INSIGHTS OF CHEMICAL, CULTURAL AND GENETIC EXPLORATION FOR SOYBEAN SUDDEN DEATH SYNDROME MANAGEMENT, AND FUSARIUM VIRGULIFORME By Amy Marie Baetsen-Young in partial fulfillment of the requirements Plant Pathology – Doctor of Philosophy A DISSERTATION Submitted to Michigan State University for the degree of 2019 ABSTRACT By Amy Marie Baetsen-Young INSIGHTS OF CHEMICAL, CULTURAL AND GENETIC EXPLORATION FOR SOYBEAN SUDDEN DEATH SYNDROME MANAGEMENT, AND FUSARIUM VIRGULIFORME Soybean sudden death syndrome, caused by Fusarium virguliforme, is a key limitation in reaching soybean (Glycine max) yield potential, stemming from limited disease management through cultural practices and partial host resistance. The research within this thesis reveals the economic potential of fungicide seed treatment SDS fluopyram to alleviate yield loss, provides insights into field management of F. virguliforme and highlights transcriptomic plasticity of diverse host-fungal interactions. Previously, farm level studies have found the fungicide seed treatment of fluopyram profitable, yet the benefit across an aggregate level of soybean production at risk to SDS yield loss is unknown. To estimate economic benefits of fluopyram adoption in SDS at risk acres, in the light of U.S public research and outreach costs, an economic surplus approach was applied to calculate ex ante net benefits from 2018 to 2032. Through this framework of fluopyram adoption for alleviation of SDS associated yield losses, we estimated a net benefit of $5,829 million over 15 years, considering public seed treatment research costs from 2014 to present and future extension communication. While chemical seed treatments aid disease management of SDS, the ability of this pathogen to colonize asymptomatic hosts may increase the prevalence of F. virguliforme. Thus, the impact of cultural tactics upon F. virguliforme colonization of an asymptomatic host, and the ability of this colonization to alter subsequent SDS symptoms when rotated to soybean were explored. The exploration of tillage, and residue management across four U.S. states provided clarity to variable reports, revealing that no-tillage inconsistently enhances F. virguliforme colonization of corn and soybean roots, while corn residue did not alter pathogen colonization. Alternatively, an asymptomatic host provides a unique application to discover genetic factors facilitating soybean sudden death syndrome through investigation host-fungal interactions. Exploring this plant disease through a comparative orthologous mRNA-Seq on soybean and corn hosts under colonization of F. virguliforme uncovered transcriptional responses enabling a robust defense response in corn, and delayed immune induction within soybean permitting pathogenic colonization and susceptibility. To colonize both hosts, F. virguliforme exhibited a massive transcriptional rewiring of an infection program. Transcriptomic responses suggest, F. virguliforme is less suited for colonization of monocots by delayed colonization, and lower induction of CAZymes and effector proteins. Integration of the data generated through the mRNA-Seq experiments, including a micro-like RNA-Seq analysis of soybean host during colonization by F. virguliforme revealed an intimate communication between the plant and fungal pathogen; we posit that a micro-like RNA cross-talk potentially regulates host susceptibility. Overall, several hypotheses were generated surrounding hemibiotrophic enhancement of host senescence, and fungal transcriptomic reprogramming, which will deliver ecological plasticity transparency upon a currently difficult and enigmatic syndrome. through I would like to dedicate this dissertation to my husband Stephen Young, who has encouraged and supported me to chase my dreams no matter the distance. iv ACKNOWLEDGMENTS I would like to acknowledge my advisor, Dr. Brad Day for providing me with academic freedom and guidance throughout the last four years. I would also like to thank my committee members, Dr. Shinhan Shiu, Dr. Scott Swinton, and Dr. Martin Chilvers, for their support and advice throughout my dissertation research. I would also thank Dr. Jennifer Wai for teaching me the ways of genomics and Dr. Kevin Childs for bioinformatics support and collaboration. Also, I must thank Adam Byrne and Janette Jacobs for collaboration and support during my field studies. Finally, I would like to thank Drs. Daren Muller, Kiersten Wise, Yuba Kandel, Grazieli Araldi Da Silva, Damon Smith for collaboration, support and incorporation in to the field crops pathology community. I would like to thank Michigan State University for supporting me through a Plant Science Fellowship, C. S. Mott Foundation and for research support through Project GREEN. I would also like to thank the Plant Resilience Institute for in part funding this research. I would like to thank all of my lab mates who have supported me throughout the years including Alex Corrion, Yi-Ju Lu, Pai Li, Miranda Haus, Saroopa Samaradivakara, Masaki Shimono, and Judy Chen. Finally, I would also like to thank the members of the fourth floor Molecular Plant Sciences, for entertaining my thoughts and providing a delightful work atmosphere. v TABLE OF CONTENTS Economic Impact of Fluopyram-Amended Seed Treatments to Reduce Soybean Yield LIST OF TABLES………………..………………..………………..………………..………………..……………….…… ix LIST OF FIGURES………………..………………..………………..………………..………………..……………………. x CHAPTER 1 ………………..………………..………………..………………..………………..………………..…………..1 Literature Review...................................................................................................................................................1 Introduction..................................................................................................................................................2 Soybean Sudden Death Syndrome………………………………………………………..........................2 SDS Management………………………………….........................................................................................4 Chemical Management……………………………………………………………………………….5 Cultural Management………………………………………………………………………………...6 Genetic Management………………………………………………………………………………….7 Conclusions………………………………………………………………………………………………………………8 REFERENCES……………………………………………………………………………………………………..12 CHAPTER 2 ………………..………………..………………..………………..………………..………………..…....18 Loss Associated with Sudden Death……………….…………………………..………………………………18 Abstract………………..………………..………………..………………..………………..………………..…….19 Introduction………………..………………..………………..………………..………………..……………….20 Materials and Methods……..………………..………………..………………..………………..…………..23 Economic Conceptual Framework………………..…………………………………………..23 Management Induced Yield Change Parameterization……………………………….25 Determining Changes in Economic Surplus……………………………………………….26 Data to Evaluate Economic Surplus and Net Present Value ……………………….28 Results………………..………………..………………..………………..………………..……………………….33 Sensitivity Analysis ………………..………………..………………..………………..…………...35 Discussion………………..………………..………………..………………..………………..…………………..38 Conclusions…………………………………………………………………………………………………………….41 Acknowledgements………………..………………..………………..………………..………………..…….41 Author Contributions………………..………………..………………..………………..……………………42 REFERENCES ………………..………………..………………..………………..………………..……………..50 CHAPTER 3………………..………………..………………..………………..………………..………………..…………56 Residue Management of Soybean Sudden Death Syndrome..……..……………………………...56 Abstract………………..………………..………………..………………..………………..………………..…….57 Introduction………………..………………..………………..………………..………………..…………….....58 Materials and Methods………………..………………..………………..………………..…………………62 Inoculum Preparation of Fusarium virguliforme………………..………………………62 Corn Residue Establishment………………..………………..………………………..………..62 Field Site Design………..………………..………………..………………..………………..………63 Implications of Fusarium virguliforme Temporal Colonization of Corn: Tillage and vi Divergence in Transcriptomic Response of Symptomatic and Asymptotic Hosts Following Fusarium virguliforme Inoculation Highlights Senescence Triggered Root Sampling and Processing………………..………………..………………..……………..64 DNA Extraction………………..………………..…………………………………………………….66 Fusarium virguliforme DNA Quantification………………..………………..………….....66 Data Analysis………………..………………..………………….....................................................67 Results ………………..………………..………………..………………..……………………............................67 Temporal Dynamics of Fusarium virguliforme Colonization of Corn Roots.…67 Temporal Dynamics of Fusarium virguliforme Colonization of Soybean Roots Under Cultural Management.………………..………………………………………………….70 Discussion…………………………………………………………………………………………………………72 Infield Detection of Fusarium virguliforme in Corn Roots.………………..………...72 Temporal Dynamics of Fusarium virguliforme Root Colonization……………….73 Fusarium virguliforme Temporal Dynamics in Soybean Roots……………..……..75 Impacts of Cultural Practices on F. virguliforme Root Colonization……..……...76 Conclusions………………………………………………………………………………………………………..76 Acknowledgements……………………………………………………………………………………………77 Author Contributions………………………………………………………………………………………….78 REFERENCES ………………..………………..………………..………………..………………..……………..88 CHAPTER 4………………..………………..………………..………………..………………..………………..…………94 Susceptibility………………………………………………………………………………………………………………94 Abstract………………..………………..………………..………………..………………..………………..…….95 Introduction………………..………………..………………..………………..…………………………………96 Materials and Methods………………..………………..………………..………………..………………….99 Plant and Fusarium virguliforme Assay ………………..………………..…………………..99 Fungal Colonization Analysis………………..………………..……………………………….100 DNA Extraction and Real Time PCR for Fusarium virguliforme…………………..101 RNA Extraction………..………………..…………………………………………………………...101 Library Preparation and Sequencing………………………………………………………102 mRNA-Sequencing Processing and Differential Analysis………………………….102 Differential Gene Co-expression Network Analysis…………………………………..103 Identification of Orthologous Genes………………………………………………………..104 Differential Analysis of Orthogroups……………………………………………………….105 Gene Ontology Enrichment Analysis………………………………………………………..105 Orthologous Transcription Factor Analysis……………………………………………..106 Results……………..………………..………………..………………..………………..………………………..106 Fusarium virguliforme Growth is Similar on Symptomatic and Asymptomatic Hosts………..………………..………………..………………..………………..……………………..106 Temporal Expression of Defense Induced Genes in Corn or Soybean…………108 Orthologous Host Pathways Reveal Different Patterns of Induced Defense Responses………..………………..………………..…………………………………………………113 Non-orthologous Defense Processes within Soybean and Corn…………………114 Constitutive Expression of Corn Genes within Orthologous Defense…………116 Divergence of Defense Expression Patterns Across Orthologous Transcription Factors………..………………..……………………………………………………………………….117 vii Fusarium virguliforme Transcriptional Plasticity Revealed by Diverged Host Discussion………………………………………………………………………………………………………..120 Acknowledgments……………………………………………………………………………………………125 Author Contributions………………………………………………………………………………………..125 REFERENCES ………………..………………..………………..………………..………………..…………...147 CHAPTER 5………………..………………..………………..………………..………………..………………..……….156 Colonization……………………………………………………………………………………………………………...156 Abstract………………..………………..………………..………………..………………..………………..…..157 Introduction…………………………………………………………………………………………………….158 Materials and Methods………………..………………..………………..………………..………………..161 Genome Sequencing, Assembly, and Annotation of Fusarium virguliforme…161 Comparative Genomics with Fusarium virguliforme…………………………………163 Plant and Fusarium virguliforme Assay……………………………………………………164 Fungal Colonization Analysis………………………………………………………………….165 DNA extraction and Real Time PCR for Fusarium virguliforme………………….165 RNA Extraction……………………………………………………………………………………...166 Library Preparation and Sequencing………………………………………………………166 mRNA-Sequencing Processing and Differential Analysis………………………….167 Gene Co-expression Network Analysis…………………………………………………….168 Gene Ontology Enrichment Analysis……………………………………………………….168 Small RNA Read Processing and Identification…………………………………………169 Target Prediction and Differential Accumulation of miRNAs…………………….169 Results……………..………………..………………..………………..………………..………………………..170 Generation of a Long Read Fusarium virguliforme Reference Genome……...170 Fungal Growth and Development Produced Differing Plant Root Phenotypes……………………………………………………………………………………………172 Host-induced Gene Expression Profiles in Fusarium virguliforme……………..174 Temporal Divergence of F. virguliforme in planta Gene Co-expression upon Host Colonization…………………………………………………………………………………..177 Host-specific Gene Expression Patterns during Root Colonization……………181 Regulation of Soybean Defenses by milRNAs……………………………………………184 Discussion………………………………………………………………………………………………………..186 Acknowledgments……………………………………………………………………………………………191 Author Contributions………………………………………………………………………………………..191 REFERENCES ………………..………………..………………..………………..………………..…………...212 viii Table 1. United States and rest of world supply and demand elasticities for soybean.………43 Table 2. Variable costs associated with fluopyram amended seed treatments...………………44 Table 3. Cost associated with fluopyram seed treatments and communication of research.………………………………………………………………………………………………………………………45 Table 4. Sensitivity analysis of economic impacts to U.S. soybean production for fluopyram SDS management, from 2018-2032 (in millions U.S. $).………………..…………………………………46 Table 5. Field trial location and management in Iowa, Indiana, Michigan, and Wisconsin during 2015 to 2017……………………………………………………………………………………………………..79 Table 6. Experimental treatments and crop rotation schedule from 2016 to 2017…………...80 Table 7. Temporal dynamics of Fusarium virguliforme colonization of corn roots in Michigan, Wisconsin, Indiana and Iowa during the 2016 and 2017 field season under tillage and residue management.………………..………………..………………..……………………………………………………………81 Table 8. Temporal dynamics of Fusarium virguliforme colonization of corn radical roots in Michigan during the 2016 and 2017 field season under tillage and residue management.….82 Table 9. Temporal dynamics of Fusarium virguliforme colonization of soybean tap roots in tillage Michigan during and residue.………………..………………..………………………………..………………..………………..…………………83 Table 10. Significantly induced defense genes in corn ………………..………………..………………...126 Table 11. Significantly induced defense genes in soybean.………………..………………..………….127 Table 12. Gene ontology enrichment of biological processes within corn genes significantly up regulated between Fusarium virguliforme inoculated and mock inoculated treatments across time course of two weeks ………………..………………………………………………………………..128 Table 13. Gene ontology enrichment of biological processes within soybean genes significantly up regulated between Fusarium virguliforme inoculated and mock inoculated treatments across time course of two weeks…………………………………………………………………129 Table 14. Gene ontology enrichment of biological processes within soybean orthogroups of uniquely upregulated significantly between Fusarium virguliforme inoculated and mock inoculated treatments across time course of two weeks………………………………………………..131 season under the 2016 and 2017 field LIST OF TABLES ix Table 15. Gene ontology enrichment of biological processes within soybean and corn orthogroups of shared in upregulated between Fusarium virguliforme inoculated and mock inoculated treatments across time course of two weeks….……………………………………………133 Table 16. Genome assembly metrics of Fusarium virguliforme versions 1 and 2…………….192 Table 17. GO enrichment of genes only contained in Fv_v2 genome……………………………….193 Table 18. Number of quality trimmed reads uniquely aligning to the Fusarium virguliforme v2 genome…………………………………………………………………………………………………………………194 Table 19. Host specific differential gene expression across timepoints of Fusarium virguliforme colonization of soybean or corn……………………………………………………………….195 Table 20. Host specific differential gene expression within timepoints of Fusarium virguliforme colonization of soybean or corn at log2 fold change > 1…………………………….196 Table 21. Number of quality trimmed milRNA reads uniquely aligning to the Fusarium virguliforme v2 genome.……………………………………………………………………………………………...197 x LIST OF FIGURES Figure 1. Distribution of soybean sudden death syndrome, since first report in Arkansas in 1971.……………………………………………………………………………………………………………………………10 Figure 2. Fusarium virguliforme disease cycle………………………………………………………………….11 Figure 3. Proposed soybean supply shifts under fluopyram seed treatment.………………….….47 Figure 4. Adoption path of soybean seed treatments …………..………………..………………………...48 Figure 5. Soybean sudden death syndrome incidence.………………………………………..……………49 Figure 6. Corn radical root colonization by Fusarium virguliforme under two tillage treatments over three timepoints during the 2016 and 2017 field season.………………………. 84 Figure 7. Corn radical root colonization by Fusarium virguliforme under two corn residue treatments over three timepoints during the 2016 and 2017 field season…………………….….85 Figure 8. Corn radical root colonization temporal dynamics by Fusarium virguliforme under tillage and residue management in Michigan, during the 2016 and 2017 field season over six time points. ………………………………………………………………………………………………………………….86 Figure 9. Soybean lower tap and lateral root colonization temporal dynamics by Fusarium virguliforme under tillage and residue management in Michigan, during the 2016 and 2017 field season over six time points…………………………………………...………………………………………..87 Figure 10. Correlation plots of differentially induced orthogroups between soybean and corn……………………………………………………………………………………………………………………………135 Figure 11. Fusarium virguliforme root inoculation time course phenotypes of soybean and corn..………………..………………..………………..………………..………………..…………………………………..136 Figure 12. Plant growth and development over infection time course…………………………….137 Figure 13. Biological reproducibility of samples from different time courses.…………………138 Figure 14. Temporal expression patterns of defense response genes in corn and soybean.………………..………………..…………………………………………………………………………………..139 Figure 15. Module hierarchy of modules hubs containing defense related genes.……………140 Figure 16. Orthologous host processes reveal different patterns of induced defense response...................................................................................................................................................................141 xi to corn aid in immune responses Figure 17. Processes unique to Fusarium virguliforme.……………………………………………………………………………………………………………….142 Figure 18. Expression of orthologous genes in defense relevant processes.…………………….143 Figure 19. Conservation of innate defense gene expression preceding inoculation with Fusarium virguliforme..………………..………………………………………………………………………………144 Figure 20. Divergence of defense expression patterns of orthologous transcription factors.………..………………..…………………………………………………………………………………………….145 Figure 21. Genome assembly of Fusarium virguliforme.………………………………………………….198 Figure 22. Syntenic regions between genome versions of Fusarium virguliforme.……………199 Figure 23. Fusarium virguliforme growth chamber assay of soybean and corn. ……………….200 Figure 24. Biological consistency of samples from different time courses....…………………….201 Figure 25. Samples of fungal plant colonization cluster by host.…………..………………………….202 Figure 26. Temporal expression patterns of Fusarium virguliforme response genes within soybean and corn hosts in comparison to germinating macroconidia……………………………..203 Figure 27. Distinct gene co-expression groups of host induced Fusarium virguliforme response…………………………………………………………………………………………………………………….204 Figure 28. Weighted gene co-expression network modules from F. virguliforme temporal colonization of corn…………………………………………………………………………………………………….205 Figure 29. Symptomatic and asymptomatic hosts uncover Fusarium virguliforme transcriptome plasticity…………….………………………………………………………………………………...206 Figure 30. Weighted gene co-expression network modules from F. virguliforme temporal colonization of soybean………………………………………………………………………………………………207 Figure 31. Host unique genes induced within Fusarium virguliforme highlight disease development………………………………………………………………………………………………………………208 Figure 32. Temporal expression patterns of Fusarium virguliforme candidate effector genes within soybean and corn hosts…………………………………………………………………………………….209 Figure 33. Temporal expression patterns of Fusarium virguliforme carbohydrate active enzyme related genes within soybean and corn hosts…………………………………………………..210 xii Figure 34. Expression of micro-like RNAs (milRNAs)in Fusarium virguliforme colonization of soybean host…………………………………………………………………………………………………………..211 xiii CHAPTER 1 Literature Review 1 Soybean is a leading annual crop, accounting for 35% of global harvestable acreage planted for oil crops (Thoenes, 2015). The sub-products of meal and oil from this crop are highly valuable, in large part stemming from wide use for food consumption and animal feed. Soybean production is primarily highly geographically concentrated in Argentina, Brazil, China, and the United States (Thoenes, 2015). For example, in the U.S. in 2018, soybean was the most planted crop, at 88 million acres planted (NASS, 2018). Within increased acreage, comes the potential for increased exposure to diseases to incur yield losses. Soybean cyst nematode (Heterodera glycines) was the most destructive pathogen from 2010 to 2014, causing between 90 to 112 million bushels of soybean to be lost annually (Allen, 2017). The top three fungal diseases within soybean from 2010-2014 included seedling diseases of Rhizoctonia spp., Fusarium spp., Phomopsis spp. and, or Pythium spp., an Oomycete; soybean sudden death syndrome caused by Fusarium virguliforme; and charcoal rot caused by Macrophomina phaseolina caused an average of 134.2 million blushes of soybean production lost annually in the U.S. (Allen et al., 2017). These yield losses highlight the need for successful management strategies to enable soybean production to reach maximum yield potential. Soybean sudden death syndrome (SDS) is a devastating disease with an estimated impact of >$330 million dollars in the U. S. (Koenning et al., 2010). Since the first report in the 1970’s Soybean Sudden Death Syndrome Introduction 2 in Arkansas, soybean SDS has quickly spread to nearly all soybean producing states, including Michigan in 2009 (Figure 1). In North America, soybean SDS is caused by the soil borne fungus Fusarium virguliforme and F. brasiliense, while in South America, an additional two closely related species (e.g., F. tucumaniae, and F. cuneirostrum) have also been identified as possible casual agents within the F. solani species complex 2 (FSSC2) (O’Donnell et al., 2010; Wang et al., 2019b). The F. solani species complex (FSSC) has been the most comprehensively studied group of plant pathogens (Coleman, 2015). Within the FSSC, over 60 phylogenetically distinct species have been identified and are thought to arose over the last 55 million years (Ma et al., 2013) which have been further characterized into formae speciales (f. sp.) by host specificity (Coleman, 2015). In the United States, F. virguliforme is an asexual hemibiotrophic fungus, infesting soil and crop residues. This fungus infects soybean roots during the early plant developmental stages (Jin et al., 1996) colonizing the xylem tissues, where the pathogen secretes phytotoxins, then degrade which initiating programmed cell death of the soybean leaf through an accumulation of oxygen radicals (Brar et al., 2011). The host response creates a characteristic symptom of interveinal leaf scorch associated with SDS (Figure 2). Root infection and leaf scorch leads to a reduction of overall plant biomass, flowering and pod loss, and thus yield. Yield losses of up to 80% have been reported in highly infested fields, but typically yield losses of 5 to 15% are observed (Hershman et al., 1990; Roy,1997). As the plant matures below ground, F. virguliforme will degrade soybean root tissue and cause tap and lateral root necrosis, decreasing root biomass. As the plant senesceses, F. virguliforme may produce characteristically blue asexual rubisco-1,5-bisphosphate carboxylase oxygenase, 3 spore mass (sporodochia) externally on soybean roots, which enables re-infestation of the soil (Roy, 1997). Development of soybean SDS is affected by environmental and biotic factors. Environmental conditions inducing high disease intensity are cool temperatures and wet compacted soil during planting, and increased soil moisture during early reproductive growth stages (Rupe, 1989; Roy et al., 1997; Scherm and Yang, 1999; Chong et al., 2005). These conditions are thought to favor fungal infection, while slowing plant growth and development. Furthermore, the interaction of soybean cyst nematode (Heterodera glycines) with F. virguliforme has been observed to synergistically promote SDS severity and incidence (Gao et al., 2006; Westphal et al., 2014), yet the underlying mechanism enhancing this interaction remains unknown. genetic strategies. At present, no single strategy can fully manage SDS. Partial genetic resistance of soybean cultivars has been developed through quantitative trait loci. However, if disease pressure is high in soybean fields, the partial resistance is broken (Njiti et al., 1996). Tillage is thought to reduce symptom development by decreasing soil moisture and increasing soil temperature (Roy et al., 1997; Vick et al., 2003). An additional strategy for field crop disease management is crop rotation, which reduces pathogen population within soils by planting non-hosts for multiple years (Agrios, 2004). Previous research on corn (Zea mays L.), soybean, and wheat (Triticum aestivum L.) rotation was found to reduce SDS yield losses SDS Management 4 when compared to continuous soybean (Von Qualen et al., 1989; Roy et al., 1997). However, more recent studies have shown corn and other crop rotations have not reduced SDS severity (Hershman, 2003; Xing and Westphal, 2009; Leandro et al., 2018). Therefore, to prevent the development and subsequent yield loss, an integrated pest management approach to control SDS through chemical, cultural, and genetic strategies is needed. Chemical Management Because cultural approaches and limited genetic resistance have not consistently mitigated SDS associated yield loss, a fungicidal strategy was recently explored. As initial infection occurs below ground, seed treatment application of SDS-specific fungicide (i.e., fluopyram) was developed; fluopyram targets succinate dehydrogenase (SDH) – complex II of the mitochondrial respiration chain – associated with the mitochondrial electron transport (Keon et al., 1991; Fraaije et al., 2012). In multiple studies, fluopyram was found to reduce soybean foliar symptoms and improve yields under SDS pressure in comparison to commercial base seed treatments of insecticide, fungicide and nematicide (Kandel et al., 2016; Gaspar et al., 2017; Kandel et al., 2018b; Kandel et al., 2018a; Kandel et al., 2019a). These reported yield gains from fluopyram have ranged from 2.8% to 12.0% in fields with a historical SDS pressure (Kandel et al., 2016; Gaspar et al., 2017; Kandel et al., 2018b; Kandel et al., 2018a; Kandel et al., 2019a). While several additional products containing fungicidal activity are registered for foliar application to manage SDS development, recent research has revealed that only fluopyram provided a reduction of root rot and foliar symptoms when compared commercial base for currently registered foliar products (Kandel et al., 2019a). 