i TRANSCRIPTOMIC, METABOLOMIC AND GENETIC ANALYSES OF AGE - RELATED RESISTANCE OF CUCUMBER TO PHYTOPHTHORA CAPSICI By Ben N athan Mansfeld A DISSERTATION Submitted to Michigan State University in partial fulfillment of the requirements f or the degree of Plant Breeding, Genetics and Biotechnology - Horticulture - Doctor of Philosophy 2019 ii ABSTRACT TRANSCRIPTOMIC, METABOLOMIC AND GENETIC ANALYSES OF AGE - RELATED RESISTANCE OF CUCUMBER TO PHYTOPHTHORA CAPSICI By Ben Nathan Mansfeld The oomycete plant pathogen Phytophthora capsici infects several agriculturally important crop species . In cucumber ( Cucumis sativus ), P. capsici primarily causes fruit rot which is characterized by tissue collapse and dense mycelial growth. Previous studies i n our lab have shown that some cucumber cultivars exhibit an age - related resistance (ARR) wherein young fruit are highly susceptible but deve lop resistance at approximately 12 - 16 days post pollination (dpp). Furthermore, the fruit peel has been shown to be important for conferring ARR and methanolic extracts from resistant peels had inhibitory effects on pathogen development. In the research he rein, we sought to elucidate the mechanism controlling this ontogenic resistance in cucumber fruit by employing a d iverse array of genetic, genomic, metabolomic and microscopic approaches. Using transcriptome analyses of peels from ARR expressing - and non - expressing cultivars, we identified unique upregulation of defense related factors in resistant - aged fruit peels, i ncluding resistance genes and transcription factors. An enrichment for genes involved in specialized metabolism in resistant fruit was also o bserved, and subsequently followed by an untargeted metabolomic analysis. We identified metabolites, specifically t erpenoid glycosides, that may act as antimicrobial components in resistant - aged fruit peels. In a second study, we characterized the response to inoculation at resistant (8 dpp) and susceptible (16 dpp) ages via microscopic and transcriptomic analyses. Sca nning electron microscopy of resistant peels showed evidence for infection failure as early as 4 hpi, including deflated or lysed spores and hyphae, that were not observed on susceptible fruit. Furthermore, transcriptome iii analysis of the first 48 hours post inoculation (hpi) revealed strong transcriptional defense responses at 4 hpi in both ages. At 24 and 48 hpi, susceptible 8 dpp fruit continu ed to mount defense along with strong downregulation of genes involved in photosynthesis and other biological proce sses. In contrast, resistant 16 dpp samples largely downregulated defense responses while upregulating photosynthesis. Weighted gene co - expre ssion network analysis was used to further understand the transcriptional dynamics of infection during the first 24 hours. We identified early defense response modules which showed patterns of increased gene expression as early as 2 and 4 hpi, uniquely in resistant fruit. Several candidate genes involved in conferring this rapid response were identified. The early path ogen death and rapid defense response to infection in resistant - aged fruit indicate developmental changes that may include both pre - formed bi ochemical defenses and developmentally regulated capacity for pathogen recognition. To genetically map loci linked to ARR we employed a bulk segregant analysis approach. However, as no easy - to - use computational tools were available for these analyses, I d eveloped a software package to use in our own experiments, but also as a tool for the plant breeding and genetics c ommunity. Using this tool, we analyzed bulks selected from two segregating populations derived from ARR expressing and non - expressing parents . We identified one locus on chromosome 3, linked to ARR, and using transcriptome data of the parental lines we ide ntified a set of genes potentially associated with ARR. Together, these studies further our understanding of ARR as a biological phenomenon i n general, as well as in the cucumber - P. capsici pathosystem specifically. iv Copyright by BEN NATHAN MANSFELD 201 9 v To Carly , with love and admiration vi ACKNOWLEDGEMENTS Completing a PhD truly takes a village. I have met so many amazing people throughout this process who have influenced me as a person and as a scientist. Here , I will try to express how grateful I am t oo all those whose paths I have crossed in the last few years. First, I must thank my PhD advisor Dr. Rebecca Grumet. Dr. Grumet has been an incredible mentor, always leading by example , whether at the office , bench , or in the muddy field. I have learnt s o much from you and truly appreciate your guidance and the freedom you have afforded me to express my scientific thought and vision. I further thank my advisory committee members, Drs. Cornelius Barry, Robin Buell an d Brad Day , who have been extremely acc essible throughout my time here at MSU. From impromptu hallway conversations to in depth meetings on method development, their contribution to my development as a scientist has been immense. I must also thank Dr. Dani el Jones who might as well have been a part of the committee for countless hours in the Mass Spectrometry C or e! Beyond my committee, I thank all the other plant science researchers at MSU for their open - door policy , of which I strongly took advantage. The advice and ear of Drs . Dan Chitwood , Dave Douches, Linda Hanson, Co u rtney Hollender, Ning Jiang, Rob Last, Tony Schilmiller, Guo - Qing Song, and Bob VanBuren have been invaluable to me . I am also greatly indebted to the fantastic feedback I have received from the members of the Horticulture Joint Labs group. I was also lucky to have amazing mentors prior to my enrollment at MSU who encouraged me on this journey, including Drs. Amram A shri, Shuki Saranga and Shmuel Wolf . Studying and conducting research with these individuals during my B. Sc. in Israel is what spurred me to pursue a PhD in plant science and I would not be where I am today without them. vii I must also thank t he staff of the Research Technolo gy Support Facility , including both Genomics and Mass Spectrometry cores , and the Center f or Statistical Training and Consulting for their dedication and hard work. I am also grateful to the administrative staff of the Horticulture Department for always be ing there with a solution and a sympathetic ear. Importantly, all of the amazing colleagu es and undergraduates that have been part of the Grumet Lab throug h out the last almost six years have been a great support system : Dr. Marivi Colle, Ying - Chen Lin, Stephanie Rett and especially Sue Hammar for sharing her experience in ou r field. I would li ke to thank my fellow graduate student colleagues for their continuous support and friendship , especially Dr. Shujun Ou, Chris Gottschalk , an d Natalie Kaiser . An important thank you goes out to the Ann Arbor East Lansing carpool group, for literally day s - Rauf and especially Lucas Michelotti, Dr. Israel Touitou and Dr. Andrew Wiersma , all of whom are true friends. I would be remiss to not mention some other great friends , i ncluding Drs. Hakeem Jefferson, Fabian Neuner and Sara Meerow , as well as Steve Brooks . Finally, I thank my family . W ithout their lov e and support , none of this would be possible. To my loving parents who encouraged me and were genuinely engaged an d inter ested in my research who was always reminding of how proud h e was I love you both. I am also thankful for the constant support from the rest of my family in Israel, Tami & Daniel, and Daphna. The support of my adoptive family in Michigan was also crucial during my time here. Thank you, Brenda, Gary, Jeffery and Bubbie I love you all! Layla you always remind me what is most important in life and I love you more than anything. Last but not least, a thank you to my incredible wife, Carly who inspir es me to be my best and without whom I would be lost. You really are amazing! viii TABLE OF CONTENTS LIST OF TABLES ................................ ................................ ................................ .......................... x LIST OF FIGURES ................................ ................................ ................................ ....................... xi CHAPTER I ................................ ................................ ................................ ................................ .... 1 Introduction ................................ ................................ ................................ ................................ ..... 1 Cucumber as a crop in the genomics era ................................ ................................ .................... 2 Phytophthora capsici is a plant pathogen that infects cucumber fruit ................................ ........ 3 Pathogen li fe cycle in the cucumber field ................................ ................................ ................... 4 Constitutive resistance to P. capsici ................................ ................................ ........................... 5 Age - related resistance ................................ ................................ ................................ ................. 7 Cucumber fruit transcriptomes transition from growth to defense with age .............................. 9 Cucumber peels are associated with pathogen defense ................................ ............................ 10 Methanolic extracts from cucumber peels inhibit P. capsici growth ................................ ........ 11 Utilizing transcriptomics to study plant - pathogen interactions ................................ ................ 12 Rationale and objectives ................................ ................................ ................................ ........... 13 WORKS CITED ................................ ................................ ................................ ....................... 15 CHAPTER II ................................ ................................ ................................ ................................ . 21 Tra nscriptomic and metabolomic analyses of cucumber fruit peels reveal a developmental increase in terpenoid glycosides associated with age - related resistance to Phytophthora capsici 21 Abstract ................................ ................................ ................................ ................................ ..... 22 CHAPTER III ................................ ................................ ................................ ............................... 23 Developmentally regulated defense rapidly inhibits Phytophthora capsici infection in cucumber fruit. ................................ ................................ ................................ ................................ ............... 23 Abstract ................................ ................................ ................................ ................................ ..... 24 Introduction ................................ ................................ ................................ ............................... 25 Materials and Methods ................................ ................................ ................................ .............. 28 Plant Material ................................ ................................ ................................ ........................ 28 Detached fruit inocul ations and sample collection ................................ ............................... 28 High - throughput RNA extraction ................................ ................................ ......................... 29 TruSeq Library preparation and sequencing ................................ ................................ ......... 30 - mRNA library preparation and sequencing ................................ ..................... 30 Sequencing read preprocessing and quasi - mapping ................................ ............................. 31 Differential expression analysis ................................ ................................ ............................ 32 Weighted gene co - expression network analysis ................................ ................................ ... 32 Gene ontology ter m enrichment analysis ................................ ................................ .............. 33 Microscopy ................................ ................................ ................................ ........................... 34 High - thr oughput infection phenotyping ................................ ................................ ............... 34 Results ................................ ................................ ................................ ................................ ....... 35 - related resistance to P. capsici ............................... 35 Age - dependent differential transcriptomic responses to infection ................................ ....... 37 ix Resistant - age fruit mount a suc cessful response by 24 hours post inoculation .................... 40 Analysis of pathogen growth provides evidence for infection failure in the first 24 hours on resistant fruit ................................ ................................ ................................ ......................... 41 Transcriptomic investig ation of the first 24 hours post inoculation ................................ ..... 44 Gene co - expression is preserved but not expression patterns over time .............................. 50 Biological processes identified by weighted gene co - expression network analysis ............. 50 Modules induced in early infection of resistant - aged fruit ................................ ................... 54 Discussion ................................ ................................ ................................ ................................ . 60 A rapid infection meets a rapid response ................................ ................................ .............. 60 A reprogramming of gene co - expression networks of infection at the resistant age ............ 63 Conclusio n ................................ ................................ ................................ ................................ 66 WORKS CITED ................................ ................................ ................................ ....................... 68 CHAPTER IV ................................ ................................ ................................ ............................... 74 QTLseqr: An R Package for bulk segregant analysis with Next - Generation Sequencing ........... 74 Abstract ................................ ................................ ................................ ................................ ..... 75 CHAPTER V ................................ ................................ ................................ ................................ 76 Conclusion and future directions ................................ ................................ ................................ .. 76 Conclusion ................................ ................................ ................................ ................................ 77 Future directions ................................ ................................ ................................ ....................... 79 WORKS CITED ................................ ................................ ................................ ....................... 83 x LIST OF TABLES Table 3. 1 . Genes with high module membership to Modules R5 and R9 that are differentially expressed in resistant - aged fruit compared to the uninoculated control and inoculated susceptible fruit ................................ ................................ ................................ ................................ ................. 5 8 xi LIST OF FIGURES ts age - related resistance to P. capsici . ........... 3 6 Figure 3.2. Age - dependent differential transcriptomic responses to infection. ............................. 39 Fi gure 3.3. Resistant - aged show transient upregulation of defense related genes. ........................ 4 2 Figure 3.4. Analysis of differentially expressed gene overlap at each timepoint. ......................... 4 3 Figure 3.5. Evidence for infection failure in the first 24 hours post inoculation on res istant fruit. ................................ ................................ ................................ ................................ ........................ 4 8 Figure 3.6. Differential transcriptional dynamics within the first 24 hours. ................................ .. 49 Figure 3 .7. Defense response network reprogramming in the two fruit ages. ............................... 5 2 Figure 3.8. Resistant network module gene expression patterns. ................................ .................. 5 3 Figure 3.9. Early response modules in the resistant network. ................................ ....................... 5 6 Figure 3.10. Genes induced in early responses to inocu lation of resistant fruit. ........................... 5 7 Figure 3.11. Hypothesized model for cucumber age - related resistance to P. capsici . .................. 6 7 1 CHAPTER I Introduction 2 Cucumb er as a crop in the genomics era The Cucurbitaceae plant family includes approximately 115 genera and several economically significant crops, of which one of the most important is cucumber ( Cucumis sativus L.). In the U.S., cucumber production is grouped into two main variety types: slicing (fresh market) and pickling (processing), and about half of all cucumbers produced in the U.S. are specifically grown for the pickling industry (USDA, http://quickstats.nass.usda.gov). Michigan is the leading producer o f pickling cucumber, producing over 170,000 tons an d accounting for more than 30% of national production in 2015. Depending on the region of production, pickling cucumbers can be hand harvested for multiple harvests per season or can be planted at high den sity for once - over mechanical harvest (Cargill et a l., 1975) . In contrast to other fruit, cucumber is typically harvested immature, several weeks prior to ripening. In 2009 , was sequenced and assembled (Huang et al., 2009) . This study compared Sanger and Illumina assemblies and ultimately used a combination of both technologies for de novo assembly . (RNA - seq) (Li et al., 2011) . Data from the above st udies are deposited on the cucurbit online database (http://www.cucurbitgenomics.org). The genome of the gynoecious, inbred, pickling , but an accom panying manuscript was not published . The north Eur (Witkowicz et al., 2011) . The recently updated, Cucurbit Genomics Database further improves access to these genomes , other genetic and genomic data for cucumber as well as other cucurbits (Zheng et al., 2019) . Th e database provides standard genomics tools such as BLAST and Genome Browser as well as new tools that allow viewing of gene expression and synteny data (Zheng et al., 2019) . 3 Utilizing Next Generation Sequ encing (NGS) tools has aided in characterizing genetic diversity of many crops (Kilian and Graner, 2012) . A set of 115 cucumber lines, representing four geographic regions of origin, was resequenced, revealing valuable information regarding the domesticati on and diversity of cucumber (Qi et al., 2013) . More recently, the full USDA plant introduction collecti on (1234 accessions) was genotyped using genotyping - by - sequencing technology, providing an extremely detailed characterization of the diversity and popu lation structure catalogued in this collection (Wang et al., 2018) . Together , these data sets help under stand the history of domestication of the crop from its origins in India and Asia. Furthermore, these data can aid in identifying markers linked to trai ts of interest using genome - wide association studies. Phytophthora capsici is a plant pathogen that inf ects cucumber fruit A major pathogen affecting c ucumber yields in the mid - western U.S. is Phytophthora capsici, an oomycete that causes foliar blightin g, damping - off, wilting, and root, stem, and fruit rot in many vegetable crops such as bell pepper, toma to, snap bean, cucumber and other cucurbits (Hausbeck and Lamour, 2004; Granke et al., 2012; Sanogo and Ji, 2012) . When climate conditions favor the dev elopment of the pathogen, it is estimated that approximately 25% of all vegetable crop yields in Michiga n are lost due to infection (Hausbeck and Lamour, 2004) . Owing to the importance of the disease, there has been research studying the development, morph ology and genetics of the pathogen (Hausbeck and Lamour, 2004) and a draft genome of P. capsici was sequ enced and assembled (Lamour et al., 2012a) . Interestingly, in cucumber, P. capsici specifically causes severe fruit rot, yet does not typically infect other plant organs (Hausbeck and Lamour, 2004) . Thus, a field may appear to be healthy but fruit, underneath the canopy, will be highly infected and unmarketable. Symptoms of 4 P. capsici in cucumber fruit include necrosis, water soaking and ultimately, who le tissue collapse (Granke et al., 2012; Colle et al., 2014) . In addition, fruit may appear healthy at harvest, but if the pathogen is present, moist post - harvest conditions may facilitate rot, thus leading to complete losses of harvested commercial loads (Hausbeck and Lamour, 2004) . Dispersal and spr ead of the disease is predominantly by run - off from rain or overhead irrigation (Granke et al., 2009) . Furthermore, irrigation water from contaminated sources contributes to inoculum dispersal, by infecting pr eviously un - inoculated fields (Granke et al., 2 012) . Infection in the field is usually very persistent and the sexually produced overwintering oospores can survive in the soil for many years (Granke et al., 2012) . Due to the high survivability of the oospo res and the wide variety of host species, it is extremely difficult to disinfest an inoculated field, and even multiple year crop rotations are typically insufficient (Hausbeck and Lamour, 2004) . While this pathogen is increasingly problematic, the world wide use of chemical pesticides is being increa singly limited due to environmental and health considerations. Furthermore, because of overuse , resistance to mefenoxam, a fungicide that was previously, commonly used to control Phytophthora , was reported in several locations throughout the United States (Parra and Ristaino, 2001; Granke et al., 2012; Sanogo and Ji, 2012) . Research aimed at identifying and introducing genetic resistance in to germplasm will be highly beneficial to growers in need of practical s olutions to this pest. Pathogen life cycle in t he cucumber field Infection of cucumber fruit is by means of motile, bi - flagellate, zoospores that are transferred to the fruit surface via rain or irrigation (Hausbeck and Lamour, 2004) . Once on the plant su rface, zoospores use electro - and chemotaxis to identify and swim towards potential 5 infection sites (Hardham, 2007) . Zoospores then encyst, that is, become affixed to the host surface and lose their flagella, and subsequently , hyphae germinate from the spo re to penetrate the host. Penetration of the fr uit surface is predominately through the formation of appressoria - like swellings turgor pressure building organs that press against the host cell and enable penetration (Hardham, 2007; Lamour et al., 2012b) . After penetration has occurred, hyphae develo p haustoria to absorb nutrients from the plant tissue, and once established, growth of the pathogen ensues to colonize the host tissue. P. capsici is a hemibiotroph and thus infection can be divided into two s tages: parasitic biotrophy in early infection s tages and subsequent necrotrophy as cells begin to die (Lamour et al., 2012b) . As infection of the fruit progresses, and as early as three days post infection, lemon - shaped sporangia emerge and asexual reprodu ction of zoospores ensues to advance infection (Gevens et al., 2006; Lamour et al., 2012b) . Optimal climate conditions for growth and infection are approximately 28°C with high relative humidity (Erwin and Ribeiro, 1998) . Infection on the fruit surface is visible as a dense white, sporulating, mycelium that quickly covers the entire fruit and can propagate infection in the field. Constitutive resistance to P. capsici The most desirable solution to this disease problem w ould be to integrate genetic resista nce using plant breeding. As there are multiple hosts for this pathogen, there have been attempts to identify sources of genetic resistance in different plant breeding programs. Research has thus far yielded mixed result s; some programs have not yet found stable resistance and often resistant varieties of some crops carry other unappealing horticultural traits (Granke et al., 2012) . Moreover, it appears that resistance is conferred through different genetic mechanisms in the different crops (Granke et al., 2012) . 6 Some success has been reported in identifying and breeding of P. capsici resistant Solanaceous crops. There are some tolerant tomato ( Solanum lycopersicum ) lines that can be used in infected fields (Quesada - Ocamp o and Hausbeck, 2010) and research i n eggplant ( Solanum melongena L.) has identified accessions with resistant fruits (Naegele et al., 2014) . In pepper ( Capsicum annuum ), several sources of resistance have been studied and implemented in some of resistant cultivars commercially available (Gr anke et al., 2012) . Most of the genes conferring these resistances have yet to be elucidated. A more recent study screened 66 recombinant inbred lines (RIL) of pepper derived from resistant and susceptible parents, again st 20 P. capsici isolates and utiliz ed a high - density linkage map to identify a quantitative trait locus (QTL) that explained between 29 to 58% of resistance to seven isolates (Rehrig et al., 2014) . The researchers proceeded to examine transcriptomic seque nce data of this QTL and suggested a n ortholog of the Arabidopsis DOWNY MILDEW RESISTANT 1 ( DMR1 ) homoserine kinase, encoded therein, to be a strong candidate for a component of this resistance. Screening against isolates of P. capsici has been performed for several cucurbit crops, however , identifying a stable source of genetic host resistance has not been as successful (Hausbeck and Lamour, 2004) . One study evaluated 115 Cucurbita pepo (squash, pumpkin and gourd) accessions for their resistance to the pathogen and found eight accessions th at could be used as sources for resistance (Padley et al., 2008) . Later research of the inheritance of the resistance trait in a Cucurbita true breedi ng line suggested that resistance was conferred by three independent dominant genes (Padley et al., 2009) . Potential, constitutive host resistance in cucumber fruit was only potentially identified recently. In a previous effort, a detached fruit screenin g method was developed and harvest - stage fruit of over 300 cucumber accessions were examined, yet no compl ete resistance was 7 discovered (Gevens et al., 2006) . Our lab performed a subsequent screen of close to 1300 cucumber accessions (Colle et al., 2014) . This attempt revealed three potential accessions that might be used as a genetic source for resistance to P. capsici in cucumber that are now being incorporated into breeding program s (Grumet and Colle, 2017) . Age - related resistance During the initial scr eening for host resistance our lab discovered that younger fruit of the be more susceptible to infection than older fruit (Gevens et al., 2006) . More specifically, the transition to resistance was at approximately 10 - 12 d ays post - pollination (dpp) and appeared to be highly correlated with the end of the period of rapid fruit expansion. Further research showed that other cucurbit fruit such as pumpkin and squash also became less susceptible with increasing age (Ando et al., 2009) . Such ontogenic, developmental or age - related resistance (ARR) h as been described in several differ ent plant - pathogen systems and in crops such as pepper, grape, rice, wheat, and several cucurbit crops (Kim et al., 1989; Gee et al., 2008; Ando et al ., 2009; Zhao et al., 2009; Zhang et al., 2012) . ARR to P. capsici was also previously observed in whole p epper plants (Kim et al., 1989) and chili pepper fruit (Biles et al., 1993) . T hough many studies have been performed, the molecular mechanisms controlling these resistances are not well understood and appear to be highly variable between pathosystems (Whal en, 2005; Develey - Rivière and Galiana, 2007) . Age - related resistance may be o rgan specific or affect the whole plant and has been observed in monocots and dicots infected by different types of pathogens including viruses, fungi, oomycetes and bacteria (De veley - Rivière and Galiana, 2007) . Over the years, research into mechanism of A RR has suggested several potential factors correlated with the onset of resistance. Early research in tobacco ARR to Phytophthora parasitica showed a correlation 8 between salicyli c acid (SA) and Pathogenesis - Related (PR) protein accumulation and leaf age (H ugot et al., 1999) . Results of this study showed that transgenic NahG plants, deficient in SA accumulation did not develop ARR. In Arabidopsis, ARR to Pseudomonas syringae pv. to mato ( Pst ) was shown to be dependent on SA accumulation yet independent of SA signaling (Kus et al., 2002; Carella et al., 2015). Though transition to flowering is correlated with ARR to Pst , flowering is not required, nor sufficient, for manifestation of this resistance (Wilson et al., 2013; Wilson et al., 2017). These studies sugg est that in Arabidopsis SA may function as an intercellularly accumulated antimicrobial compound in ARR in response to Pst (Carella et al., 2015). A study of the Nicotiana bent hamiana P. infestans pathosystem revealed that age played a role in resistance to the pathogen (Shibata et al., 2010) . Using virus - induced gene silencing , the researchers determined that