106.1 Galactokinase Peaxi162Scf00331g01018.1 calcium-dependent protein kinase 15 Peaxi162Scf00168g01738.1 Unknown protein Peaxi162Scf00444g00616.1 SIGNAL PEPTIDE PEPTIDASE-LIKE 1 Peaxi162Scf00570g00314.1 Unknown protein Peaxi162Scf00825g00027.1 Protein yippee-like Peaxi162Scf00550g00548.1 CCT motif family protein Peaxi162Scf00650g00221.1 sugar transport protein [Coffea canephora] Peaxi162Scf00739g00125.1 potassium transporter 2 Peaxi162Scf00463g00416.1 Metal tolerance protein 4 Peaxi162Scf00213g00934.1 Protein phosphatase 2C family protein Peaxi162Scf00699g00638.1 Peaxi162Scf00105g01011.1 Enod93 protein [Medicago truncatula] gb|AES58699.1| Enod93 protein [Medicago truncatula] gb|AFK42368.1| unknown [Medicago truncatula] Subtilase family protein Peaxi162Scf00264g00838.1 CASP-like protein Peaxi162Scf00703g00210.1 Octicosapeptide/Phox/Bem1p family protein Module bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 bisque4 deepskyblue4 deepskyblue4 deepskyblue4 deepskyblue4 deepskyblue4 Peaxi162Scf00003g00719.1 26S protease regulatory subunit 10B homolog A deepskyblue4 Peaxi162Scf00031g00823.1 LisH and RanBPM domains containing protein Peaxi162Scf01112g00112.1 TBP-associated factor 7 Peaxi162Scf00957g00018.1 proline transporter 2 Peaxi162Scf00852g00012.1 Unknown protein Peaxi162Scf00074g02027.1 product [Oryza sativa Japonica Group] Peaxi162Scf00538g00229.1 Peaxi162Scf01039g00023.1 Peaxi162Scf01161g00322.1 Peaxi162Scf00038g00920.1 -- cullin 1 Transmembrane amino acid transporter family protein Protein transport protein SEC13 Peaxi162Scf00769g00416.1 alpha/beta-Hydrolases superfamily protein Peaxi162Scf00014g00085.1 Proteasome subunit alpha type-4 Peaxi162Scf00870g00011.1 Unknown protein Peaxi162Scf00404g00061.1 Peaxi162Scf01312g00070.1 conserved hypothetical protein [Ricinus communis] gb|EEF33671.1| conserved hypothetical protein [Ricinus communis] Protein kinase superfamily protein Peaxi162Scf00110g00146.1 BURP domain-containing protein Peaxi162Scf00463g00127.1 Peaxi162Scf00620g00918.1 Mediator of RNA polymerase II transcription subunit 7b Cox19-like CHCH family protein Peaxi162Scf00746g00057.1 TRAF-like family protein deepskyblue4 deepskyblue4 deepskyblue4 deepskyblue4 deepskyblue4 deepskyblue4 deepskyblue4 deepskyblue4 deepskyblue4 deepskyblue4 deepskyblue4 deepskyblue4 deepskyblue4 deepskyblue4 deepskyblue4 deepskyblue4 deepskyblue4 deepskyblue4 221 Table APP-3-1 (cont’d) GeneID Peaxi162Scf00620g00011.1 Peaxi162Scf00114g00811.1 Peaxi162Scf00681g00332.1 Peaxi162Scf00067g01924.1 Functional description Zinc finger A20 and AN1 domain-containing stress- associated protein 1 Charged multivesicular body protein 3 GRAM domain-containing protein / ABA- responsive protein-related cullin 1 Peaxi162Scf00585g00318.1 Mechanosensitive ion channel protein Peaxi162Scf00332g00755.1 Unknown protein Peaxi162Scf00380g00710.1 GDT1-like protein 4 Peaxi162Scf01414g00010.1 Exostosin family protein Peaxi162Scf00303g00513.1 Rer1 family protein Peaxi162Scf00496g00003.1 Peaxi162Scf00136g00620.1 P-loop containing nucleoside triphosphate hydrolases superfamily protein ferrochelatase 1 Peaxi162Scf40203g00005.1 mitochondrial acyl carrier protein 2 Peaxi162Scf00009g00624.1 Galactosyltransferase family protein Peaxi162Scf00094g00210.1 auxin response factor 1 Peaxi162Scf00128g00020.1 alpha/beta-Hydrolases superfamily protein Peaxi162Scf01944g00014.1 Bifunctional protein FolD 2 Peaxi162Scf00329g00410.1 alpha-galactosidase 1 Peaxi162Scf00000g00397.1 Unknown protein Peaxi162Scf01053g00210.1 Peaxi162Scf00235g00323.1 Peaxi162Scf00146g00132.1 Peaxi162Scf00073g01423.1 Peaxi162Scf00276g00312.1 Peaxi162Scf00251g00053.1 UPF0510 protein INM02 [Theobroma cacao] gb|EOY09739.1| UPF0510 protein INM02 [Theobroma cacao] AUTOPHAGY 8E Disease resistance protein (CC-NBS-LRR class) family conserved hypothetical protein [Ricinus communis] gb|EEF52477.1| conserved hypothetical protein [Ricinus communis] Branched-chain-amino-acid aminotransferase-like protein 3 [Morus notabilis] RING/U-box superfamily protein Peaxi162Scf00481g00628.1 BTB/POZ domain-containing protein Peaxi162Scf00454g00524.1 O-fucosyltransferase family protein Peaxi162Scf00815g00234.1 60S ribosomal protein L13-2 Peaxi162Scf00698g00417.1 14 kDa zinc-binding protein Peaxi162Scf00001g00284.1 Peaxi162Scf00519g00710.1 P-loop containing nucleoside triphosphate hydrolases superfamily protein Diphosphomevalonate decarboxylase Peaxi162Scf00089g00637.1 C2H2-like zinc finger protein Peaxi162Scf00009g00333.1 Peaxi162Scf00282g00225.1 Peaxi162Scf00067g02427.1 SPFH/Band 7/PHB domain-containing membrane- associated protein family Peptide-N(4)-(N-acetyl-beta- glucosaminyl)asparagine amidase glutathione S-transferase F4 Peaxi162Scf00330g00715.1 DHHC-type zinc finger family protein Peaxi162Scf00052g00820.1 Protein kinase superfamily protein Peaxi162Scf00007g01610.1 Calcium-binding EF-hand family protein Module deepskyblue4 deepskyblue4 deepskyblue4 deepskyblue4 deepskyblue4 deepskyblue4 deepskyblue4 deepskyblue4 deepskyblue4 deepskyblue4 