5 Cultural Management In annual row crop production systems, such as soybean, the application of several cultural practices can reduce disease development. Tillage of soil was thought to reduce root infection by F. virguliforme from decreasing soil moisture and increasing soil temperature through increased porosity of the soil (Roy et al., 1997; Vick et al., 2003). Yet, recent exploration through long-term tillage and no-till management revealed that alteration of these practices did not impact soybean root and foliar SDS disease severity or yield loss (Kandel et al., 2019a). Additionally, infected soybean roots from no-till fields supported summer level CFUs of F. virguliforme through the fallow season, whereas soil did not harbor detectable CFUs of F. virguliforme to spring (Luo et al., 2001), suggesting residue, especially in no-till fields may promote disease development. Through inoculated field and greenhouse experiments Navi and Yang (2016) explored this hypothesis that plant residue contributes to F. virguliforme populations and found that corn (Zea mays) kernel residue increases soil CFUs of F. virguliforme at three- and 12-months post treatment establishment. Corn residue colonization by F. virguliforme, also indicates corn maybe able to support F. virguliforme, and therefore may support this soybean fungal pathogen through crop rotations of corn to soybean. Field studies spanning three decades of research have not resolved if corn is an asymptomatic host of F. virguliforme (Von Qualen et al., 1989; Roy et al., 1997 Hershman, 2003; Xing and Westphal, 2009; Leandro et al., 2018). Similarily, greenhouse studies have found root colonization by F. virguliforme, however, contrasting conclusions regarding the asymptomatic host status of corn (Kolander et al., 2012; Kobayashi-Leonel et al., 2017). Therefore, inconsistencies persist within the research community regarding whether corn is 6 an asymptomatic host of F. virguliforme and if crop rotation of corn and soybean enhances or reduces the severity of SDS. Genetic Management Host resistance to F. virguliforme would prevent pathogen colonization and SDS symptom development. However, over 80 quantitative trait loci (QTL) and many single nucleotide polymorphisms (SNPs) have been discovered in association with phenotypic resistance to SDS, suggesting genetic resistance to SDS highly complex (Chang et al., 2018). The diverse methodology applied to decipher QTL and SNPs has further complicated resolution of genetic loci, as several techniques apply laboratory screening germplasm in a manner distinct from field trials. In total, these methods often do not take into consideration the biotic and abiotic factors enabling disease development, which in turn promote the discovery of loci tied to foliar resistance, and not root resistance to F. virguliforme (Chang et al., 2018 and Kazi et al., 2008). Although these traits are tied to reducing above ground symptom development, breeding genetically resistant varieties have produced soybeans with partial resistance to SDS (Kandel et al., 2016; Kandel et al., 2017). More recently, efforts have focused upon root colonization by F. virguliforme and root symptom development, as field studies have revealed dis-correlation between root and foliar symptomology development (Wang et al., 2019). Phenotyping below ground root symptoms across a soybean diversity panel discovered 13 putative SDS resistance genes associated with foliar and root rot resistance for SDS (Swaminathan et al., 2018). Future introgression of these loci may assist 7 with developing cultivars with greater resistance to root and foliar components of SDS than current commercial cultivars. Exploration of transcriptomic expression tied to disease development and soybean susceptibility have uncovered potential loci that are modulated during infection and root necrosis development, when comparing F. virguliforme inoculated and mock inoculated single cultivars (Ngaki et al., 2016). Transgenic overexpression of an ankyrin repeat containing proteins discovered through this transcriptomic approach elevated SDS resistance. This suggests comparative transcriptomics during infection by F. virguliforme may yield novel genes tied to susceptibility, which can be employed resistance. Fusarium virguliforme is a causal agent for soybean sudden death syndrome (SDS), a destructive soil-borne fungal disease. Since the first report in the 1970’s in Arkansas, soybean SDS has quickly spread to nearly all soybean producing states, highlighting the need for integrated disease management to mitigate yield losses. Through Chapter 2 of this dissertation, I developed a framework to evaluate economic returns of public research and outreach investment surrounding grower adoption of a F. virguliforme seed treatment fungicide: fluopyram. This framework enabled parameterization of economic SDS incidence and grower adoption of fluopyram at national level, to estimate economic impacts of SDS over 15 years and the alleviation of SDS associated yield losses by fluopyram, which has only been researched at the farm gate level prior. 8 Conclusions Multiple approaches are likely necessary to manage this complex disease. To this end, I also explored the integration of tillage and corn residue management to alter F. virguliforme colonization of soybean roots and an asymptomatic host of corn in Chapter 3. These field studies across the North Central region of the U.S. revealed that corn is an asymptomatic host of F. virguliforme, yet this host’s residue does not alter F. virguliforme root colonization of either soybean or corn. Additionally, manipulating the soil environment through tillage or no-till did not consistently change root colonization of soybean and corn, suggesting implications of tillage management upon F. virguliforme root colonization is soil- or site- specific. The development of SDS starts within the genetic capacity of the host to be susceptible, thus a resistant host would prevent F. virguliforme infection and lessen the dependency up on fungicide chemistries and cultural management. Through a comparative transcriptomic approach of a symptomatic host and asymptomatic host under F. virguliforme colonization in Chapter 4, I revealed transcriptional responses enabling a robust defense response in corn and delayed immune induction within soybean permitting pathogenic colonization and susceptibility. Additionally, as described in Chapter 5, evaluation of the fungal transcriptomic response to two diverged hosts identified that F. virguliforme can manipulate its genetic repertoire to enable colonization of hosts with distinct phenotypic outcomes. 9 Year Sudden Death Syndrome Reported Figure 1. Distribution of soybean sudden death syndrome, since first report in Arkansas in 1971. Transition in color denotes decade change. 1980 1990 2000 2010 Sudden Death Syndrom Reported 1980 1990 2000 2010 10 Figure 2. Fusarium virguliforme disease cycle. 1. Macroconidia, microconidia and chlamydospores infested within soil, germinate and colonize roots of soybean and corn, causing rot and necrosis in soybean, but is non-symptomatic corn. 2. Colonized corn roots may enable pathogen persistence in the soil. 3. F. virguliforme releases toxins within the soybean xylem causing foliar chlorosis and necrosis. 4. 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Field Crops Res. 112, 107-117. 17 CHAPTER 2 Loss Associated with Sudden Death Syndrome This chapter is unpublished, with intention to submit to Plant Disease. Economic Impact of Fluopyram-Amended Seed Treatments to Reduce Soybean Yield Baetsen-Young, A.M., Chilvers, M.I., Swinton, S.M. 20XX. Economic Impact of Fluopyram- Amended Seed Treatments to Reduce Soybean Yield Loss Associated with Sudden Death Syndrome. 18 Abstract Soybean sudden death syndrome (SDS), caused by F. virguliforme, is a key limitation in reaching soybean yield potential, stemming from limited disease management through cultural practices and partial host resistance. A fungicidal seed treatment was released in 2014 with the active ingredient fluopyram and was the first chemical management strategy to reduce soybean yield loss stemming from sudden death syndrome. While farm level studies have found fluopyram profitable, we were curious if fluopyram would be beneficial nationally if targeted to soybean fields at-risk to SDS yield loss. To estimate economic benefits of fluopyram adoption in SDS at-risk acres, in the light of U.S. public research and outreach from a privately developed product, we applied an economic surplus approach, calculating ex ante net benefits from 2018 to 2032. Through this framework of logistic adoption of fluopyram for alleviation of SDS associated yield losses, we estimated a net benefit of $5,829 million over 15 years, considering the costs of public seed treatment research and future extension communication. While the sensitivity analysis indicates that overall net benefits from fluopyram adoption on SDS at-risk acres are highly dependent upon the market price of soybeans, the incidence of SDS, and the adoption path of this seed treatment, the net benefits still exceeded 1.3 billion in the worse-case scenario. 19 Soybean sudden death syndrome (SDS) is an economically damaging disease in the United States (Koenning and Wrather, 2010). Since the first report in the 1970’s in Arkansas, soybean SDS has quickly spread to nearly all soybean producing states. In North America, soybean SDS is predominantly caused by the soil borne fungus Fusarium virguliforme (O'Donnell et al., 2010). Fusarium virguliforme is an asexual hemibiotrophic fungus, infesting soil and crop residues. This fungus infects soybean roots during the early plant developmental stages (Jin et al., 1996), colonizing the xylem tissues, where the pathogen secretes the phytotoxins, initiating programmed cell death of the soybean leaf (Brar, 2012). The host response creates the characteristic symptom of interveinal leaf scorch associated with SDS. Root infection and leaf scorch leads to a reduction of overall plant biomass, flowering and pod loss, and thus yield. Yield losses of up to 80% have been reported in highly infested fields, but typically yield losses of 5 to 15% are observed (Roy et al., 1997; Hershman, 2003). Currently, no strategy can fully manage SDS. Partial genetic resistance of soybean cultivars has been developed through quantitative trait loci. However, if disease pressure is high in soybean fields, the partial resistance is broken (Njiti et al., 1996). Conditions inducing high disease pressure are thought to be cool temperatures, and wet compacted soil conditions during planting and early developmental stages (Rupe, 1989; Roy et al., 1997; Scherm and Yang, 1999; Chong et al., 2005). Tillage is thought to reduce symptom development by decreasing soil moisture and increasing soil temperature (Roy et al., 1997; Vick et al., 2003). Introduction 20 An additional strategy for field crop disease management is crop rotation, which reduces pathogen population within soils by planting non-hosts for multiple years (Agrios, 2004). Previous research on corn (Zea mays L.), soybean, and wheat (Triticum aestivum L.) rotation was found to reduce SDS yield losses when compared to continuous soybean (Von Qualen et al., 1989; Roy et al., 1997). However, more recent studies have shown corn and other crop rotations have not reduced SDS severity (Hershman, 2003; Xing and Westphal, 2009; Leandro et al., 2018). Because cultural approaches and limited genetic resistance have not consistently mitigated SDS associated yield loss, a fungicidal seed treatment was released in 2014 with the active ingredient fluopyram, commercially designated as ILeVO (BASF, Research Triangle Park, NC). Fluopyram is a succinate dehydrogenase inhibitor and was the first chemical management strategy to reduce soybean foliar symptoms and improve yields under SDS pressure in comparison to pre-existing commercial base seed treatments of insecticide, fungicide and nematicide (Kandel et al., 2016; Gaspar et al., 2017; Kandel et al., 2018b; Kandel et al., 2018a; Kandel et al., 2019a). These reported yield gains have ranged from 2.8 to 12.0% in fields with a historical SDS pressure. But the cost of planting seeds treated with fluopyram is also elevated, potentially reducing economic gains for growers. Thus, the profitability of the fluopyram seed treatment and probability of a positive return on investment have been explored, revealing an increasing probability of capturing return on investment with increasing disease pressure; however, profitability decreased with increasing seed costs (Kandel et al., 2018b) or decreasing grain sale price (Gaspar et al., 2017). 21 Previous studies have also compared costs and benefits of seed treatments of fungicide, insecticide and nematicide activity against a commercial base or untreated controls (Gaspar et al., 2017; Kandel et al., 2018b; Rossman et al., 2018). As with the exploration of fluopyram, these analyses focused on the farm level, calculating profitability and return on investment with fixed soybean prices and quantities. Interestingly, Kandel et al., assessed the impacts of fluopyram seed treatments upon soybean yield under SDS pressure across 200 field trials in 12 U.S. states (2018b). From these trials, a meta-analysis was conducted that found a 7.6% yield increase across the U.S. soybean production region. The parameterization of fluopyram yield benefits under SDS disease pressure across soybean production could enable the aggregate estimation of economic benefits to growers at-risk to SDS associated yield losses. Previous research into national economic impacts of agricultural damage agents has dealt primarily with insect pests. The ease of scouting insects enabled the development of economic thresholds for management decision making (Stern et al., 1959). Economic evaluations of aggregate returns to public research from the use of insect integrated pest management (IPM) economic thresholds for field crop growers have found favorable returns (Musser et al., 1981; Greene et al., 1985; Norton and Mullen, 1994; Song and Swinton, 2009). Yet, while an increasing proportion of agricultural research and development occurs within the private sector (Alston et al., 1998; Alston et al., 2009; Wang et al., 2013), the economic returns to public validation research have not been explored. We propose to contribute a plant disease study to evaluate economic impact of a privately developed prophylactic application of fluopyram seed treatments as a disease management tactic for SDS. 22 Economic Conceptual Framework An economic surplus approach modeled after Alston et al. (1998) was applied to determine the economic impact of SDS fluopyram research. The widespread adoption of SDS controls may cause changes in soybean quantities that affect market prices, so the management tactic was evaluated as part of U.S. national soybean production. Changes in total economic surplus were computed from effects on revenues above costs for producers (producer surplus) and the willingness to pay of consumers above the initial equilibrium price (consumer surplus). The fundamental elements expected to change economic surplus can be illustrated using supply and demand curves. As shown in Figure 1A the supply curve (ST) and demand curve (D) represent the soybean market before the management intervention. The consumer surplus represents the area above the equilibrium price (Po) and below the demand curve (1) and the producer surplus is the area below the equilibrium price (Po), but above the supply curve (2). The total economic surplus is the union of areas 1 and 2. This analysis compares the current U.S. soybean market where fluopyram is gradually being adopted to avert SDS losses to a counterfactual where fluopyram does not become available. If the fluopyram tactic was adopted in SDS affected areas this would increase supply, shifting the supply curve to the right from ST to S1FT, causing the equilibrium price to fall (P1), which would occur if the value of reduced SDS associated yield losses were greater than the Material and Methods 23 increased costs of SDS control (Figure 3B). The consumer surplus would grow from the decline in equilibrium price from Po to P1, because consumers would be paying a lower price at P1 (adding regions 3, 4, 5), and more consumers can enter the market at the lower price. The producer surplus would experience a mixed effect. It would grow from the decrease in marginal costs (region 6) and increased soybean production (region 7), but also shrink from the fall in price (loss of region 3). The amount of net change in total surplus depends on the proportionate increase in soybean production, variable input costs and the elasticities of supply and demand, which reveal how producers and consumers respond to changes in price. We estimated the benefits of the fluopyram tactic through a three-step strategy. First, as a baseline we evaluated U.S. soybean production affected by SDS without the adoption of fluopyram treated seed. To discover potential damage to soybean yield by F. virguliforme, we predict soybean production under SDS pressure without fluopyram seed treatment. We next model the adoption of fluopyram seed treatments applied to soybean production at risk of SDS. We assume that growers adopt fluopyram treated seeds as they learn about this prophylactic management strategy and begin to plant treated seeds in areas at risk for economic loss to SDS. While the use of fluopyram treated seeds will incur additional production costs, the resulting reduction of SDS associated yield loss shift the supply curve to the right. To estimate the value of potential gains to growers adopting fluopyram treated seed, we compared the quantities and prices of soybean produced with and without adoption of fluopyram treated seeds annually throughout the time horizon of the fluopyram adoption path. 24 Management Induced Yield Change Parameterization The economic impact was determined by comparing costs and benefits from the fluopyram seed treatment. The current U.S. soybean production acted as a baseline estimate of soybean market that has incorporated losses due to SDS, allowing a comparison to evaluate the impacts of SDS to soybean yield by the application of fluopyram seed treatments (NASS, 2017). To develop the counterfactual, we applied the percent yield change reported in Kandel et al., (2018b) meta-analysis of over 200 field trails in 12 states during 2013-2015, between the soybean seed treated with a commercial base plus fluopyram and soybeans treated with a commercial base alone. To isolate benefits from fluopyram, the commercial base treatment included a nematistat and insecticide (Bacillus firmus with clothianidin) and three fungicides (metalaxyl, penflufen and prothioconazole) known not to target F. virguliforme. While B. firmus is intended to account for nematistatic activities of current commercial base seed treatments, in comparison to nematistatic activity against nematodes by fluopyram, growers may receive yield benefits beyond the conventional base, which were included in this analysis. These additional benefits from fluopyram could occur by preventing a potential synergistic activity of Heterodera glycines and F. virguliforme causing elevated SDS severity (Xing and Westphal, 2006). Additionally, all field site locations had a historical presence of SDS with a diversity of disease severity that enabled characterization of yield benefits under a range of disease pressure (Kandel et al., 2018b). Also, field site locations included multiple soybean cultivars with varying levels of SDS resistance. The yield difference between the fluopyram-enhanced seed treatments and the counterfactual of soybean production with only a commercial base seed treatment was a 7.6% yield increase 25 under F. virguliforme pressure (Kandel et al., 2018b). This yield gain only applies to fields affected by SDS, and assumes no future advancement of soybean genetic resistance to SDS during the 15-year time horizon of this study. Determining Changes in Economic Surplus The precise spatial extent of SDS in the U.S. remains unclear. Specific environmental predictors of this disease remain ambiguous to growers and researchers as SDS incidence is very heterogeneous, even within an infested field (Roy et al., 1997; Allen et al., 2017). SDS is spreading and has been reported in new soybean production states over the last two decades, but the incidence of this disease is not reported. Often the impact of SDS is estimated by bushels lost based on disease surveys, feedback from university, industry, extension and farmer personnel, and personal exposure to disease severity or incidence. From these reports, percent disease loss is estimated and formulated to total bushels lost for each soybean disease based upon predicted yield before estimated losses (Crop Protection Network, 2016). Total incidence or area of soybean production with SDS present and at economic risk to yield loss across the soybean production region can be estimated from annual reports of percent yield loss (Crop Protection Network, 2016; Allen et al., 2017; United Soybean Board, 2017) divided by an average disease severity (e.g., the intensity of disease when presence) across affected soybean acres of 10.0% (Roy et al., 1997). This calcuated incidence of SDS across U.S. soybean production was determined to be at economic risk of SDS associated yield loss and assumed for gradual adoption of fluopyram treated soybean seed to alleviate SDS associated yield losses within this analysis. 26 To place an economic value on potential reduction in SDS-related yield loss from the use of fluopyram as a seed treatment, we assumed an adoption path similar to that observed for neonicotinoid soybean seed treatments, which were introduced to prophylactically manage insect pests of soybean, starting in 2004. Neonicotinoid seed treatments were rapidly adopted by 6% of U.S. growers in 2006, leading to a conservative estimate of 34% of soybean production planting neonicotinoid treated seeds in 2011 (Douglas and Tooker, 2015). Applying the key values of percent soybean area planted with neonicotinoid seed treatments in 2006 and 2011, with an upper limit 90%, revealed that neonicotinoid seed treatments were primarily adopted over a 15-year time frame (Figure 4). We applied the adoption path estimated for neonicotinoid treatments on soybean seeds to model the adoption of fluopyram seed treatments as a logistic trend, with a maximum adoption of 90% (rather than 100%, as some growers are averse to adopting new technologies (Fernandez-Cornejo et al., 1994)). Early evidence of the adoption of fluopyram amended seed treatment appears strikingly similar to that of neonicotinoid seed treatments. Fluopyram treated soybean seed were commercially available to the U.S. soybean production from 2014 to 2018. Fluopyram- treated soybean seed was planted across 3 million acres in 2016, and increased to 8.5 million acres in 2018 (personal communication, Jeremiah Mullock, BASF). To model the future adoption of fluopyram, we assumed 1) that only growers with fields at risk of SDS infestation might adopt it, and 2) that not all growers with fields infested with soybean SDS would adopt this specific seed treatment, as some are averse to new technologies (Fernandez-Cornejo et al., 1994), thus the ceiling for future adoption was selected at 90%. Fitting the observed 27 percentages of soybean area planted with fluopyram seed treatments in 2016 and 2018, with a logistical adoption function and an upper adoption limit of 90%, implied that fluopyram seed treatments would be primarily adopted over a 15-year time frame as well (Figure 4). The technologies presented in Figure 4 have similar capital inputs and benefits to growers, and the adoption area is very similar of a pest specific seed treatment. The barriers for growers to adopt fluopyram treated seed are lower than other technologies as many growers already own the equipment needed to plant the seeds and only require capital for purchasing treated seeds, along with an awareness of seed treatments as a cost-effective management tactic. Therefore, when considering barriers to adoption, we also propose to evaluate economic impacts from fluopyram treated seed adoption timeline explored over 15 years. Data to Evaluate Economic Surplus and Net Present Value To assess surplus changes in a parallel supply shift of ST to S1FT, we determined the vertical supply shift from Po to P1, denoted as K, and Z, the percent change in the equilibrium soybean price between soybean production with no adoption of fluopyram and the gradual adoption of fluopyram within SDS affected areas (Figure 3) (Alston et al., 1998; Song and Swinton, 2009). Soybean quantity changes between the scenarios of with or without fluopyram adoption were determined through the yield loss reduction equation below: !"#$% = (1−+−#)∙!′ + +∙(1−%[1−1∙2%345%])∙!′+ #∙(1−%[1−1∙0])∙!′ 28 Where Yield is the quantity soybean yield produced under each scenario. s is the low incidence of soybean area with economic damage from SDS and e is the epidemic jump in proportion during five-yearly epidemics. Y’ represents the soybean yield that is SDS free, assumed to be the USDA projected soybean quantities. The damage proportion from SDS of 7.6% is represented by d. Parameter f represents the proportional damage reduction from fluopyram, assumed to be 100%. Adopt%, is the proportion of growers adopting fluopyram in areas normally experiencing SDS economic damage. We assume 0% fluopyram adoption in epidemic areas that do not normally experience SDS damage, so damage from SDS epidemics in these areas is unmitigated. Changes to soybean quantities produced were analyzed by categorizing soybean yield into disease free proportion ((1−+−#)∙!