both SA and ethylene signaling pathways were important in conferrin g ARR. Additionally, the authors s how that production of the phytoalexin capsidiol is controlled by ethylene signaling in N. benthamiana and is important in ARR to P. infestans . Fruit specific ARR, such as the kind observed in cucumber fruit, was also pr eviously shown in multiple cases. One such example was described in grapevine ( Vitis spp.) , where young berries are susceptible to black rot (Hoffman et al., 2002) as well as powdery mildew ( Uncinula necator ) (Gadoury et al., 2003) . The genetics, inheritan ce and mode of resistance were not described. The research showed, however, that the previously suggested mechanism of resistance accumulation of soluble solids was probably not the casual factor associated with the onset of ARR. Other examinations of fruit cuticle thickness, papillae formation and phenolic compound 9 accumulation concluded that these factors were not related to grape berry ARR to powdery mildew (Ficke et al., 2002) . Theoretically speaking, ARR could be conferred by means of preformed d efenses such as physical barriers or chemical defenses that accumulate thoughout development (Meldau et al., 2012; Barton and Boege, 2017) mulates in the leaves with plant age (Cambier et al., 2000) . Alternatively, a developmentally regulated inducible defense response could b e the mechanism. For example, in rice, a developmental increase in expression of leucine - rich repeat (LRR) - kinase type genes , Xa3/Xa26 and Xa21 , that peaks at the maximum - tillering stage of growth confers ARR to bacterial blight (Cao et al., 2007; Zhao et al., 2009) . Recently, transcriptional control of the canonical immune receptor FLS2 was also shown to regulate ontogen ic resistance in Arabidopsis to P. syringae (Zou et al., 2018) . It is, of course, possible that a combination of preformed and development ally induced mechanisms contributes to ARR. Cucumber fruit transcriptomes transition from growth to defense with age Previous research in our laboratory investigated the transcriptome of cucumber fruit tissue of different ages from 0 dpp to 16 dpp (Ando e t al., 2012) . This study showed that late/post exponential fruit growth which is associated with the onset of ARR is highly enriched for genes involved in response to abiotic and biotic stresses and extracellular functions. Moreover, 12 and 16 dpp frui t were uniquely and significantly enriched for putative transcription factor (TF) genes, including primarily stress re lated and development related factors. These observations suggested a programmatic shift from growth to defense which was correlated with the transition to resistance at 12 dpp. Interestingly, there appears to be genetic variability for the 10 ability of cucu mber fruit to develop ARR. Of the 19 cultivars subsequently screened, only three loped ARR; the homozygous, P. capsici even beyond 16 dpp. Cuc umber peels are associated with pathogen defense To further understand the underlying mechanisms controlling ARR in cucumber we observed that the fruit surface was associated with resistance; peeled fruit were highly susceptible, though confounding effect s of wounding could not be eliminated (Ando et al., 2009) . When excised peels were placed over intact whole fruits and subsequently inocul ated with P. capsici spore suspension, the peel sections showed the same infection rating as whole fruit, indicating t hat the peels alone responded equivalently to whole fruit (Ando et al., 2015) . Interestingly, peels excised from 15 dpp fruit that were pl aced over intact 8 dpp fruit conferred resistance to the whole fruit beneath them, while 1 5 dpp fruit underneath infec ted 8 dpp peels did not become infected. This suggested that factors in the cucumber fruit peel were responsible for ARR and prompted furt her research into differences of young and old peels. Developmental changes in chemical or physical properties of the fruit peel could potentially influence resistance. Transcriptomic analysis comparing fruit peel and pericarp tissues at 8 and 16 dpp sho wed that the genes that were most highly expressed in peel tissue were enriched for fruit surface - associated functions such as extracellular, endoplasmic reticulum , cell wall and plastid - related genes (Ando et al., 2015) . Moreover, peel tissue was enriched for putative TF genes annotated to be involved in biotic and abiotic stress responses, while TF associated with devel opment were excluded from peel and primarily expressed in the pericarp. Interestingly, genes that were specifically highly expressed in pe el tissue of 16 dpp fruit were annotated to be associated with 11 response to stress, response to abiotic or biotic stimu lus, signal transduction, and extracellular and transport functions. Methanolic extracts from cucumber peels inhibit P. capsici growth No research has yet specifically examined the metabolome of cucumber fruit peel; however, it has been reported that cucu mber leaves synthesize chemicals such as C - glycosyl in response to powdery mildew infection and chemical elicitati on (McNally et al., 2003a; McNally et al., 2003b) - fluorescence in confocal laser microscopy of treated tissue showed that synthesis was site specific in response to infection. An attempt to down - regulate synthesis of flavonoid phytoalexin precursors yielded mixed r esults, however, an increase in infection rates of powdery mildew was c orrelated with reduced chalcone synthase activity, a key enzyme upstream in the pathway (Fofana et al., 2005) . A comprehensive study of compounds in cucumber whole fruit identified the existence of some of these same inhibitory compounds in fruit extracts, yet their effect on pathogen infection was not studied (Abu - Reidah et al., 2012) . Other research of cucumber fruit volatiles found evidence that they had antimicrobial properties (Soti roudis et al., 2010) . To further our understanding of the potential e ffects of chemicals within the peel on infection we examined the effects of chemical extracts on pathogen growth. We developed a high - throughput screening method using microtiter plates ; wells were filled with clarified V8 media and treated with chemical e xtracts from fruit peels of different fruit ages and genotypes. The wells were subsequently inoculated with P. capsici spores from strains genetically engineered to express red or green fluorescent proteins (RFP and GFP) (kindly provided by C. Smart, Corne ll University; Dunn et al., 2013) and read with a fluorescence plate reader over the 12 course of five days. Results showed that methanolic extracts from 16 dpp fruit peels inhibited P. ca psici growth more than those extracted from 8 dpp fruit peels (Ando et al., 2015) . Utilizing transcriptomics to study plant - pathogen interactions The ability to examine the whole transcriptome of an organism, organ or cell can shed light on complex deve lopmental processes such as pathogen infection. A transcriptomic explor ation of the interaction of tomato and P. capsici used a custom designed microarray to concurrently examine host and pathogen over the course of infection (Jupe et al., 2013) . The resea rchers described transcriptional programs for distinct stages of infect ion. The pathogen showed expression of genes enriched for Gene Ontology (GO) terms associated with protein metabolism, gene expression and biosynthetic processes during the biotrophic s tage. Genes involved with catabolic processes, including peptidases and proteasomal subunits, were highly prevalent in the transition to necrotrophy. In later infection stages, pathogen gene expression was enriched in expression of signal transduction and metabolic process genes. Furthermore, the researchers examined the expr ession patterns of genes coding for effectors, which inhibit plant defense response, and identified groups that were specific for different stages, including prior to infection. Exami nation of the tomato transcriptome revealed that there were large group s of genes that were specifically differentially regulated during the first 8 hours of infection. Furthermore, there were relatively few genes differentially expressed during the transi tion from 0 to 8 hours post - inoculation (hpi) that were also differenti ally expressed during the transition from 24 to 48 hpi, suggesting two different, key, transcriptional profiles at those time points. The initial stages of infection, 0 to 8 hpi, showed a shift from general metabolic to catabolic and specific metabolic pro cesses. GO enrichment analysis of the transition from 24 to 48 hpi showed enrichment for 13 many different ontologies and suggested a distinct change in regulation of several metabolic and biosynthetic processes associated with the beginning of the necrotroph ic stage. The recent advancement, accessibility , and decrease in costs of NGS technologies have highly benefited the scientific community. Due to the developmental nature of ARR and th e several, different potential molecular mechanisms that could be emplo yed to confer this resistance, a n NGS transcriptome - wide approach should prove useful in identifying the key factors involved. To our knowledge, only one transcriptomic study of an ARR pathosystem was performed (Gusberti et al., 2013) . Apple leaves show AR R to Venturia inaequalis , and by performing RNAseq on inoculated tissue at 72 and 96 hpi genes potentially contributing to ARR were identified. Rationale and objectives Though there have been recent advancements, little is understood about the regulation and molecular mechanisms conferring ARR in general, and specifically in the cucumber - P. capsici pathosystem. Using genetic and genomic analyses to elucidate these mechanisms wil l be valuable in understanding ARR as a biological phenomenon. Furthermore, pin pointing the components regulating ARR could aid in development of young fruit resistance that could benefit agricultural production. The objectives of the research herein wer e: 1) utilize the genetic diversity in cucumber, and developmental regulation o f this trait to identify genes and metabolites potentially contributing to ARR; 2) Use gene co - expression networks to identify differentially regulated processes in response to infection in susceptible and resistant - aged fruit; 3) Develop a software packag e to facilitate genetic mapping of loci linked to the trait; and 4) Use the software and 14 segregating populations derived from ARR expressing and non - expressing parents, to ident ify such loci. ARR may arise by means of either preformed or induced defenses, in either case an age - regulated difference in expression of the defense components is required. In objective 1, we identified two cucumber genotypes, one exhibiting ARR and one that remains susceptible throughout development. We sought to understand what genes and specialized metabolites were differentially expressed uniquely in the ARR expression genotype, specifically in the resistant age. We expected that by using both geneti c diversity and development to filter the thousands of developmentally regulate d factors we could focus on those which are ARR specific. For the second objective, we were interested in characterizing the infection process in an ARR expressing cucumber, b oth in susceptible - and resistant - aged fruit. To this end, we examined inoculat ed samples using a combination of microscopy, high - throughput phenotyping and transcriptomic experiments. The goals were to identify the mechanisms and timing at which ARR manif ests in resistant - aged fruit. The transcriptomic and metabolomic approaches i dentified several genes that were associated with ARR, however genetic analyses are necessary to identify genomic loci linked to the trait. We wished to use a bulk segregant ana lysis approach for this purpose, however as no easy - to - use tools were available , I developed a software package to aid in the analysis. Using this tool, we analyzed segregating populations derived from ARR expressing and non - expressing parents to identify loci linked to this trait. 15 WORKS CITED 1 16 WORKS CITED Abu - Reidah IM, Arráez - R omán D, Quirantes - Piné R, Fernández - Arroyo S, Segura - Carretero A, Fernández - Gutiérrez A (2012) HPLC ESI - Q - TOF - MS for a comprehensive characterization of b ioactive phenolic compounds in cucumber whole fruit extract. 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Plant Cell 30 : 2779 - 2794; 21 CHAPTER II Transcriptomic and metabolomic analyses of cucumber fruit peels reveal a developmental increase in terpenoid glycosides associated with age - related resistance to Phytophthora capsici Ben N Mansfeld, Marivi Colle, Yunyan K ang, A Daniel Jones & Rebecca Grumet This work is published. For the full text of this work go to: Horticul ture Research (4), 17022 (2017), doi: 10.1038/hortres.2017.22 Author contributions: BNM and RG Conceived and designed the experiments. BNM performed the transcriptomic analysis with support from RG. MC performed the fruit disease assay. YK an d BNM performed the qRT - PCR analysis. Metabolomic analysis was performed by BNM with support from ADJ. The text was written by BM and RG with comments from MC and ADJ. All authors reviewed and approved the manuscript. 22 Abstract The oomycete, Phy tophthora capsici , infects cucumber ( Cucumis sativus L.) fruit. An age - related resistance (ARR) to this pathogen was previously observed in fruit of cultivar own to be associated with the peel. Young fruits are highly susceptible, but develop resistance at ~10 e, and m ethanolic extracts from resistant age peels inhibit pathogen growth. Here we compared developing fruits ied 80 g enes that were and/or specialized metabolism, including four putative resistance (R) genes, and numerous genes involved in flavonoid and terpenoid synthesis and decoration. Untargeted metabolomic Ultra - Performance Liquid Chro matograp hy and Quadrupole Time - of - Flight Mass Spectrometry. Multivariate analysis of the metabolomes identified 113 ions uniquely group had relative mass defects cons istent w ith terpenoid glycosides. Two of the three most abundant ions were annotated as glycosylated nor - terpenoid esters. Together, these analyses reveal potential mechanisms by which ARR to P. capsici may be conferred. CHAPTER III 1 2 Developmentally regulated defe nse rapidly inhibits Phytophthora capsici infection in 3 cucumber fruit. 4 5 Ben N Mansfeld, Marivi Colle, Chunqiu Zhang, Ying - Chen Lin & Rebecca Grumet 6 7 Author contributions: 8 All work was performed by Ben N Mansfeld except for the fruit disease assays and cult ivar 9 screens which were performed by Marivi Colle . 