deepskyblue4 deepskyblue4 deepskyblue4 deepskyblue4 deepskyblue4 deepskyblue4 deepskyblue4 deepskyblue4 deepskyblue4 deepskyblue4 deepskyblue4 deepskyblue4 deepskyblue4 deepskyblue4 deepskyblue4 deepskyblue4 deepskyblue4 deepskyblue4 deepskyblue4 deepskyblue4 deepskyblue4 deepskyblue4 deepskyblue4 deepskyblue4 deepskyblue4 deepskyblue4 deepskyblue4 222 Table APP-3-1 (cont’d) GeneID Functional description Peaxi162Scf01716g00006.1 catalase 2 Peaxi162Scf00007g02541.1 2-nonaprenyl-3-methyl-6-methoxy-1,4-benzoquinol hydroxylase [Theobroma cacao] gb|EOY12769.1| 2- nonaprenyl-3-methyl-6-methoxy-1,4-benzoquinol hydroxylase [Theobroma cacao] Module deepskyblue4 deepskyblue4 Peaxi162Scf00418g00732.1 Peaxi162Scf00332g00337.1 cytochrome P450, putative [Ricinus communis] gb|EEF40808.1| cytochrome P450, putative [Ricinus communis] DCN1-like protein 4 Peaxi162Scf00472g00824.1 RING/U-box superfamily protein Peaxi162Scf00263g01423.1 Nodulin MtN3 family protein Peaxi162Scf00166g00424.1 Unknown protein Peaxi162Scf00129g01228.1 Peaxi162Scf00038g02442.1 Peaxi162Scf00141g00138.1 Peaxi162Scf01003g00012.1 Proteasome assembly chaperone [Medicago truncatula] ref|XP_003630456.1| Proteasome assembly chaperone [Medicago truncatula] gb|AES72307.1| ATP synthase subunit epsilon, mitochondrial conserved hypothetical protein [Ricinus communis] gb|EEF37990.1| conserved hypothetical protein [Ricinus communis] DWNN domain, a CCHC-type zinc finger Peaxi162Scf00981g00019.1 allantoate amidohydrolase orange1 orange1 orange1 orange1 orange1 orange1 orange1 orange1 orange1 orange1 Peaxi162Scf01015g00114.1 Pyrimidine-specific ribonucleoside hydrolase RihA orange1 223 Table APP-3-2: Go enrichment analysis of differentially expressed genes and modules identified in the WGCNA analysis (p <0.05). GO ID Term Category Count p-value Downregulated genes in the slow lines (Slow vs. Fast pooled comparison) GO:0016021 GO:0031224 GO:0044425 GO:0016020 GO:0090406 GO:0042995 GO:0048226 GO:0044426 GO:0044462 GO:0008289 GO:0004553 GO:0016798 GO:0015299 GO:0015298 GO:0016641 GO:0016638 GO:0046524 GO:0005215 GO:0008131 GO:0015198 GO:0015197 GO:0016705 GO:0033907 GO:0090439 GO:0050113 GO:0015297 GO:0005506 integral component of membrane intrinsic component of membrane membrane part membrane pollen tube cell projection Casparian strip cell wall part external encapsulating structure part lipid binding hydrolase activity, hydrolyzing O-glycosyl compounds hydrolase activity, acting on glycosyl bonds solute:proton antiporter activity solute:cation antiporter activity oxidoreductase activity, acting on the CH-NH2 group of donors, oxygen as acceptor oxidoreductase activity, acting on the CH-NH2 group of donors sucrose-phosphate synthase activity transporter activity primary amine oxidase activity oligopeptide transporter activity peptide transporter activity oxidoreductase activity, acting on paired donors, with incorporation or reduction of molecular oxygen beta-D-fucosidase activity tetraketide alpha-pyrone synthase activity inositol oxygenase activity antiporter activity iron ion binding C C C C C C C C C F F F F F F F F F F F F F F F F F F 224 92 95 99 193 6 6 2 2 2 26 51 52 10 10 5 6 3 83 4 4 4 51 2 2 3 16 50 7.4e-14 8.8e-14 5.6e-08 9.5e-06 0.0024 0.0081 0.0165 0.0241 0.0425 3.3e-06 4.3e-06 1.3e-05 7.6e-05 0.00012 0.00043 0.00046 0.00083 0.00094 0.00128 0.00128 0.00204 0.00351 0.00361 0.00361 0.00378 0.00427 0.00703 Table APP-3-2 (cont’d) GO ID Term Category Count GO:0015291 GO:0016843 GO:0016844 GO:0004044 GO:0080083 GO:0016887 GO:0016157 GO:0015086 GO:0022804 GO:0004650 GO:0022892 GO:0008422 GO:0030414 GO:0061134 GO:0022857 GO:0008061 GO:0016298 GO:0004618 GO:0015385 GO:0017089 GO:0051861 GO:0015926 GO:0010208 GO:0085029 GO:0010927 GO:0010584 GO:0030198 GO:0043062 GO:0080110 GO:0006869 secondary active transmembrane transporter activity amine-lyase activity strictosidine synthase activity amidophosphoribosyltransferase activity beta-gentiobiose beta-glucosidase activity ATPase activity sucrose synthase activity cadmium ion transmembrane transporter activity active transmembrane transporter activity polygalacturonase activity substrate-specific transporter activity beta-glucosidase activity peptidase inhibitor activity peptidase regulator activity transmembrane transporter activity chitin binding lipase activity phosphoglycerate kinase activity sodium:proton antiporter activity glycolipid transporter activity glycolipid binding glucosidase activity pollen wall assembly extracellular matrix assembly cellular component assembly involved in morphogenesis pollen exine formation extracellular matrix organization extracellular structure organization sporopollenin biosynthetic process lipid transport F F F F F F F F F F F F F F F F F F F F F F P P P P P P P P 225 20 4 4 2 2 29 3 2 27 7 50 2 6 6 57 3 7 2 2 2 2 3 14 14 14 11 15 15 6 14 p-value 0.00844 0.0104 0.0104 0.0104 0.0104 0.01227 0.01891 0.01998 0.02391 0.02903 0.03082 0.03198 0.03835 0.03835 0.0394 0.0394 0.04567 0.04608 0.04608 0.04608 0.04608 0.04797 