′) and the proportion of soybean yield at-risk to SDS associated losses (+∙(1−%[1−1∙2%345%])∙!′)+ #∙(1−%[1−1∙0])∙!′. If growers adopted fluopyram treated seed, then SDS associated yield losses would decrease by 7.6% per acre within the proportion of soybean yield at-risk to SDS associated losses. The proportionate difference of soybean quantities produced by adoption of fluopyram treated seeds when compared to non-adoption of fluopyram in areas affected by SDS, represents total yield loss averted. To quantify the vertical supply shift, we adjusted four parameters that underpin changes in economic surplus: soybean and demand supply elasticities, costs of fluopyram adoption, incidence of SDS, and the projected rate at which farmers adopt fluopyram. The value of changes to surplus between the scenarios of fluopyram adoption and non-adoption was summed from 2018 to 2032 and discounted at an annual rate of 5% to determine the net present value (NPV) (Alston et al., 1998). 29 Supply and demand elasticities. Elasticities were reviewed from the literature and presented in Table 1. Since similar values for supply elasticities were recorded from several different literature sources, we chose 0.30 to represent the supply elasticity. Cost change. The change in variable input costs were accounted from variable cost differences in annual production budgets between the two management options of the commercial base seed treatment or using commercial base with a fluopyram amended seed treatment. The commercial base seed treatment costs $12 per seed unit (140,000 seeds per seed unit), versus $25 per seed unit for the commercial base seed treatment with fluopyram (Gaspar et al., 2017). The increased cost of $13/acre for fluopyram amended seed treatments was assumed to entail an additional $0.32 per acre in borrowing costs at a 5% interest rate, resulting in a total increased cost for fluopyram seed treatment of $13.32 per acre, a 2.8% increase in total production costs (Ward et al., 2018) (Table 2). Incidence of SDS in United States soybean production. The incidence of SDS in U.S. soybean production was estimated from annual yield losses from 1996 to 2015 (Allen et al., 2017), and 2011 (United Soybean Board, 2017), and soybean production data (USDA, 2019) from 1996 to 2015. Annual percent yield loss was estimated as follows and used as incidence of SDS in the U.S soybean production: !>?@@ !ABCDE@F+!>?@@ !"#$% 93++ :ℎ<=#= 30 !>?@@ !ABCDE@F+!>?@@ =G=3H(:I:)∙!>?@@|:I: Where YLoss share is the estimated proportional loss in soybean production due to SDS. Which is divided by total potential yield, measured as the total bushels harvested in the U.S. (YHarvest) plus the additional soybean bushels lost to SDS (Yloss). Proportional yield loss was utilized to estimate incidence from the following equation: The percent yield loss occurring from SDS is a function of the incidence of SDS (Prob(SDS)) and the average percent yield loss occurring when severe SDS is present (!>?@@|:I:). We used this equation to estimate incidence by dividing proportional yield loss by average disease severity of 10% across affected soybean acres (Roy et al., 1997). Holding disease severity constant, assumes incidence will reflect the variability of years with greater SDS yield loss. The incidence of SDS with an economic impact across the soybean production region was estimated from yield loss data collected annually from 1996 to 2015 (Figure 5). Over the last two decades of U.S. soybean reports, SDS incidence appeared to group at two distinct levels (Figure 5A). The first level we deemed SDS low incidence, or incidence without epidemics (below 11.90%) ranging from 1.5% in 1996 to 11.0% in 2015, increasing at a linear trend of 0.168% annually (Figure 5A). While we can estimate an annual rate of increase to a very precise rate, the accuracy of this annual increase of incidence may be much lower than calculated. Interestingly, a second group of SDS incidence was observed at levels of incidence above 11.9% (Figure 5A), which corresponded to years with epidemic levels of SDS associated yield loss (Leandro et al., 2013). SDS epidemics have been observed to cause 31 spikes in yield loss approximately every 4-5 years; the epidemics seem to correspond to decreases in annual mean temperature in the prior year (Figure 5B). The low incidence of SDS was modeled over the 15-year analysis using the low incidence values starting at 9.8% in 2018 and increasing annually at 0.168%, to a final incidence of 12.2% in 2032. Every 5 years the average incidence of the four previous epidemics was applied at 19.0%, with the first epidemic occurring in 2019, as five years had passed since the last epidemic. Adoption of fluopyram amended seeds. The adoption path of fluopyram across SDS affected soybean production was based upon the estimated logistical adoption path of fluopyram treated seed (Figure 4). We applied the logistical adoption pattern of fluopyram, seed treatments from 2014 to 2032 to model the adoption path of fluopyram seed treatments. Fluopyram has been commercially available on the market since 2014, thus the adoption rate starts in 2018 at 8.0%. Following a logistical trend, the adoption rate swiftly increases to 12.5% in 2019, and 19.2% in 2020 (Figure 4). This increasing adoption rate of fluopyram corresponds in part to public outreach activities after the research was completed in 2018. Price of soybeans. Projections of prices were taken from USDA through 2029 values (USDA, 2019). Projections were held constant at 2029 prices through 2032, mimicking trends from 2026-2029. Quantity of soybeans. The quantity of soybeans produced was taken from historical accounts of soybeans produced in the U.S. (USDA, 2019) as well as projections to 2028 from the USDA (2019) and held stable at 2029 quantities through 2032. 32 Costs from research and outreach. Previous research costs associated with fluopyram were gathered from National Soybean Checkoff Base, which occurred from 2014 to 2019 totaling $844,000 in across seven U.S. states (United Soybean Board, 2019) (Appendix 1). Future research costs were estimated from National Soybean Checkoff Base funds allocated to SDS and fluopyram research and communication in 2019 at $93,500, and which was presumed to be allocated again in 2020 (Table 3). Additionally, we assumed there was one principal investigator and one technician for each field trial in Delaware, Indiana, Iowa, Kentucky, Michigan, South Dakota, and Wisconsin that would dedicate 5% of their annual time to conducting individual trials (Table 3). These associated research costs were estimated at $17,600 per trial site after incorporating 30% fringe benefits and 50% indirect costs (Table 2). Outreach costs were estimated from 20 extension educators in each state allocating 2% of time annually from 2018 to 2032, repeated in each of the 33 soybean producing states in a similar method to Song and Swinton (Song and Swinton, 2009), at a total of $1,783,000 annually. An average salary was estimated from the 2019 reported values of Michigan extension educators’ salaries on a continuing track, after incorporating 30% fringe benefits and 50% indirect costs and applied as salary for all extension agents. The net present value of adopting fluopyram for SDS control was estimated based upon comparison of two scenarios across soybean acreage at risk of SDS with and without Results 33 fluopyram adoption. Without the prophylactic control from this fungicidal seed treatment, soybean production would have suffered an estimated loss of 645 million bushels from 2018 to 2032 stemming from SDS, representing 0.96% of projected U.S. soybean production. Adopting fluopyram treated seeds reduces SDS-related losses to U.S. soybean production by an estimated 46.4%, decreasing production losses from 645 million bushels to $299 million bushels across 2018 to 2032. The increase of the estimated 346 million bushels of soybean represents the benefit of adopting fluopyram treated seeds on SDS at-risk acreage by averting associated yield loss. Net present value analysis of this increase over 15 years comes to $5,829 million in net benefits. We applied an economic surplus approach to determine the value of fluopyram adoption, as not only do costs of production rise, but also the change in soybean output may alter market prices. Specifically, the increased soybean production entering the market from fluopyram adoption in SDS at-risk areas will cause a predicted drop in soybean prices. While the price reduction will slightly shrink producer surplus, the increase in soybean quantity produced due to fluopyram adoption will expand consumer surplus. The mechanism for this is that the decrease in soybean prices allows more consumers to enter the market and purchase soybeans at the lower price. Therefore, a shift of the supply curve to the right will expand consumer surplus from adoption of fluopyram in at-risk SDS areas, increasing the estimated benefits. 34 Sensitivity Analysis Key parameters that may affect the economic impact of fluopyram treated seeds on soybean acreage at risk of SDS includes supply elasticity, disease incidence, price change, and adoption rate. To understand the impacts of multiple variables, the sensitivity of this analysis was explored through combining all variables in either a worst case or best-case scenario (Table 4). Since similar values for supply elasticities were recorded from several different literature sources and were similar to the value we used for this analysis, we did not model changes to values of supply elasticities. As additional fungicidal seed treatment chemistries targeting SDS will be on the market starting in 2019 (Syngenta, 2019), we adjusted the adoption rate of fluopyram based upon observed adoption of a neonicotinoid seed treatment that became available to growers five years after initial neonicotinoid soybean seed treatments came on the market. For the other two key variables of SDS incidence and soybean prices, we adjusted outcomes by one trend-adjusted standard deviation up or down as appropriate for “worst” and “best” case scenarios. The level of incidence of soybean acres at economic risk to SDS in the U.S. is a primary factor to the economic benefits of fluopyram treated seeds on alleviating yield losses attributable to SDS. Currently no aggregate level of SDS incidence has been estimated across the U.S. soybean production. Therefore, we estimated this parameter based on percent annual yield loss estimates of total soybean production attributed to SDS, and an average severity of 10%. This method overlooks many local scale environmental factors, in addition to assuming a strong linear relationship between severity and incidence to generate yield loss. Also, SDS is 35 being discovered in new regions of the U.S. each growing season, thus, the rate at which incidence increase may be lower herein than actually realized. To account for uncertainties of the incidence of soybean acres at risk to SDS we calculated the trend-adjusted standard deviation at low SDS incidence (i.e. incidence below 11.9%) during 1996-2015 to be 5.8%. This standard deviation represented 77.1% change of incidence from the mean low incidence of 7.6% from 1996-2015. To represent the recorded variation of SDS low incidence we then reduced SDS low incidence 77.1% in 2018 to 2.2% for the worse-case scenario. In the best-case scenario, we increased SDS incidence by one standard deviation by 77.1% to 17.4%. While low incidence was altered in the sensitivity analysis, both scenarios still had epidemics in 2019, 2024 and 2029 with an incidence of 19%, and SDS low incidence still increased at a rate of 0.168% annually. Soybean prices are a key factor to valuation of benefits in this analysis. Prices of soybeans over thirty years from 1989-2018 have varied from a high of $15.54 per bushel in 2012 to a low of $6.29 per bushel in 2002 after converting all prices to 2018 U.S. dollars (NASS, 2019; USDA, 2019). We wanted account for price fluctuations in the soybean market, to explore impacts upon benefits generated from fluopyram seed treatment in SDS at risk areas. Therefore, we adjusted soybean prices from 1989 to 2018 for inflation by converting to 2018 U.S. dollars. The standard deviation of inflation-adjusted soybean prices was $2.43, for a coefficient of variation of 23.2% around the mean soybean price of $10.45. To account for this observed variation in soybean prices in the sensitivity analysis, we decreased soybean prices by 23.2% annually in the worst-case scenario and added 23.2% to soybean prices annually in the best-case scenario. 36 As an ex-ante analysis, we projected future adoption of fluopyram based on adoption rates from 2016 and 2018. However, additional seed treatments registered for SDS management are coming on the market in 2019 (Kandel et al., 2019b, Syngenta, 2019). To take these additional products entering the SDS seed treatment market into consideration, we reviewed the adoption path of different neonicotinoid soybean seed treatments, as neonicotinoids had a similar adoption path as fluopyram. Imidacloprid (Bayer Crop Science) and thiamethoxam (Syngenta) are both neonicotinoid seed treatments that were registered in 2004 for use on soybean and were quickly adopted (Schulz et al., 2007; Douglas and Tooker, 2015). Clothianidin (Bayer Crop Science) was registered five years later. By 2012, after just three years, this product was estimated to be planted on 1 million soybean acres, or 1.2 % of U.S. soybean area (Myers and Hill, 2014). As a parallel, five years post registration of fluopyram for SDS management, new chemistries are currently proposed to be registered as seed treatments for SDS of soybean (Syngenta, 2019). With similar timelines for registration of products, as well as similar adoption paths of the seed treatments, we applied the observed values of clothianidin to model future adoption of new SDS seed treatments that could affect fluopyram adoption. Therefore, we reduced the adoption of fluopyram by 1.2 % in 2022 in the worst-case scenario, and increased it by 1.2% in 2022 in the best-case scenario. The sensitivity analysis indicated a 77.5% decrease in worse-case and 93.9% increase in best-case scenario of net benefits, relative to the baseline analysis of $5,829 million in net benefits (Table 4). Changing variables critical to this analysis revealed that this analysis is highly sensitive to soybean prices, adoption path and incidence of SDS at risk soybean acres. 37 Discussion Soybean sudden death syndrome is a leading cause of soybean yield losses, and was ranked among the top 5 yield-reducing diseases in U.S. soybeans during 2010 to 2014 (Allen et al., 2017). Industry reports SDS incidence up to 80% of U.S. soybean acreage (Bayer Crop Science, 2017). However, only part of this area reflects “economic incidence” where soybean yield loss occurred. Based on severity reports and 20 years of yield loss data we found an economic incidence near 10% where SDS is causing yield loss. At this economic incidence rate, we find moderate economic benefits of fluopyram amended seed treatments of 5,829 million for alleviation of SDS associated yield losses. Overall, this net present value represents an estimated 1.4% of the projected discounted national gross value of soybean production over the 15 year estimation. While the estimated benefit of fluopyram is a small percentage of the gross value of this crop, this value reflects the proportion of the national soybean production with SDS incidence that would receive an economic benefit from fluopyram adoption. As our parameter assumptions here are conservative and yield losses from soil borne diseases are often underreported (Crop Protective Network, 2017), the actual economic benefits may be greater than projected here. The moderate economic benefits estimated from this frame suggests that public investments in validation research and outreach of privately developed technology are beneficial to society. Previous studies have compared adoption of management practices to alleviate yield loss from pests or diseases at an aggregate level and provide a framework to explore our findings. The concern of Asian soybean rust (Phakopsora pachyrhizi), sparked the investigation of 38 economic impacts through fungicide management applications. Johansson et al., (2006) estimated within the 2010 growing season a total of $623 million of soybean production would be lost if no treatment was applied. Additionally, exploration of economic losses stemming from soybean aphid (Aphis glycines), with and without IPM scouting management or fungicide management within 2010 as well, ranged from $274 to $698 million across soybean affected production (Song and Swinton, 2009). We found that if fluopyram were not adopted annual losses to SDS ranged from $297 to 407 million in non-epidemic years to $583 to 636 million during epidemic years. The non-epidemic annual loss estimates from SDS fall within range of soybean aphid and below Asian soybean rust. The higher impact of epidemics, greater than Asian soybean rust, originates from the nearly doubling of acres at risk to economic loss from SDS when these outbreaks occur. Previously estimated annual soybean yield losses stemming from SDS have been valued at annual soybean prices to determine economic losses. During non-epidemic years economic valuation of estimated annual yield losses stemming from SDS have varied from $29 million in 1996, to $474 million in 2016, with each non-epidemic year from 2009-2016 causing an excess of $300 million in losses (Bandara et al., 2019; Navi and Yang, 2016). Epidemic years caused greater losses, ranging from $275 to $445 million during 2004, and 2000 growing seasons, respectively, to $673 to $863 million during the 2014 and 2010 growing seasons (Bandara et al., 2019). Our estimates of annual losses to SDS fall within the range of estimations economic losses stemming from SDS during more recent growing seasons, as incidence of SDS increased from 1996-2016 to a level similar within our framework. 39 The profitability of fluopyram amended seed treatments and probability of a positive return on investment have previously been investigated at the farm level (Gaspar et al., 2017; Kandel et al., 2018b). These localized studies found that fluopyram was beneficial in sites with a history of SDS pressure, but the probability of returns to growers decreased with increasing seeds costs or decreasing soybean prices (Gaspar et al., 2017; Kandel et al., 2018b). Our sensitivity analysis reaches similar findings. In the worst case we model, if soybean prices fell by one standard deviation (23.2 %) while disease incidence decreased by one standard deviation (77.1% in 2018), and the adoption rate of fluopyram dropped by 1.2% in 2022, then the net benefits would decrease by 77.5%. By contrast, in the best case, if the rate of disease incidence rose by 77.1%, soybean prices increased by 23.2%, and the adoption rate rose by 1.2% in 2022, then this seed treatment net benefits rise by 93.990%. The adoption of fluopyram across the soybean acres at risk to SDS associated yield losses could pose environmental costs that are difficult to value. While fluopyram is active against F. virguliforme, the SDHI fungicide also has activity against other fungi (Santísima-Trinidad et al., 2018), which could cause environmental microbial communities to change in response to this seed treatment or aid in fungal communities losing sensitivity to this mode of action (Nettles et al., 2016). The costs associated with these environmental changes are difficult to parameterize and evaluate but should be evaluated when considering SDS management and the potential benefits derived from fungicidal seed treatments. 40 The economic impacts of public investment through research and outreach of a privately developed technology estimated from adoption of fluopyram to alleviate SDS associated yield losses in at risk acres revealed an overall benefit with a net present value of $5,829 million over 15 years, indicating fluopyram can reduce the economic impact of SDS. This analysis also illustrates the investment of validation research and outreach to discover and implement privately developed management tactics of diseases do have an overall economic benefit. Sensitivity analysis of this framework indicates that the market price of soybeans, the incidence of SDS, and the adoption path of this seed treatment can alter overall net benefits from fluopyram. Nonetheless, even in the worst case scenario where all three of these factors were less favorable to fluopyram impacts, the net present value of fluopyram amended seed treatment targeting SDS management still reached $1.3 billion. In sum, if growers adopt this fluopyram seed treatment to reduce economic yield losses caused by SDS as predicted, the overall economic benefits to soybean producers and consumers should be significant. We would like to recognize Michigan State University (MSU) project GREEEN, Plant Resilience Institute and the C.S. Mott Foundation for funding of ABY and research. Research is supported by the MSU AgBioResearch and the U.S.D.A. National Institute of Food and Agriculture for S.M.S. Acknowledgements Conclusions 41 Author Contributions Designed framework: A.B.Y., S.M.S. M.I.C.; Analyzed data: A.B.Y., S.M.S.; Wrote the manuscript: A.B.Y., S.M.S. M.I.C. 42 Table 1. United States and rest of world supply and demand elasticities for soybean. Elasticity U.S. Supply U.S. Demand U.S. Export Demand Rest of World Supply Kim and Moschini, 2018 Haile et al., 2016 Reimer et al., 2012 0.38 0.30 Masuda and Goldsmith, 2009 -0.61 Zilberman et al., 2010 Lybbert et al., 2014 0.22 0.30 -0.90 0.34-0.55 43 Table 2. Variable costs associated with fluopyram amended seed treatments. Total cost of growing soybeans in the U.S with a commercial base treated seed containing nematistat and insecticide (Bacillus firmus with clothianidin) and three fungicides (metalaxyl, penflufen and prothioconazole) or with the commercial base and fluopyram. Farm budget taken from (Ward et al., 2018). Cost (U.S.D./Acre) ITEM VARIABLE COSTS 62.00 Commercial Base Treated Seed 75.00 Commercial Base with Fluopyram Treated Seed 4.70 Interest on Operating Capital 5.02 66.70 TOTAL OF VARIABLE COSTS 80.02 44 Table 3. Cost associated with fluopyram seed treatments and communication of research. Cost were gathered from locations in Delaware (DE), Indiana (IN), Iowa (IA), Kentucky (KY), Michigan (MI), South Dakota (SD) and Wisconsin (WI) and associated research costs from 2014 to 2020 in U.S. $. Costs Per Trial Across States Year 2014 2015 2016 2017 2018 2019 2020 IA - 129,159 121,196 - - - - MI - - 21,500 - - 15,200 15,200 Multilocation and Region Trials 121,196 164,811 50,000 72,369 65,328 78,354 78,354 Associated Research Costs 125,844.00 59,711.00 82,248.00 93,998.00 82,248.00 82,248.00 82,248.00 45 fluopyram SDS management, from 2018-2032 (in millions U.S. $). Table 4. Sensitivity analysis of economic impacts to U.S. soybean production for Best Case Parameter Parameters Changed: Incidence: Starting Rate in 2018 17.4% Adoption Rate: 8.0% 2018 Percentage 40.2% 2022 Percentage Soybean Price: $11.40 Price Per Bushel Average (2019- 2032) +23.2% Price change (%) 18,548 Gross Benefits (m) Net Benefits (m) 11,303 Percent Change to Reference (%) 93.9% Worse Case 2.2% 8.0% 37.7% $7.34 -23.2% 2,204 1,311 -77.5% Fluopyram Baseline 9.8% 8.0% 38.9% $9.33 9,531 5,829 46 A) Price Po 1 2 ST B) Price Po P1 1 2 4 5 7 3 6 ST S1FT Qo D Quantity Figure 3. Proposed soybean supply shifts under fluopyram seed treatment. A) Current U.S. soybean production market. ST: soybean supply curve with current adoption seed treatment for SDS (T). B) Proposed shift in U.S. soybean production market if fluopyram seed treatment was adopted. S1FT: soybean supply curve under fluopyram seed treatment for SDS (FT). Quantity Qo Q1 D 47 100% 90% ) % t ( e a R n o i t p o d A 80% 70% 60% 50% 40% 30% 20% 10% Neonicotinoid Observed Neonicotinoid Fluopyram Observed Fluopyram 2008 2013 2018 0% 2003 Figure 4. Adoption path of soybean seed treatments. Percent adoption area of soybean neonicotinoid seed treatments from 2004 to 2025. Predicted fluopyram adoption from 2018 to 2032 for SDS associated yield loss aversion. 2023 Year 2028 2033 2038 48 Figure 5. Soybean sudden death syndrome incidence. Scatter plot of incidence of soybean sudden death syndrome from 1996 to 2015 across U.S. soybean production region. Low incidence (blue) with trend line and high or epidemic incidence (orange) (A). Scatter plot of relationship of yield loss to soybean sudden death syndrome (SDS) to mean annual temperature one year before yield loss occurred. Red circle denotes epidemic years when low temperatures preceded the year of an epidemic loss (B). 49 REFERENCES 50 REFERENCES Agrios, G.N. (2004). Plant Pathol. 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Plant Pathol. 28, 77-83. 54 Von Qualen, R.H., Abney, T.S., Huber, D.M., and Schreiber, M.M. (1989). Effects of rotation, tillage, and fumigation on premature dying of soybeans. Plant Dis. 73, 740- 744. Wang, S.L., Heisey, P.W., Huffman, W.E., Fuglie, K.O. (2013). Public R&D, and U.S. agricultural productivity growth: dynamic and long-run relationships. Amer. J. Agr. Econ. 95, 1287-1293. Ward, B., Lindsey, L., and Loux, M. (2018). Soybean production budget (RoundUp ready)- 2019. In Farm Office (Ohio State University). Xing, L., and Westphal, A. (2006). Interaction of Fusarium solani f. sp. Glycines and Heterodera glycines in sudden death syndrome of soybean. Phytopathol. 96, 763-770. Xing, L., and Westphal, A. (2009). Effects of crop rotation of soybean with corn on severity of sudden death syndrome and population densities of Heterodera glycines in naturally infested soil. Field Crops Res. 112, 107-117. 