10 24 Abstract Cucumber ( Cucumis sativus ) fruit are susceptible to infection by Phytophthora capsici. However, some cucumber cultivars develop a fruit surface - associated age - related resistance (ARR) to P. capsi ci . Young, rapidly growing fruit are highly susceptible, but become resistant as they complete exponential growth [~16 days post - pollination (dpp); 2 - 3 weeks prior to ripening]. Analyses of peel of ARR expressing and non - expressing uninoculated fruit ident ified gene expression and metabolomic changes associated with resistance that potentially function s as preformed defenses. Here we performed transc riptomic analyses of inoculated fruit at resistant (16 dpp) and susceptible (8 dpp) ages, providing a unique opportunity to examine compatible and incompatible interactions in the same genotype. Strong transcriptional changes were observed at 4 hours post inoculation (hpi), with approximately 1800 genes differentially expressed in either age, suggesting an early initial response to infection. At 24 and 48 hpi, susceptible 8 dpp fruit continued to mount defense along with strong downregulation of genes invol ved in photosynthesis and other biological processes. In contrast, resistant 16 dpp samples largely downregul ated defense responses while upregulating photosynthesis. Scanning electron microscopy of resistant peels showed evidence for infection failure as early as 4 hpi, including deflated or lysed spores and hyphae, that were not observed on susceptible fruit. W eighted gene co - expression network analysis identified early defense response modules uniquely expressed in resistant fruit as early as 2 and 4 hpi . Several candidate genes involved in conferring this rapid response were identified. The early pathogen deat h and rapid defense response to infection in resistant - aged fruit indicate developmental changes that may include both pre - formed biochemical defen ses and developmentally regulated capacity for pathogen recognition. 25 Introduction Ontogenic, developmental, or age - related resistance (ARR), wherein plants or plant organs transition from susceptibility to resistance as a result of developmental changes ( Whalen, 2005; Develey - Rivière and Galiana, 2007) , has been described in several different plant - pathogen syst ems and in crops such as pepper, grape, rice, wheat, and several cucurbit crops (Kim et al., 1989; Gee et al., 2008; Ando et al., 2009; Zhao et al. , 2009; Zhang et al., 2012) . While providing protection in agricultural systems and potentially playing an im portant role in the evolution and optimization of host resistance (Meldau et al., 2012) , the molecular mechanisms controlling these resistances are not well understood. Evidence from various systems suggests possible physical, chemical, or physiological c hanges that may result from age - dependent, non - pathogen specific investment in defense such as cell wall modifications, production of anti - microbia l phytoanticipins, or altered hormone balance (Develey - Rivière and Galiana, 2007; Meldau et al., 2012) . There are also some examples where ARR may result from developmentally regulated expression of a pathogen receptor, allowing for pathogen - specific induc ed resistance at the resistant age. In rice, a developmental increase in expression of leucine - rich repeat (L RR) - kinase type genes, Xa3/Xa26 and Xa21 , that peaks at the maximum - tillering stage of growth , confers ARR to bacterial blight (Cao et al., 2007; Zhao et al., 2009) . Recently, in Arabidopsis, transcriptional control of the canonical immune receptor FLS2 wa s also shown to regulate ontogenic resistance to Pseudomonas syringae (Zou et al., 2018) . It is, of course, possible that a combination of p reformed and developmentally induced mechanisms contributes to ARR. Utilizing an ARR pathosystem also allows a uniq ue opportunity to examine both compatible and incompatible interactions within the same plant genotype. In this work we sought 26 to understand the basis for ARR in cucumber fruit to infection by the oomycete pathogen, Phytophthora capsici (Gevens et al., 200 6) . This soilborne hemibiotroph is a pathogen of many agriculturally important crops including numerous solanaceous and cucurbit species (Ha usbeck and Lamour, 2004) . Infection is initiated by means of biflagellate zoospores which arrive via water from rain or irrigation (Granke et al., 2012) . Upon reaching the host target tissue, zoospores encyst, lose their flagella and germinate forming germ ination tubes (Lamour et al., 2012) . The germination tubes penetrate the plant surface using appressoria and continu e growing hyphae. During the early, biotrophic stages of infection haustoria are formed and used for direct interaction with the host cells and nutrient acquisition; the pathogen then transitions to necrotrophy at approximately 48 hours post - inoculation (h pi) and can produce sporangia for asexual reproduction as soon as 72 hpi (Lamour et al., 2012; Jupe et al., 2013) . The transcriptome of P. c apsici infection has been described in tomato leaves using microarray technology (Jupe et al., 2013) . The authors id entified two major transcriptomic responses in both pathogen and host, at initial infection (8 hpi), and at the transition to necrotrophy (4 8 hpi) (Jupe et al., 2013) . In cucumber , the primary targets of infection of P. capsici are the fruit, which displa y symptoms of rot and tissue collapse followed by appearance of white mycelia and sporangia. In screening for genetic resistance to P. capsici , ARR was discovered (Gevens et al., 2006) . Young, rapidly growing fruit are extremely susceptible to infection, a nd then transition to resistance as they near the end of their exponential growth phase, starting at 12 - 15 days post - pollination (dpp). The fruit peel has been shown to be important for ARR (Ando et al., 2015) . Excised peels exhibit equivalent responses to whole fruit and methanolic extracts from resistant - aged peels had inhibitory effects on pathogen growth (Ando et al., 2015) . Cucumber peels of resistant - aged fruit 27 are enriched for genes associated with defense against biotic and abiotic stresses (Ando et al., 2015) . ARR may arise by means of either preformed or in duced defenses; in either case a developmentally - regulated difference in expression of the defense components is required. A comparison of uninoculated peel transcriptomes of ARR expressing and non - expressing cucumber fruit revealed the potential for eithe r or both cases (Mansfeld et al., 2017) . Of the 80 genes that were uniquely upregulated in ARR expressing fruit at the resistant age, four putative resistance genes (R - genes) as well as resista nce related transcription factors were identified. Furthermore, this set of genes was highly enriched for specialized metabolism genes, including terpenoid synthesis and decoration genes, and untargeted metabolomic analyses of the same tissues revealed an increased accumulation of glycosylated terpenoids in the resist ant tissue (Mansfeld et al., 2017) . The accumulation of these preformed compounds may work in inhibiting infection, while at the same time developmentally regulated expression of R - genes may pr ovide the ontogenic capacity to sense and respond to infection. In this study, we characterized the response of cucumber peel to inoculation with P. capsici at resistant (8 dpp) and susceptible (16 dpp) ages. To our knowledge, only one other transcriptomi c study of ARR was performed. This study examined apple leaves of different ages inoculated with Venturia inaequalis , at 72 and 96 hpi (Gusberti et al., 2013) . Thus, no thoroughly resolved comparisons of the transcriptome analyses of early infections in an ARR pathosystems have been performed. A detailed characteriza tion of infection in ARR response could shed light on the mechanism by which this resistance is controlled, revealing if preformed or induced defenses are recruited. Here, we observed a rapid tr anscriptional defense response at the resistant age coinciding with observed death and collapse of the pathogen by 8 hpi . Two co - expression 28 modules were uniquely upregulated in resistant - aged fruit by 8 hpi and were enriched for defense response. These mod ules contained WRKY transcription factors, ethylene synthesis genes, peroxidases and other defense related genes. These findings suggest that this early response may be crucial in conferring ARR to P. capsici in cucumber. Materials and Methods Plant Materi al A set of 22 inbred cucumber cultivars was tested for ARR . T hree to ten fruits at 16dpp were collected from each cultivar were grown in the greenhouse as described in Ando et al. (2012) . For all further experiments, greenhouse production of cucumber frui except plants were drip fertigated (1 L/ day at 1 - 2% 20 - 20 - 20 fertilizer) . In transcriptome experiment one , flowers were hand pollinated, while in transcriptome experiment two flowers were tagged at anthesis and bee pollinated. In both cases pollination was staggered, such that 8 and 16 dpp fruit were harvested on the same day. In all experiments only one fruit per plant was grown to limit competition. Detached fruit inoculations and sample collection Harvested fruit were briefly washed with distilled water and allowed to air - dry. Fruits were placed in incubation trays lined with wet paper towels, to maintain high humidity and covered with clear plastic tops. Zoospore suspensions were prepared from P. c apsici isolate OP97 or NY - 0644 - 1 ex pressing RFP (Dunn et al., 2013) cultured on diluted V8 agar media (V8 juice 200 mL, CaCO 3 3 g, agar 15 g, distilled water 800 mL). After 7 days, the plates were flooded with 10 mL sterile distilled water to release zoosp ores. Two 10 µL aliquots were 29 remov ed for quantitation by a Countess Cell Counter (Invitrogen) and the mean concentration was used for dilution. The suspension was diluted to a concentration of 5 x 10 5 zoospores/mL. Fruits were then inoculated with ~6 (8 d pp fruit) or ~12 (16 dpp fruit), eq ually spaced, 30 µL droplets of the diluted zoospore suspension. Incubation was performed under constant light at 23 to 25 °C. For ARR screening development of disease symptoms such as water soaking and mycelial growth on each fruit was monitored daily for ten days. Fruits were evaluated using a disease rating in scale of 1 - 9 (1=no symptom; 9=extensive mycelial growth and sporulation). Plant material was inoculated and harvested for two transcriptome experiments; the first included fruit sampled at 0, 4, 24 , and 48 hours post inoculation (hpi), and the second at 0, 2, 4, 8, 12, 18, and 24 hpi. In both experiments t imepoint 0 was collected at 12:30 pm . A t each subsequent timepoint , samples were collected from 6 - 12 inoculation sites per fruit. Samples from a g iven fruit were pooled to form a biological replicate. Three replicate fruits were samples for each age at each timepoint . In e xperiment t w o , at timepoint 0, the three r eplicate samples were prepared from a single fruit . Fruits were removed from the incuba tion chamber and punches were made around each inoculation site using a No. 4 cork borer. Peel discs were subsequently collected by peeling the punched area using a vege table peeler and immediately frozen in liquid nitrogen and stored at - 80°C until RNA ex traction. In experiment two, samples taken from uninoculated parts of the fruit were used as the respective control for each time point. High - throughput RNA extraction S amples were ground using a mortar and pestle in liquid nitrogen. RNA extraction was per formed using the MagMAX Plant RNA Isolation Kit protocol (Thermo Fisher) with slight modifications: 100 - 150 mg of ground tissue were added to 1000 µL of lysis buffer. Hi gh - throughput RNA extraction was performed in 96 - well format, on a KingFisher Flex Puri fication 30 System (Thermo Fisher). Immediately after the run was complete, the 96 - well plate was transferred to storage at - 80°C. RNA concentration and quality were measur ed using Qubit 2.0 Fluorometer (Life Technologies, Inc.) and LabChip GX (Perkin Elmer) respectively. All samples had a minimum RNA quality score of 8. TruSeq Library preparation and sequencing h Technology Support Facility, using the Illumina TruSeq Stranded mRNA Library Preparat ion Kit on a Sciclone G3 magnetic beads was performed after completion of librar y preparation. Quality control and quantification of completed libraries was performed using a combination of Qubit dsDNA HS and Advanced Analytical Fragment Analyzer High Sensitivity DNA assays. The libraries were divided into two pools of 15 libraries ea ch. Pools were quantified using the Kapa Biosystems Illumina Library Quantification qPC R kit. Each pool was loaded onto one lane of an Illumina HiSeq 4000 flow cell and sequencing was performed in a 1x50 bp single read format using HiSeq 4000 SBS reagents. Base calling was accomplished by Illumina Real Time Analysis (RTA) v2.7.7 and output o f RTA was demultiplexed and converted to FastQ format with Illumina Bcl2fastq v2.19.1. - mRNA library preparation and sequencing For the second experiment, Qu - mRNA FWD libraries (Lexogen) were prepared by the Cornell University, Institu te of Biotechnology, Genomics Facility using the manufacturers guidelines. Quality control and quantification of completed libraries was performed using a combination of Qubit dsDNA HS and Advanced Analytical Fragment Analyzer High Sensitivity 31 DNA assays. The libraries where then loaded on a single Illumina NextSeq500 lane and sequenced in a 1x86 bp single end format. Base calling was achieved by Illumina RTA v2.4.11 and output of RTA was demultiplexed and converted to FastQ format with Illumina Bcl2fastq v 2.18. Sequencing read preprocessing and quasi - mapping Experiment one : Reads were cleaned, and adaptor sequences were removed using Trimmomatic v. 0.34 (Bolger et al., 2014) with the following settings: LEADING:3 TRAILING:3 SLIDINGWINDOW:4:15 MINLEN:35. Qu ality control was performed using FastQC (http://www.bioinformatics.bbsrc.ac.uk/projects/fastqc). A cucumber transcriptome fasta (Huang et al., 2009; Li et al., 2011) genome using the gffread function from the cuf flinks software package (Trapnell et al., 2010) and high - quality reads were then quasi - mapped to the transcriptome using Salmon v. 0.9.1 (Patro et al., 2017) with default settings. Experiment two : Reads quality was assessed with FastQC and visualized usin g multiQC ( Ewels et al., 2016) . Subsequently, reads were processed using BBMap (https://jgi.doe.gov/data - and - tools/bbtools/) with the following settings: ftl=12; k =13; ktrim=r; useshortkmers=t; mink=5; qtrim=r; trimq=10; minlength=20; int=f, and trimmed of any poly A sequences, adaptors and the first 12 nt (as recommended by the manufacturer of the library kit). To increase the mapping 2000 bases using a custom R script. The extended gene models were then used to extract a transcriptome fasta file as above. Reads were then quasi - mapped to this new transcriptome file using S almon v 0.12.0 with the -- noLengthCorrection option. 