1.6e-10 1.6e-10 4.9e-10 1.7e-08 2.5e-08 2.5e-08 2.6e-07 5.9e-05 Table APP-3-2 (cont’d) Term GO ID Category Count GO:0048646 GO:0006857 GO:0015833 GO:0042886 GO:0010876 GO:0045471 GO:0006885 GO:0045229 GO:0030638 GO:0030639 GO:0019310 GO:0055085 GO:0042542 GO:0046685 GO:0005975 GO:0044765 GO:0032989 GO:0055067 GO:0051098 GO:0006541 GO:1902578 GO:0055114 GO:0009555 GO:0006414 GO:0009686 GO:0010092 GO:0010093 GO:0010262 GO:0015691 GO:0034755 anatomical structure formation involved in morphogenesis oligopeptide transport peptide transport amide transport lipid localization response to ethanol regulation of pH external encapsulating structure organization polyketide metabolic process polyketide biosynthetic process inositol catabolic process transmembrane transport response to hydrogen peroxide response to arsenic-containing substance carbohydrate metabolic process single-organism transport cellular component morphogenesis monovalent inorganic cation homeostasis regulation of binding glutamine metabolic process single-organism localization oxidation-reduction process pollen development translational elongation gibberellin biosynthetic process specification of organ identity specification of floral organ identity somatic embryogenesis cadmium ion transport iron ion transmembrane transport P P P P P P P P P P P P P P P P P P P P P P P P P P P P P P 226 19 10 10 11 14 3 4 26 2 2 3 65 8 4 71 105 24 5 2 5 105 130 19 6 3 3 3 2 2 2 p-value 0.00019 0.00021 0.00021 0.00023 0.00025 0.00075 0.00114 0.0016 0.00339 0.00339 0.00345 0.00406 0.00533 0.00536 0.00658 0.00734 0.0084 0.00928 0.00978 0.01117 0.0133 0.01472 0.0167 0.01672 0.01738 0.01738 0.01738 0.01881 0.01881 0.01881 Table APP-3-2 (cont’d) Term GO ID Category Count GO:0048317 GO:0098771 GO:0046174 GO:0050801 GO:0071705 GO:0048444 GO:0048563 GO:0048449 GO:0055080 GO:0006810 GO:0006020 GO:0016102 GO:0009691 GO:0035264 GO:0043090 GO:0009739 GO:0051234 GO:0009685 GO:0009113 GO:0046836 GO:0016998 GO:0046164 seed morphogenesis inorganic ion homeostasis amine metabolic process polyol catabolic process ion homeostasis nitrogen compound transport floral organ morphogenesis post-embryonic organ morphogenesis floral organ formation cation homeostasis transport inositol metabolic process diterpenoid biosynthetic process cytokinin biosynthetic process multicellular organism growth amino acid import response to gibberellin establishment of localization gibberellin metabolic process purine nucleobase biosynthetic process glycolipid transport cell wall macromolecule catabolic process alcohol catabolic process Upregulated genes in the slow lines (Slow vs. Fast pooled comparison) GO:0042752 GO:0042753 GO:0009649 GO:0008152 GO:0042742 regulation of circadian rhythm positive regulation of circadian rhythm entrainment of circadian clock metabolic process defense response to bacterium 2 9 10 3 10 17 5 5 4 8 127 3 3 2 2 2 8 127 3 2 2 3 3 3 2 2 90 6 P P P P P P P P P P P P P P P P P P P P P P P P P P P P 227 p-value 0.01881 0.01987 0.0221 0.02288 0.02382 0.02416 0.02457 0.02457 0.02626 0.02678 0.02811 0.02921 0.02921 0.03015 0.03015 0.03015 0.03438 0.03634 0.03637 0.0435 0.0435 0.04433 0.04433 0.00094 0.00096 0.00134 0.00359 0.00505 Table APP-3-2 (cont’d) Term GO ID Category Count GO:0055114 GO:0009617 GO:0009605 GO:0007623 GO:0048511 GO:0010017 GO:0000272 GO:0071489 GO:0051241 GO:0009733 GO:0051093 GO:0009627 GO:0009926 GO:0006355 GO:1903506 GO:2001141 GO:0010114 GO:0003700 GO:0001071 GO:0016705 GO:0005506 GO:0043565 GO:0020037 GO:0046906 GO:0016161 GO:0016491 GO:0016160 GO:0051213 GO:0030246 oxidation-reduction process response to bacterium response to external stimulus circadian rhythm rhythmic process red or far-red light signaling pathway polysaccharide catabolic process cellular response to red or far red light negative regulation of multicellular organismal process response to auxin negative regulation of developmental process systemic acquired resistance auxin polar transport regulation of transcription, DNA-templated regulation of nucleic acid-templated transcription regulation of RNA biosynthetic process response to red light transcription factor activity, sequence-specific DNA binding nucleic acid binding transcription factor activity oxidoreductase activity, acting on paired donors, with incorporation or reduction of molecular oxygen iron ion binding sequence-specific DNA binding heme binding tetrapyrrole binding beta-amylase activity oxidoreductase activity amylase activity dioxygenase activity carbohydrate binding P P P P P P P P P P P P P P P P P F F F F F F F F F F F F 228 25 6 11 3 3 2 2 2 3 5 3 2 2 15 15 15 2 14 14 12 12 9 13 13 2 25 2 3 5 p-value 0.00727 0.01217 0.016 0.01938 0.01938 0.02312 0.02464 0.0262 0.02744 0.03384 0.034 0.03458 0.03636 0.04692 0.04751 0.04751 0.04779 0.00024 0.00024 0.00195 0.00215 0.00267 0.00292 0.00326 0.00524 0.00873 0.0106 0.01575 0.02211 Table APP-3-2 (cont’d) Term GO ID GO:0004553 GO:0016758 GO:0016798 GO:0042803 GO:0003824 GO:0019829 hydrolase activity, hydrolyzing