55 CHAPTER 3 Implications of Fusarium virguliforme Temporal Colonization of Corn: Tillage and Residue Management of Soybean Sudden Death Syndrome This chapter is unpublished, with intention to submit to Plant Disease. Araldi Da Silva, G., Baetsen-Young, A. M., Jacobs, J. L., Byrne, A., Kandel Y., Leandro, L., Mueller, D. S., Smith, D. L., Teutna, A., Wise, K. A., Day, B., Chilvers, M. I. 20XX. Implications of Fusarium virguliforme Temporal Colonization of Corn: Tillage and Residue Management of Soybean Sudden Death Syndrome. Plant Dis. XXX:XX-XX. 56 Abstract The asymptomatic host range of Fusarium virguliforme includes corn, a common crop rotation with soybean that we hypothesize may alter F. virguliforme population dynamics and disease management. A field-based approach explored the temporal dynamics of corn and soybean root colonization by F. virguliforme under tillage and residue management. Experiments were conducted in IA, IN, MI, Ontario and WI for two years. Corn and soybean roots were sampled at 1, 2, 4, 8, 12, and 16 weeks after planting (WAP) in MI, and corn roots 4 or 5, 8 and 16 WAP for IA, IN, and WI. DNA was extracted and analyzed by real-time qPCR for F. virguliforme quantification. Trials were rotated between corn and soybean, containing a two factorial of no tillage or tillage and no- corn residue or residue. In 2016, low (ca. 100 fg/10 mg root tissue) F. virguliforme was detected in the F. virguliforme inoculated MI, IA and IN, and non-inoculated WI corn fields, but in 2017 higher levels of F. virguliforme DNA was detected in MI, IA and IN across sampling time points. Overall, trials in MI, IA, IN and WI revealed consistent low-level detection of F. virguliforme in corn roots. Cultural tillage practices showed inconsistent effects on F. virguliforme root colonization between trials and locations. Yet, residue management did not alter root colonization of corn or soybean by F. virguliforme. 57 Introduction Sudden death syndrome (SDS) is a devastating soybean (Glycine max) disease with an estimated annual yield loss impact around $330 million dollars in the United States, placing this disease in the top 5 most impactful on soybean production (Koenning and Wrather, 2010; Allen et al., 2017). Since the first report in 1971 in Arkansas, soybean SDS has quickly spread to nearly all soybean producing states (Hartman et al., 2015). In North America, soybean SDS is caused by the soilborne fungus F. virguliforme and F. brasiliense, while in South America, an additional two closely related species (e.g., F. tucumaniae and F. cuneirostrum) have also been identified as casual agents within the Fusarium solani species complex clade 2 (FSSC2) (O'Donnell et al., 2010; Wang et al., 2019b). Fusarium virguliforme is an asexual fungus that survives in soil and crop residues (Roy et al., 1997; O'Donnell et al., 2010). This fungus infects soybean roots during early plant growth stages causing root rot (Jin et al., 1996). As the pathogen colonizes the xylem tissues, it secretes phytotoxins, initiating programmed cell death of the soybean leaf (Brar, 2012). This host response creates a symptom of interveinal leaf scorch, characteristic of SDS. Root rot and leaf scorch lead to a reduction of overall plant biomass, flowers, pods, and yield (Roy et al., 1997). Yield losses of up to 80% have been reported in highly infested fields, but typically yield losses of 5 to 15% are observed (Hershman et al., 1990). Integrated pest management is the strongest approach to control SDS, because no single strategy can fully manage the disease. One of the tactics to control SDS is the use of soybean cultivars with partial genetic resistance (Chang et al., 2018). However, if disease intensity is high in soybean fields, the partial resistance can be broken (Njiti et al., 1996). Environmental 58 conditions inducing high disease intensity are cool temperatures and wet compacted soil during planting, and increased soil moisture during early reproductive growth stages (Rupe, 1989; Roy et al., 1997; Scherm and Yang, 1999; Chong et al., 2005). Thus, planting into warmer soils by delaying planting date can been considered to reduce severity of SDS (Hershman et al., 1990), but, recently, multi-location research demonstrated that earlier planting is not strictly correlated with higher SDS severity, indicating that yield maximization in SDS infested fields can be achieved without altering planting date (Kandel et al., 2016a). Furthermore, the interaction of soybean cyst nematode (Heterodera glycines) with F. virguliforme (Gao et al., 2006; Westphal et al., 2014), and the disease pressure specific profitability of seed treatments for SDS (Kandel et al., 2016b; Gaspar et al., 2017) have further confounded disease management recommendations. Cultural practices of tillage and residue management have independently been explored to reduce SDS symptoms development. Tillage can reduce root infection from F. virguliforme, by decreasing soil moisture and increasing soil temperature through increased porosity of the soil (Roy et al., 1997; Vick et al., 2003). However, a new study in a field with a long-term till and no-till treatments revealed that tillage does not impact soybean root and foliar SDS disease severity or yield (Kandel et al., 2019). Implementation of tillage has reduced SDS severity and root colony forming units (CFUs) of F. virguliforme, but has not decrease soil CFUs (Vick et al., 2003). Subsequently, infected soybean roots from no-till fields maintained summer level CFUs of F. virguliforme through the fallow season, whereas soil did not harbor detectable CFUs of F. virguliforme to spring (Luo et al., 2001).This indicates that plant residue, especially in no-till fields may lead to increased F. virguliforme populations and SDS 59 severity. Navi and Yang (2016) explored this hypothesis that plant residue contributes to F. virguliforme populations and found that corn (Zea mays) kernel residue increases soil CFUs of F. virguliforme in inoculated greenhouse and field experiments three and 12 months post treatment establishment. Freed et al. (2017) also demonstrated that corn kernels exudates can increase F. virguliforme population in vitro, and infested corn kernels can cause high soybean foliar and root rot disease severity. These studies suggest that tillage and residue management provide an opportunity to control F. virguliforme populations and SDS disease severity. Another strategy for field crop disease management is crop rotation, which reduces pathogen population within soils by planting non-hosts for multiple years (Agrios, 2004). Previous research regarding corn, soybean, and wheat (Triticum aestivum) rotation found reduced SDS yield losses, when compared to continuous soybean (Von Qualen et al., 1989; Roy et al., 1997). However, more recent studies have shown that corn-soybean rotation has not reduced SDS incidence and severity (Hershman, 2003; Xing and Westphal, 2009; Leandro et al., 2018). A greenhouse study in 2012 indicated that corn can be an asymptomatic host for F. virguliforme. Kolander et al., (2012) found that F. virguliforme is able to colonize corn roots, as well as wheat, ryegrass, pigweed and lambsquarters roots without developing root or foliar symptoms, or reducing biomass, opposed to infected soybean, alfalfa, Canadian milk vetch, pinto and navy bean, white and red clover, pea, sugar beets and canola. Whereas, more recently Kobayashi-Leonel et al., (2017) explored the susceptibly cover and rotation crops to F. virguliforme and detected similar levels of F. virguliforme DNA as Kolander et al. (2012) in corn roots, but suggested that corn is a non-host because the F. virguliforme DNA from 60 corn roots was lower than soybean roots. Therefore, inconsistencies persist within the research community regarding whether crop rotation of corn and soybean enhances or reduces the severity of SDS, but have instead revealed a potential broad host range for F. virguliforme. Although tillage and residue management practices have been explored for SDS management, no research has explored the impact of cultural tactics upon F. virguliforme colonization of corn, and the ability of this colonization to alter subsequent SDS symptoms when rotated to soybean. The dissection of tillage and residue management in combination with corn-soybean rotation tactics should provide clarity to variable reports, revealing if the interaction of these two cultural practices promotes or suppresses SDS. To examine the potential of cultural practices to alter disease development, trials were established in four states (IN, IA, MI, WI) and Ontario, Canada during the 2015 to 2017 field seasons. The objectives of this study were to: (i) identify if F. virguliforme can colonize corn, a predominate rotation crop, (ii) determine the temporal dynamics of corn and soybean root colonization by F. virguliforme under field conditions, and (iii) decipher if tillage and plant residue management affect F. virguliforme root colonization, SDS foliar symptom development and soybean yield. 61 Materials and Methods Inoculum Preparation of Fusarium virguliforme Inoculum was created for field experiments in Indiana, Iowa and Michigan by transferring a single-spore-derived isolate of F. virguliforme (Table 5) onto Nash Snyder agar (NSA) (Leslie and Summerell, 2006) in 100 mm x 15 mm petri dishes, and allowed to colonize the medium at 21°C ± 1°C in ambient light for three weeks. A sorghum grain inoculum was created by soaking sorghum grains in deionized water overnight. After draining, sorghum grains were placed in bags and sterilized by autoclaving. Media colonized with F. virguliforme was added to the cooled sorghum grains and the bags were sealed. The bags were incubated at 21°C ± 1°C with ambient light for 30 days and mixed every other day. Upon uniform grain colonization, the inoculum was spread on trays covered with paper and dried at room temperature. Colony forming units were utilized to determine strain concentration and contamination by counting colonies of 104 and 105 diluted spore suspensions on nutrient agar. Corn Residue Establishment In 2015, a three-year study was established in Indiana, Iowa, Michigan, Ontario and Wisconsin within a site location with a history of SDS. Each site was tilled and planted with corn at 84,014 seeds/ha on a 76.2 cm row spacing (Table 5). In Iowa, F. virguliforme inoculum was broadcasted after planting at 1.4 kg per 46 m2. In Indiana, the trial was 62 inoculated with 4.9 g per row meter of inoculum at planting, and 414 mL per plot was broadcasted after planting at growth stage V5 on June 24. The Wisconsin site was not inoculated, and in Michigan, the site was inoculated at 11.5 ml/row meter and then broadcasted at 31.7 g per row meter on June 18th. Fertility and weed control followed local agronomic recommendations, and no foliar fungicides were applied throughout the growing season. Field Site Design Treatments of tillage and residue arrangement varied by location, but all treatments were applied in the fall of 2015. Iowa, Indiana and Ontario, Canada arranged treatments in a split- split plot design, with chisel plow tillage or no-till as the whole plot, soybean or corn as the sub-plot, and residue or no-residue as the sub-sub plot. Each sub-sub plot treatment consisted of four rows 6 m long, replicated four times and randomized within the sub-plot. Michigan designed the treatments in a strip-split plot design with corn or soybean as the whole plot. Then a strip of chisel plow tillage and no-till, 13 feet wide were randomly assigned within a block, and repeated four times within each whole plot. Opposing to strips of tillage, strips of residue or no-residue were applied to distinguish individual plots as four rows wide and was repeated four times, creating four replicates of each treatment within a single block. Wisconsin treatments were designed in a split plot design, with corn or soybean as the whole plot, but assigned the factors of chisel plow tillage or no-till and residue or no- residue to four blocks, in a completely randomized design. For all locations crop rotation included, in 2016 soybean followed where corn was planted in 2015 in half of the treatments, 63 and corn followed corn in the remaining treatments. In 2017, corn followed soybean 2016 planting, and soybean followed corn 2016 planting (Table 6). In 2016 and 2017, planting date and cultivar varied by site (Table 5). Corn was planted at 98,840 and 308,875 seeds/ha for soybean in Iowa and Indiana, and 84,014 corn seeds/ha and 345,940 soybean seeds/ha in Michigan and Wisconsin into either no-tilled soil or tilled soil, that was chisel plowed in spring. Wisconsin did not inoculate either corn or soybean at planting. Michigan inoculated corn at planting with 4 ml per row meter. Indiana inoculated corn in 2016 at 4.1 mL per row meter but corn or soybean were not inoculated in 2017. Iowa did not inoculate corn in 2016, but soybean was inoculated at 8.2 mL per row meter in 2017 only. Soybean was irrigated in Michigan with a drip tape at V3 growth stage (Fehr et al., 1971) in 2016 and 2017. Both corn and soybean were irrigated in Indiana throughout the trial. In 2016, sandhill cranes (Grus canadensis) did extensive feeding damage to the corn stand in the Michigan trial, and thus had to be replanted on June 24 using different corn variety (DK5261), which was not inoculated. Fertility and weed control followed local agronomic recommendations, and no foliar fungicides were applied throughout the growing season. Following soybean harvest, corn residue was removed and/or soil was tilled for respective treatments. Root Sampling and Processing Michigan. In 2016 and 2017, roots were sampled at one, two, four, eight, 12 and 16 weeks after planting for both corn and soybean. Three roots were taken from the outer two rows of 64 one plot of each treatment from each block (n=4). The aboveground biomass was removed at the soil line, and loose soil was removed from the roots. The remaining soil was then washed off the roots, and roots were dried at ambient temperature. In 2016, due to crane feeding damage, remaining corn plants from the first planting were sampled throughout the growing season, but plants from the no-till treatments were sampled at a whole block level rather individual plot for blocks three and four, until no plants remained. Iowa, Indiana and Wisconsin. In 2016, three corn plants were sampled from the two outer rows of each plot and sent to Michigan State University. Roots were sampled at 4, 8 and 16 weeks after planting for Iowa, Indiana and Wisconsin in 2016. In 2017, roots were received from Iowa and Indiana at 5, 8, and 16 weeks after planting. The aboveground biomass was removed at the soil line, and loose soil was removed from the roots before shipping. Upon arrival the roots were washed, then air dried at ambient temperature and stored at room temperature. The primary root from the dried corn roots were sampled at each time point and ground into a fine powder by a TissueLyser (Qiagen) with a steel bead. Lower portions of soybean lateral and tap roots were sampled at each time point and were also ground into a fine powder by a TissueLyser with a steel bead. The homogenized roots were stored at room temperature until further processing. 65 DNA Extraction DNA was extracted with an automated protocol with KingFisher Flex (Thermo Fisher Scientific) and Mag-BIND Plant DNA Plus 96 kit (Omega) from 10 mg of dried root power for both soybean and corn samples. The powder was loaded into 96 well 2 mL plate (Denville), and mixed with 700 µL of lysis buffer with 20 µL of proteinase K. Plates were incubated for one hour at 65°C with mixing every 20 minutes, after which plates were spun at 5,000 g for 20 minutes to clear the supernatant. Four hundred and fifty microliters were transferred to a KingFisher 96-deep well plate, and the DNA was extracted following manufacturers recommendations. DNA concentration was determined using a Nanodrop (ThermoFisher Scientific). Fusarium virguliforme DNA Quantification Fusarium virguliforme DNA detection was performed using a real time quantitative PCR (qPCR) F. virguliforme specific assay (Wang et al., 2015). The real time reactions were performed in duplicate on the ABI StepOnePlus thermocycler (Applied Biosystem), with a total reaction volume of 20 µL containing 2 µL of sample DNA. A three-point standard curve ranging from 1 ng to 100 fg of genomic F. virguliforme DNA was utilized in each 96-well experimental run to ensure PCR efficiency and to calculate sample DNA concentration. 66 Data Analysis Data were analyzed in R v3.4.1 (R Development Core Team, 2010). Analysis of variance (ANOVA) was calculated using the “lme4” (Bates, 2015) “nlme” (Pinheiro, 2019) and “Car” (Fox and Weisberg, 2011) package. The average was taken of plants sampled within a plot to represent a single observation. Tillage and residue treatments were defined as fixed effects. For repeated measures sampling time was considered a fixed effect. Year by location interactions were tested and if significant (P < 0.05) each location was analyzed within year separately for the effects of tillage and residue. Means were separated within treatments or time by Tukey HSD “multicomp” (Hothorn, 2008) by a < 0.05. Graphs were visualized using “ggplot2” (Wickham, 2016). Results Tillage and time significantly affected F. virguliforme DNA detected within soybean and corn roots. Thus, the main effects of tillage and residue are presented, as each site year was analyzed independently from significant location by year interaction. Temporal Dynamics of Fusarium virguliforme Colonization of Corn Roots 2016. To understand the temporal pattern of corn primary root colonization, DNA levels were determined across three timepoints in Iowa, Indiana and Wisconsin sites and six timepoints in Michigan. Fusarium virguliforme DNA was detected within all treatments at 67 four weeks after planting (WAP) at low levels (less than 250 fg/10 mg dried root tissues, Figure. 6A and 7A). Additionally, F. virguliforme was detected at one and two WAP sampling time points in Michigan (Figure 8A and B). However, no root or foliar chlorosis and necrosis could be assigned to as symptoms derived from F. virguliforme colonization. DNA quantities changed throughout the growing season and varied by location. Across residue treatments, F. virguliforme DNA quantities were significantly higher at 4 WAP than 16 WAP in Indiana (P = 0.0209 and P = 0.0087, Table 7). Michigan and Iowa had similar levels of DNA quantities at four and 16 WAP in both tillage treatments (Iowa P < 0.0389, Michigan P < 0.0258) and Michigan no-residue treatment (P = 0.0486). The highest F. virguliforme DNA quantities were detected at 16 WAP across all tillage and residue treatments in Wisconsin (P < 0.0001, Table 5). Within earlier timepoints in the Michigan site, the treatment of tillage or residue had the highest DNA quantities at 1 WAP (P < 0.0001). Yet, in the no-till or no-residue treatment F. virguliforme DNA quantities were highest at 2 WAP (P < 0.0001, Table 8). Comparison of F. virguliforme DNA quantities across tillage or residue treatments at each timepoint did not reveal any significant difference within each location after four WAP (P > 0.0651, Figure 6A and 7A). Two weeks after planting, F. virguliforme DNA quantities of this fungus in Michigan were significantly higher in the no-till treatment, when compared to the tillage treatment (P = 0.0027, Figure 8A). To explore the effect of tillage or residue management over time, the area under the DNA progress curve (AUDPC) of F. virguliforme was calculated for each treatment. Comparing AUDPCs across tillage or residue treatments did not reveal any significant differences within 68 Iowa, Indiana and Wisconsin (P > 0.2349, Fig 4B and 5B). However, when considering the AUDPCs across the six sampling timepoints in Michigan, significantly higher F. virguliforme AUDPC was observed from samples from no-till treatments when compared to the tillage treatment (P = 0.0038, Figure 8C). 2017. The impacts of tillage and residue cultural practices on corn primary root colonization by F. virguliforme were evaluated in Iowa and Indiana across three timepoints, as well as Michigan across six timepoints. The first sampling timepoint from Iowa and Indiana in 2017 was at five WAP rather than four WAP, as in 2016. Similarly, to 2016, F. virguliforme DNA was detected at five WAP in all treatments, across all locations (Figure 6A, 7A and Table 7), ranging from 389.1 fg/10 mg of dried root tissue within the tillage treatment at the Indiana site, to 13,607.5 fg/10 mg of dried root tissue within the no-residue treatment at the Iowa site. Detected DNA quantities within the first sampling timepoint varied greatly by location, ranging from 389.1 to 655.0 fg/10 mg of dried root tissues in Indiana, Michigan fluctuated from 3,574.5 to 7,904.4 fg/10 mg dried root tissues, and Iowa varied from 5,924.6 to 13,607.5 fg/10 mg dried root tissues. The first sampling time point after planting contained the highest DNA quantities across all treatments within Iowa and Michigan no-till or residue treatments at four WAP (P < 0.04). At the Indiana site, tillage, residue and no-residue treatments all exhibited higher F. virguliforme DNA quantities detected at five WAP when compared to 16 WAP, but similar eight WAP (P < 0.02, Table 7). Sampling of corn roots at one or two WAP in Michigan revealed statistically similar levels of F. virguliforme DNA quantities to four WAP in all treatments (P < 0.0001), Table 8). Interestingly, tillage and no- 69 residue treatments at 16 WAP had similar DNA levels to one, two and four WAP (P < 0.0001, Table 7). Treatment differences comparing tillage to no-till treatments were apparent within sampling timepoints at the Iowa location, with higher F. virguliforme DNA quantities in no- till at five WAP (P = 0.001) and eight WAP (P = 0.0334) (Figure. 6A). No differences of F. virguliforme DNA quantities were detected at Indiana or Michigan locations for tillage or residue treatments (P > 0.2649, Figure 6A, 5A, 6A). When analyzing treatments over time, a comparable pattern appeared of F. virguliforme colonization of corn primary roots in Iowa within time points (Figure 6B). The AUDPC was significantly higher in the no-till treatment than the tillage treatment (P = 0.0004). While similar trends of elevated F. virguliforme AUDPCs were detected Indiana and Michigan in no- till treatments, no significant differences were found between till and no-till treatments (P > 0.3115, Figure 6B). Treatments of residue or no-residue did not have a significant effect on AUDPCs in any location (P > 0.0783, Figure 7B). Temporal Dynamics of Fusarium virguliforme Colonization of Soybean Roots Under Cultural Management 2016. Fusarium virguliforme DNA was discovered in soybean tap root tissues as early as one WAP, across all treatments in Michigan (Figure 9 A and B, Table 9). During 2016, the DNA quantities varied from 102.1 to 476.7 fg/10 mg of dried root tissues) at one WAP across 70 treatments and did not significantly increase until eight WAP for all treatments (P < 0.0001). DNA quantities of F. virguliforme were significantly higher at 12 WAP than eight WAP (P < 0.0001). The highest F. virguliforme DNA quantities were observed at 16 WAP (P < 0.0001), ranging from 1,004,828.8 to 1,607,338.6 fg/10 mg dried root tissues (Table 9). Comparing F. virguliforme DNA quantities across residue or tillage treatments did not reveal any significant effects (P > 0.9633, Figure 9A and B). Analyzing the AUDPC of each cultural treatment over time sampling time course found similar DNA quantities between tillage or residue treatments (P > 0.3139, Figure 9C and D). 