32 Differential expression analysis Read qu antification data was imported into R using the tximport R package (Soneson et al., 2015) and differential expression analysis was performed using DEseq2 (Love et al., 2014) with log - fold - change - shrinkage. Contrasts were analyzed comparing sequential timep oints as well as each timepoint vs. uninoculated samples. Differentially expressed genes were called significant using an adjusted p - value (Benjamini - Hochberg adjustment) and a false discovery rate of less than 5%. A cutoff expression change of above two - f old was used to define biological significance. Alluvial plots were drawn using the ggalluvial R package and venn diagrams were created using the ove rLapper script from: http://faculty.ucr.edu/~tgirke/Documents/R_BioCond/My_R_Scripts/overLapper.R . Weighted gene co - expression network analysis Integer value trans cript counts from e xperiment two , were imported into DESeq2 using the tximport R package (Soneson et al., 2 015) . Genes with less than 10 reads in greater than 75 of the total 78 samples were considered lowly expressed and excluded from the analysis, and 15 202 genes remained. The normalized counts matrix was then transformed using the variance stabilizing transf ormation (VST) (Anders and Huber, 2010) using DESeq2 and imported into the WGCNA package pipeline (Langfelder and Horvath, 2008) . Two separate signed networks were assigned for the inoculated susceptible - and resistant - aged fruit. For each network, the VST counts were used to calculate adjacency matrices using the biweight midcorrelation and a soft thresholding power of 12 (yielding a scale free topolo gy fit of greater than 0.8). The adjacency matrices were used to calculate two topological overlap dissimil arity matrices which were subsequently used for forming gene clustering trees, using 33 average distances. The gene trees were used for assigning co - exp ression modules using the dynamic tree cut algorithm with a minimum module size of 30 genes. Module eigenge nes were correlated to each other and modules with similar expression patterns (dissimilarity < 0.25) were merged. Gene expression profiles of module genes from the infected resistant network (16 dpp) were plotted based on VST values and compared to contro l and 8 dpp expression patterns. To identify modules with different expression patterns in inoculated tissue, read counts were first extracted and n ormalized by library size using DESeq2 counts() function. For each of the resistant network modules, the lo g 2 (+ 0.5) of the normalized counts of all genes in that module was the dependent variable in a linear model where a natural cubic spline with 3 inte rnal knots, at 3, 8, 15 hpi (as determined by the 0.25, 0.5 and 0.75 quantiles of time), was applied to the time variable using the ns() function from the splines R package: An analysis of variance was then performed to identify module with significant a ge X splined - time interactions. The summary of each of those linear models contains the interaction effects for each segment of the spline. Modules with interaction effects in segment 1 and/or 2 (0 3 and 3 8 hpi, respectively) where identified as early induced modules. Gene ontology term enrichment analysis Gene Ontology (GO) term enrichment analysis was pe rformed using the TopGO R package (Alexa et al., 2006) with the entire set of fruit peel expressed genes set as background. Terms were considered enriched if they passed a p - value of 0.05 on the Fisher test with the weight01 size of 100 genes. The previously updated GO term list for cucumber genes (Mansfeld et al., 2017) , was used for analysis. To visua lize change of 34 GO terms over consecutive contrasts heatmaps of - log 10 (Fisher weight01 p - values) were plotted using only terms with P < 0.01. Microscopy Preliminary fluorescent microscopy of infection was performed using an EVOS FL Auto imaging system (Ther moFisher). Excised cucumber peels were affixed to the lid of a 100 mm petri dish using petroleum jelly and inoculated with 10 µL zoospore suspension (~ 5 x 10 5 spores/ml) of RFP - e xpressing isolate NY - 0664 - 1 (Dunn et al., 2013) . Petri dishes were then sealed with parafilm and carefully inverted and placed on the microscope table. Samples were observed at 4x magnification and images were captured every 30 minutes for 72 hours. While collecting samples for transcriptome experiment two , a ~2 mm peel plug from the middle section of each fruit was also excised using a razor blade and fixed in 4% glutaraldehyde in 0.1M phosphate buffer fo r scanning electron microscopy (SEM). After overnight fixation in glutaraldehyde, samples were soaked in 0.1 M phosphate bu ffer for 40min. After consecutive dehydration in rising ethanol concentrations (25, 50, 75, 90, 100, 100, 100%; 1 hour each), samples were transferred to a Leica Microsystems EM CPD300 critical point dryer (Leica Microsystems) using liquefied carbon dioxid e as the transitional fluid. Samples were then mounted on aluminum stubs using adhesive tabs (M.E. Taylor Engineering) and coated wit h osmium (~10 nm thickness) in an NEOC - AT osmium coater (Meiwafosis Co., Ltd.). Samples were examined in a JEOL JSM - 6610LV scanning electron microscope (JEOL Ltd.). High - throughput infection phenotyping High - throughput in vivo disease phenotyping was as de scribed in Zhang et al. (2018) . Briefly, sixteen 6 mm diameter, 5 mm thick, peel tissue plugs were collected from each of three 35 8 and 16 dpp fruit using a biopsy punch. Plugs were placed in a 96 - well black plate and subsequently inoculated with the constit utively fluorescing P. capsici isolate NY - 0664 - 1 (Dunn et al., 2013) , or with distilled water (control 4 plugs/fruit). Plates were read using a Tecan Spark Plate Reader (Tecan). Fluorescent measurements were taken in each well every hour, over the course o f 24 hours at 28 °C. The excit ation and emission settings were 536 and 586 nm , respectively. Gain was calculated from a well containing a mycelial mat, and the Z - position was set at 20000 µm. Results - related resistan ce to P. capsici Our previous ARR studies (Gevens et al., 2006; Ando et al., 2009; Ando et al., 2015; Mansfeld et al., 2017) 1 hybrid commonly grown for processing cucumber production in the Midwest USA. For further genetic and transcriptomic analyses, we sought to determine whether ARR was a common trait in cucumber and to identify a homozygous inbred cultivar expressing ARR. Testing of several inbred cultivars (Supplementary Table 1) identified a s mall number that exhibit ARR i ncluding 3. are initially extremely susceptible to infection, and fruits then become increasingly resistant as they complete th eir exponential growth phase ( Figure 3. 1 A). As fruits reached full size, at ~16 dpp, they primarily exhibited localized necrosis at sites of inoculation, with occasional successful infection at inoculation sites ( Figure 3. 1B). 36 Figure 3.1. Cucumbe - related resistance to P. capsici . ( A ) Fruit length and disease rating (DR) as a function of fruit age. Fruit were ranked on a 1 - 9 DR scale (1=no symptom; 9=extensive mycelial growth and sporulation) at 5 days post - inoc ulation. The dotted line at DR = 3 represents localized necrosis. Points are means of 5 - 6 fruit, error bars represent +/ - standard error of the mean. ( B ) Representative fruit and disease progression at 5 dpi. ( C ) Fluorescently labeled P. capsici on <8 dpp cucumber fruit at 4 and 72 hpi. sp spore; gt germ tube; spg sporangia. A B C sp gt spg 37 Age - dependent differential transcriptomic responses to infection As a first step to explore the early stages of infection in susceptible age fruit (8 dpp) we observed germinati on and growth of a constitutively fluorescent isolate of P. capsici NY - 0644 - 1 (Dunn et al., 2013) . Consistent with observations of P. capsici development on tomato leaves (Jupe et al., 2013) , microscopic images taken at 30 - minute intervals showed germinati on and appressoria formation by 4 hpi, extensive growth by 24 hpi, and sporangia formation by 72 hpi ( Figure 3. 1C). Based on these results we compared transcriptomic responses of resistant (16 dpp) and susceptible (8 dpp) fruit peels at 0 (uninoculated), 4, 24, and 48 hpi. For each age and timepoint three fruit were inoculated with 10 - 15 droplets. All inoculation sites for a given fruit were harvested and pooled for sequencing providing ~20M reads per sample. An average of ~82% r eads uniquely quasi - mapped to the cucumber transcriptome (Supplementary Figure 1). high reproducibility among replicates. Principal component analysis (PCA) con firmed the high within trea tment reproducibility ( Figure 3. 2 A). The first principal component largely reflected time post inoculation, while the second largely reflected fruit age. A similar transcriptional shift in direction and magnitude was observed al ong the positive direction of PC1 at 4 hpi regardless of age (8 dpp circles, 16 dpp triangles) of the tissue, suggesting a somewhat comparable initial response to infection. In contrast, subsequent timepoints (colors) exhibited differential transcripti onal responses to infection as evidenced by the PCA. The susceptible 8 dpp samples progressively moved along the positive direction of PC1 with time, while resistant 16 dpp samples largely stayed in same position relative to PC1, suggesting little subseque nt change in gene expressio n. 38 As was observed by infection phenotyping, successful infection can occasionally occur on resistant 16 dpp fruit. We observed one 16 dpp sample in each of 24 and 48 hpi timepoints to exhibit transcription signatures with simil arities to those of infecte d 8 dpp fruit at the same respective timepoints. These samples had little effect on results (due to treatment of outlier genes in DESeq2), therefore analysis of differential gene expression proceeded including the two samples. Di fferential expression analy sis showed that approximately 1800 genes were differentially expressed (up or down) at 4 hpi compared to uninoculated tissue, regardless of age, evidence of an extremely rapid and strong response to infection ( Figure 3. 2 B). As s uggested by the PCA, differ ential expression was markedly different in the susceptible and resistant tissues at subsequent contrasts. While there was an increase in differentially expressed genes in the susceptible 8 dpp fruit peels with time, (4758 and 25 05 DEG at 24 and 48 hpi, re spectively) the resistant 16 dpp samples had a smaller number of differentially expressed genes at 24 hpi vs 4 hpi (2223) and by 48 hpi only about 500 genes were differentially expressed compared to 24 hpi. 39 Figure 3. 2. A ge - dependent differential transcrip tomic responses to infection. (A) Principal component analysis (PCA) of 8 and 16 dpp inoculated fruit at 0, 4, 24, and 48 hpi . (B) Number of differentially expressed genes in each of three within - age consecutive contrasts: 4 vs. 0 hpi, 24 vs 4 hpi, and 48 vs 24 hpi. A B 40 Resistant - age fruit mount a successful response by 24 hours post inoculation To further understand the biological processes involved in the two responses, the most significantly enriched GO - terms (Fisher - weight01 p - value < 0.01) were compared f or each consecutive contrast: 4 vs. 0 hpi, 24 vs. 4 hpi, and 48 vs. 24 hpi ( Figure 3. 3). At 4 hpi, both 8 and 16 dpp fruit up regulated genes in inoculated fruit were strongly enriched for defense related rogen in both ages at this time point . Although the number and GO categories of genes differentially expressed at 4 hpi was comparable between the two ages, fewer than half of the differentially expressed genes in the resistant samples was shared with those differentially expressed in the sus ceptible samples ( Figure 3. 4A; blue shading). Analysis of the 613 genes uniquely upregulated in the resistant 16 dpp samples at 4 hpi revealed a potentially unique set of defense related genes involved in an early incompatible interaction. When comparing 24 hpi to 4 hpi, and 48 hpi to 24 hpi, less than 15% of the thousands of up - and downregulated genes were shared between the ages, respectively ( Figure 3. 4 B and C). - regulat ing photosynthetic processes and other homeostatic processes, such as carbohydrate metabolic processes. In contrast, by 24 hpi, res istant 16 dpp fruit, were upregulating photosynthetic and growth - related genes and downregulating defense (top five downregul - 41 suggesting a return to normal state ( Figure 3. 3 B). This is especially evidenced by the l arge number of inversely regulated genes in the 8 dpp vs. 16 dpp samples at 24 hpi indicating an opposite response (red shading). T he set of 251 genes upregulated in 16 dpp and downregulated in 8 dpp was strongly enriched for photosynthesis ( p - value = 5.2e - 13). Collectively these observations suggest that the resistant fruit have successfully mounted a defense by 24 hpi. Analysis of pathogen growth provides evidence for infection failure in the first 24 hours on resistant fruit The transcriptomic suggestion of a rapid and potentially successful defense response within 24 hours prompted us to more closely investigate pathogen growth du ring the first 24 hours of infection using electron microscopy and a high throughput microplate a ssay. Samples were collected for SEM from 8 dpp and 16 dpp aged cucumber fruit inoculated with droplets of zoospore suspension ( 5 x 10 5 spores/ml) at time inte rvals of 0, 2, 4, 8, 12, 18 , and 24 hpi. At each timepoint, three samples were collected, each fr om a distinct inoculated fruit. The morphological differences between 8 dpp and 16 dpp fruit were readily observed; the younger susceptible fruits had smaller more densely packed cells and trichomes, as well as warts that produced valley regions that in so me cases increased spore density due to the surface topography ( Figure 3. 5 A). 42 Figure 3. 3. Resistant - aged show transient upregulation of defense related g enes. Gene Ontology enrichment of up - and down - regulated genes ( A and B , respectively). Each row represents an enriched term at one of the three consecutive contrasts: 4 vs. 0 hpi, 24 vs 4 hpi, and 48 vs 24 hpi. Enrichment p - value threshold of 0.01. Terms clustered by Euclidean distances. A B 43 Figure 3. 4. Analysis of differentially expressed gene overlap at each timepoint. Venn genes in 8 and 16 dpp inoculated fr uit at ( A ) 4 vs. 0 hpi, ( B ) 24 vs 4 hpi, and ( C ) 48 vs 24 h pi. Counts in red and blue denote up - and downregulated gene s, respectively. Blue and red highlighted areas represent shared - and inverted - differential - expression, respectively. A B C 44 At 2 hpi, encysted P. capsici spores were observed germinating on fruit of both ages. Formation of some appressoria was also observed as early as 2 hpi. By 4 and 8 hpi, some differences were observable between the resistant (16 dpp) and susceptible (8 dpp) fruit. While spores on susce ptible fruit continued to germinate and form appressoria, in four out of the six 16 dpp samples, lysed spor es and germ tubes were observed, suggesting either preformed antimicrobial compounds or a rapidly induced defense response may inhibit successful inf ection as early as 4 hpi. As infection progressed, more evidence of failure to infect was observable in the majority of the resistant samples. By 18 and 24 hpi, deflated spores, germ tubes , and hyphae were observed on most of the resistant fruit samples, s uggesting that spores that survived an initial defense response may be stopped at a later time, during the first 24 hours. No such histological signs of deflated or burst pathogen structures were observed at any timepoint in samples from susceptible 8 dpp fruit. Quantitative fluorescence based in vivo infection assays provided further evidence of inhibited infe ction in resistant aged fruit by 24 hpi. After a short lag phase, the signal from fluorescently labeled P. capsici on inoculated susceptible 8 dpp fr uit grew linearly throughout the 24 - hour period ( Figure 3. 