O-glycosyl compounds transferase activity, transferring hexosyl groups hydrolase activity, acting on glycosyl bonds protein homodimerization activity catalytic activity cation-transporting ATPase activity WGCNA module: Bisque4 GO:0005777 GO:0042579 GO:0016021 GO:0031224 GO:0012505 GO:0005783 GO:0005886 GO:0044425 GO:0003824 GO:0048037 GO:0050662 GO:0016616 GO:0016614 GO:0019787 GO:0005215 GO:0004842 GO:0070011 GO:0016787 GO:0008233 GO:0042578 GO:0022892 GO:0004175 peroxisome microbody integral component of membrane intrinsic component of membrane endomembrane system endoplasmic reticulum plasma membrane membrane part catalytic activity cofactor binding coenzyme binding oxidoreductase activity, acting on the CH-OH group of donors, NAD or NADP as acceptor oxidoreductase activity, acting on CH-OH group of donors ubiquitin-like protein transferase activity transporter activity ubiquitin-protein transferase activity peptidase activity, acting on L-amino acid peptides hydrolase activity peptidase activity phosphoric ester hydrolase activity substrate-specific transporter activity endopeptidase activity Category Count F F F F F F C C C C C C C C F F F F F F F F F F F F F F 8 8 8 3 79 2 23 23 90 90 78 41 86 100 501 48 36 19 24 19 71 19 41 161 42 20 48 26 p-value 0.02217 0.02642 0.03122 0.04202 0.04551 0.04867 3e-07 3e-07 2e-06 1.7e-05 0.00024 0.00045 0.00144 0.00567 3.7e-08 6.4e-07 6.4e-06 2.8e-05 6.9e-05 6e-04 0.00045 0.00045 0.00054 0.00058 0.00071 0.0013 0.00157 0.00169 229 Table APP-3-2 (cont’d) Term GO ID Category Count GO:0043492 GO:0016798 GO:0022857 GO:0000287 GO:0046873 GO:0008324 GO:0015075 GO:0004553 GO:0016772 GO:0044281 GO:0006732 GO:0009108 GO:0006733 GO:0006970 GO:0044763 GO:0046496 GO:0019362 GO:0044712 GO:0043436 GO:0043436 GO:0006082 GO:0072524 GO:0019752 GO:0051186 GO:0048437 GO:0006629 GO:0009651 GO:0044699 GO:0044282 GO:0016567 ATPase activity, coupled to movement of substances hydrolase activity, acting on glycosyl bonds transmembrane transporter activity magnesium ion binding metal ion transmembrane transporter activity cation transmembrane transporter activity ion transmembrane transporter activity hydrolase activity, hydrolyzing O-glycosyl compounds transferase activity, transferring phosphorus-containing groups small molecule metabolic process coenzyme metabolic process coenzyme biosynthetic process oxidoreduction coenzyme metabolic process response to osmotic stress single-organism cellular process nicotinamide nucleotide metabolic process pyridine nucleotide metabolic process single-organism catabolic process oxoacid metabolic process oxoacid metabolic process organic acid metabolic process pyridine-containing compound metabolic process carboxylic acid metabolic process cofactor metabolic process floral organ development lipid metabolic process response to salt stress single-organism process small molecule catabolic process protein ubiquitination F F F F F F F F F P P P P P P P P P P P P P P P P P P P P P 230 13 37 53 16 15 26 35 33 97 109 27 17 17 38 292 15 15 29 66 66 66 15 63 28 18 56 31 424 14 17 p-value 0.00237 0.00238 0.00376 0.00379 0.0043 0.00665 0.00799 0.00805 0.00903 2.5e-08 1.7e-06 7.6e-06 1.6e-05 2.1e-05 3.2e-05 5.3e-05 6e-05 2e-04 0.00011 0.00011 0.00012 0.00013 0.00025 0.00028 0.00034 0.00048 0.00054 0.00058 0.00063 0.00067 Table APP-3-2 (cont’d) Term GO ID Category Count GO:0071704 GO:0000003 GO:0005975 GO:0001101 GO:0032446 GO:0048569 GO:0048646 GO:0010243 GO:0005996 GO:0051188 GO:0044723 GO:0080134 GO:0019637 GO:0032504 GO:0044710 GO:0031347 GO:0009751 GO:0030001 GO:0009555 GO:1901700 GO:1901698 GO:0009117 GO:0070647 GO:0055086 GO:0046686 GO:0006753 GO:0006793 GO:0042221 GO:0002376 GO:0051321 organic substance metabolic process reproduction carbohydrate metabolic process response to acid chemical protein modification by small protein conjugation post-embryonic organ development anatomical structure formation involved in morphogenesis response to organonitrogen compound monosaccharide metabolic process cofactor biosynthetic process single-organism carbohydrate metabolic process regulation of response to stress organophosphate metabolic process multicellular organism reproduction single-organism metabolic process regulation of defense response response to salicylic acid metal ion transport pollen development response to oxygen-containing compound response to nitrogen compound nucleotide metabolic process protein modification by small protein conjugation or removal nucleobase-containing small molecule metabolic process response to cadmium ion nucleoside phosphate metabolic process phosphorus metabolic process response to chemical immune system process meiotic cell cycle P P P P P P P P P P P P P P P P P P P P P P P P P P P P P P 231 429 25 70 50 17 22 16 12 11 17 35 19 36 15 239 15 12 24 20 61 17 23 19 27 20 23 112 100 19 11 p-value 0.00069 0.00087 0.00122 0.00139 0.00141 0.00147 0.00155 0.00164 0.00164 0.00165 0.00175 0.00202 0.0023 0.00255 0.00307 0.00322 0.00327 0.00329 0.00334 0.00359 0.00415 