2017. As observed in 2016, F. virguliforme DNA was detected in soybean roots at one WAP. However, the overall quantities of DNA detected at one WAP tended to be lower in 2017 (Table 9). Fusarium virguliforme DNA quantities detected increased at two WAP in no-till and no-residue treatments (P < 0.0001), whereas DNA detected in tilled and residue treatments did not significantly increase until four WAP (P < 0.0001). Fusarium virguliforme DNA quantities were significantly higher at 12 WAP than two WAP, but not at four or eight WAP, across all treatments (P < 0.0001, Table 9). Quantities from F. virguliforme were the highest at 16 WAP for all treatments (P < 0.0001), ranging from 126,716.9 to 2,488,780.1 fg/10 mg dried root tissues (Table 9). Across tillage or residue treatments at each timepoint, no significant effect was found upon F. virguliforme DNA quantities from one to 12 WAP (P > 0.0875, Figure 9A and B). Yet, at 16 WAP F. virguliforme DNA quantities were significantly higher in the no-till treatment (P < 0.0169, Figure 9A) when compared to the tillage treatment. Exploring the effects of 71 Discussion treatments upon F. virguliforme colonization of soybean lower tap and lateral roots over time revealed comparable trends to the temporal results (Figure 9C and D). Specifically, no significant differences of AUPDCs between residue treatments (P = 0.2845). However, tillage management, particularly the implementation of no-till increased detected F. virguliforme DNA AUPDC when compared to tilled (P = 0.0069, Figure 9C). The detection of F. virguliforme in corn roots across four states demonstrates the ability of this soybean pathogen to colonize living corn tissue. Additionally, this fungus was quantified inside both soybean and corn roots at one WAP, highlighting the early interactions of F. virguliforme and hosts. Cultural treatment of no-till may enhance F. virguliforme root colonization in corn and soybean roots, but this elevated colonization was not consistently observed in all field sites, indicating potential need for site specific management. Corn residue did not alter F. virguliforme root colonization of corn or soybean. This gained knowledge suggests that corn in addition to soybean should be considered as a host for SDS disease management. Infield Detection of Fusarium virguliforme in Corn Roots. Soybean are often rotated with corn to reduce soilborne pathogen populations. Yet, corn rotations with soybeans have not reduced SDS severity and incidence (Xing and Westphal, 2009; Leandro et al., 2018). In field experiments during 2016 and 2017 across four North 72 Central soybean production states, F. virguliforme was detected in the primary root tissues of corn. Fusarium virguliforme was detected in living corn roots within experimental sites with variable management, including without pathogen inoculation, irrigation, and differing corn cultivars (Table 1), highlighting the relevance of this detection across soybean production. Previously colonization of corn roots by F. virguliforme was discovered in greenhouse settings (Kolander et al., 2012; Kobayashi-Leonel et al., 2017), but our findings are the first report of living corn root colonization in field settings. The colonization of corn roots among other crops by F. virguliforme, demonstrates the broad host range of this soybean pathogen (Kolander et al., 2012). The increasing reports of F. virguliforme colonization of corn roots, communicates the need to consider corn as an asymptomatic host. Therefore, crop rotations to manage SDS should contain crops outside of corn or soybean to reduce SDS severity (Leandro et al., 2018) and potentially alter the soil microbiome of with rotation of diverse crop species (Gdanetz and Trail, 2017) . Temporal Dynamics of Fusarium virguliforme Root Colonization Development of root diseases are distinct from foliar counterparts, as within soil environments the fungal propagules are majorly immobilized, and the plant roots move throughout the soil. Thus, the probability of infection or fungal root colonization highly depends on the growth stage of the host root during initial contact with the fungus (Huisman, 1982). The elevated levels of F. virguliforme DNA across early (one to four WAP), corresponding to active growth and development of the primary root system within corn 73 (Fusseder, 1987). These developing corn roots secrete exudates, which are documented to stimulate F. virguliforme growth (Freed et al., 2017). However, as post embryonic roots (i.e. crown and nodal roots) develop, nutrient acquisition and water uptake from the primary root decreases (Hochholdinger et al., 2004; Ahmed et al., 2018). Corn samples in this study at four or five WAP had minimal crown root development and at eight WAP, corn roots had matured. Decreases in F. virguliforme DNA biomass at eight WAP may correspond with these changes in root development and metabolic activity. The decrease in this fungal DNA biomass at eight WAP and root activity could potentially be caused by senescence of cortical root cells by programed cell (Schneider and Lynch, 2017). Fusseder (1987) noted a decrease of viable nuclei in the cortex of the primary root after the nine-leaf stage, indicating cell death of primary root cortex, which corresponds to the aboveground growth stage and a reduction of F. virguliforme biomass during the eight WAP timepoint. Senescence of roots attenuates plant defenses, enabling saprophytic colonization by F. virguliforme at 12 and 16 WAP (Schneider and Lynch, 2017). The quantities of F. virguliforme DNA within corn roots varied substantially between 2016 and 2017 field seasons. There were two major differences that could account for variation of F. virguliforme colonization of corn roots. The weather during months of May and June, when early root fungal interactions occurred, differed between 2016 and 2017. The temperature was 0.82 °C in May, and 0.48 °C in June cooler in 2017 when compared to 2016 when averaged across all locations. Precipitation in 2017 had additional 1.42 cm in May, and 1.37 cm in June, when compared to 2016 (NOAA, 2019). Both temperatures and soil moisture play a key role in colonization of plant roots by F. virguliforme (Rupe, 1989; Roy et al., 1997; 74 Scherm and Yang, 1999; Chong et al., 2005) and these annual differences may have altered temporal dynamics of root colonization. Secondly, crop rotation from soybean to corn may have added additional F. virguliforme inoculum to the soil than a corn-corn rotation. Infected tap roots with sporulating F. virguliforme may have increased fungal inoculum, more than colonized corn roots. Infested soybean residue provides an overwintering structure for F. virguliforme (Luo et al., 2001). Recently, two-year corn-soybean rotations were found to contain five-fold greater F. virguliforme density in soil than a four-year corn-soybean- oat+alfalfa-alfalfa rotations, indicating short crop rotations could promote greater F. virguliforme soil persistence (Leandro et al., 2018). Thus, the rotation to soybean, between corn plantings could have increased the opportunity for infection of corn roots in 2017. Fusarium virguliforme Temporal Dynamics in Soybean Roots Similar to F. virguliforme corn root colonization, F. virguliforme DNA was detected within soybean roots at one WAP across 2016 and 2017 experiments. This detection at one WAP illustrates the early interactions between soybean roots and F. virguliforme, as observed by (Gongora-Canul et al., 2011). Fusarium virguliforme DNA quantities gradually increased at four and eight WAP, but rapidly increased at 12 and 16 WAP. This rapid rate of colonization by F. virguliforme in senescensing soybean roots was noted by Wang et al. (2019a) and indicates a potential change to saprotrophy from necrotrophy. The temporal dynamics of F. virguliforme in soybean roots initializes at a similar level to corn root colonization at one- four WAP (Table 6 and 7), but then diverges as F. virguliforme proliferates in soybean roots. Detecting F. virguliforme in soybean roots at one WAP, highlights the need to protect soybean 75 seedlings through the use of resistant germplasm or seed treatments such as fluopyram to prevent colonization (Kandel et al., 2018). Impacts of Cultural Practices on F. virguliforme Root Colonization The cultural practice of tillage has previously yielded variable management of SDS root or foliar severity. Herein, we discovered similar findings of inconsistent effects of F. virguliforme root colonization by use of tillage. Only two of the eight corn field experiments found an increase in F. virguliforme root colonization under no-till management, and one of the two soybean field experiments. The remaining experiments did not differ in temporal dynamics of F. virguliforme root colonization under tillage or no-tillage management. These experiments support recent findings by Kandel et al. (2019), that tillage does not impact symptom development on root components. However, the increase in F. virguliforme colonization under no-tillage implementation in three experiments may indicate site specific factors that enhanced root colonization such as soil chemical and chemical properties (Roy et al., 1997; Vick et al., 2003). In combination with no-tillage, residue left on the soil surface could potentially act as an inoculum source for F. virguliforme. Previous studies have indicated that corn residue could harbor viable F. virguliforme, when inoculated (Kolander et al., 2012), and that corn kernels could increase F. virguliforme soil populations (Navi and Yang, 2016). Interestingly, we did not observe any effects of corn residue management on F. virguliforme colonization of corn or soybean roots. These findings contrast with the common conviction that residue of a host 76 left on the soil surface can further promote a soilborne disease (Agrios, 2004; Katan, 2017). While F. virguliforme can saprophytically colonizes aboveground corn tissues when inoculated (Kolander et al., 2012) the potential occurrence in field settings does not promote subsequent root colonization of corn or soybean across the North Central production region. In total, these field experiments revealed that corn is an asymptomatic host for F. virguliforme across the North Central soybean production region. We were able to establish that corn and soybean interactions with F. virguliforme occur within one week of planting, highlighting the need to prevent root colonization through use of prophylactic management strategies. Surprisingly, corn residue management did not alter root colonization of soybean or corn. Tillage management for root colonization did not provide clear conclusions, indicating additional variables relating to soil ediphatic factors may be critical to understanding the complex nature of this soilborne pathogen. We would like to thank the North Central Soybean Research Program, Michigan Soybean Promotion Committee, Michigan State University Project GREEEN, Michigan State University Plant Resilience Institute, and the Charles Stewart Mott Foundation for funding. We would also like to thank the Michigan State University College of Agriculture and Natural Resources Statistical Consulting Center for data analysis suggestions. Acknowledgements Conclusions 77 Author Contributions Designed framework: K.Y., L.L., D.S.M., A.T., K.A.W., G.A.D.S., A.B.Y., M.I.C.; Conducted Experiments: K.Y., L.L., D.S.M., A.T., K.A.W., G.A.D.S., A.B.Y., M.I.C., J.L.J., A.B.; Analyzed data: A.B.Y., G.A.D.S., K.Y.; Wrote the manuscript: K.Y., L.L., D.S.M., A.T., K.A.W., G.A.D.S., A.B.Y., M.I.C.; J.L.J., A.B. 78 Table 5. Field trial location and management in Iowa, Indiana, Michigan, and Wisconsin during 2015 to 2017. Year Irrigatio Planting Date Inoculation n z + June 11 2015 + Corn - May 22 + Corn + May 28 + Corn - - June 6 2016 - + + Corn - - - May 19 - - May 6 - - - - May 24 + + + Corn - - + 2017 May 11 + Corn - April 26 - - - + Soybean + + - - May 17 zCorn or Soybean plants were inoculated at planting with sorghum seeds infested with isolate NE305 in Iowa, INS12-10 in Indiana, and Mont-1 in Michigan. Cultivar Corn:P9807AM Corn: DKX57-75 RIB Corn: P9917AMX Corn:P9807AM Soybean:DF5242R2Y Corn:197-31V2PRIB Soybean:DSR-2110/R2Y Corn: N41Y Soybean: AG2431 Corn:197-31V2PRIB Soybean:5N293R2 Corn 5556VT2RIB Soybean:DF5242R2Y Corn: N41Y Soybean: AG2431 Corn: P9917AMX Soybean:5N293R2 State MI IA IN WI MI WI IA IN MI IA IN 79 Table 6. Experimental treatments and crop rotation schedule from 2016 to 2017. Crop rotation schedule Treatment 2017 1 Soybean following 2 years of corn Soybean following 2 years of corn 2 Corn following soybean 3 Corn following soybean 4 Soybean following 2 years of corn 5 6 Soybean following 2 years of corn 7 Corn following soybean Corn following soybean 8 Description Chisel plow, no residue Chisel plow, residue Chisel plow, no residue Chisel plow, residue No-till, no residue No-till, residue No-till, no residue No-till, residue 2016 Corn following corn Corn following corn Soybean following corn Soybean following corn Corn following corn Corn following corn Soybean following corn Soybean following corn 80 Wisconsin Indiana Michigan, Wisconsin, Indiana and Iowa during the 2016 and 2017 field season under Table 7. Temporal dynamics of Fusarium virguliforme colonization of corn roots in tillage and residue management. Weeks After Planting P-value WEEK 4/5Z WEEK 8 WEEK 16 Location Treatment Year F. virguliforme DNA (Fg/10 mg Dried Root Tissue) 2016 TILL 227.6 aby 9.69 b 72.5 a 0.0258 0.1082 215.7 a 32.8 a NO TILL 160.2 a 6,266.7 a 2017 TILL 3,86.9 b 5,001.4 ab 0.0005 0.0021 656.0 b 1,48.3 b 5,212.1 a NO TILL Michigan 2016 RESIDUE 221.5 a 0.0617 17.5 a 61.4 a 166.3 a 25.0 a 226.8 a 0.0486 NO RESIDUE 0.0001 486.3 b 4,26.6 b 7,904.4 a 2017 RESIDUE 3,574.5 a 4,17.5 b 200.3 ab 0.0046 NO RESIDUE 2016 TILL 7.0 b 49.7 ab 44.4 a <0.0001 <0.0001 20.7 a 15.2 ab 4.6 b NO TILL 2016 RESIDUE 5.9 b 33.8 ab 28.9 a <0.0001 NO RESIDUE 5.3 b 21.9 ab 32.1 a <0.0001 0.2366 11.6 a 46.8 a 67.9 a 2016 TILL NO TILL 164.2 a 167.4 ab 55.2 b 0.0003 2017 TILL 627.9 a 184.8 b 244.1 b 0.0030 0.1665 252.4 a 184.1 a 369.1 a NO TILL 2016 RESIDUE 181.2 a 226.0 ab 53.6 b 0.0209 133.0 a 71.9 ab 40.7 b 0.0087 NO RESIDUE 0.1441 326.1 a 249.9 a 526.6 a 2017 RESIDUE 655.1 a 211.2 b 296.5 b 0.0043 NO RESIDUE TILL 88.7 a 43.4 a 114.9 a 0.0389 2016 0.1380 55.6 a 45.2 a 48.0 a NO TILL TILL 5,924.6 a 903.5 b 1,212.4 b <0.0001 2017 NO TILL 12,408.9 a 1,157.9 b <0.0001 1,627.5 b Iowa 69.2 a 82.5 a RESIDUE 0.1907 47.3 a 2016 0.4210 63.9 a NO RESIDUE 78.1 a 54.4 a 1,350.2 b RESIDUE 10,930.5 a <0.0001 1,205.6 b 2017 <0.0001 1,434.7 b NO RESIDUE 13,607.5 a 2,303.4 b ZIn 2017 at Iowa and Indiana sites the first sampling occurred at 5 weeks after planning, and in 2016 the first sampling occurred at 4 weeks after planting y Asterisks or letters denote significance as determined by α ≤ 0.05 by ANOVA and HSD means separation. 81 Year 2016 2017 2016 2017 Treatment TILL NO TILL TILL NO TILL RESIDUE NO RESIDUE RESIDUE NO RESIDUE and 2017 field season under tillage and residue management. Table 8. Temporal dynamics of Fusarium virguliforme colonization of corn radical roots in Michigan during the 2016 WEEK 2 WEEK 4 WEEK 8 WEEK 12 WEEK 16 P-value WEEK 1 F. virguliforme DNA (Fg/10 mg Dried Root Tissue) 72.5 bc 0.0001 55.2 c 1,118.4 a 1,164.1 ay 5,434.5 a 215.7 cd 0.0001 45.9 cd 1,082.3 b 464.2 c 5,001.4 bc 0.0001 1,784.3 ab 2,531.2 ab 656.0 ab 0.0001 5,665.6 a 4,137.2 a 222.4 b 3,079.6 a 57.4 c 1,442.2 a 226.8 b 0.0001 43.7 c 804.1 b 3,473.4 a 61.4 b 0.0001 200.3 b 5,230.45 ab 0.0001 4,547.1 a 2,282.1 a 486.3 b 3,639.4 a 3,649.8 a 426.9 b 0.0001 y Asterisks or letters denote significance as determined by α ≤ 0.05 by ANOVA and HSD means separation Weeks After Planting 9.7 d 227.6 b 160.2 c 32.8 d 6,266.7 a 386.9 c 148.2 b 5,212.1 a 166.2 b 25 c 221.5 b 17.5 c 417.45 b 3,574.5 a 7,904.4 a 117.7 b 82 and 2017 field season under tillage and residue. Treatment TILL NO TILL TILL NO TILL RESIDUE NO RESIDUE RESIDUE NO RESIDUE Table 9. Temporal dynamics of Fusarium virguliforme colonization of soybean tap roots in Michigan during the 2016 P-value Year 0.0001 2016 0.0001 0.0001 2017 0.0001 0.0001 2016 0.0001 0.0001 2017 0.0001 y Asterisks or letters denote significance as determined by α ≤ 0.05 by ANOVA and HSD means separation Weeks After Planting WEEK 1 WEEK 8 WEEK 2 WEEK 4 WEEK 12 WEEK 16 F. virguliforme DNA (Fg/10 mg Dried Root Tissue) 464.6 dy 217.7 cd 88,539.5 b 1,367,292.6 a 12,163.8 c 128.7 d 114.2 d 512.4 cd 415.8 cd 11,056.7 c 380,959.6 b 1,244,924.9 a 126,716.9 a 4,812.5 b 283.5 c 739.5 c 31.0 d 243.3 cd 6,958.1 c 79.7 d 426.9 c 1,139.7 c 211,027.7 b 2,488,780.1 a 102.1 d 194.5 cd 176.9 cd 5826.5 c 240,623.3 b 1,004,828.8 a 228,875.8 b 1,607,388.6 a 17,394.1 c 476.7 d 535.6 cd 367.5 cd 73.4 d 341.7 cd 806.7 c 583.9 cd 198,486.4 b 1,580,731.5 a 328.4 d 1,072.5 cd 37.2 e 6657.7 d 17,353.8 bc 1,034,765.5 a 83 2016 2017 * * 2016 2017 b I n d i a n a a I o w a M i c h g a n i i W s c o n s n i 8 4 12 16 Tillage No Tillage Treatment No−Tillage Tillage No−Tillage Tillage 12 8 Weeks After Planting 16 4 Figure 6. Corn radical root colonization by Fusarium virguliforme under two tillage treatments over three timepoints during the 2016 and 2017 field season. Treatment values of F. virguliforme DNA are the average of three samples replicated within four blocks. DNA temporal dynamics of F. virguliforme under tillage management in Indiana, Iowa, Michigan, or Wisconsin (A). Area under the DNA progress curve (AUDPC) violin plots, containing boxplots was calculated for tillage management in Indiana, Iowa, Michigan or Wisconsin (B). Asterisks or letters denote significance as determined by α ≤ 0.05 by ANOVA and HSD means separation. Figure 1. Treatment No−Tillage Tillage Tillage i ) e u s s T t o o R d e i r D g m 0 1 g F ( / A N D v F A. 2.0e+04 1.5e+04 1.0e+04 5.0e+03 0.0e+00 2.0e+04 1.5e+04 1.0e+04 5.0e+03 0.0e+00 2.0e+04 1.5e+04 1.0e+04 5.0e+03 0.0e+00 2.0e+04 1.5e+04 1.0e+04 5.0e+03 0.0e+00 B. 9e+05 6e+05 3e+05 0e+00 9e+05 6e+05 3e+05 0e+00 9e+05 6e+05 3e+05 0e+00 9e+05 6e+05 3e+05 0e+00 I n d i a n a ) I o w a A N D e m r o f i l u g r i v . F ( C P D U A M i c h g a n i i W s c o n s n i 84 2016 2017 I i n d a n a ) I o w a A N D e m r o f i l u g r i v i M c h g a n i . F ( C P D U A i W s c o n s i n B. 9e+05 6e+05 3e+05 0e+00 9e+05 6e+05 3e+05 0e+00 9e+05 6e+05 3e+05 0e+00 9e+05 6e+05 3e+05 0e+00 2016 2017 I n d i a n a I o w a M i c h g a n i i W s c o n s n i A. 1.5e+04 1.0e+04 5.0e+03 0.0e+00 1.5e+04 1.0e+04 5.0e+03 0.0e+00 1.5e+04 1.0e+04 5.0e+03 0.0e+00 1.5e+04 1.0e+04 5.0e+03 0.0e+00 i ) e u s s T t o o R d e i r D g m 0 1 g F ( / A N D v F 4 8 16 12 Residue No Residue Treatment Treatment No−Residue Residue No−Residue Residue 12 8 Weeks After Planting 16 4 Figure 7. Corn radical root colonization by Fusarium virguliforme under two corn residue treatments over three timepoints during the 2016 and 2017 field season. Treatment values of F. virguliforme DNA are the average of three samples replicated within four blocks. DNA temporal dynamics of F. virguliforme under residue management in Indiana, Iowa, Michigan or Wisconsin (A). Area under the DNA progress curve (AUDPC) violin plots, containing boxplots was calculated for residue management in Indiana, Iowa, Michigan, or Wisconsin (B). Asterisks or letters denote significance as determined by α ≤ 0.05 by ANOVA and HSD means separation. Figure 2 No−Residue Residue Residue 85 * A. 1.2e+04 9.0e+03 6.0e+03 3.0e+03 0.0e+00 1.2e+04 9.0e+03 6.0e+03 3.0e+03 0.0e+00 i ) e u s s T t o o R d e i r D g m 0 1 g F ( / A N D v F i ) e u s s T t o o R d e i r D g m 0 1 g F ( / A N D v F B. 1.2e+04 9.0e+03 6.0e+03 3.0e+03 0.0e+00 1.2e+04 9.0e+03 6.0e+03 3.0e+03 0.0e+00 4 8 12 Weeks After Planting Treatment No Tillage Tillage 16 2 0 1 6 2 0 1 7 2 0 1 6 2 0 1 7 4 16 Residue Residue No−Residue Residue No−Residue Residue Treatment No−Residue Treatment 8 12 Weeks After Planting Figure 3. Figure 8. Corn radical root colonization temporal dynamics by Fusarium virguliforme under tillage and residue management in Michigan, during the 2016 and 2017 field season over six time points. Treatment values of F. virguliforme DNA are the average of three samples replicated within four blocks, for tillage (A) and residue (B) management. Area under the DNA progress curve (AUDPC) violin plots, containing boxplots was calculated for tillage (C) and residue (D) management. Letters denote significance as determined by α ≤ 0.05 by ANOVA and HSD means separation. b a No−Tillage Treatment Tillage Tillage No−Tillage Tillage 2 0 1 6 2 0 1 7 2 0 1 6 2 0 1 7 C. 6e+05 4e+05 2e+05 0e+00 6e+05 4e+05 2e+05 0e+00 ) A N D e m r o f i l u g r i v . F ( C P D U A D. 6e+05 4e+05 2e+05 0e+00 6e+05 4e+05 2e+05 0e+00 ) A N D e m r o f i l u g r i v . F ( C P D U A 86 C. 1.25e+08 1.00e+08 7.50e+07 5.00e+07 2.50e+07 0.00e+00 1.25e+08 1.00e+08 7.50e+07 5.00e+07 2.50e+07 0.00e+00 b a Tillage No−Tillage Treatment Tillage No−Tillage Tillage ) 2 0 1 6 A N D e m r o f i l * u g r i v . 2 0 1 7 F ( C P D U A 4 8 Weeks After Planting 12 16 Treatment No Tillage Tillage ) 2 0 1 6 A N D e m r o f i l u g r i v . D. 1.25e+08 1.00e+08 7.50e+07 5.00e+07 2.50e+07 0.00e+00 1.25e+08 1.00e+08 7.50e+07 5.00e+07 2.50e+07 0.00e+00 2 0 1 7 F ( C P D U A 16 A. 3e+06 2e+06 1e+06 0e+00 3e+06 2e+06 1e+06 0e+00 B. 2.5e+06 2.0e+06 1.5e+06 1.0e+06 5.0e+05 0.0e+00 2.5e+06 2.0e+06 1.5e+06 1.0e+06 5.0e+05 0.0e+00 i ) e u s s T t o o R d e i r D g m 0 1 g F ( / A N D v F ) e u s s i T t o o R d e i r D g m 0 1 / g F ( A N D v F 2 0 1 6 2 0 1 7 2 0 1 6 2 0 1 7 4 Residue Residue No−Residue No Residue Treatment Treatment 8 12 Weeks After Planting Figure 4. Figure 9. Soybean lower tap and lateral root colonization temporal dynamics by Fusarium virguliforme under tillage and residue management in Michigan, during the 2016 and 2017 field season over six time points. Treatment values of F. virguliforme DNA are the average of three samples replicated within four blocks, for tillage (A) and residue (B) management. Area under the DNA progress curve (AUDPC) violin plots, containing boxplots was calculated for tillage (C) and residue (D) management. Letters denote significance as determined by α ≤ 0.05 by ANOVA and HSD means separation. No−Residue Residue Residue 87 REFERENCES 88 REFERENCES Agrios, G.N. (2004). Plant pathology. (San Diego: Elsevier Academic Press). A. (2018). Root type matters: Measurement of water uptake by seminal, crown, and lateral roots in maize. J. Exp. Bot. 69, 1199-1206. 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Effects of crop rotation of soybean with corn on severity of sudden death syndrome and population densities of Heterodera glycines in naturally infested soil. Field Crops Res. 112, 107-117. 93 CHAPTER 4 Divergence in Transcriptomic Response of Symptomatic and Asymptotic Hosts Following Fusarium virguliforme Inoculation Highlights Senescence Triggered Susceptibility This chapter is unpublished, with intention to submit to Plant Cell. Baetsen-Young, A.M., Shiu, S.H., Day, B. 20XX. Divergence in transcriptomic response of symptomatic and asymptotic hosts following Fusarium virguliforme inoculation highlights senescence triggered susceptibility. Plant Cell. XX:XX-XX 94 Abstract Broad host-range plant pathogens have evolved the ability to modulate defense and immune signaling in a broad array of hosts. However, recent transcriptome analyses have only explored pathogenic interactions within species; thus, a comparative transcriptomic approach across diverse plant species will reveal complex processes that regulate host immunity and defense. In the current study, we exploited the broad host range of Fusarium virguliforme as a comparative model to identify and define differentially induced root responses between an asymptomatic monocot, corn, and a symptomatic eudicot, soybean. We posit that this approach offers a unique perspective to uncover immune regulatory responses encoding tolerance and susceptibility. Following pathogen inoculation, enriched orthologous defense response transcripts within both hosts varied in duration of upregulation, indicating earlier temporal induction of immunity may play a role in tolerance of F. virguliforme by corn. Transcription factors upregulated within corn suggest tolerance is potentially aided by no apical meristem (NAM), ATAF1/2, cup-shaped cotyledon2 (CUC2) (NAC) and Ethylene Responsive Factor (ERF) stress reduction whereas, many senescence promoting NACs were differentially regulated within soybean as the infection time-course proceeded. Activation senescence processes occurred only within the susceptible host during colonization by F. virguliforme through NACs transcription factors, reinforcing the concept of necrotrophy activated senescence. In total, this supports our hypothesis that F. virguliforme induces an environment within symptomatic hosts which favor susceptibility through transcriptomic reprogramming to promote cell senescence. 95 Introduction Contrasting phenes have answered large scale questions across biology and provided clues to the molecular underpinnings of host susceptibility and resistance following pathogen perception. Indeed, comparative approaches between phenotypically distinct responses of the same plant species to a single pathogen has been applied to numerous pathosystems, providing details of the intimate interactions from physiological- to genetic/genomic-scale resolution (O'Connell et al., 2012; Lorrain et al., 2018). In total, each of these has shed light on the varied responses plants induce to thwart pathogen infection and colonization. In brief, these typically include 1) the hypersensitive response (HR), a key hallmark of effector- triggered immunity (Chisholm et al., 2006; Dangl and Jones, 2006), 2) pattern triggered immunity (PTI; (Bagnaresi et al., 2012; Zhang et al., 2018), 3) the expression of secondary antimicrobial metabolites (Dupont et al., 2015), leading to the activation of resistance (R) genes (Etalo et al., 2013) and 4) the temporal infection dynamics between susceptible and resistant cultivars (Kong et al., 2015; Burkhardt and Day, 2016). While many critical processes underlying plant immunity have been clarified by pairwise genomics- and transcriptomics-based approaches, most studies have focused on comparison(s) of host response within cultivars of a single species or species within a genera. As a result, numerous knowledge gaps remain in understanding how pathogens with broad host ranges modulate immunity of diverged hosts. Many fungal pathogens can colonize a broad suite of agronomically important crops, yet only a few are true pathogens (Gdanetz and Trail, 2017; Banerjee et al., 2019). One exception to 96 this is phytopathogenic Ascomycetes, found within the species complex of Fusarium solani and F. oxysporum, which can colonize over 220 diverse hosts, including both monocots and eudicots (Michielse and Rep, 2009; Šišić et al., 2018). Comparing host responses across such highly diverged species colonized by the same fungus could provide novel insights into immunity induced pathways relevant to resistance and tolerance. These comparisons would be highly relevant to agroecosystems containing monocultures of rotated crops, interacting with the same microbial community each growing season (Gdanetz and Trail, 2017; Katan, 2017; Leandro et al., 2018). Surprisingly, while growers often rotate to different crops to prevent pathogen associated yield loss, recent explorations have discovered many field crops asymptomatically support fungi pathogenic to other crops (Kolander et al., 2012; Malcolm et al., 2013; Lofgren et al., 2018). Interestingly, these asymptomatic hosts could provide a comparative system to explore regulatorily networks and pathways promoting tolerance to severe phytopathogens. The fundamentals of plant immunity were built upon the framework of the eudicot Arabidopsis thaliana. Therefore, while a robust understanding of eudicot immune signaling and pathways exists, immune signaling in monocots remain enigmatic (Balmer et al., 2012; Balmer et al., 2013). Often similar genetic orthologs or paralogs are encoded within the genome of monocots, yet these well elucidated pathways in eudicots do not consistently express in the same manner in monocots (Lu et al., 2011; Balmer et al., 2013). Furthermore, attempting to translate host immune responses below ground to root-pathogen interactions remains an even larger challenge as the vast majority of phytopathology studies have focused on above ground infections and symptom development (De Coninck et al., 2015; 97 Chuberre et al., 2018). Exploring differential induced root responses to between a monocot and eudicot may unveil an immune genetic regulatorily response encoding tolerance upon an asymptomatic host that was previously unknown. To explore such a hypothesis, soybean sudden death syndrome (SDS) provides an excellent model. Differing phenotypic host responses are prominent by crops colonized by Fusarium virguliforme, the causal agent SDS (Kolander et al., 2012). Disease management is limited in soybean to chemical, cultural practices and partial host resistance, enabling an estimated annual yield loss impact greater than $330 million dollars in the United States, placing this disease in the top 5 most impactful on soybean production (Koenning and Wrather, 2010; Allen et al., 2017). Soybean and corn are often rotated in the vast majority of soybean production for agronomic and disease management reasons (Katan, 2017), yet recent research has revealed corn is an asymptomatic host for F. virguliforme (Kolander et al., 2012). With the limited resolution of molecular cross talk between this root fungal pathogen and soybean to derive mechanisms of susceptibility (Hartman et al., 2015) and the large gaps within the scientific literature surrounding asymptomatic root responses to fungal colonization, the comparison F. virguliforme colonization of corn and soybean roots provides an unique system to explore pathway rewiring of transcriptional response underlying tolerance and susceptibility within roots. To uncover genetic signatures responsible for visible phenes in corn and soybean roots, we explored (1) early temporal interactions between F. virguliforme and soybean or corn through in planta assays, (2) what early transcriptional responses of corn or soybean to F. virguliforme colonization are induced, (3) if orthologous transcriptional responses are induced in a similar temporally dependent 98 manner between soybean and corn roots, (4) to what extent non-orthologous induced defense responses aid in tolerance of corn and susceptibility of soybean to F. virguliforme. Materials and Methods Plant and Fusarium virguliforme Assay Soybean c.v. Sloan (provided by Martin Chilvers, Michigan State University), and corn hybrid E13022S (Epley Brothers Hybrids Inc, Shell Rock, IA, provided by Martin Chilvers) were surface sterilized in 70% ethanol for 30 sec, 10% bleach solution for 20 min, and then triple rinsed in sterile water for 1 min. Soybeans seeds were placed between two sheets of sterile 100 mm Whatman filter paper with 5 mL of sterile water inside a petri dish. Seeds were incubated for five days in total darkness at 21°C. Corn seeds were incubated in sterile water for 24 hours in darkness and placed between two sheets of sterile 100mm Whatman filter paper with 5 mL of sterile water inside a petri dish. Seeds were incubated for five days in total darkness at 21°C. Fusarium virguliforme Mont-1 isolate (provided by Martin Chilvers) was propagated on potato dextrose agar (Difco, Fisher Scientific) for seven weeks. Asexual macroconidia spores were collected, diluted to 1 x 105 macroconidia mL-1 and sprayed onto five-day old corn or soybean seedlings with a 3 oz travel spray bottle. Twenty-five sprays were applied to the seedlings at angles of 0°, 90°, 180°, and 270° to ensure seeds were properly covered. For mock inoculated samples, water was sprayed onto the seedlings. Seedlings were incubated 99 for 30 min with the inoculum, then excess inoculum was removed, and seedlings were incubated for an additional hour. Following incubation, three corn or soybean seedlings were placed into seed germination pouches (Mega International), containing 25 mL of sterile distilled water. Pouches containing seedlings were placed in a BioChambers Bigfoot Series Model AC-60 growth chamber with 140 µE m-2 sec -1 and 14:10 h light/dark cycle at 12°C for seven days and then 25°C for seven days. Plants were watered as needed with sterile water. Tap root from soybean or radical from corn root samples were taken at the same time (16:00 h) of day from the original 4 cm inoculation site throughout the time course. The two-week time course was repeated three independent times in the same growth chamber, with sampling of six plants for RNA isolation and three plants for DNA isolation at 0, 2, 4, 7, 10, and 14 days post inoculation (DPI) in each biological repeat. Time point 0 was sampled after completion of fungal or mock inoculation. Plant growth and disease symptomology was recorded at each timepoint by photography with a D50 Nikon camera. Fungal Colonization Analysis To visualize fungal growth on samples, microscopic analyses of corn and soybean roots were conducted at each time point for all treatments. Roots were cleared in 100% ethanol, followed by staining in a 0.05% trypan blue solution containing equal parts of water, glycerol and lactic acid (Savory et al., 2012). Fungal structures were observed using a MZ16 dissecting scope (Lecia). 