5 B). However, on 16 dpp resistant - aged fruit, in tensity of the fluorescent signal plateaued by 8 10 hpi, further suggesting early inhibition of pathogen growth. Together, the SEM and bioassay res ults bolster the transcriptional evidence suggesting that infection may be thwarted by 24 hpi in 16 dpp cuc umber fruit. Transcriptomic investigation of the first 24 hours post inoculation A second transcriptomic experiment was performed in parallel to the SEM study. Concurrent with the collection of samples for SEM (0, 2, 4, 8, 12, 18 and 24 hpi), inoculated an d 45 uninoculated tissue was harvested for transcriptome analysis using 3 mRNA sequencing. In total - mRNA libraries were sequenced to an average d epth of ~5M reads/sample and an average of ~60% reads quasi - - extended cucumber transcript sequences (Supplementary Figure 3). Two samples (8dpp_T12_Inoc_1 and 8dpp_T18_Cont_2) had aberrantly low read coverage (<0.5 M reads) and were exclud ed from analysis. A PCA comparison of timepoints shared between the two transcriptome experiments ( 0, 4 and 24 hpi) showed tight clustering of samples within their respective timepoints, indicating high reproducibility between the two experiments (Suppleme ntary Figure 4). The PCA of data from experiment two revealed, modest changes in transcriptomic patterns fo r uninoculated samples of both ages (open symbols) relative to timepoint 0 (asterisks), likely reflecting a combination of diurnal changes and the ef fects of fruit detachment from the vine ( Figure 3. 6 A). In contrast, the inoculated samples (closed symbols ) showed strong transcriptional changes, especially for the susceptible 8 dpp fruit. From 4 hpi and beyond, the inoculated 8 dpp samples (circles) ex hibited a sequential transcriptomic transition. Conversely, samples collected from the resistant - aged 16dpp fruit (triangles) all clustered together, and relatively closely to uninoculated fruit, from 4 hpi and beyond. Notably, at 2 hpi, samples from unino culated 8 dpp fruit clustered with uninoculated control, while 2 hpi samples from 16 dpp fruit showed a cle ar difference from the uninoculated controls, suggesting an earlier response to infection in the resistant - aged fruit. To further understand the overall trends in gene expression changes in response to infection, all genes that are differentially expresse d in at least one timepoint vs. the respective control were displayed as an alluvial plot ( Figure 3. 6 B). Alluvial plots are compri sed of: 1) Strata stacked bars that represent relative counts in each category, in this case upregulated, 46 downregulated or not differentially expressed (nonDE); and 2) Alluvia curves connecting strata, represent the change in number of observations fro m one category to another along the x - axis, in this case, the different timepoints post inoculation. Each alluvium can be fol lowed from 2 hpi to 24 hpi and represents groups of genes that share an expression pattern over time (e.g. Up Down rge alluvia from nonDE strata (blue) progressively connecting to either up - or downregulated strata shows a phased response t o infection, in which certain genes are involved in early timepoints, while others in later stages of infection. The figure reveal s that response to infection is dramatically different in the two ages. Most evident is that more than double the number of u nique genes is differentially expressed at least once in susceptible fruit compared to resistant fruit. Examination of the trends i n 8dpp susceptible fruit revealed a pattern of sequential accumulation of differentially expressed genes, as previously sugge sted by the PCA. The number of differentially expressed genes grew continuously until 18 hpi, and most genes which were differentia lly expressed at one timepoint continued to be differentially expressed at following timepoints. Of the 679 upregulated genes at 4 hpi, 417 are continuously upregulated in every subsequent timepoint. By 24 hpi, 1411 of the 1585 upregulated genes had been p reviously upregulated at one and all subsequent timepoints. A similar trend is observable in downregulated genes. Conversely , by following alluvia of genes initially upregulated at 4 hpi in the 16 dpp resistant - aged samples, it is evident that most are su bsequently not differentially expressed at further timepoints. Of the 339 genes upregulated at 4 hpi, 139 genes are not diffe rentially expressed at 8 hpi, and only 40 are continuously upregulated through 24 hpi. Though the number of differentially expresse d genes grows until 12 hpi, most genes are timepoint specific, being differentially expressed once or at most at two timepoin ts. Most striking is the fact that at 24 hpi, 47 only 255 and 67 genes are up - and down - regulated, respectively, compared to uninoculated tissue, further confirming the culmination of the defense response in resistant - aged fruit. 48 Figure 3. 5. Evidence fo r infection failure in the first 24 hours post inoculation on resistant fruit. ( A ) Scanning electron micrographs o f inoculated 8 and 16 dpp fruit. tr trichrome; ap appressoria; gt germ tube; bgt burst germ tube; bs burst spore; deflated spores (d s), deflated germ tubes (dgt), and deflated hyphae (dh). Bottom right scale bar for all frames, except for insets. ( B ) High - throughput in vivo analysis of pathogen growth on fruit plugs. For inoculated and control treatments, each point is a mean of 36 and 12 replicates, respectively. Error bars are +/ - SEM. A B 49 Figure 3. 6. Differential transcriptional dynamics within the first 24 ho urs. ( A ) Principal component analysis of inoculated and control fruit of two ages, 8 and 16 dpp. Samples collected at time 0, which represent both Inoculated and Control treatments, are denoted by an asterisk. ( B ) Alluvial plots showing all genes with sign ificantly changed expression at each timepoint compared to respective controls. Each stacked bar represents the numbers of genes either up - down - or not - differentially (nonDE) regulated at sequential time points. Genes are grouped based on expression patter ns throughout time. B 50 Gene co - expression is preserved but not expression patterns over time To better identify transcrip tional co - expression patterns in the data we employed Weighted Gene Co - expression Network Analysis (WGCNA) (Langfelder and Horvath, 2008) . Two independent response networks for the susceptible and resistant fruit ( Figure 3. 7 A, B respectively) were assembl ed. After module merging (based on eigengene correlation), a total of 15 and 27 modules were defined for the susceptible and resistant inoculated networks, respectively. Module preservation statistics (Langfelder et al., 201 1) showed that gene co - expressi on was preserved in many modules when comparing the susceptible and resistant networks (Supplementary Figure 5). Module gene overlap ( Figure 3. 7 C) also showed significant gene co - expression between man y modules of the two networks. For example, large subsets of the genes in Susceptible Module 1 were also co - expressed in the resistant network, however these subsets were distributed amongst eight distinct modules, all showin g different expression patterns . Thus, while many groups of genes were expressed in concert over time, indicating coordinate regulation regardless of fruit age, these groups also showed diverse module assignment and thus age - specific patterns of expression in response to infection. This suggests a reprogramming of the response network in resistant - aged fruit. Biological processes identified by weighted gene co - expression network analysis Using gene module assignment defined by the resistant network, express ion patterns of the genes withi n a given module were compared in control and inoculated fruit of both ages ( Figure 3. 8). GO - term enrichment analysis of the different modules demonstrated the biological 51 relevance and function of identified modules (Suppleme ntary File 1). For example, Mod ule R1 (Resistance Module 1) (n genes = 1982) showed patterns of increased expression in response to inoculation in both ages. Uninoculated tissue largely showed unchanging expression levels throughout the entire time course. GO term enrichment showed that this module was strongly associated with translation and ribosome biogenesis suggesting induction of protein synthesis in response to infection. In Modules R2 and R3 (n genes = 1893 and 1670, respectively) all genes, regardl ess of age or treatment, showed a similar pattern of change over time. Genes in Module R2, which exhibited gradual decline, were enriched for carbohydrate metabolic process, lipid metabolic process, signal transduction and response to abiotic stimulus. Hig her baseline expression of 8 dp p fruit is likely due to the different stage of development, as unharvested fruit at this age are still rapidly growing ( Figure 3. 1B). Genes in Module R3 showed gradual increase and were ompound catabolic modules is probably a result of the fruit being detached during the analysis, i.e. deprived of carbohydrate sour ce and subject to water loss. M odule R13 (n genes = 425) showed a potentially circadian controlled expression pattern, with peak expression at 18 hpi (collected at 6:30 AM) and almost identical patterns of expression in uninoculated fruit of both ages. Ino culated fruit showed a gated ex pression pattern, wherein the diurnal rhythm is offset in amplitude and period. This module was enriched for processes involved in transcriptional regulation as well as response to light stimulus. 52 Figure 3. 7 . Defense response network reprogramming in the two fruit ages. Weighted gene co - expression analysis (WGCNA) was used to analyze all expressed genes in inoculated samples. Separate signed networks were analyzed for resistant ( A ) and susceptible ( B ) fruit. Dendrograms cluster the genes based on thei r topological dissimilarity. The colored bars underneath the dendrograms show co - expression module assignment. ( C ) Module overlap between the susceptible and resistant networks. Each curve connecting two modules in the different network represents a shared group of genes. Curve width is proportionate to the number of genes. Curve color shows if the overlap between mo Exact test and an FDR of < 0.001. A B C 53 Figure 3. 8. Resistant network module gene expression patterns. Eac h panel represents a module (number of genes indicated in label) from the infection co - expression network of resi stant fruit ( Figure 3. 7A). Comparison of expression patterns of the module - defined genes in the different ages and treatments across time. Data are mean and SEM of variance stabilized normalized read counts for all genes in the module. 54 Modules induced in early infection of resistant - aged fruit Several modules indicated differential response to infection between the resistant and susceptible age s. Expression patterns of all genes, in each of the identified resistant network modules, were compared across in oculated samples using regression analysis . A cubic spline basis function was applied to the time variable with three internal knots, splitting the time course into four segments (0 - 3, 3 - 8, 8 - 15, and 15 - 24 hpi). Most of the modules (22/27) showed significa nt interaction effects between age and splined modeled time in at least one timepoint , indicating that genes in each module behave differently across time in the two ages, further reflecting the reprogramming of module expression patterns (Supplementary Ta ble 2). Based on the PCA, we were especially interested in modules that showed very early response differences. Six modules had significant int eraction effects during the first two spline fractions, 0 - 3 hpi and 3 - 8 hpi ( Figure 3. 9). Of specific interest w ere early induced modules that were also associated with defense. Module R5 (n = 970), is defined by a spike of increased expression in respons e to inoculation of resistant - aged fruit. Expression levels peak at 2 and 4 hpi followed by decrease in expressio n starting at 8 hpi. In the inoculated susceptible fruit, the genes identified in this module showed a minimal change in expression prior to 4 hpi . GO term enrichment showed that this module was strongly associated with defense related genes, the five most enriched terms being r Similarly, Module R9 (n = 813) also showed statistically diff erent expression during early infection, wit h increased expression by 2 and 4 hpi in inoculated resistant - aged fruit. In susceptible fruit these genes show a more gradual increase in expression, which only matches 55 that of the resistant - aged fruit at 8 hpi. This module was also strongly associated wi th a resistance response as evidenced - Genes with high module membership are str ongly correlated to the module eigeng ene and thus represent highly connected genes within the module. We used this measure to identify highly connected genes in Modules R5 and R9, as these modules showed patterns of increased expression at early infection timepoints in resistant fruit. The se cond criterion for selection was those genes that also showed significantly increased expression in resistant inoculated fruit at 2 and/or 4 hpi compared to both the uninoculated control and the inoculated susceptible f ruit. We identified 25 genes with a g reater than 2 - fold expression at 2 and/or 4 hpi in both comparisons as well as a module membership greater than 0.75 (Table 3. 1). As expected, the expression patterns of these genes strongly conform to those of the Modu le eigengene, with a uniquely strong expression at early timepoints in resistant inoculated fruit ( Figure 3. 10). Many of t hese genes are annotated to have canonical functions in early defense response, for example associated with reactive oxygen species (R OS) metabolism, gibberellin and ethyl ene balance, vesicle transport, protein phosphorylation, as well as pathogen perception and response. 56 Figure 3. 9. Early response modules in the resistant network. Points represent the mean log 2 (normalized read count) of inoculated 8 dpp or 16 dpp fruit. Lines and grey ribbons are predicted values (and standard errors ) based on regression of log 2 (normalized read count) by a natural cubic spline of time with three internal knots (0 - 3, 3 - 8, 8 - 15, a nd 15 - 24 hpi). Modules selected are those with a significant interaction effect in one or both first spline fractions (0 - 3 and 3 - 8 hpi), i.e. have age - dependent response to infection in early timepoints. The five most significantly enriched gene ontology t erms are indicated next to each module. 