0.00519 0.00546 0.0055 0.00591 0.00663 0.00671 0.00737 0.00762 0.00777 Table APP-3-2 (cont’d) Term GO ID GO:0006790 GO:0014070 GO:0006950 GO:0048438 sulfur compound metabolic process response to organic cyclic compound response to stress floral whorl development WGCNA module: Deepskyblue4 GO:0044464 GO:0005623 GO:0032991 GO:0044428 GO:0044424 GO:0031974 GO:0005739 GO:0043234 GO:0005622 GO:0030163 GO:0009057 GO:1901575 GO:0009056 GO:0006996 GO:0019538 GO:0051641 GO:0009987 GO:0006508 GO:0043170 GO:0044238 GO:0044267 cell part cell macromolecular complex nuclear part intracellular part membrane-enclosed lumen mitochondrion protein complex intracellular protein catabolic process macromolecule catabolic process organic substance catabolic process catabolic process organelle organization protein metabolic process cellular localization cellular process proteolysis macromolecule metabolic process primary metabolic process cellular protein metabolic process WGCNA module: Orange1 GO:0031090 GO:0098588 GO:0031967 organelle membrane bounding membrane of organelle organelle envelope p-value 0.00798 0.00804 0.00808 0.00907 0.0114 0.0115 0.0132 0.0145 0.02 0.021 0.0264 0.0285 0.0395 2.1e-06 0.00011 0.00273 0.00429 0.0051 0.0101 0.0151 0.01612 0.01987 0.02466 0.0321 0.04522 0.0041 0.0102 0.0213 Category Count 14 19 119 13 101 101 29 13 90 11 15 20 90 12 12 14 14 16 45 11 109 13 70 88 36 18 14 15 P P P P C C C C C C C C C P P P P P P P P P P P P C C C 232 Table APP-3-2 (cont’d) Term GO ID GO:0031975 GO:0098805 GO:0065008 GO:0009628 GO:0010035 GO:0055085 GO:0044763 GO:0050794 GO:0051234 GO:0065007 GO:0051179 GO:0006810 GO:0044281 envelope whole membrane regulation of biological quality response to abiotic stimulus response to inorganic substance transmembrane transport single-organism cellular process regulation of cellular process establishment of localization biological regulation localization transport small molecule metabolic process WGCNA module: Black GO:0005730 GO:0031981 GO:0044424 GO:0044428 GO:0070013 GO:0043233 GO:0031974 GO:0005622 GO:0005634 GO:0043231 GO:0043227 GO:0005623 GO:0044464 GO:0043229 GO:0043226 GO:0005739 nucleolus nuclear lumen intracellular part nuclear part intracellular organelle lumen organelle lumen membrane-enclosed lumen intracellular nucleus intracellular membrane-bounded organelle membrane-bounded organelle cell cell part intracellular organelle organelle mitochondrion Category Count p-value 15 11 12 21 12 16 59 32 28 38 28 27 19 29 38 283 45 39 39 39 285 106 224 224 302 301 237 237 39 0.0218 0.0259 0.00415 0.00457 0.00551 0.00694 0.01007 0.01959 0.02055 0.02233 0.02997 0.03114 0.03464 6.3e-10 1.4e-07 1.6e-07 2.3e-07 3.4e-07 3.6e-07 5.1e-07 5.2e-07 1.3e-06 5.7e-05 6.1e-05 3e-04 0.00053 0.00056 0.00061 0.00879 C C P P P P P P P P P P P C C C C C C C C C C C C C C C C 233 Table APP-3-2 (cont’d) Term GO ID Category Count GO:0043228 GO:0043232 GO:0005737 GO:0009536 GO:0003723 GO:0005515 GO:0005488 GO:0016779 GO:0003676 GO:0000166 GO:1901265 GO:0036094 GO:0016791 GO:0042578 GO:0004518 GO:0017111 GO:0006396 GO:0034470 GO:0034660 GO:0006364 GO:0016072 GO:0022613 GO:0042254 GO:0006807 GO:0034641 GO:0046483 GO:0006725 GO:0010467 GO:0016070 GO:1901360 non-membrane-bounded organelle intracellular non-membrane-bounded organelle cytoplasm plastid RNA binding protein binding binding nucleotidyltransferase activity nucleic acid binding nucleotide binding nucleoside phosphate binding small molecule binding phosphatase activity phosphoric ester hydrolase activity nuclease activity nucleoside-triphosphatase activity RNA processing ncRNA processing ncRNA metabolic process rRNA processing rRNA metabolic process ribonucleoprotein complex biogenesis ribosome biogenesis nitrogen compound metabolic process cellular nitrogen compound metabolic process heterocycle metabolic process cellular aromatic compound metabolic process gene expression RNA metabolic process organic cyclic compound metabolic process 51 51 203 76 50 217 453 15 132 129 129 132 11 12 13 25 52 26 30 17 17 20 19 161 145 127 128 112 93 129 C C C C F F F F F F F F F F F F P P P P P P P P P P P P P P 234 p-value 0.01176 0.01176 0.03667 0.04072 1.5e-11 6.9e-08 6.1e-05 0.00032 0.0029 0.00661 0.00661 0.00664 0.0084 0.02775 0.03518 0.04356 6.6e-20 2.4e-13 3e-12 1.1e-11 1.9e-11 1.1e-10 1.1e-10 1.2e-10 3.9e-09 1.2e-08 2.5e-08 2.6e-08 5e-08 6.3e-08 Table APP-3-2 (cont’d) Term GO ID Category Count p-value GO:0006139 GO:0090304 GO:0032502 GO:0007275 GO:0009790 GO:0044767 GO:0009793 GO:0044707 GO:0048856 GO:0008652 GO:0048316 GO:0009987 GO:0006397 GO:0010154 GO:0016071 GO:0032501 GO:1901566 GO:0016458 GO:0006520 GO:0008380 GO:0048731 GO:0044260 GO:0009451 GO:1901607 GO:0009791 GO:0048608 GO:0061458 GO:0044237 GO:0043170 GO:0003006 nucleobase-containing compound metabolic process nucleic acid metabolic process developmental process multicellular organismal development embryo development single-organism developmental process embryo development ending in seed dormancy single-multicellular