100 DNA Extraction and Real Time PCR for Fusarium virguliforme DNA for real-time quantitative polymerase chain reaction (qPCR) was extracted from flash- frozen root tissue to determine the amount of fungal biomass present in samples throughout the time course. A total of 60 mg of ground root tissue from individual corn or soybean plants from each time point were extracted with a NucleoSpin Plant II Kit (Macherey-Nagel), with an additional incubation on 1 hour at 65°C during lysis. Samples were prepared for F. virguliforme DNA detection by qPCR following (Wang et al., 2015). Analysis of variance (ANOVA) was calculated for DNA quantities using the “lme4” (Bates, 2015) and “Car” (Fox, 2011) package in R v3.4.1(R Development Core Team, 2010). Means were separated at P ≤ 0.05 using Tukey’s least significant different test using the “multicomp” package (Hothorn, 2008). RNA Extraction Total RNA was isolated from 200 mg of ground flash frozen germinating macroconidia and plant root samples for messenger RNA (mRNA) sequencing with a miRNeasy Mini Kit (Qiagen). Contaminating DNA was removed with TURBO DNA Free DNase (Invitrogen). RNA quality was determined by gel electrophoresis and the 2100 Bioanalyzer (Agilent) with the Agilent RNA 6000 Pico kit. 101 Library Preparation and Sequencing The same extraction for each sample was used for mRNA and sRNA library preparation. Libraries were prepared using the Illumina TruSeq mRNA Library Preparation Kit from three biological repeats of each time point of F. virguliforme or mock inoculated corn or soybean or germinating macroconidia samples by the Michigan State Research Technology and Support Facility. Samples were pooled and sequenced on the Illumina HiSeq 4000 under single end 50 bp mode. Base calling was done by Illumina Real Time Analysis (RTA) v2.7.7 and output of RTA was demultiplexed and converted to FastQ format with Illumina Bcl2fastq v2.19.1 Samples for sRNA were prepared using the NEB Next Small RNA Sample Prep Kit at University of Illinois at Urbana-Champaign W.M Keck Center. Samples were pooled and sequenced on the Illumina HiSeq 4000 under single end 50 bp mode. Fastq files were generated and demultiplexed with the bcl2fastq v2.20 Conversion Software (Illumina). mRNA-Sequencing Processing and Differential Analysis Reads were trimmed for adapter presence and quality score by Trimmomatic (v0.33) (Bolger et al., 2014). The trimmed reads were uniquely mapped to the corresponding reference genomes of soybean (Wm82.a2.v1), and corn (B73 RefGen_v4, AGPv4) HISAT2 (v 2.1.0) (Kim et al., 2015) with the following parameters --dta --rna-strandness F. Hits from (v 2.18.1) HISAT2 were converted (http://broadinstitute.github.io/picard/). Alignments were then counted by HTSeq (v0.6.1) (Anders et al., 2014) with the following options: --minaqual 50 -m intersection-strict -s format by Picard 102 from SAM to BAM reverse --idattr=gene_id. Gene counts were imported into DESeq2 (v1.22.2) (Love et al., 2014) conducted in R, normalized for library size and log2 transformed to determine correlation of biological replicates at each time point. To determine differential gene expression DESeq2 (v1.22.2) executed in R (R Development Core Team, 2010) with raw HTSeq counts. Gene counts were filtered less than 10 across 90% of samples. DESeq2 was applied to determine significant genes with an adjusted P ≤ 0.05 and greater than 1-fold difference between mock and inoculated. Pairwise comparison within each time point and within each host, of F. virguliforme and mock inoculated samples. Differential Gene Co-expression Network Analysis Genes were also filtered for differential gene correlation analysis (DGCA) (McKenzie et al., 2016) implemented in R (R Development Core Team, 2010) for 90% of genes with less than 10 across all samples. These 43,308 soybeans or 28,956 corn genes were variance stabilized transformed for importation and Pearson correlation of individual gene pairs within each treatment of were calculated and compared to across mock or inoculated treatments to assign differential correlation by significance of median difference in z-score in default settings, with an Bonferroni correction of p-values (McKenzie et al., 2016). Significant gene pairs from differentially induced classes (e.g. +/0, -/0, 0/-, +/0, +/- and -/+) were weighted by the z-score difference between treatments to convert into a planar filtered network (PFN). These gene pairs were imported into MEGENA and multiscale modules and hubs were identified, with a hub detection significance threshold of P < 0.05, module significance 103 threshold of P < 0.005, network permutations of 100 and module size greater than 10 (Song and Zhang, 2015). Overall differential gene expression correlation within corn discovered 129 significant gene pairs from 419,152,582 gene pairs between mock and inoculated. Soybean differential gene expression correlation identified 5,526,057 significant gene pairs from 904,379,186 gene pairs between treatments. While corn differential gene correlation PFN was limited, soybean differential gene pairs were clustered by MEGENA into 1,161 modules. Modules were visualized in ggplot2 (v3.1.1) package (Wickham, 2016) in R. Identification of Orthologous Genes The longest protein sequences for genes from soybean and corn were analyzed by OrthoFinder (v2.2.7) in default settings. Protein sequences were accessed from Ensembl Plants (http://plants.ensembl.org/index.html). A total of 25,362 soybean and 15,158 corn genes were discovered to be contained within 10,700 orthogroups. This dataset was then applied to filter log2 fold changes between mock and inoculated corn or soybean at the orthogroup level. As many orthogroups contain more than a one: one relationship, the median of gene log2 fold changes of all genes within one host orthogroup was completed, as the median transformation of genes within an orthogroup captured the most variation between soybean and corn (Figure 10) 104 Differential Analysis of Orthogroups To determine differential orthogroup expression DESeq2 (v1.22.2) executed in R (R Development Core Team, 2010) with median transformed gene HTSeq counts within each orthogroup. Orthogroup counts were filtered less than 10 across 90% of samples. DESeq2 was applied to determine significant genes with an adjusted P ≤ 0.05 and greater than 1-fold difference between mock treatments between soybean and corn, as well as, inoculated treatments between soybean and corn at each time point. Gene Ontology Enrichment Analysis The gene ontology was retrieved from the GFF file of the soybean genome annotation (Wm82.a2.v1) and the revised gene ontology annotation for corn (Wimalanathan et al., 2018). The gene ontology annotation varies in completeness between soybean and corn. For example, the defense response term (GO:0006952), has 374 genes annotated in corn, however, in soybean only 86 genes are annotated. To have an ontology that is similar for an ortholog comparison, we merged gene ontologies from corn and soybean within each orthogroups, keeping only unique ontology terms. In total, all 10,700 orthogroups had gene ontology assignment. Orthogroup lists from either differential analysis were analyzed by TopGO (2.34.0) conducted (Alexa A and Rahnenfuhrer J, 2018) in R. Fishers Exact Test was conducted on each orthogroup set with an adjusted P-value ≤ 0.05 to determine significance of enrichment 105 across all orthogroups. Additionally, lists of genes from differential analysis or gene differential clustering modules were analyzed for GO term enrichment by the singular enrichment analysis within AgriGO (v2) website (Tian et al., 2017) with the Fisher statistical adjustment method at a significance level of 0.05. Orthologous Transcription Factor Analysis Transcription factors for corn were retrieved from http://planttfdb.cbi.pku.edu.cn/. Genome v3 gene ID were converted to v4 IDs for the 2,290 transcription factors and orthologous genes with soybean were discovered within the orthogroup dataset, entailing 508 orthogroups. These orthogroups were filtered for significant defense induction over the time course. Results Fusarium virguliforme Growth is Similar on Symptomatic and Symptomatic Hosts To explore induced defense genes, time-series samples between water (mock) and F. virguliforme-inoculated soybean and corn was conducted over a 2-week period. This experimental design permits the discovery of stage-specific immune/susceptibility responses enabling a better understanding of the genetic processes required for immunity (Burkhardt and Day, 2016a). As shown in Figure 11 and Figure 12, plant growth and development were observed to be similar between mock and inoculated plants of soybean 106 and corn. As shown, in soybean, the radical elongated from 0-4 days post inoculation (DPI), with lateral roots developing at 7 DPI. Secondary laterals formed at 10 DPI, with unfolding of the cotyledons. By 14 DPI, prolific lateral and tap root growth had occurred in both treatments, and full expansion of cotyledons, representing a final VC plant developmental stage for soybean. Consistent with the hemibiotrophic lifestyle of F. virguliforme, no symptoms were apparent on soybean roots form 0-4 DPI, indicative of a biotrophic stage (Ngaki et al., 2016). At 7 DPI, a chlorotic discoloration of the lower tap root of the F. virguliforme treatment was observed, which developed into necrotrophic streaking of the tap root by 10 DPI. Total necrosis engulfed lower hypocotyl, upper tap root and spread to lower lateral roots adjoining the tap root (Figure 12A). No necrosis was noted in mock treatments. Inoculated and mock inoculated corn seedlings exhibited comparable growth and development patterns as well. The radical and seminal roots were slowly extending and by 4 DPI had expanded in length. At 7 DPI lateral roots were initializing along the primary root, and the crown roots had started emerging (Figure 12B). By 14 DPI, the seminal, crown, and primary roots had grown prolifically, and above ground, leaves had fully expanded to the V1 growth stage. As no visual symptom development was observed on F. virguliforme-inoculated corn roots, we applied a more sensitive approach to evaluate fungal growth and penetration. To this end, we employed qPCR to identify and quantify fungal level from our time-course inoculation analysis (Figure 11B). Fusarium virguliforme DNA concentrations increased on both soybean and corn roots throughout the time-course, with 0 and 2 DPI displaying significantly lower levels of F. virguliforme DNA quantities than 14 DPI (P < 0.0001, Fig. 9B). 107 Both inoculated soybean and corn exhibited similar levels of F. virguliforme, as determined by qPCR at each time-point and were significantly different from mock treatments (P < 0.001). Background levels of F. virguliforme DNA were detected in mock inoculated roots, which corresponds to the non-specific nature of the qPCR assay below 10 fg of DNA (Wang et al., 2015). Once we established that F. virguliforme had colonized both hosts over our time-course in both corn and soybean, we next conducted a comparative RNA-sequencing at six time-points across a 0-14-day interval, post-inoculation to explore induced defenses between soybean and corn. After trimming for adapters and low-quality reads, 76-83% of soybean and 77- 81% of the corn reads could be uniquely mapped to the respective genomes (Fig. 9C, Supplemental Data Set 1-1). The percent of reads aligning varied throughout the time- course, indicating a host mRNA production varied across the time-course and between the hosts. However, in F. virguliforme inoculated samples, mRNA alignments decreased over time, particularly within soybean at 10 DPI. We hypothesize that this decrease in read alignment may correspond to necrosis development and cell death of tissues, thus resulting in a lack of mRNA production (Chen et al., 2008). Temporal Expression of Defense Induced Genes in Corn or Soybean To determine if defense-induced responses underpin phenotypic symptomatic and asymptomatic disease development, a comparative transcriptional approach was undertaken. We posit that this approach will reveal pathway rewiring of transcriptional 108 response underlying tolerance and susceptibility within roots (Chen et al., 2016; Chowdhury et al., 2017) We identified a high correlation (i.e., >96%) between biological replicates; this indicates the robustness of both the experiment and the sampling (Figure 13). To explore expression patterns between treatments of mock and F. virguliforme, we clustered samples by principle coordinate analysis. As shown in Figure 14A, gene expression from corn roots tended to be primarily affected by time, instead of fungal treatment. This indicates that plant (i.e., root) development had a larger impact on gene expression than fungal colonization, with distinct clustering at 0, 2-7, 10, or 14 DPI. While this highlights significant changes in gene expression throughout the time course may be driven by development, the comparison of mock and fungal-inoculated treatments at each time point provided us an opportunity to discover genes that are specific to defense and immunity. Conversely, samples from soybean clustered by treatment throughout the time-course (Figure 14B). Indeed, at 0 and 2 DPI, both gene expression profiles clustered together; however, by 4 DPI, mock and fungal inoculated treatments had separated into distinct groups. The remaining samples were noticeably separated by treatment over the time-course. We posit that the large separation between treatments suggests large global expression changes within soybean roots, a response that is coincident with F. virguliforme-induced disease symptom development. Next, to explore genes that were induced by F. virguliforme treatment, we filtered the dataset for genes that were significantly up or down regulated (adjusted P-value < 0.05, |log2(fold change)| > 1) in the fungal-inoculated samples compared to the mock inoculated. In total, 28,956 genes were expressed in at least one timepoint over the time course in corn roots. A threshold of 1-log2 fold change was selected, as only 600 unique in corn was were 109 differentially expressed at this threshold across all time points. Of the significantly induced genes in corn roots to F. virguliforme, 267 were up regulated across the entire time course, with the majority of expression occurring at 0 and 14 DPI (Figure 14C, Table 10). In soybean root tissue, a total of 43,308 genes were expressed at one or more time points. Of these, more were significantly induced by F. virguliforme than in corn roots, within the time course, for a total of 10,898 unique genes. There was an almost equal ratio of positively and negatively regulated genes (5,326 and 5,644, respectively). But majority of induced genes primarily occurred after 7 DPI (Figure 14D, Table 11). This contrasts with the temporal induction of defense related genes in corn, highlighting this differential expression may promote the divergence in phenotype between corn and soybean. To understand the function of genes underlying temporal differences of expression, we next conducted a Gene Ontology (GO) gene set enrichment analysis of the defense-response associated induced genes. Previous studies have observed that fungal interactions within roots elicit an array of immune responses, including the production of both reactive oxygen species (ROS) and secondary metabolites associated with immunity (Pusztahelyi et al., 2016; Zhang et al., 2017). Interestingly, we identified an enriched defense related response specifically from corn roots upregulated genes throughout the time course (Table 12). These genes were enriched for defense response, oxidation reduction, and terpenoid biosynthetic processes (Figure 14E), suggesting that corn, while phenotypically asymptomatic, recognized F. virguliforme as a pathogen. As expected, a broader response was enriched in samples from soybean roots, with 47 significant biological processes identified (Table 13). Among these processes were also defense responses and oxidation reduction pathways, but 110 additionally, phosphate metabolism, protein ubiquitination and cell to cell processes are indicative of a larger disruption and alteration of soybean root processes by F. virguliforme. Interestingly, the response of defense related processes were upregulated at earlier (0-4 DPI) time points in corn than soybean roots (Figure 14F). Specifically, defense responses and oxidation processes appeared to be up regulated in soybean 7-14 DPI. The divergence of temporal reactions related to defense may support tolerance in corn and susceptibility in soybean. To define the broader implications of defense response induction in corn and soybean, we next explored the global co-expression of each host’s transcriptome. As such, we hypothesized that changes in gene interactions stem from specific fungal interactions with each host. To address this, a differential gene correlation analysis (DGCA, see methods) was undertaken to discover significant changes within gene pairs resulting from treatment by employing the median z-score difference of the gene pair correlations in the first condition compared to the second condition, and then compared to all gene pairs. Investigating individual gene pair interactions enables one to discover gene co-expression on a much finer scale (McKenzie et al., 2016). Through this approach, in samples from corn, we found that 0.00003% of gene pairs were significantly differentially correlated, representing 129 gene pairs (Supplemental Dataset 1-2). Surprisingly, within this analysis, two R-genes were identified as having significant differential correlations. Resistance to Pseudomonas syringae pv. maculicola (RPM1) (Zm00001d014099) was positively correlated with xyloglucan galactosyl-transferase (Zm00001d029862) in the fungal treatment, which was negatively correlated in the mock treatments. Expression of RPM1 was non-significantly down 111 regulated by 4.29 log2 fold change at 0 DPI, but gradually increased over the time course to 1 log2 fold change. Interestingly, this correlation analysis also highlighted the negative correlation of disease resistance protein RPP13-like 4 (Zm00001d018786) with Rho-N domain containing protein (Zm0001d051967); this correlation was positive under mock- inoculation conditions. Interestingly, this R-gene was identified as a candidate in SDS resistance through a genome-wide association study (GWAS) of soybean populations (Zhang et al., 2015). Herein, this R-gene was significantly down regulated at 4 DPI between treatments, indicating while this trait is associated with resistance in soybean, it is not significantly upregulated in corn and is likely not associated with pathogen tolerance. Similar to the differential gene expression analysis within soybean, significantly more gene pairs were observed as significantly differentially correlated when compared to corn, totaling 5,526,057, or 0.61% gene pairs. To understand how these differentially correlated gene pairs were co-expressed, we built a planar filtered network to resolve a multi-scale co- expression network of these genes (see Methods). The fine scale of the DGCA enabled 1,161 modules to be formed. Of these modules, 198 contained hub genes related to defense and disease development and were hierarchically organized into four clusters (Figure 15). However, these small modules were not enriched by GO. The lack of enrichment could stem from the small scale (< 100 genes) of these clusters. 112 Orthologous Host Pathways Reveal Different Patterns of Induced Defense Responses Previous studies comparing susceptible and resistant cultivars have identified temporal changes in gene expression, a process hypothesized to be associated with both compatible and incompatible pathogen interactions (Dupont et al., 2015; Kong et al., 2015). As noted above, we observed temporal changes in induction of defense related gene expression in both soybean and corn (Figure 14F). To understand if defense induced responses between soybean and corn response in a temporally similar or diverged manner, we compared expression of orthologous genes between hosts. As we observed temporal patterns of earlier upregulation of corn defense processes, we focused upon these genes within orthogroups that were significantly regulated within both hosts. To explore if defense response induction was dependent upon respective host, we clustered orthogroup response by significant upregulation unique to each host or across both hosts (Figure 16A). The vast majority of orthogroups that were significantly upregulated, were uniquely induced in soybean, accounting for 94% of up regulated orthogroups. Only 1.2% of orthogroups were uniquely upregulated in corn, and 4.7% were conserved in upregulation across both hosts in response to F. virguliforme. Orthogroups that were uniquely upregulated in soybean were enriched for ROS processes, responses to defense and external stimuli, terpenoid metabolism, and abscisic acid metabolism (Table 14). Similarly, orthogroups that were upregulated in both hosts in response to F. virguliforme were enriched for multi-organismal processes, defense (Table 15). No orthogroups that were upregulated only in corn were enriched by GO. This lack of 113 enrichment stems from the few orthogroups (19) within this classification. While both clusters of orthogroup regulation unique to soybean and shared between soybean and corn were related to defense, we were interested in exploring how temporal patterns of orthogroup induction differed between soybean and corn. Therefore, we applied upregulated defense relevant GO groups that were enriched within corn at earlier (0-4 DPI) (Figure 14E) to capture the temporal response of orthologous genes that were enriched within soybean. Filtering upregulated orthogroup for GO processes of defense response, defense response to fungus, negative regulator protein metabolism processes, ROS and terpenoid biosynthesis (Figure 16B) revealed a shift in temporal expression between hosts. This change in upregulation of orthologous genes occurred at 2 and 4 DPI, in corn, whereas this regulation did not initialize until 4 to 7 DPI in soybean. Genes within these upregulated orthogroups encoded antifungal genes, including pathogenesis related protein 10 (PR10), stress induced protein 1, terpene synthase and Browman-Birk type trypsin inhibitor, suggesting corn activates specific responses to thwart F. virguliforme growth earlier than soybean. Interestingly, ROS production was similar during interactions between soybean and corn, indicating ROS bursts from fungal interaction upon roots were consistent in both hosts at early time points. Non-orthologous Defense Processes within Soybean and Corn While orthologous defense-related processes between soybean and corn identified temporal divergence in host response to this pathogen, we were also curious if these induction patterns were host-specific, as well as potentially relevant to the observed disease 114 phenotype. To address this, we interrogated the same defense related GO categories that were classified as enriched in corn (Figure 14E) and asked if non-orthologous genes were also significantly upregulated following fungal infection. Using this approach, we found that non-conserved temporal gene expression patterns related to defense were similar between soybean and corn; this includes processes associated with terpenoid biosynthesis, defense response to fungus, and ROS (Figure 17A). Similar to orthologous defense genes comparisons, broad defense processes related to antifungal activity were upregulated earlier in corn. Genes associated with this response include pathogenesis related maize seed (PRSM), TIFY10B (containing JASMONATE ZIM DOMAINE repressors), and hevein-like. Interestingly, both PRSM and hevein