57 Figure 3. 10. Genes induced in early responses to inoculation of resistant fruit. Genes are identified by a module membership > 0.75 and significant fold change of >2 in inoculated resistant fruit (16 dpp) compared to both control resista nt fruit and inoculated susceptible fruit (8 dpp). Mean expression for three biological replicates i n each age and treatment. Error bars are +/ - standard error of the mean . 58 Table 3. 1: Genes with high module membership to Modules R5 and R9 that are differe ntially expressed in resistant - aged fruit 1 compared to the uninoculated control and inoculated suscep tible fruit. Mod. Mem. Module membership; LFC log 2 (Fold 2 Change); Padj Benjamini - Hochberg adjusted p - value; R resistant 16 dpp; S susceptible 8 dpp ; RI Resistant inoculated; RC 3 Resistant control. 4 Mod ule Gene Description (Arabidopsis best BLAST hit) Mod. Mem. LFC R vs. S Padj R vs. S LFC RI vs. RC Padj RI vs. RC LFC R vs. S Padj R vs. S LFC RI vs. RC Padj RI vs. RC 2 hpi 4 hpi 5 Csa3G7061 70 unknown protein 0.96 1.39 0.003 1.05 0.394 1.13 0.024 1.22 0.043 Csa2G35154 0 chaperone protein dnaJ - related 0.94 1.36 0.032 2.14 0.014 0.49 0.517 2.53 <0.001 Csa3G782680 3 syntaxin of plants 121 0.92 1.52 <0.001 1.37 0.014 0.80 0.066 0.77 0.176 Cs a4G009900 inositol polyphosphate kinase 2 beta 0.92 1.82 <0.001 1.93 0.006 0.65 0.256 2.29 <0.001 Csa5G524780 NAD(P) - binding Rossmann - fold superfamily protein 0.91 1.24 0.037 2.21 0.006 0.77 0.249 1.91 0.006 Csa3G271380 Regulator of Vps4 activity in th e MVB pathway 0.91 1.73 0.008 2.22 0.015 - 0.26 0.772 1.77 0.032 Csa2G069200 cinnamate - 4 - hydroxylase 0.90 1.63 0.031 2.71 0.007 - 0.56 0.541 1.38 0.184 Csa1G439830 gibberellin 2 - oxidase 0.89 1.61 0.001 1.76 0.013 1.37 0.008 1.65 0.008 Csa1G025960 WRKY DNA - binding protein 33 0.89 1.17 0.005 1.54 0.007 - 0.05 0.934 1.12 0.035 Csa4G640960 HXXXD - type acyl - transferase family protein 0.89 1.52 0.045 2.56 0.01 - 0.28 0.782 2.31 0.006 Csa5G471600 EXS (ERD1/XPR1/SYG1) family protein 0.88 1.48 0.001 1.94 0.001 - 0.09 0.898 0.92 0.143 Csa1G086390 receptor - like kinase in in flowers 3 0.87 1.36 0.002 1.50 0.024 - 0.58 0.255 1.47 0.013 59 5 Csa4G411390 Glycosyltransferase family 61 protein 0.87 1.57 0.009 2.04 0.019 - 0.19 0.822 1.47 0.066 Csa2G07 0840 Calcium - dependent phospholipid - binding Copin 0.83 1.19 0.005 1.49 0.015 0.18 0.751 1.13 0.039 Csa4G049610 1 - aminocyclopropane - 1 - carboxylic acid (acc) synthase 6 0.82 1.72 0.019 2.33 0.023 0.59 0.518 2.43 0.003 Csa6G367150 ACT domain repeat 4 0.80 1.42 0.006 0.73 0.999 1.33 0.012 1.74 0.006 9 Csa6G495000 root hair specific 19 0.95 1.68 0.004 0.41 0.999 1.42 0.022 1.98 0.004 Csa3G567330 WRKY DNA - binding protein 75 0.94 1.74 0.004 2.51 0.006 - 0.38 0.618 2.72 <0.001 Csa6G213910 Peroxidase superfam ily protein 0.93 2.14 <0.001 0.31 0.999 1.88 0.003 1.97 0.007 Csa6G403620 unknown protein 0.92 1.17 0.037 0.47 0.999 1.17 0.04 1.71 0.009 Csa7G432140 HOPW1 - 1 - interacting 1 0.90 1.03 0.005 - 0.05 0.999 1.31 <0.001 1.05 0.015 Csa6G517020 alternative NAD (P)H dehydrogenase 1 0.90 1.96 0.005 2.41 0.022 0.56 0.518 2.70 0.001 Csa6G454420 unknown protein 0.86 1.23 0.029 0.30 0.999 1.34 0.013 1.36 0.041 Csa6G160180 ethylene - forming enzyme 0.84 0.74 0.104 1.04 0.263 1.77 <0.001 1.55 0.001 Csa4G109010 alter native oxidase 2 0.80 1.28 <0.001 1.17 0.015 0.08 0.867 1.57 <0.001 5 60 Discussion A rapid infection meets a rapid response The infection process of Phytophthora spp. has been studied extensively on many plants and crops (Erwin and Ribeiro, 1998) . However, t here are clear differences between the rates and severity of infection in different species and hosts. For example, P. infestans infections on potato and tomat o are largely symptomless until 3 days post infection (dpi) (Nowicki et al., 2011) . As such, many transcriptome studies examine much later time points post inoculation (eg. Gyetvai et al., 2012; Zuluaga et al., 2016) . In contrast, P. capsici has a rapid in fection cycle with visible symptoms, often within 24 hpi, and can reach asexual sporulation by 2 - 3 dpi (Lamour et al., 2012) . Using both fluorescent and scanning electron microscopy, we observed that P. capsici infection of susceptible young cucumber fruit is also extremely rapid. On susceptible - aged fruit, hyphal growth was observed as early as 8 hpi and progressed rapidly throughout the first 24 hours, indicating a successful biotrophic infection. Using in vivo high - thro ughput bioassay, we quantitatively observed a detectable linear growth rate on cucumber fruit peel samples as early as 4 - 6 hpi, agai n suggesting quick establishment and rapid progression during this time. As described on other hosts (Jupe et al., 2013) , ou r fluorescent microscopy showed that by 72 hpi asexual reproductive sporangia have been formed in cucumber. It is this rapid infecti on and reproductive cycle that allows P. capsici to be such a devastating pathogen in the field. As we observed in our prev ious studies using other cucumber cultivars (Ando et al., 2015; Mansfeld et al., 2017) ibit ARR at approximately 16 dpp , coinciding with the end of exponential growth . Evidently, inhibition of 61 zoospore germinati on is not the mechanism of ARR; regardless of fruit age, within two hours of inoculation most P. capsici zoospores have already ency sted and germinated ( Figure 3. 5), and some had formed appressoria. Strikingly however , the SEM of inoculated resistant fruit revealed histological signs, such as burst or lysed spores, as early as 4 hpi, and consistently by 8 hpi. By 24 hpi, all resistant - aged samples examined either showed no pathogen present or deflated/unviable spores and hyphae. None of these histological signs were present in susceptible samples, on which infection proceeded normally. Similarly, a study of P ort - O rford - cedar plants res istant to P. lateralis showed a reduction in pathogen tissue as well as deflated hyphae and spores at 24 hpi (Oh, 2004) . Tho ugh there is limited histopathological evidence of such a severe defense response, similar signs of Phytophthora spp. (including cap sici) inoculum death are observed after exogenous application of phytochemicals such as garlic root exudates or terpenoid - containing essential oils from oregano and other plants (Malajczuk, 1988; Soylu et al., 2006; Khan and Zhihui, 2010; Khan et al., 2011 ) . Evi dence in other fungal pathosystems of spore - and hyphal membrane disruption by preformed or induced Defensin proteins also exists (eg. de Beer and Vivier, 2011; Sagaram et al., 2011) . Thus, the rapid histological signs of pathogen death might result from p reformed potentially antimicrobial compounds, as implicated from peel extract assays and transcriptomic analysis of non - inoculated developing peels (Ando et al., 2015; Mansfeld et al., 2017) , or a rapidly induced defense response. Rapid responses to P. cap sici ha ve been observed in incompatible reaction on Arabidopsis leaves where failure to penetrate, ROS bursts, callose deposition, and hypersensitive cell death occurred within 24 hpi (Wang et al., 2013) . Our in vivo bioassay further confirmed that p athoge n growth was inhibited by 8 - 10 hpi on resistant fruit. The findings indicating rapid inhibition of infection were further supported by transcriptional evidence showing that induced 62 rapid activation of defense related genes at 4 hpi was followed by ac tive d ownregulation of defense related genes in the peels of resistant age, 16 dpp fruit, by 24 hpi (Figures 3A and B). This downregulation of defense makes intuitive sense if the pathogen has been eradicated or is no longer infecting the host. Conversely, succe ssful infection of tomato by P. capsici infection, was accompanied by downregulation of genes associated with primary metabolism processes (Jupe et al., 2013) . While we observed similar down - regulation in susceptible cucumber fruit, by 24 hpi resista nt - age d fruit showed upregulation of photosynthesis and other metabolic processes, Together the evidence from microscopy, bioassay and preliminary transcriptome study suggested t hat bi ological processes prior to 24 hpi are important in conferring ARR. We thus performed a high - resolution transcriptomic time course analysis within that time frame including inoculated and control tissue at 0, 2, 4, 8, 12, 18 and 24 hpi. Congruent wit h our first transcriptome experiment, gene expression changes over time were drastically different between resistant and susceptible aged fruit. Double the number of genes were involved in the susceptible response, and susceptible fruit exhibited progressi ve wav es of gene expression changes , peaking at 18 hpi ( Figure 3. 7 B). A high - resolution transcriptional time series of the compatible response of Arabidopsis to infection by the fungal pathogen Botrytis cinerea showed that most differential expression occ urred at ~18 - 30 hpi (Windram et al., 2012) . In Botrytis infection of Arabidopsis , these timepoints are post - pathogen penetration , and thus the increased gene expression at these times may represent a response wh ich is too late to inhibit infection (Windram et al., 2012) . The increased expression at comparable timepoints in our data might suggest a similarly failed defense response in susceptible - aged fruit. In contrast, in the resistant fruit of our study, 63 relati vely few genes were differentially expressed at 24 hpi compared to uninoculated samples, providing an additional indication that the pathogen defense response is largely completed. A reprogramming of gene co - expression networks of infection at the resista nt age To further understand the effect of fr uit age on gene expression patterns in response to P. capsici inoculation, gene co - expression networks of both the susceptible and resistant interactions were analyzed using WGCNA (Langfelder and Horvath, 2008) . The difference in the number of modules iden tified and module preservation patterns suggest that the co - expression response in resistant - age fruit was reprogrammed as compared to the network on susceptible fruit ( Figure 3. 7). While genes within a co - expre ssed set share similar regulatory mechanisms, these are employed at different times and with different patterns during the infection of resistant and susceptible - aged fruit. Network reprograming in resistant wild type plants and effector - triggered - immunity by P. syringae was similarly observed in Arabidopsis (Mine et al., 2018) . Specifically, similar genes are activated in compatible and incompatible interactions; however, their timing and expression patterns were altered. While we identified several module s that exhibit similar expression patterns regardless of infection (i.e. likely a response of fruit detached from the plant), most modules were impacted by infection ( Figure 3. 8). We focused our analysis on the differences in gene expression patterns of ge nes identified to be induced by inoculation in the resistant network. In PCA, susceptible samples at 2 hpi clustered with non - inoculated controls ( Figure 3. 6 A). Conversely, resistant - aged samples at 2 hpi showed a distinct transition along both PC1 and 2 away from the uninoculated controls, suggesting a more rapid transcriptional defense response in these samples. We thus were interested in modules showing differential expression patterns at early timepoin ts. As o ptimal timing of defense response has been shown to be crucial for successful resistance 64 (Tang et al., 2017) , the ability to mount a successful defense could be attributed to this early response. By performing cubic spline regression on module gene expression curves, we identified gene co - expressio n modules associated with defense that are differentially activated in resistant - aged fruit as early as 2 and 4 hpi ( Figure 3. 9). Of specific interest were module R5 and R9; in both cases increases in gene expression were delayed in susceptible relative to resistant - aged fruit. Genes of interest were identified using a combination of module membership statistics and differential expression analysis ( Figure 3. 10). Among the 25 candidate genes of interest sho wing resistance - specific increase at 2 or 4 hpi, we re two WRKY transcription factor homologs Csa1G025960 ( AtWRKY33, BLAST E = 2.8e - 119) and Csa3G567330 ( AtWRKY75, BLAST E = 7.4e - 44). While AtWRKY75 is reported to be associated with phosphate deficiency (De vaiah et al., 2007) , its cucumber homologue may fun ction in defense to pathogen infection . AtWRKY33, however, has been shown to be important in resistance to Alternaria brassicicola and Botrytis cinerea in Arabidopsis (Zheng et al., 2006; Birkenbihl et al. , 2012) . AtWRKY33 is also rapidly induced by the fl g22 epitope as part of microbe - associated molecular pattern immunity, with downstream targets involved in ethylene and camalexin synthesis as well as other transcription factors and pathogen receptors (Bir kenbihl et al., 2016) . Furthermore, in a proteomic study of P. capsici infection, the tomato WRKY33 homolog protein was found to be induced by 8 hpi and localized to the nucleus (Howden et al., 2017) . Among the targets of WRKY33 in Arabidopsis are ethylen e biosynthesis genes (Birkenbihl et al., 2016) . We further identified that the two ethylene synthesis genes Csa4G049610 and Csa6G160180 , encoding 1 - aminocyclopropane - 1 - carboxylate synthase and 1 - aminocyclopropane - 1 - carboxylate oxidase, respectively, are bo th induced as part of Modules R5 65 and R9 respectivel y. Ethylene is generally thought to be important in defense response against fungi and necrotrophic pathogens (Broekgaarden et al., 2015) . Specifically, ethylene response, but not SA or JA, was shown to be crucial for inhibition of P. capsici growth in hab anero pepper, (Núñez - Pastrana et al., 2011) . Blocking ethylene perception by means of exogenous application of silver nitrate reduced this inhibition (Núñez - Pastrana et al., 2011) . Moreover, s ilencing of ethylene signal transduction in Nicotiana benthamian a resulted in loss of ARR to P. infestans ( Shibata et al., 2010) . The uniquely increased expression of the cucumber WRKY33 and downstream upregulation of ethylene synthesis observed in resistant fruit could thus be a central component in regulation of the successful defense response in cucumber ARR . Consistent with a successful hypersensitive response in resistant - age fruit, this group of early induced genes also included two putative peroxidases ( Csa6G213910 and Csa6G495000 ), an NAD(P)H - dehydrogenase (Csa6 G517020), as well as an alternative oxidase (Csa4G109010) that could potentially serve in modulating ROS within the first few hpi (Quan et al., 2008) . Other genes identified to be potentially involved in defense are genes involved in vesicle transport ( Csa 3G782680 and Csa3G271380 ), as well as a sig nal transduction ( Csa4G009900 and Csa1G086390 ) and specialized metabolism ( Csa2G069200 , Csa4G640960 and Csa4G411390 ). Finally, Csa2G070840, which putatively encodes a calcium - dependent phospholipid - binding copine family protein, might also be important in fine - tuning the response to infection, as its homolog in Arabidopsis functions in stomatal closing during infection and regulation of several resistance receptor genes (Li et al., 2009; Gou et al., 2015) . All thes e genes are canonically involved with respo nse to pathogens, and so their early activation in response to inoculation in resistant fruit could be crucial in conferring ARR by limiting pathogen establishment in early stages of infection. 