organism process anatomical structure development cellular amino acid biosynthetic process seed development cellular process mRNA processing fruit development mRNA metabolic process multicellular organismal process organonitrogen compound biosynthetic process gene silencing cellular amino acid metabolic process RNA splicing system development cellular macromolecule metabolic process RNA modification alpha-amino acid biosynthetic process post-embryonic development reproductive structure development reproductive system development cellular metabolic process macromolecule metabolic process developmental process involved in reproduction 116 104 85 77 26 82 24 77 76 18 29 297 14 29 17 77 53 13 26 12 56 175 12 14 47 39 39 225 189 43 1e-07 1.1e-07 1.4e-07 1.7e-07 1.7e-07 3.9e-07 4e-07 4.5e-07 5e-07 1.7e-06 3.4e-06 5.4e-06 7e-06 7.3e-06 9e-06 9.7e-06 1.3e-05 1.8e-05 2e-05 2.9e-05 3.3e-05 5e-05 6.5e-05 6.7e-05 0.00011 0.00013 0.00013 0.00015 0.00016 0.00018 P P P P P P P P P P P P P P P P P P P P P P P P P P P P P P 235 Table APP-3-2 (cont’d) Term GO ID Category Count GO:0010629 GO:1901605 GO:0071840 GO:0044711 GO:0044702 GO:0022414 GO:0009892 GO:0010605 GO:0016053 GO:0046394 GO:1901564 GO:0044283 GO:0040029 GO:0044085 GO:0048229 GO:0044763 GO:0009888 GO:0048507 GO:0044249 GO:1901576 GO:0048519 GO:0009058 GO:0071704 GO:0046907 GO:0033036 GO:0008104 GO:0044238 GO:0009653 GO:0070727 GO:0051641 negative regulation of gene expression alpha-amino acid metabolic process cellular component organization or biogenesis single-organism biosynthetic process single organism reproductive process reproductive process negative regulation of metabolic process negative regulation of macromolecule metabolic process organic acid biosynthetic process carboxylic acid biosynthetic process organonitrogen compound metabolic process small molecule biosynthetic process regulation of gene expression, epigenetic cellular component biogenesis gametophyte development single-organism cellular process tissue development meristem development cellular biosynthetic process organic substance biosynthetic process negative regulation of biological process biosynthetic process organic substance metabolic process intracellular transport macromolecule localization protein localization primary metabolic process anatomical structure morphogenesis cellular macromolecule localization cellular localization P P P P P P P P P P P P P P P P P P P P P P P P P P P P P P 236 17 17 66 51 38 45 19 17 22 22 59 26 11 31 17 161 22 12 112 113 24 118 239 19 23 18 227 26 16 23 p-value 0.00018 0.00025 0.00048 0.00059 0.00075 0.00087 0.00088 0.00131 0.00145 0.00145 0.00146 0.00152 0.0016 0.00168 0.00184 0.00247 0.00281 0.00312 0.00517 0.00568 0.0059 0.00698 0.0076 0.0076 0.00818 0.00825 0.0085 0.00873 0.00992 0.01182 Table APP-3-2 (cont’d) Term GO ID Category Count GO:0015031 GO:0034613 GO:0044699 GO:0014070 GO:0045184 GO:0006886 GO:0048513 GO:0051649 GO:1902582 GO:0048523 GO:0019752 GO:0006996 GO:0009605 GO:1901137 GO:0071310 protein transport cellular protein localization single-organism process response to organic cyclic compound establishment of protein localization intracellular protein transport organ development establishment of localization in cell single-organism intracellular transport negative regulation of cellular process carboxylic acid metabolic process organelle organization response to external stimulus carbohydrate derivative biosynthetic process cellular response to organic substance WGCNA module: Mistyrose2 GO:0009507 GO:0016020 GO:0009536 GO:0004672 GO:0003006 GO:0022414 chloroplast membrane plastid protein kinase activity developmental process involved in reproduction reproductive process WGCNA module: Sienna2 GO:0005840 GO:0030529 GO:0043228 GO:0043232 GO:0044464 GO:0005623 GO:0044391 ribosome ribonucleoprotein complex non-membrane-bounded organelle intracellular non-membrane-bounded organelle cell part cell ribosomal subunit 16 15 234 12 16 13 27 19 12 14 31 29 27 12 16 18 34 18 14 11 12 57 61 88 88 358 358 25 P P P P P P P P P P P P P P P C C C F P P C C C C C C C 237 p-value 0.01197 0.01254 0.01282 0.0132 0.01392 0.02138 0.02391 0.03735 0.03767 0.03835 0.04036 0.04466 0.04643 0.04698 0.04739 0.0232 0.0316 0.037 0.0246 0.0334 0.0368 2.4e-14 2e-13 5.8e-12 5.8e-12 1.4e-10 1.5e-10 2.4e-09 Table APP-3-2 (cont’d) Term GO ID Category Count p-value GO:0005622 GO:0044424 GO:0043229 GO:0043226 GO:0005634 GO:0015935 GO:0044445 GO:0022626 GO:0044428 GO:0032991 GO:0015934 GO:0070013 GO:0043233 GO:0031974 GO:0031981 GO:0005730 GO:0005829 GO:0005654 GO:0043231 GO:0043227 GO:0044444 GO:0044451 GO:0005737 GO:0003735 GO:0005198 GO:0003676 GO:0003723 GO:0097159 GO:1901363 GO:0036094 intracellular intracellular part intracellular organelle organelle nucleus small ribosomal subunit cytosolic part cytosolic ribosome nuclear part macromolecular complex large ribosomal subunit intracellular organelle lumen organelle lumen membrane-enclosed lumen nuclear lumen nucleolus cytosol nucleoplasm intracellular