were previously identified as induced by fungal colonization (Majumdar et al., 2017; Wong et al., 2017). Similarly, TIFY10B was up-regulated in corn roots, and further analysis exploring the jasmonate biosynthesis pathway revealed minimal changes in expression between mock and treated, demonstrating jasmonic acid pathways in corn roots were repressed (Figure 17B, Figure 18). As jasmonate precursor expression was not altered by F. virguliforme colonization in corn, we were curious as to what role, and through what mechanism(s) corn roots might perceive this fungal pathogen. To explore this question, we next interrogated several defense makers specific to corn root fungal interactions, as previously described (Balmer et al., 2013; Chuberre et al., 2018). As show in Figure 16B and Figure 17, pathogenesis related (PR) genes were induced over the selected time-course. However, while the array of PR genes that were expressed indicates that defense pathways were induced, NONEXPRESSER OF PR GENES 1 (NPR1) was not up regulated in corn roots (Figure 18), suggesting upregulation of PR genes 115 is salicylic acid independent (Balmer et al., 2012). Early responses of callose deposition in response to chitin perception did not seem to be activated either, and instead CYP7A12 was downregulated at 4 DPI. Auxin and gibberellic acid expression did not appear to be modified in transcriptional expression throughout the time course. Ethylene biosynthesis was upregulated in corn roots (Figure 18), additionally ETHYLENE RESPONSIVE FACTOR 105- LIKE (ERF-105-like) was significantly upregulated in corn (Figure 17B) at 0 DPI. This is significant, as ethylene biosynthesis has been recently reported to be induced in SDS resistant soybean cultivars (Abdelsamad et al., 2019), a mechanism proposed to enhance host resistance to necrotrophs (Laluk and Mengiste, 2010). ERF-105-like transcription factor is associated with ethylene-responsive ROS regulation, limiting cellular damage (Bolt et al., 2017). In total surveying the phytohormonal landscape through transcriptomic expression has suggested that ethylene regulation may play key role in activating corn root defenses to F. virguliforme. Constitutive Expression of Corn Genes within Orthologous Defense While our primary goal herein was to characterize induced defense responses following fungal inoculation, as well to define the orthologous defense networks between during infection of a symptomatic (i.e., soybean) and asymptomatic (i.e., corn) host, the role of constitutive defense-associated responses remains. Previous studies suggest divergence in the temporal induction of defense responses between plants (Liu et al., 2018). To determine if the duration of constitutive orthologous gene expression in corn might underpin these responses, we next compared expression of soybean and corn orthogroups following mock 116 inoculation. Using this approach, we identified 182 orthogroups that were significantly upregulated in corn, in comparison to soybean, over the duration of the inoculation time- course (Figure 19). Based on this, we hypothesized that these orthogroups provide tolerance to F. virguliforme within corn through elevated expression during early interactions and moreover their expression would decrease in contrast to an induction in defenses in F. virguliforme inoculated soybeans. Surprisingly, a similar pattern of temporal expression of the constitutively up-regulated orthogroups was observed from 2-14 DPI in both hosts inoculated with F. virguliforme (Figure 19). Additionally, constitutively induced corn responses were 2 log2 higher in the F. virguliforme treatment when compared to the constitutive expression of the same genes under mock conditions. Orthologs in soybean do not appear to be upregulated to a similar extent upon colonization by F. virguliforme colonization, suggesting these responses within corn may assist in tolerance to F. Divergence of Defense Expression Patterns Across Orthologous Transcription Factors The temporal expression patterns of defensed induced orthologous genes by F. virguliforme colonization varied by host, indicating potential differences in pathway activation or repression by transcription factor expression. Within our orthologous dataset between soybean and corn, we extracted the transcription factors, and compared the changes in differential expression induced within each host by F. virguliforme, across the infection time course. Similar to orthologs that were upregulated by F. virguliforme, the vast majority of the differentially expressed transcription factors were uniquely to soybean. Only four virguliforme. 117 transcription factor orthogroups were uniquely induced in corn, while 11 were induced in both soybean and corn in response to F. virguliforme. Being we were interested in transcription factors altering temporal expression across both hosts, we focused our attention on transcription factors induced within both hosts. Within the selected cluster of transcription factors orthogroups, three were expressed at 2 log2 fold change greater in corn when compared to soybean at 0 DPI (Black box, Figure 20A). A NAC042 transcription factor (No Apical Meristem (NAM), Arabidopsis Transcription Activation Factor (ATAF), and Cup-Shaped Cotyledon (CUC)) exhibited the largest expression difference when comparing soybean changes between mock and inoculated treatments to the expression differences in corn induced by treatment. This transcription factor is responsive to hydrogen peroxide (Wu et al., 2012). Hydrogen peroxide production was elevated by transcription factor ZAT12 (C2H2-zinc finger proteins) (Mittler et al., 2006) in corn at 0 DPI, indicating ROS production stimulated NAC042 mRNA accumulation in corn more so than soybean, which may account for the >2 log2 fold change difference. The third transcription factor that was induced greater in corn was DREB1A (DEHYDRATION- RESPONSIVE ELEMENT-BINDING PROTEIN 1A). NAC042 can bind and activate DREB1A to lower oxidative stress homologs in tomato (Thirumalaikumar et al., 2018). This process suggests that corn is able to quickly attenuate ROS production after defense responses are activated and lowers plant cell stress. However, NAC042 is also a negative regulator of senescence (Wu et al., 2012). If F. virguliforme activates ethylene defense responses in corn, we hypothesize that it may be as a result of host recognition of F. virguliforme as a necrotroph, and that prolonged expression of NAC042 is promoting cell longevity, thereby 118 inhibiting the induction of cell death. Interestingly, NAC042 expression in soybean did not exceed levels of > 2 log2 fold change till 7 DPI, after symptom development. This made us wonder if host senescence plays a key role in sudden death syndrome development. To explore this, we filtered the soybean transcriptome for significantly expressed NACs. Using this approach, we identified an additional seven NACs that were differentially expressed in soybean; interestingly, previous work demonstrated that these NACs play a role in both the negative and positive regulation of senescence, as well as in root development (Majid and Abbas, 2019) (Figure 20B, Supplemental Data Set 1-3). Three of these NACs have roles in senescence development, and include NAC047, NAC055 and NAC087, of which NAC055 and NAC087 are abscisic acid responsive (Huysmans et al., 2018). In addition to ROS production, an increase of 9-CIS-EXPOSYCAROTENOID DIOXYGENASE (NCED), the first step in ABA production (Xiong and Zhu, 2003) increased at 2 DPI to 4 log2 fold change and NACs upregulation was initialized at 4 and 7 DPI. However, these genes were not up regulated in corn roots. Several senescence associated genes (SAGs) were identified as up-regulated in soybean as early as 4-7 DPI. This is interesting, as previous work showed that SENESCENCE ASSOCIATED GENE 13 (SAG13) is associated with the cell death response (Pell et al., 2004), SAG21 and SAG24 are both upregulated during early senescence (Salleh et al., 2012). Intriguingly, SAG20 is activated by Necrosis and Ethylene Inducing Peptides (NEPs) from Fusarium spp, including similar phytotoxins produced in F. virguliforme (Chang et al., 2016). Also, SENESCENCE REGULATORY GENE 1 (SAG 1) mRNA was elevated at 7 DPI. Again, none of these senescence-inducing processes, or associated genes, were significantly induced in corn across the time course of infection. 119 Plant development is also regulated by NACs transcription factors, which were induced in soybean roots when exposed to F. virguliforme. Lateral root growth is induced by NAC022 (Xie et al., 2000), which was down regulated in soybean when colonized by F. virguliforme. In similar fashion, NAC074 positively regulates xylem development (Xia et al., 2018), which was down regulated in soybean starting at 7 DPI. Vascular development, negatively regulated by NAC083 by repressing VASCULAR-RELATED NAC-DOMAIN7 (VND7) (Yamaguchi et al., 2010) was upregulated across the time course. However, root hydraulic conductivity was upregulated by decreasing the expression of NAC104, which negatively regulates water conductivity in association with differentiating tracheary elements (Zhao et al., 2008; Tang et al., 2018). In total, these processes demonstrate root development was altered when colonized by F. virguliforme, reducing development of vascular system and altering differentiation. Transcriptomic dissection of resistant and susceptible host responses to phytopathogens is common, and numerous studies have illuminated our understanding as to how host-specific pathogens modulate immunity. This approach provides a foundation to comparatively explore how immune pathways respond across diverged hosts to a single pathogen with a broad host range. Here, we applied the broad host range of F. virguliforme as a comparative system to probe differentially induced root responses between a monocot and a eudicot to uncover immune regulatory responses encoding tolerance and susceptibility. To do this, we Discussion 120 generated 72 transcriptomes across mock and inoculated hosts to pinpoint host responses specific to fungal root colonization throughout a two-week infection time course. Soybean and corn have distinct phenotypic responses to F. virguliforme colonization. Soybean becomes symptomatic with root chlorosis at 7 DPI, and tap root necrosis at 14 DPI, and corn roots do not develop symptoms. Yet, underlying phenotypic differences are similar levels of fungal colonization, supporting corn as an asymptomatic host for F. virguliforme (Kolander et al., 2012). Corn root growth and development did not appear to shift in response to F. virguliforme colonization and were reflected by changes in gene expression when colonized, enriched for immune responses. Inversely, soybean roots exhibited a massive reprogramming of transcriptomic responses induced by symptom development. The divergence in magnitude of host responses has been observed within studies of a single host being colonized by pathogenic and non-pathogenic isolates (Lanubile et al., 2015). While the number of genes induced by F. virguliforme root colonization varied substantially, both hosts exhibited gene expression that were enriched for defense, indicating temporal induction of immunity may play a role in tolerance of F. virguliforme by corn. Comparing orthologous expression of defense related processes highlighted the differential host responses between soybean and corn. Very few orthologous genes were differentially express in both hosts, suggesting a large transcriptomic shift in soybean. Orthologs encoding defense responses that were induced by F. virguliforme exhibited disparate temporal patterns of induction. Gene expression of corn and soybean changed the most between 2 and 7 DPI, representing recognition of the fungal interactor, and initiating defense responses, as 121 well as the fungus shifting form a biotrophic phase to a necrotrophic phase on soybean. Shifts in host gene expression derived from fungal life style changes during colonization have been recorded in hemibiotrophic interactions (Njiti et al., 1996; Chowdhury et al., 2017). Defense related process are upregulated both hosts encoding hydrogen peroxide bursts, and anti- microbial proteins to limit fungal growth. However, as the infection time course proceeds many orthologous defense genes within corn are lower in expression, indicating a return to normalization, similar to pathogen resistant cultivars (Chen et al., 2016). Inverse to corn, soybean defense responses were increasing to highest levels at 14 DPI, highlighting a delayed response to F. virguliforme colonization. Non-orthologous defense induced responses exhibited similar trends between hosts for majority of defense processes, suggesting the temporal dynamics of orthologous response maybe key to enabling tolerance in corn. Expression of orthologous anti-fungal metabolites by 2 DPI may restrict further spread of this fungus to other root structures (Allardyce et al., 2013; Kuhar et al., 2013). Overall, this suggests early recognition and activation of defense s in critical for tolerance. Early activation of defense responses in corn may stem from induction of ethylene production and ROS production. Induction of ethylene in soybean roots lowered disease severity and activated PR gene and ROS (Abdelsamad et al., 2019), as observed herein. Reactive oxygen species also induce transcription factors regulating cell stress, such as ERF- 105 (Bolt et al., 2017) and NAC042 (Thirumalaikumar et al., 2018). The reduction. The reduction of ROS may limit further colonization as the fungus switches from a biotrophic to necrotrophic lifestyle (Chowdhury et al., 2017). 122 As F. virguliforme transitions from a biotrophic to necrotrophic lifestyle on soybean, a large transcriptomic rewiring occurs. Plant pathogens acting as necrotrophs will promote plant cell death, which results in a drastic shift in host gene expression. Indeed, over the time- course of analysis, we observed several critical senescence related transcription factors from soybean as being distinctly activated, or repressed, following pathogen infection. This in turn activated several senescence related genes within soybean root, promoting inhibition of protein synthesis, hydrolysis of macromolecules and degeneration of cells (Podzimska- Sroka et al., 2015). The up-regulation of cell senescence would promote further colonization of by a necrotrophic fungus, such as F. virguliforme. Colonization of soybean roots by F. virguliforme triggers further ROS production, which will in turn actives the induction of NAC senescence-associated transcription factors (Haffner et al., 2015). Additionally, senescence processes through NAC transcription factors are also induced by abscisic acid; this is interesting, as we observed induced expression in soybean roots at 2 DPI, concomitant with NAC055 and NAC087 were upregulation at 4 and 7 DPI, respectively. We posit that F. virguliforme susceptibility in soybean is mediated in part by pathogen-induced senescence responses during infection. This activation of senescence by necrotrophs has been noted for other fungi. When Sclerotinia sclerotium colonized a compatible host, the pathogen will secrete oxalic acid, triggering ROS production and program cell death to the advantage of the necrotrophy (Williams et al., 2011). Similarly, F. virguliforme can secrete toxins to initiate cell death; however, this has only been documented in foliar tissues (Chang et al., 2016). Hemibiotrophic vascular pathogen F. oxysporum activates senescence by targeting the COI1 123 of the JA-binding JAZ-COI1 co-receptor (Thatcher et al., 2009) We demonstrated the activation senescence processes occurred only within the susceptible host during colonization by F. virguliforme through NACs transcription factors, reinforcing the concept of necrotrophy activated senescence. The lack of information surrounding the molecular signature(s) responsible for senescence in roots lessens our ability to precisely define the factors critical for triggered senescence. Further studies regarding root senescence within or without pathogen colonization would provide clarity to mechanisms of senescence with plant’s organs below ground. Plant senescence and defense have many critical pathways that overlap (Yuan et al., 2019). Several NAC transcription factors discovered to be altered in expression herein, have played roles in immunity and susceptibility. Knock out of NAC055 increases resistance of Arabidopsis to Botrytis spp. infection (Bu et al., 2008). Also, NAC042 activates an anti-microbial camalexin biosynthesis in Arabidopsis to Alternaria brassicicola (Saga et al., 2012). Additionally, hypersensitive responses within plant defenses utilizes similar pathways as host natural senescence by induce program cell death (Yuan et al., 2019). The resolution to decipher if gene expression is indicative of pathogen triggered senescence or host induced susceptibility remains currently enigmatic. However, the mounting evidence presented herein, and previously seems to suggest that necrotrophic pathogen manipulation of genetic pathways leading to a favorable host environment are evident (Häffner et al., 2010; Williams et al., 2011; Haffner et al., 2015; Podzimska-Sroka et al., 2015; Chowdhury et al., 2017). The culmination of pathogen lifestyle, host genetics and plant development should be further 124 Acknowledgments explored to decipher the interaction of defense responses and pathogen accelerated senescence. We would like to recognize Michigan State University (MSU) project GREEEN and the C.S. Mott Foundation for funding of ABY and research. Marty Chilvers for providing the Fusarium virguliforme mont-1 isolate. We would also like to recognize the support staff at the MSU Institute for Cyber enabled Research High Performance Computing Cluster for assistance in software optimization. This research was supported by funding from the MSU Plant Resilience Institute. Author Contributions Designed framework: B.D., S.H.S., A.B.Y.; Conducted Experiments: A.B.Y; Analyzed data: A.B.Y., S.H.S., B.D. Wrote the manuscript: A.B.Y., S.H.S., B.D. 125 Table 10. Significantly induced defense genes in corn. To be considered significantly induced genes needed to be 1-fold change or greater in F. virguliforme treated compared to mock inoculated with an adjusted P > 0.05. DPI 0 2 4 7 10 14 DPI 0 2 4 7 10 14 DPI 0 2 4 7 10 14 2 10 13 57 10 2 7 35 Corn Inoculated Down Regulated 2 10 5 17 Corn Inoculated Up Regulated Corn Inoculated 4 7 11 24 7 4 7 21 4 7 4 3 14 457 14 151 14 306 0 92 0 86 0 6 k c o M n r o C k c o M n r o C k c o M n r o C 126 Table 11. Significantly induced defense genes in soybean. To be considered significantly induced genes needed to be 1-fold change or greater in F. virguliforme treated compared to mock inoculated with an adjusted P > 0.05. Soybean Inoculated 14 DPI 7 4 0 2 266 4 7 1,737 10 9,542 14 DPI 4 7 14 0 2 232 4 1,538 7 10 14 4,957 14 7 4 DPI 0 2 34 4 7 199 10 4,585 14 10 2 151 4,564 Soybean Inoculated Up Regulated 2 10 121 3,256 Soybean Inoculated Down Regulated 2 10 35 1,308 0 771 0 724 0 47 k c o M n a e b y o S 127 k c o M n a e b y o S k c o M n a e b y o S GO term Ontology Description Adjusted P- significantly up regulated between Fusarium virguliforme inoculated and mock Table 12. Gene ontology enrichment of biological processes within corn genes treatments across the time-course of two weeks. GO:0055114 GO:0016114 GO:0006721 GO:0006952 GO:0051346 GO:0044710 GO:0008299 GO:0051248 GO:0032269 GO:0050832 GO:0006720 GO:0009620 oxidation-reduction process terpenoid biosynthetic process terpenoid metabolic process defense response negative regulation of hydrolase activity single-organism metabolic process isoprenoid biosynthetic process negative regulation of protein metabolic process negative regulation of cellular protein metabolic process defense response to fungus isoprenoid metabolic process response to fungus value 0.0012 0.0012 0.002 0.0026 0.0098 0.0098 0.0098 0.016 0.016 0.021 0.022 0.032 P P P P P P P P P P P P 128 Adjusted P-value GO term Ontology Description significantly up regulated between Fusarium virguliforme inoculated and mock Table 13. Gene ontology enrichment of biological processes within soybean genes treatments across time course of two weeks. 2.40E-25 GO:0055114 1.40E-21 GO:0008152 4.20E-19 GO:0043687 1.50E-18 GO:0006464 1.10E-17 GO:0006468 7.80E-17 GO:0043412 GO:0016310 2.60E-16 2.60E-16 GO:0006796 GO:0006793 2.60E-16 GO:0051704 3.70E-11 4.40E-11 GO:0009875 GO:0008037 4.40E-11 GO:0048544 4.40E-11 4.40E-11 GO:0009856 GO:0000003 8.00E-11 GO:0022414 8.00E-11 4.90E-10 GO:0032501 GO:0006979 1.80E-05 9.60E-05 GO:0016567 GO:0070647 0.00012 0.00012 GO:0032446 GO:0006952 0.00031 GO:0007154 0.00048 0.0014 GO:0009607 GO:0016052 0.0015 GO:0044283 0.0028 0.0028 GO:0042221 GO:0043436 0.0031 GO:0044282 0.0031 0.0031 GO:0006082 GO:0019752 0.0031 oxidation reduction metabolic process post-translational protein modification protein modification process protein amino acid phosphorylation macromolecule modification phosphorylation phosphate metabolic process phosphorus metabolic process multi-organism process pollen-pistil interaction cell recognition recognition of pollen pollination reproduction reproductive process multicellular organismal process response to oxidative stress protein ubiquitination protein modification by small protein conjugation or removal protein modification by small protein conjugation defense response cell communication response to biotic stimulus carbohydrate catabolic process small molecule biosynthetic process response to chemical stimulus oxoacid metabolic process small molecule catabolic process organic acid metabolic process carboxylic acid metabolic process 129 P P P P P P P P P P P P P P P P P P P P P P P P P P P P P P P Table 13. (cont’d) GO term GO:0044267 GO:0016053 GO:0046394 GO:0008610 GO:0044281 GO:0046164 GO:0044275 GO:0006066 GO:0032787 GO:0006006 GO:0006631 GO:0006720 GO:0008299 GO:0005996 GO:0019318 Ontology P P P P P P P P P P P P P P P Description cellular protein metabolic process organic acid biosynthetic process carboxylic acid biosynthetic process lipid biosynthetic process small molecule metabolic process alcohol catabolic process cellular carbohydrate catabolic process alcohol metabolic process monocarboxylic acid metabolic process glucose metabolic process fatty acid metabolic process isoprenoid metabolic process isoprenoid biosynthetic process monosaccharide metabolic process hexose metabolic process Adjusted P-value 0.005 0.0072 0.0072 0.0096 0.011 0.012 0.017 0.019 0.029 0.029 0.031 0.038 0.038 0.041 0.045 130 Description Adjusted P- value GO term Ontology orthogroups of uniquely upregulated significantly between Fusarium virguliforme Table 14. Gene ontology enrichment of biological processes within soybean inoculated and mock treatments across time course of two weeks. response to stimulus 5.33E-14 GO:0050896 respiratory burst involved in defense re... GO:0002679 1.78E-05 response to chemical GO:0042221 1.78E-05 1.78E-05 respiratory burst GO:0045730 response to abiotic stimulus GO:0009628 1.78E-05 defense response GO:0006952 3.82E-05 multi-organism process GO:0051704 3.91E-05 0.00023134 ion transport GO:0006811 organic acid transport GO:0015849 0.00032902 GO:0006082 0.00061075 organic acid metabolic process 0.00061075 response to stress GO:0006950 GO:0043436 oxoacid metabolic process 0.00061075 GO:0046942 carboxylic acid transport 0.00062641 0.00079318 carboxylic acid metabolic process GO:0019752 GO:0015711 organic anion transport 0.00103642 GO:0032787 monocarboxylic acid metabolic process 0.00115672 0.00155853 drug metabolic process GO:0017144 response to oxygen-containing compound GO:1901700 0.00155853 GO:0002252 immune effector process 0.00155853 0.00203583 response to organic substance GO:0010033 small molecule biosynthetic process GO:0044283 0.00253817 GO:0080167 0.0025574 response to karrikin 0.00257496 anion transport GO:0006820 GO:0001101 0.00431842 response to acid chemical GO:0009607 response to biotic stimulus 0.00540989 0.00540989 small molecule metabolic process GO:0044281 GO:0046394 carboxylic acid biosynthetic process 0.00603207 GO:0016053 organic acid biosynthetic process 0.00663717 0.00663717 organic substance transport GO:0071702 GO:0055085 transmembrane transport 0.00764977 phenylpropanoid metabolic process GO:0009698 0.00883584 cell communication GO:0007154 0.01226122 0.01226783 cellular response to stimulus GO:0051716 131 P P P P P P P P P P P P P P P P P P P P P P P P P P P P P P P P P Table 14. (cont’d) GO term GO:1903825 GO:0051707 GO:0009605 GO:0043207 GO:0010038 GO:0002376 GO:1905039 GO:0010035 GO:0000165 GO:0019748 GO:0023014 GO:0098656 GO:0006810 GO:0006721 GO:0034220 GO:0009687 GO:0043288 GO:1902644 GO:0006865 GO:1900424 Ontology P P P P P P P P P P P P P P P P P P P P Adjusted P- Description value 0.01226783 organic acid transmembrane transport 0.01439472 response to other organism 0.01460592 response to external stimulus 0.0185075 response to external biotic stimulus 0.0185075 response to metal ion 0.0185075 immune system process 0.02166732 carboxylic acid transmembrane transport 0.02291405 response to inorganic substance MAPK cascade 0.030285 0.030285 secondary metabolic process signal transduction by protein phosphory... 0.03454733 anion transmembrane transport 0.03540565 transport 0.037015 terpenoid metabolic process 0.037015 0.04079204 ion transmembrane transport abscisic acid metabolic process 0.0424987 apocarotenoid metabolic process 0.0424987 0.0424987 tertiary alcohol metabolic process amino acid transport 0.0424987 0.0424987 regulation of defense response to bacter... 