66 Conclusion M any tr anscriptomic comparisons of plant - pathogen interactions have been performed utilizing both susceptible and resistant genotypes. Yet often the resistant material is of wild or ancestral nature and hence these comparisons may have inherent biases due to any genomic presence/absence variances of resis tance or other genes (i.e. , Bayer et al., 2016) . Even in cases where transgenic plants, mutants or near isogenic lines are compared, pleotropic effects might be present (eg. Krattinger et al., 2016) . The ability a fforded by an ARR pathosystem to examine bo th susceptible and resistant responses within the same plant genotype helps us gain valuable insight into the mechanisms that confer plant disease resistance. We have previously shown that developmentally accumula ted chemical compounds have an inhibitory e ffect on pathogen growth (Ando et al., 2015; Mansfeld et al., 2017) . Thus, the findings herein, of extremely rapid pathogen defeat could be due to such preformed specialized metabolites . We also observed an ontog enically manifested capacity to rapidly res pond to inoculation, suggesting developmental regulation of an upstream signaling factor. In our previous study we observed developmental upregulation of four pathogen receptors, uniquely in ARR expressing fruit ( Mansfeld et al., 2017) . Similar in mode to the rice Xa3/Xa26 and Xa21 receptors (Cao et al., 2007; Zhao et al., 2009) , a developmental upregulation of one of those receptors in cucumber could allow the perception of P. capsici at resistant ages, and thus a ctivation of transcrip tion factors like WRKY33 and its downstream ethylene and other defense response targets. Ultimately, these two developmentally regulated mechanisms, chemical defenses and rapid response (Figure 3.11) , could be working in concert to pr otect the cucumber fru it and seed, and thus the investments made in producing future generations. 67 Figure 3. 11. Hypothesized model for cucumber age - related resistance to P. capsici . In young susceptible fruit there is low accumulation of potentially anti microbial specialized metabolites. Furthermore, a delayed response (> 8 hpi) to pathogen sensing may be too late to limit pathogen establishment. In resistant - aged fruit, the accumulation of metabolites could directly inhibit pathogen growth. Developmental ly regulated expressio n of receptor - like gene(s) allows the sensing of pathogen - associated molecular patterns or effectors, and thus mediates an early response to infection. 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Plant J 48 : 592 605 Zou Y, Wang S, Zhou Y, Bai J, Huang G, Liu X, Zhang Y, Tang D, Lu D (2018) Transcription al regulation of the immune receptor FLS2 controls the ontogeny of plant innate immunity. Plant Cell 30 : tpc.00297.2018 Zuluaga AP, Vega - Arreguín JC, Fei Z, Matas AJ, Patev S, Fry WE, Rose JKC (2016) Analysis of the tomato leaf transcriptome during succes sive hemibiotrophic stages o f a compatible interaction with the oomycete pathogen Phytophthora infestans . Mol Plant Pathol 17 : 42 54 74 CHAPTER IV QTLseqr: An R Package for b ulk s egregant a nalysis with Next - Generation Sequencing Ben N Mansfeld & Rebecca Gr umet This work is published. For the full text of this work go to: Plant Genome 11:180006. doi:10.3835/plantgenome2018.01.0006 Author contributions: BNM wrote the code for the software with support from RG. The manuscript text was written by B N M and both authors reviewed and approved the manuscript. 75 Abstract Next - Generation Sequencing Bulk Segregant Analysis (NGS - BSA) is efficient in detecting quantitative trait loci (QTL). Despite the popularity of NGS - BSA and the R statistical platform, no R packages are currently available for NGS - BSA. We present QTLseqr, an R package for NGS - BSA that identifies QTL using two statistical approaches: QTL - These approaches use a simulation method and a tr icube smoothed G statistic, respectively, to identify and assess statistical significance of QTL. QTLseqr can import and filter SNP data, 10 (p - values), enabling identification and plotting of QTL. The source code is available at https://github.com/bmansfeld/QTLseqr . 76 CHAPTER V 1 2 Conclusion and future directions 3 4 77 Conclusion The complexity and appeal of studying ARR stem from the intersection of developmental biology and disease resis tance. There are several hypothesized mechanisms by which ARR could be conferred (Whalen, 2005; Develey - Rivière and Galiana, 2007) . Accumulated p hysical or chemical barriers could inhibit pathogen penetration or germination (Meldau et al., 2012; Barton and Boege, 2017) , while developmentally regulated components in defense signaling could (Cao et al., 2007; Zhao et al., 2009; Zou et al., 2018) . In the research herein, we sought to elucidate the mechanism controll ing this ontogenic resistance in cucumber fruit by employing an array of genetic, genomic, metabolomic and micr oscopic approaches. In any of the cases above, whether physical, chemical, or signaling - associated, a developmentally regulated transcriptional c hange should be observable. In experiments described in Chapter II we sought to identify such a change. Though fruit development is a transcriptionally complex process that involves thousands of genes, by coupling developmental transitions with genetic di versity for ARR, we were able to filter out developmental effects and focus on genes potentially contributing t o ARR. We identified unique upregulation of defense related factors in resistant - aged fruit peels, including resistance genes and transcription f actors. We also identified an enrichment for genes involved in specialized metabolism which prompted an untarge ted metabolomic analysis. Though this approach we identified ARR - associated accumulation of metabolites, specifically terpenoid glycosides, that may act as antimicrobial components. We also confirmed previous observations in our lab indicating marked trans criptional transitions to defense at the end of fruit growth (Ando et al., 2012; Ando et al., 2015) . Defense related gene ontology terms were of the most enrich ed in 16 dpp fruit, regardless of genotype suggesting a model in which a developing fruit finalizes its 78 growth and transitions to defend its metabolic investment before ripening. Such a model is consistent with recent understanding about growth/defense tra deoffs and the optimal defense hypothesis, which posits that defensive resources are allocated to tissues that are most valuable or vulnerable to biotic attack (Meldau et al., 2012; Neilson et al., 2013; Huot et al., 2014) . Research in Chapter II was published in the journal Hor ticulture Research (Mansfeld et al ., 2017) . We were also interested in the biological processes involved in response to infection by P. caps ici . Furthermore, we anticipated that comparisons between susceptible fruit and resistant - aged fruit would help elucidate the mechanism of resist ance. In Chapter III, we utilized both microscopic and transcriptomic approaches and strikingly observed that p athogen infection on resistant age fruit (or ARR expressing fruit) is largely inhibited by 8 - 10 hpi. Additionally, using weighted gene co - express ion analysis, a strong early response to infection was observed uniquely in resistant - aged fruit. Using a combi nation of differential expression analyses and module membership we observed an early upregulation of several genes in resistant - age fruit. For e xample, transcription factors such as WRKY33 are expressed and their downstream targets including ethylene synt hesis genes and other defense genes are activated. Together these results suggested that a developmentally regulated ability to respond to pathog en sensing was crucial for resistance. This induced response, however, could also function alongside the accumu lated chemical defenses observed in Chapter II. While the above experiments identified several genes that are putatively associated with ARR and shed light on a potentially induced mechanism, genetic analyses are necessary to directly link genomic loci l inked to the trait. To this end we chose to employ a bulk segregant analysis approach. However, as no easy - to - use computati onal tools were available for these 79 analyses, I developed a software package, QTLseqr to use in our own experiments, but also as a to ol for the plant breeding and genetics community. A manuscript describing and testing the software was published in the jou rnal Plant Genome (Mansfeld and Grumet, 2018) . QTLseqr has already aided researchers working in a diversity of crops for agricultural traits as well as in genetic studies of other organisms such as Drosophila and yeast. In conclusion, the research herein suggests the ARR to P. capsici in cucumber is conferred by developmentally acquired induced responses, potentially combined with developmentally accumulated chemical defenses. Th ese induced responses are activated within 2 - 4 hpi in resistant - aged fruit, suggesting that some mechanism of defense signa l transduction must be in place prior to contact by the pathogen. More research is necessary to further elucidate the genes and casua l variants that confer ARR. Future directions We have utilized several approaches to identify genes and loci associated wi th ARR, and while several candidate genes have been identified, more work is required to confirm the function of these candidates in conferring resistance. I further suggest using the QTLseqr tool to perform bulk segregant analysis. Segregating populations derived from ARR expressing and non - expressing parents should be screened for resistance to P. capsici and resistant and susceptible bulks should be selected for the analysis. We have started in screening such populations and segregation data suggested a major gene component. Another approach to further narrow the candidate list, would be to test express ion of these genes throughout fruit development, in a set of resistant and susceptible doubled haploid (DH) lines. This will further verify if developmental changes in expression of these candidates is directly correlated with resistance. Though several tr anscriptional analysis methods can serve 80 this purpose, the nCounter method (NanoString Technologies) could prove to be accurate and affordable for testing this handful of genes in several genotypes and developmental stages. I expect that genes associated w ith ARR will show consistent d ifferential expression in the resistant DH lines compared to the susceptible lines. Expression patterns of resistant DH lines A polymorphism between t he two parents could result in a change in expression which is associated with the trait, as described above. An alternate explanation for the phenotypic - synonymous mutation in a gene conferring resistance. A regu latory or signaling factor could be altered such that its function has changed, but its expression levels between the two alleles remains the same. In such a case, our current transcriptional comparisons would be insufficient in detecting the true causal g ene. A complementary approach would be to compare all detected polymorphisms within an identified locus and scanning for amino acid alterations. We can utilize other genotypes, differing for ARR, to test if these polymorphisms are congruent with the respec tive ARR phenotype. Concurrently, I propose fine mapping of any identified loci using larger mapping population developed from backcrossing resistant DH lines with the ARR - a population will narrow down the region s of interest and he lp verify if any of the proposed candidates are still valid. I have also started developing an F 2:3 population which could also be used for this purpose. Tissue has already been collected from the F 2 lines for further DNA base d analyses. NGS based BSA allo ws us to detect variants between the parents in the population, these can be used as markers (either InDel or KASP (Semagn et al., 2014) type assays, for 81 example). These segre gating populations and markers can be used to identify recombinant lines within that locus to fine map the trait. Once further narrowing of candidates has been accomplished, several functional genomics approaches should be employed to further understand the function and regulatory me chanisms controlling these genes. While genetic engineering approaches are limited in cucumber, there are a few reports where such methods, as well as genome editing using CRISPR/Cas9, have been used successfully (Sherma n et al., 2016; Hu et al., 2017) . Another option would be to utilize a TILLING population developed in cucumber, if mutations in this region exist in that population (Fraenkel et al., 2014) . Finally, melon ( Cucumis melo ), a close relative of cucu mber is more amenable to transformation methods and several transgenic melon lines have been produced in our lab . Many cucumber genomic regions are syntenic with me lon, t hus several of the identified genes should contain homologues in melon which might be available for genetic manipulation. Apart from identifying the developmentally regulated factor that confers ARR, further research into the response mechanisms sh ould b e pursued. Using our network analyses, we identified several early induced pathways associated with defense in resistant - aged fruit. More research should be pursued in identifying downstream targets of WRKY33 and WRKY75 in cucumber. Chromatin immunop recipi tation followed by sequencing could be used to identify targets of WRKY75, and to confirm if in fact WRKY33 directly activates transcription of ethylene synthesis genes in response to P. capsici infection in cucumber. Ethylene has been shown to be im portan t in ARR of Nicotiana benthamiana to P. infestans (Shibata et al., 2010) and we showed that key ethylene synthesis genes were upregulated in resistant - aged fruit in response to inoculation. Thus, the importance of the ethylene signaling pathway in cucumber 82 should be examined in early infec tion. An easy assay would be to chemically block ethylene receptors by exogenous silver nitrate application on the fruit surface, and to subsequently inoculate resistant fruit. 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