membrane-bounded organelle membrane-bounded organelle cytoplasmic part nucleoplasm part cytoplasm structural constituent of ribosome structural molecule activity nucleic acid binding RNA binding organic cyclic compound binding heterocyclic compound binding small molecule binding 324 317 281 281 118 12 19 18 43 96 13 36 36 36 33 18 64 14 233 233 214 12 228 56 56 182 55 291 290 150 4.4e-08 3.3e-07 5.4e-07 6.1e-07 9e-07 5.5e-06 1.2e-05 1.7e-05 3.6e-05 6.2e-05 9.3e-05 9.6e-05 1e-04 0.00013 0.00024 0.00306 0.0034 0.02099 0.02983 0.03111 0.0354 0.03836 0.04499 3.1e-21 1.9e-18 6.9e-15 3e-14 1.6e-09 2.6e-09 9.4e-06 C C C C C C C C C C C C C C C C C C C C C C C F F F F F F F 238 Table APP-3-2 (cont’d) Term GO ID Category Count GO:0003700 GO:0001071 GO:0008135 GO:0000166 GO:1901265 GO:0016874 GO:0003677 GO:0035639 GO:0043168 GO:0043565 GO:0042802 GO:0032553 GO:0097367 GO:0005488 GO:0001883 GO:0032550 GO:0032555 GO:0005524 GO:0017076 GO:0032549 GO:0001882 GO:0005525 GO:0032561 GO:0019001 GO:0032559 GO:0030554 GO:0006412 GO:0043043 GO:0006518 GO:0043604 transcription factor activity, sequence-specific DNA binding nucleic acid binding transcription factor activity translation factor activity, RNA binding nucleotide binding nucleoside phosphate binding ligase activity DNA binding purine ribonucleoside triphosphate binding anion binding sequence-specific DNA binding identical protein binding ribonucleotide binding carbohydrate derivative binding binding purine nucleoside binding purine ribonucleoside binding purine ribonucleotide binding ATP binding purine nucleotide binding ribonucleoside binding nucleoside binding GTP binding guanyl ribonucleotide binding guanyl nucleotide binding adenyl ribonucleotide binding adenyl nucleotide binding translation peptide biosynthetic process peptide metabolic process amide biosynthetic process 39 39 11 140 140 16 89 103 125 23 13 109 109 438 105 105 105 88 105 105 105 15 15 15 90 90 74 74 74 75 F F F F F F F F F F F F F F F F F F F F F F F F F F P P P P 239 p-value 3.2e-05 3.4e-05 0.00016 0.00017 0.00017 0.00022 0.00079 0.00156 0.00249 0.00286 0.00322 0.00423 0.00586 0.00866 0.00935 0.00935 0.00935 0.00952 0.01009 0.01009 0.0102 0.03407 0.03407 0.03699 0.04287 0.04444 5.1e-24 7.2e-24 3.2e-23 4.4e-23 Table APP-3-2 (cont’d) Term GO ID Category Count GO:0043603 GO:1901566 GO:0010467 GO:0006807 GO:0034641 GO:0044271 GO:0034645 GO:0009059 GO:0044249 GO:1901564 GO:1901576 GO:0044260 GO:0009058 GO:0043170 GO:0044238 GO:0044237 GO:0090304 GO:0071704 GO:0044267 GO:0016070 GO:0006139 GO:0009987 GO:0019538 GO:0040008 GO:2000026 GO:0048831 GO:0051239 GO:0046483 GO:1901360 GO:0006399 cellular amide metabolic process organonitrogen compound biosynthetic process gene expression nitrogen compound metabolic process cellular nitrogen compound metabolic process cellular nitrogen compound biosynthetic process cellular macromolecule biosynthetic process macromolecule biosynthetic process cellular biosynthetic process organonitrogen compound metabolic process organic substance biosynthetic process cellular macromolecule metabolic process biosynthetic process macromolecule metabolic process primary metabolic process cellular metabolic process nucleic acid metabolic process organic substance metabolic process cellular protein metabolic process RNA metabolic process nucleobase-containing compound metabolic process cellular process protein metabolic process regulation of growth regulation of multicellular organismal development regulation of shoot system development regulation of multicellular organismal process heterocycle metabolic process organic cyclic compound metabolic process tRNA metabolic process 76 100 168 215 198 151 146 146 184 104 182 245 189 262 315 290 122 318 134 106 130 357 146 14 28 16 29 135 140 16 P P P P P P P P P P P P P P P P P P P P P P P P P P P P P P 240 p-value 1.8e-22 4e-22 3.3e-21 5.3e-19 1e-17 2.2e-17 8.9e-16 2.1e-15 7.2e-15 3.3e-14 1.2e-13 1.7e-13 1.8e-13 2.4e-12 2.4e-10 2.6e-08 9.8e-08 1e-07 2e-07 2.5e-07 3.1e-06 7.9e-06 7.9e-06 1e-05 1.5e-05 1.7e-05 2.1e-05 2.3e-05 2.6e-05 3e-05 Table APP-3-2 (cont’d) Term GO ID Category Count p-value GO:0006725 GO:0050793 GO:0006520 GO:0008652 GO:0031326 GO:0050789 GO:0009889 GO:0080090 GO:2000112 GO:0010556 GO:0051252 GO:0048580 GO:0009909 GO:0040007 GO:0006355 GO:0065007 GO:0051254 GO:1903506 GO:2001141 GO:0060255 GO:0034660 GO:0006351 GO:0097659 GO:2000241 GO:0032774 GO:0048589 GO:0010015 GO:0034654 GO:1901607 GO:0051171 cellular aromatic compound metabolic process regulation of developmental process cellular amino acid metabolic process cellular amino acid biosynthetic process regulation of cellular biosynthetic process regulation of biological process regulation of biosynthetic process regulation of primary metabolic process regulation of cellular macromolecule biosynthetic process regulation of macromolecule biosynthetic process regulation of RNA metabolic process regulation of post-embryonic development regulation of flower development growth regulation of transcription, DNA-templated biological regulation positive regulation of RNA metabolic process regulation of nucleic acid-templated transcription regulation of RNA biosynthetic process regulation of macromolecule metabolic process ncRNA metabolic process transcription, DNA-templated nucleic acid-templated transcription regulation of reproductive process RNA biosynthetic process developmental growth root morphogenesis nucleobase-containing compound biosynthetic process alpha-amino acid biosynthetic process regulation of nitrogen compound metabolic process P P P P P P P P P P P P P P P P P P P P P P P P P P P P P P 241 136 30 28 16 68 123 68 72 65 65 64 18 11 25 62 128 12 62 62 73 18 68 68 14 68 20 13 74 13 66 4.4e-05 5.7e-05 8.9e-05 0.00029 0.00039 5e-04 0.00054 0.00061 0.00062 0.00066 0.00067 8e-04 9e-04 0.00095 0.00106 0.00108 0.00109 0.00111 0.00111 0.00116 0.00117 0.00119 0.00124 0.00126 0.00129 0.00135 0.00152 0.00161 0.00162 0.00167 Table APP-3-2 (cont’d) Term GO ID Category Count GO:0031323 GO:0009791 GO:0019219 GO:0010468 GO:0009888 GO:0048364 GO:0031328 GO:0051173 GO:0022622 GO:0045893 GO:0019438 GO:0045935 GO:1902680 GO:1903508 GO:0010557 GO:1901362 GO:0009908 GO:0006397 GO:0016071 GO:0048731 GO:0018130 GO:0009891 GO:0010628 GO:0009887 GO:0090567 GO:0048367 GO:0007275 GO:0048513 GO:0050794 regulation of cellular metabolic process post-embryonic development regulation of nucleobase-containing compound metabolic process regulation of gene expression tissue development root development positive regulation of cellular biosynthetic process positive regulation of nitrogen compound metabolic process root system development positive regulation of transcription, DNA-templated aromatic compound biosynthetic process positive regulation of nucleobase-containing compound metabolic process positive regulation of RNA biosynthetic process positive regulation of nucleic acid-templated transcription positive regulation of macromolecule biosynthetic process organic cyclic compound biosynthetic process flower development mRNA processing mRNA metabolic process system development heterocycle biosynthetic process positive regulation of biosynthetic process positive regulation of gene expression organ morphogenesis reproductive shoot system development shoot system development multicellular organismal development organ development regulation of cellular process P P P P P P P P P P P P P P P P P P P P P P P P P P P P P 242 72 50 64 68 26 20 13 13 20 11 81 12 11 11 12 84 20 11 14 57 80 13 12 12 20 30 71 35 102 p-value 0.00168 0.00169 0.00183 0.00184 0.0019 0.00202 0.00209 0.00209 0.00211 0.0023 0.00233 0.00237 0.00247 0.00247 0.00253 0.00254 0.00272 0.00283 0.00289 0.00298 0.003 0.00301 0.00346 0.00368 0.00391 0.00405 0.00422 0.00456 0.00477 Table APP-3-2 (cont’d) Term GO ID Category Count p-value GO:0006396 GO:0031325 GO:0009893 GO:0032259 GO:0019222 GO:0044707 GO:0010604 GO:0051128 GO:0044767 GO:0071310 GO:0044702 GO:0070887 GO:0048522 GO:0048507 GO:0048608 GO:0061458 GO:0048856 GO:0051716 GO:0033554 GO:0032502 GO:0048366 GO:0032501 GO:0008152 GO:0051276 GO:1901605 GO:0009653 GO:0003006 GO:0006974 GO:0022414 RNA processing positive regulation of cellular metabolic process positive regulation of metabolic process methylation regulation of metabolic process single-multicellular organism process positive regulation of macromolecule metabolic process regulation of cellular component organization single-organism developmental process cellular response to organic substance single organism reproductive process cellular response to chemical stimulus positive regulation of cellular process meristem development reproductive structure development reproductive system development anatomical structure development cellular response to stimulus cellular response to stress developmental process leaf development multicellular organismal process metabolic process chromosome organization alpha-amino acid metabolic process anatomical structure morphogenesis developmental process involved in reproduction cellular response to DNA damage stimulus reproductive process WGCNA module: Tan2 P P P P P P P P P P P P P P P P P P P P P P P P P P P P P 243 25 14 16 11 75 71 13 11 75 21 39 24 18 12 37 37 68 53 25 75 14 73 365 16 14 28 40 13 44 0.005 0.0054 0.0057 0.00573 0.00681 0.00742 0.00746 0.00855 0.01122 0.01245 0.01284 0.01341 0.01414 0.01432 0.01458 0.01458 0.01569 0.01614 0.01838 0.01928 0.02021 0.02109 0.02172 0.02222 0.02595 0.03041 0.03179 0.0426 0.04412 Table APP-3-2 (cont’d) GO ID Term Category Count p-value GO:0009536 GO:0043231 GO:0043227 GO:0044446 GO:0044422 GO:0043229 GO:0043226 GO:0044424 GO:0005622 GO:0005737 GO:0044444 plastid intracellular membrane-bounded organelle membrane-bounded organelle intracellular organelle part organelle part intracellular organelle organelle intracellular part intracellular cytoplasm cytoplasmic part C C C C C C C C C C C 11 18 18 12 12 18 18 19 19 16 15 3e-04 0.00129 0.00131 0.00359 0.00363 0.00571 0.00579 0.0128 0.0172 0.02021 0.02747 244