132 GO term Ontology Description Adjusted P- value orthogroups of shared in upregulated between Fusarium virguliforme inoculated and Table 15. Gene ontology enrichment of biological processes within soybean and corn mock treatments across the time-course of two weeks. multi-organism process GO:0051704 response to endogenous stimulus GO:0009719 response to hormone GO:0009725 response to chemical GO:0042221 defense response to other organism GO:0098542 response to organic substance GO:0010033 cellular response to endogenous stimulus GO:0071495 response to external stimulus GO:0009605 GO:0042493 response to drug cellular response to hormone stimulus GO:0032870 response to oxygen-containing compound GO:1901700 GO:0050896 response to stimulus cellular response to organic substance GO:0071310 response to acid chemical GO:0001101 GO:0014070 response to organic cyclic compound response to other organism GO:0051707 regulation of immune response GO:0050776 GO:0070887 cellular response to chemical stimulus response to external biotic stimulus GO:0043207 GO:0009607 response to biotic stimulus GO:0007154 cell communication cellular response to acid chemical GO:0071229 defense response GO:0006952 GO:0007165 signal transduction response to salicylic acid GO:0009751 secondary metabolite biosynthetic proces... GO:0044550 GO:0023052 signaling hormone-mediated signaling pathway GO:0009755 regulation of signal transduction GO:0009966 GO:0019748 secondary metabolic process cellular response to organic cyclic GO:0071407 comp... 133 3.70E-05 3.70E-05 8.39E-05 0.000192478 0.000192478 0.000285544 0.000285544 0.000481195 0.000501759 0.000636658 0.0007403 0.000925375 0.001216207 0.001216207 0.001233833 0.002844311 0.002844311 0.002844311 0.002844311 0.0037015 0.0037015 0.0037015 0.0037015 0.0037015 0.00384956 0.003986231 0.005483704 0.007931786 0.008636833 0.008636833 0.01146271 P P P P P P P P P P P P P P P P P P P P P P P P P P P P P P P Table 15. (cont’d) GO term GO:0023051 GO:0035556 GO:0006955 GO:0010646 GO:0002682 GO:0032501 GO:0010374 GO:1901701 GO:1901615 GO:0010941 GO:0032101 GO:0009595 GO:0009697 GO:0031640 GO:0044364 GO:0048583 GO:0000165 GO:0023014 GO:0009617 GO:0046677 GO:0006468 GO:0043900 GO:0032502 GO:0042742 GO:0001906 GO:0007584 GO:0031347 GO:0042445 Ontology P P P P P P P P P P P P P P P P P P P P P P P P P P P P Description regulation of signaling intracellular signal transduction immune response regulation of cell communication regulation of immune system process multicellular organismal process stomatal complex development cellular response to oxygen-containing c... organic hydroxy compound metabolic proce... regulation of cell death regulation of response to external stimu... detection of biotic stimulus salicylic acid biosynthetic process killing of cells of other organism disruption of cells of other organism regulation of response to stimulus MAPK cascade signal transduction by protein phosphory... response to bacterium response to antibiotic protein phosphorylation regulation of multi-organism process developmental process defense response to bacterium cell killing response to nutrient regulation of defense response hormone metabolic process Adjusted P- value 0.012193176 0.012193176 0.0133254 0.014806 0.014806 0.016948974 0.018982051 0.022209 0.024173061 0.024173061 0.024173061 0.024173061 0.024173061 0.024173061 0.024173061 0.024173061 0.024173061 0.0251702 0.029031373 0.031320385 0.036316604 0.037015 0.037688 0.038336964 0.041560702 0.043396897 0.046885667 0.046885667 134 A B C Orthogroups Uniquely Differentially Regulated in Soybean 0 R = 0.11 , p = 2.2e−08 2 R = 0.0053 , p = 0.78 4 R = -0.03 , p = 0.11 7 R = 0.046 , p = 0.015 10 R = 0.016 , p = 0.4 14 R = 0.14 , p = 2.5e−13 10 5 0 −5 −10 10 i n a d e M n a e b y o S 5 0 −5 −10 10 5 0 −5 −10 10 5 0 −5 −10 5 0 −5 5 0 i n a d e M n a e b y o S i n a d e M n a e b y o S −5 −10 Orthogroups Differentially Regulated in Corn and Soybean Corn Median 10 −10 −5 5 0 5 0 10 −10 −5 0 5 10 0 R = 0.47 , p = 1.3e−06 2 R = -0.065 , p = 0.53 4 R = 0.033 , p = 0.75 7 R = 0.51 , p = 9.4e−08 10 R = 0.16 , p = 0.11 14 R = 0.54 , p = 1.4e−08 −10 −5 0 Corn Median Orthogroups Uniquely Differentially Regulated in Corn 5 10 −10 −5 0 5 10 −10 −5 0 5 10 0 R = 0.042 , p = 0.67 2 R = -0.0076 , p = 0.94 4 R = -0.36 , p = 2e−04 7 R = 0.12 , p = 0.23 10 R = 0.041 , p = 0.68 14 R = 0.03 , p = 0.77 0 5 0 5 0 5 −5 −5 −5 −5 Corn Median Figure 10. Correlation plots of differentially induced orthogroups between soybean and corn. Correlation of median transformed log2 fold changes between F. virguliforme inoculated and mock inoculated orthogroups within soybean against corn at each timepoint within the infection time course for (A) orthogroups uniquely differentially regulated in soybean, (B) orthogroups differentially regulated in corn and soybean, (C) orthogroups uniquely differentially regulated only in corn. 135 0 2 4 7 10 14 Sloan Mock E13022S Mock Sloan Fv E13022S Fv C ) % ( t s o H o t g n n g i i l A s d a e R 82 80 78 i ) e u s s T t o o R g m 0 7 g F ( / A N D v F 100000 10000 1000 100 10 1 0.1 0 2 4 7 10 14 0 2 4 7 10 14 Trt Corn Mock Corn Inoculated Soybean Inoculated Days Post Inoculation Days Post Inoculation Figure 11. Fusarium virguliforme root inoculation time course phenotypes of soybean and corn. (A) Representative images plant growth and development over 14 days post inoculation with F. virguliforme. White scale bar is equivalent to 4 cm. (B) F. virguliforme DNA on inoculated and mock soybean and corn roots as detected by quantitative PCR. Values are average of two plants from three biological replicates ± SEM (n=6). (C) The percent of unique trimmed reads aligning to each respective genome within corresponding treatments of corn or soybean inoculated with F. virguliforme or mock (water), across 0-14 days post inoculation. Each sample is indicated by a colored dot and lines are the average of three biological repeats. Grey shade indicates SEM. Figure 1 Soybean Mock A A B 136 A 0 2 4 7 10 14 B 0 2 4 7 10 14 Figure 12. Plant growth and development over infection time course. Representative images plant growth and development over 14 days post inoculation with Fusarium virguliforme for soybean (A) and corn (B). White scale bar is equivalent to 4 cm. Mock inoculated host is of the left of each pair, F. virguliforme is on the right side of each timepoint. Representative images are also duplicated in Figure 10. 137 R2=0.9809902 5 1 0 1 5 0 0 5 10 15 logcounts[, "E13022S_D14_Fv1"] R2=0.9765572 0 5 15 logcounts[, "E13022S_D14_Mock1"] 10 R2=0.9820692 0 10 5 logcounts[, "Sloan_D14_Fv1"] 15 ] " 3 v F _ 4 1 D _ S 2 2 0 3 1 E " , [ s t n u o c g o l ] " 1 k c o M _ 4 1 D _ S 2 2 0 3 1 E " , [ s t n u o c g o l 5 1 0 1 5 0 5 1 0 1 5 0 ] " 3 v F _ 4 1 D _ n a o S " , [ s t n u o c g o l l 0 2 5 1 0 1 5 0 R2=0.9791466 0 5 15 logcounts[, "E13022S_D14_Fv1"] 10 R2=0.975802 0 5 15 logcounts[, "E13022S_D14_Mock3"] 10 R2=0.9662437 0 10 5 logcounts[, "Sloan_D14_Fv1"] 15 ] " 3 v F _ 4 1 D _ S 2 2 0 3 1 E " , [ s t n u o c g o l ] " 2 k c o M _ 4 1 D _ S 2 2 0 3 1 E " , [ s t n u o c g o l 5 1 0 1 5 0 5 1 0 1 5 0 ] " 2 v F _ 4 1 D _ n a o S " , [ s t l n u o c g o l 5 1 0 1 5 0 R2=0.9837071 0 5 15 logcounts[, "E13022S_D14_Fv2"] 10 R2=0.9903795 0 5 15 logcounts[, "E13022S_D14_Mock3"] 10 R2=0.9722611 0 10 5 15 logcounts[, "Sloan_D14_Fv3"] 20 R2=0.9658379 5 1 0 1 5 0 ] " 3 k c o M _ 4 1 D _ n a o S " , [ s t l n u o c g o l R2=0.9703801 5 1 0 1 5 0 ] " 2 k c o M _ 4 1 D _ n a o S " , [ s t n u o c g o l l R2=0.9809539 5 1 0 1 5 0 5 0 0 5 20 10 20 10 15 logcounts[, "Sloan_D14_Mock1"] 15 logcounts[, "Sloan_D14_Mock1"] Figure 13. Biological reproducibility of samples from different time courses. Correlation of gene expression values in three biological replicates from 14 DPI samples of corn inoculated with Fusarium virguliforme (A) or mock (B) inoculated with water, and soybean inoculated with F. virguliforme (C) or mock (D) inoculated with water. logcounts[, "Sloan_D14_Mock3"] 0 5 10 15 A ] " 2 v F _ 4 1 D _ S 2 2 0 3 1 E " , [ s t n u o c g o l B ] " 2 k c o M _ 4 1 D _ S 2 2 0 3 1 E " , [ s t n u o c g o l 5 1 0 1 5 0 C ] " 2 v F _ 4 1 D _ n a o S " , [ s t l n u o c g o l 5 1 0 1 5 0 D ] " 2 k c o M _ 4 1 D _ n a o S " , [ s t l n u o c g o l 138 A 20 10 0 −10 −20 e c n a i r a v % 8 : 2 C P C 10000 −25 0 25 PC1: 73% variance 50 s e n e G E D f o r e b m u N 7500 5000 2500 0 E Samples Samples s e n e G E D f o r e b m u N 400 300 200 100 0 0 2 0 2 Regulation Up Down 14 4 10 Days Post Inoculation 7 4 7 10 Days Post Inoculation Samples Samples 14 0 0 2 2 Samples Samples 4 4 7 7 10 14 10 14 group B 40 20 0 −20 Fv:0dpi Fv:10dpi Fv:14dpi Fv:2dpi e c n Fv:4dpi a i r a Fv:7dpi v % 6 Mock:0dpi 1 Mock:10dpi Mock:14dpi Mock:2dpi Mock:4dpi Mock:7dpi 2 C P : D 10000 7500 Regulation 5000 f s e n e G E D Up Down o r e b m u N 2500 0 F group Fv:0dpi Fv:10dpi Fv:14dpi Fv:2dpi Fv:4dpi Fv:7dpi Mock:0dpi Mock:10dpi Mock:14dpi Mock:2dpi Mock:4dpi Mock:7dpi Regulation Up Down 14 −50 0 PC1: 65% variance 50 0 2 4 7 10 Days Post Inoculation 0 0 10 14 4 7 2 4 2 7 Samples Samples Samples Samples 10 14 Process Process defense response defense response defense response to fungus defense response to fungus negative regulation of cellular protein metabolism negative regulation of cellular protein metabolism negative regulation of hydrolase activity negative regulation of hydrolase activity oxidation-reduction process oxidation-reduction process terpenoid biosynthetic process terpenoid biosynthetic process l e e g g n n a a h h C C d d o o F F 2 2 g g o o L L l e g n a h C d o F 2 g o L e g n a h C d o F 2 g o L l l Process Log2 Fold Change Log2 Fold Change Process 10 10 phosphate metabolic process phosphate metabolic process Log2 Fold Change Log2 Fold Change 5 5 alcohol metabolic process alcohol metabolic process 6 6 carbohydrate catabolic process carbohydrate catabolic process 0 0 4 4 oxidation reduction oxidation reduction -5 -5 2 2 response to chemical stimulus response to chemical stimulus Process Log2 Fold Change Log2 Fold Change Process 0 0 10 10 response to oxidative stress response to oxidative stress Log2 Fold Change Process Process phosphate metabolic process phosphate metabolic process small molecule biosynthetic process small molecule biosynthetic process -2 -2 10 10 5 5 alcohol metabolic process alcohol metabolic process phosphate metabolic process phosphate metabolic process carboxylic acid metabolic process carboxylic acid metabolic process -4 -4 5 5 carbohydrate catabolic process carbohydrate catabolic process alcohol metabolic process alcohol metabolic process 0 0 fatty acid metabolic process fatty acid metabolic process e oxidation reduction oxidation reduction carbohydrate catabolic process carbohydrate catabolic process 0 0 Process Process Log2 Fold Change g -5 -5 oxoacid metabolic process oxoacid metabolic process n oxidation reduction oxidation reduction response to chemical stimulus response to chemical stimulus 6 6 -5 -5 defense response defense response a protein ubiquitination protein ubiquitination response to chemical stimulus response to chemical stimulus response to oxidative stress response to oxidative stress h 4 4 defense response to fungus defense response to fungus cell communication cell communication C response to oxidative stress response to oxidative stress small molecule biosynthetic process small molecule biosynthetic process 2 2 negative regulation of cellular protein metabolism negative regulation of cellular protein metabolism multi-organism process multi-organism process d small molecule biosynthetic process small molecule biosynthetic process carboxylic acid metabolic process carboxylic acid metabolic process negative regulation of hydrolase activity negative regulation of hydrolase activity 0 0 o defense response defense response carboxylic acid metabolic process carboxylic acid metabolic process fatty acid metabolic process fatty acid metabolic process F oxidation-reduction process oxidation-reduction process response to biotic stimulus response to biotic stimulus -2 -2 fatty acid metabolic process fatty acid metabolic process oxoacid metabolic process oxoacid metabolic process 2 terpenoid biosynthetic process terpenoid biosynthetic process g oxoacid metabolic process oxoacid metabolic process -4 -4 protein ubiquitination protein ubiquitination o protein ubiquitination protein ubiquitination L cell communication cell communication cell communication cell communication multi-organism process multi-organism process multi-organism process multi-organism process defense response defense response defense response defense response response to biotic stimulus response to biotic stimulus response to biotic stimulus response to biotic stimulus e g n a h C d o F 2 g o L e g n a h C d o F 2 g o L e g n a h C d o F 2 g o L Log2 Fold Change l l l l Log2 Fold Change 2 c 4 c 4 c 7 c 7 c 0 1 c 0 1 c 4 1 c 4 1 c 4 4 c c 2 2 c c 7 7 c c 0 0 c c 0 X 0 X 2 X 2 X 4 1 X 7 X 7 X 0 1 X 0 1 c 0 1 c 4 X 4 X 4 1 X 0 1 X 4 1 c 4 1 c s s e c o r P s s e c o r P s s s s e e c c o o r r P P Figure 14. Temporal expression patterns of defense response genes in corn and soybean. (A) and (B) Principle component analysis (PCA) of gene expression values of either corn or soybean inoculated with Fusarium virguliforme or mock inoculated with water. (C) and (D) Number of significant differentially regulated genes with log2(FC) > 1 between mock and inoculated of corn with inset of early differentially regulated genes in corn or soybean over six timepoints. (E) and (F) Heatmap of significant gene ontology enrichment of up- regulated genes across pooled time points for corn (n=70) and soybean (n=1,792). Figure 2 s 0 2 s X X e c o r P s 0 s X e c o r P s s e c o r P s s e c o r P 4 4 1 1 X X 4 0 4 1 1 1 X X X 4 X 7 7 X X 0 1 X 7 X 0 1 X 0 1 X 2 4 4 X X X 0 2 X X 2 4 X X 7 X 0 X 139 c1_2525 ● c1_1691 ● c1_1735 ● c1_485 c1_516 c1_1666 c1_467 c1_420 c1_408 c1_1750 ● ● ●● ● ● ● ● ● ● ● ● ● c1_11 c1_7 c1_8 c1_2 c1_13 c1_530 c1_250 c1_1609 c1_1356 c1_2622 c1_2448 ● ● c1_371 c1_2594 c1_2765 c1_1509 c1_318 c1_1892 c1_1886 c1_1517 c1_2607 ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● c1_1207 c1_107 c1_135 c1_224 c1_781 c1_74 root c1_960 c1_346 c1_180 c1_173 c1_1562 c1_1467 c1_2726 c1_2734 ●● ●● c1_2407 c1_2417 c1_1481 ●● c1_1482 c1_1091 ● c1_2687 c1_2211 c1_2155 c1_1142 c1_1070 ● ● ● ● ●● ● ●● ● ● c1_1138 c1_1216 c1_1137 c1_1152 c1_1176 c1_2209 c1_257 c1_1484 c1_2418 c1_271 c1_216 c1_331 c1_308 c1_306 c1_305 c1_133 c1_128 c1_1530 c1_1488 c1_1395 c1_1660 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●●● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ●●●● ● c1_1643 c1_1297 c1_1300 c1_1339 c1_1445 c1_182 c1_210 c1_212 c1_292 c1_299 c1_300 c1_365 c1_389 c1_390 c1_399 c1_124 c1_177 c1_215 c1_285 c1_347 c1_4 c1_370 c1_217 c1_1273 c1_2262 ● ●● ● c1_2235 c1_1198 c1_144 c1_2488 c1_1645 c1_1591 c1_1331 ● ● ● ● ●● ● c1_1246 c1_168 c1_2514 c1_1234 c1_1251 c1_189 c1_1353 c1_1470 c1_1214 c1_2296 c1_2270 ● c1_2277 ● c1_2707 c1_2699 c1_2788 ● ● c1_2701 ● c1_2247 ●● c1_2248 ● c1_2756 c1_2565 ● ● c1_2597 c1_1873 ●● c1_1878 c1_890 c1_2079 ● c1_903 c1_1934 c1_2057 c1_2039 ● c1_2147 c1_845 ● c1_715 c1_3 c1_79 c1_38 c1_96 c1_95 c1_90 c1_70 c1_66 c1_61 c1_29 c1_109 c1_979 c1_893 c1_887 c1_886 c1_659 c1_855 c1_760 c1_121 c1_116 c1_113 c1_112 c1_108 c1_1031 c1_1012 c1_1059 ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●● ●●● ● ● ● ● ● ● c1_1902 c1_1906 c1_1914 c1_1962 c1_2027 c1_1768 c1_562 c1_723 c1_724 c1_725 c1_728 c1_764 c1_779 c1_816 c1_616 c1_637 c1_655 c1_732 c1_733 c1_830 c1_865 c1_920 c1_924 c1_932 c1_16 c1_21 c1_26 c1_64 c1_84 c1_97 c1_72 c1_99 c1_1910 c1_2011 ● ●● ●● ● ● c1_1967 c1_2002 c1_1050 c1_945 c1_120 c1_105 ● ● c1_1004 c1_51 c1_701 c1_699 c1_2552 c1_1964 Figure 15. Module hierarchy of modules hubs containing defense related genes within soybean. These modules were identified using MEGENA. Node size are proportional to the number of connections for each gene. node_size * node.scaleFactor ●a ●a 0.0 0.1 0.2 0.3 ●a ●a 140 A Soybean 0 2 4 7 10 14 0 Samples 2 Corn 4 7 10 14 e g n a h C d o F 2 g o L l Orthogroups DE in Soybean DE in Corn DE in Soybean and Corn Log2 Fold Change 10 5 0 -5 -10 . I . I S S S Response to Fv P P defense response D D . . 4 0 X X P D . 2 X . I s p u o r g o h t r O S . I P D . 7 X S . I S . I C . I C . I C . I C . I P P defense response to fungus D D . . 0 2 1 X X P D . 4 1 X P D . 0 X P Neg. regulation of cellular protein D . 4 X P D . 7 X C . I P D . 0 1 X C . I P D . 4 1 X Oxidation Reduction terpenoid biosynthetic process 0 2 4 7 10 14 Host Corn Soybean B e g n a h C d o F 2 g o L l 6 4 2 0 -2 -4 6 4 2 0 4 7 0 2 2 7 4 -2 -4 14 10 10 0 14 Days Post Inoculation Figure 16. Orthologous host processes reveal different patterns of induced defense response. (A) Heatmap of log2(FC) > 1 of significantly upregulated genes at a single timepoint in at least one host between mock and inoculated (n=1,453). Blue indicates orthologous genes from soybean and corn were significant, grey indicates orthologous genes from soybean were significant, blue, that corn orthologous genes were significantly differential expressed (DE). (B) Mean expression patterns of log2(FC) > 1 of significantly upregulated orthologous genes at a single timepoint in both hosts, over the infection time course of orthologous gene ontology pathways enriched in corn. Error bars indicated 1 standard deviation of the mean. Figure 3 141 Response to Fv defense response defense response to fungus Oxidation Reduction terpenoid biosynthetic process Host Corn Soybean A e g n a h C d o F 2 g o L l 7.5 5.0 2.5 0.0 -2.5 7.5 5.0 2.5 0.0 -2.5 B e g n a h C d o F 2 g o L l e g n a h C d o F 2 g o L l s s e c o r P 0 2 Samples 4 0 2 7 10 14 Log2 Fold Change 10 5 0 -5 -10 Process Auxin Biotic stress DAMP ET GA JA Metabolites/sugars PTI Log2 Fold Change 10 5 0 -5 -10 7 4 0 2 14 10 14 10 Days Post Inoculation CHS C2 TPS23 ERF ACO31 Corn Pep1 PR5 7 4 Samples PR1 PR3 OPR2 PAL1 MYB51 Beta-glucosidase 44 cyp71A12 TIP1-1 PIP1-5 GID1L2 MYB NAC PR14 WIP1 AOS LOX5 JAR1 MFS TIR1 Aux-RFB3, iaa24 Bx1 IGL ERF-105 like Process ABA/salt stress PR5 auxin PR1 biotic stress DAMP PR3 DIMBOA OPR2 ET flavonoid biosynth. PR14 GA JA PAL1 metabolites/sugars PTI Pep1 SA/biotic stress MYB51 Shoot-root signal volatiles Beta-glucosidase 44 CYP71A12 TIR1 Aux-RFB3, iaa24 MFS GID1L2 WIP1 AOS-1 LOX5 0 X 2 X 4 X 7 X 0 1 X 4 1 X 2 X 4 X 7 X 0 X 0 1 X 4 1 X s s e c o r P Figure 17. Processes unique to corn aid in immune responses to Fusarium virguliforme. (A) Mean expression patterns of log2(FC) > 1 of significantly upregulated non- orthologous genes in at least a single timepoint in both hosts over the infection time course of genes identified within the same gene ontology categories but not orthologous. Error bars indicated 1 standard deviation of the mean. (B) Gene expression of corn root defense markers across infection time course, without a soybean orthologous group. Figure 4 142 Corn 4 7 0 2 Soybean 10 14 0 Samples 2 4 7 10 14 e g n a h C d o F 2 g o L l NCED NPR1 PR10.1 PR2 ZAT4 ROS WRKY45 WRKY11 MAPK6 PEN2 GCN5 JAR1 CytP450 AOC LOX1 Aux-IAA9 MYB7 HPL ACS6 ACO31 EIN2-25 EKOx Log2 Fold Change 4 2 0 -2 -4 Pathway SA Auxin JA ET ABA PTI Terpenoids Chromatin Signalling Figure 18. Expression of orthologous genes in defense relevant processes. Heatmap of gene expression log2-fold changes between F. virguliforme and mock treatment of corn and soybean hosts, for marker genes relevant to SA (salicylic acid), auxin, JA (jasmonic acid), ethylene, ABA (Abscisic Acid), PTI (Pattern Triggered Immunity), terpenoids, chromatin, and signaling. y a w h a P 0 1 C 4 1 C 4 1 S 0 1 S 7 C 0 S 2 S 4 S 7 S 0 C 2 C 4 C t 143 A Orthogroup Response F. virguliforme Orthogroup Response F. virguliforme Mock Orthogroup Response F. virguliforme Mock Mock 15 10 5 i n o s s e r p x E 1 + e g n a h C d o F 2 g o L l 15 10 5 i n o s s e r p x E 1 + e g n a h C d o F 2 g o L l 0 0 2 2 7 7 4 4 2 2 0 0 Mock Treatment F. virguliforme 10 10 Days Post Inoculation 4 10 4 Days Post Inoculation Fusarium virguliforme. Line graph of expression patterns of orthogroups that are uniquely up regulated in corn when compared to soybean log2(FC) > 1, and corresponding regulation when corn and soybean are colonized by F. virguliforme. Figure 19. Conservation of innate defense gene expression preceding inoculation with Treatment Treatment 10 10 Days Post Inoculation 14 14 2 0 F. virguliforme F. virguliforme Mock Mock 7 7 4 7 10 2 4 7 0 14 0 14 14 Figure 5 144 Samples Samples Samples Orthogroups Log2 Fold Change Glyma15G254000 Zm00001d002285 Zm00001d014405 Zm00001d024268 Glyma15G254000 Zm00001d041472 Zm00001d002285 Glyma02G284300 Zm00001d014405 Glyma08G307100 Zm00001d024268 Glyma14G030700 Zm00001d041472 Glyma18G110700 Glyma02G284300 Zm00001d007850 Glyma08G307100 Zm00001d013151 Glyma14G030700 Zm00001d018773 Glyma18G110700 Zm00001d031164 Zm00001d007850 Zm00001d034277 Zm00001d013151 Glyma16G043200 Zm00001d018773 Glyma16G151500 Zm00001d031164 Glyma19G108800 Zm00001d034277 Zm00001d008399 Glyma16G043200 DE in Soybean Zm00001d028999 Glyma16G151500 Glyma12G221500 Glyma19G108800 Glyma13G279900 Zm00001d008399 DE in Corn Zm00001d005208 Zm00001d028999 Glyma12G221400 Glyma12G221500 Glyma13G280000 Glyma13G279900 DE in Soybean and Corn Zm00001d005208 Glyma03G164200 Glyma10G037700 Glyma12G221400 Orthogroups Glyma19G165600 Glyma13G280000 Zm00001d003052 Glyma03G164200 Log2 Fold Change Zm00001d018435 10 Glyma10G037700 Zm00001d025861 DE in Soybean Glyma19G165600 Zm00001d051140 Zm00001d003052 4 Zm00001d051956 5 Zm00001d018435 10 DE in Corn Glyma16G051800 Zm00001d025861 Glyma17G185000 Zm00001d051140 Glyma18G301500 Zm00001d051956 0 2 DE in Soybean and Corn 5 Zm00001d039004 Glyma16G051800 Glyma15G254000 Glyma02G070600 Glyma17G185000 Zm00001d002285 -5 Glyma16G042900 Glyma18G301500 0 Zm00001d014405 Glyma16G152100 Zm00001d039004 0 Zm00001d024268 Glyma19G109100 Zm00001d041472 -10 Glyma02G070600 Zm00001d005028 -5 4 Glyma16G042900 Glyma02G284300 Zm00001d022517 Glyma16G152100 Glyma08G307100 Zm00001d028995 -2 Glyma19G109100 Glyma14G030700 Zm00001d047554 -10 Zm00001d005028 2 Glyma18G110700 Zm00001d052390 Zm00001d022517 Zm00001d007850 Glyma04G167200 Zm00001d028995 Zm00001d013151 Glyma06G195500 -4 Zm00001d047554 Zm00001d018773 0 Glyma09G235700 Zm00001d052390 Zm00001d031164 Zm00001d041886 Zm00001d034277 Glyma04G167200 Glyma05G192500 Glyma06G195500 Glyma16G043200 -2 Zm00001d003626 Glyma09G235700 Glyma16G151500 Zm00001d016814 Zm00001d041886 Glyma19G108800 Zm00001d008399 Glyma02G158700 Glyma05G192500 -4 Zm00001d028999 Glyma13G203800 Zm00001d003626 Glyma13G203900 Zm00001d016814 Glyma12G221500 Glyma17G252200 Glyma13G279900 Glyma02G158700 Zm00001d011438 Zm00001d005208 Glyma13G203800 Zm00001d044122 Glyma13G203900 Glyma12G221400 Glyma02G009800 Glyma17G252200 Glyma13G280000 Glyma10G010400 Zm00001d011438 Glyma03G164200 Glyma19G218800 Zm00001d044122 Glyma10G037700 Zm00001d007191 Glyma02G009800 Glyma19G165600 Zm00001d010933 Glyma10G010400 Zm00001d003052 Zm00001d024726 Glyma19G218800 Zm00001d018435 Glyma13G070900 Zm00001d007191 Zm00001d025861 Glyma19G011700 Zm00001d010933 Zm00001d051140 Zm00001d035854 Zm00001d024726 Zm00001d051956 Glyma01G137700 Glyma13G070900 Glyma16G051800 Glyma01G138000 Glyma19G011700 Glyma17G185000 Glyma03G029100 Zm00001d035854 Glyma18G301500 Zm00001d039004 Glyma02G132500 Glyma01G137700 Glyma07G212400 Glyma01G138000 Glyma02G070600 Glyma03G029100 Glyma16G042900 Glyma12G059200 Glyma16G152100 Glyma12G059300 Glyma02G132500 Glyma19G109100 Glyma07G212400 Zm00001d005028 Zm00001d016664 Zm00001d022517 Zm00001d018335 Glyma12G059200 Zm00001d028995 Glyma12G059300 Glyma18g022700 Zm00001d047554 Glyma11g234300 Zm00001d016664 Zm00001d052390 Zm00001d028808 Zm00001d018335 Glyma04G167200 Zm00001d022569 Glyma18g022700 Glyma06G195500 Zm00001d047696 Glyma11g234300 Glyma09G235700 Glyma03G253200 Zm00001d028808 Zm00001d041886 Zm00001d022569 Glyma20g250200 Glyma05G192500 Zm00001d047696 Glyma03G066100 Zm00001d003626 Zm00001d013777 Glyma03G253200 Zm00001d016814 Zm00001d033128 Glyma20g250200 Glyma02G158700 Glyma01G056100 Glyma03G066100 Glyma13G203800 Glyma02G124700 Zm00001d013777 Glyma13G203900 Glyma02G124900 Zm00001d033128 Glyma17G252200 Glyma05G137500 Zm00001d011438 Glyma01G056100 Glyma05G137800 Zm00001d044122 Glyma02G124700 Glyma08G092800 Glyma02G124900 Glyma02G009800 Glyma12G235300 Glyma05G137500 Glyma10G010400 Glyma13G263900 Glyma05G137800 Glyma19G218800 Glyma15G090100 Glyma08G092800 Zm00001d007191 Zm00001d012819 Glyma12G235300 Zm00001d029636 Zm00001d010933 Glyma13G263900 Zm00001d040597 Zm00001d024726 Glyma15G090100 Zm00001d048547 Glyma13G070900 Zm00001d012819 Glyma20G219000 Glyma19G011700 Zm00001d029636 Zm00001d034984 Zm00001d035854 Zm00001d040597 Zm00001d048547 Zm00001d028667 Glyma01G137700 Glyma18G287200 Glyma01G138000 Glyma20G219000 Glyma02G094500 Glyma03G029100 Zm00001d034984 Glyma10G290200 Glyma02G132500 Zm00001d028667 Glyma07G212400 Glyma18G287200 Glyma02G094500 Glyma12G059200 Glyma10G290200 Glyma12G059300 Zm00001d016664 Zm00001d018335 Glyma18g022700 Glyma11g234300 Zm00001d028808 Zm00001d022569 Zm00001d047696 Glyma03G253200 Glyma20g250200 Glyma03G066100 Zm00001d013777 Zm00001d033128 Glyma01G056100 Glyma02G124700 Glyma02G124900 Glyma05G137500 Glyma05G137800 Glyma08G092800 Glyma12G235300 Glyma13G263900 Glyma15G090100 Zm00001d012819 Zm00001d029636 Zm00001d040597 Zm00001d048547 Glyma20G219000 Zm00001d034984 Zm00001d028667 Glyma18G287200 Glyma02G094500 Glyma10G290200 PADOX-1 ZAT11 e g n a h C d o F 2 g o L Log2 Fold Change Log2 Fold Change Log2 Fold Change Log2 Fold Change Log2 Fold Change 4 1 X 4 1 X ID ID ID ID D ID l I Log2 Fold Change Log2 Fold Change ID ID 10 10 10 10 5 5 5 5 0 0 0 0 10 -5 -5 -5 -5 5 -10 -10 -10 -10 0 -5 -10 10 10 5 5 0 0 -5 -5 -10 -10 A Soybean 7 4 0 2 10 14 Samples 0 Samples Corn 7 4 2 10 14 e g n a h C d o F 2 g o L l e g n a h C d o F 2 g o L l l e g n e a g h n C a h d C o d F l o 2 F g o 2 L g o L S . I P H 2 X . S . I P D 2 X . S . I P D 4 X . S . I P D 7 X . s p u o r g o h t r O B S S S S S S . I s p u o r g o h t r . I P H S . 2 P X D 4 1 X . O S . I . P D 0 1 X . I . I . I . I . I . . . . . I P D 4 X P D C 2 P X H 2 X . . I P D 0 1 X P D C 7 P X D 2 X P D C 4 P 1 D X 4 X . I . . . . I C e g C n a . h I P P C D D d C l . o 4 7 . F I P X X 2 D g . o 0 L 1 X . C C . I . I . . . I P D 4 1 X P D C 0 P 1 D X 4 1 X . C C . I . I . . . . I P H 2 X P D C 2 P X D 7 Samples Samples Samples Samples X ROS Samples Negative Regulator Samples of Senescence e g n a h C d o F 2 g o L l I I l ID D D 7 X 4 X 2 X 0 X 4 1 X 0 1 X 0 X 0 X 2 X 2 X 4 X 4 X 7 X 7 X 0 1 X 0 1 X 10 5 0 -5 -10 Log2 Fold Change NACs Samples Samples of Senescence ANAC047 ANAC055 ANAC087 Positive Regulator Glyma15G254000 Glyma15G254000 Glyma15G254000 Glyma15G254000 Zm00001d002285 Zm00001d002285 Zm00001d002285 Zm00001d002285 Zm00001d014405 Zm00001d014405 Zm00001d014405 Zm00001d014405 Zm00001d024268 Zm00001d024268 Zm00001d024268 Zm00001d024268 Zm00001d041472 Zm00001d041472 Zm00001d041472 Zm00001d041472 Glyma02G284300 Glyma02G284300 Glyma02G284300 Glyma02G284300 Glyma08G307100 Glyma08G307100 Glyma08G307100 Glyma08G307100 Glyma14G030700 Glyma14G030700 Glyma14G030700 Glyma14G030700 Glyma18G110700 Glyma18G110700 Glyma18G110700 Glyma15G254000 Glyma18G110700 Zm00001d007850 Zm00001d007850 Zm00001d007850 Samples Zm00001d002285 Zm00001d007850 Zm00001d013151 Zm00001d013151 Zm00001d013151 Zm00001d014405 Zm00001d013151 Zm00001d018773 Zm00001d018773 Zm00001d018773 Zm00001d024268 Zm00001d018773 Zm00001d031164 Zm00001d031164 Zm00001d031164 Zm00001d041472 Zm00001d031164 Zm00001d034277 Zm00001d034277 Zm00001d034277 Zm00001d034277 Glyma02G284300 Glyma16G043200 Glyma16G043200 Glyma16G043200 Glyma08G307100 Glyma16G043200 Glyma16G151500 Glyma16G151500 Glyma16G151500 Glyma14G030700 Glyma16G151500 Glyma19G108800 Glyma19G108800 Glyma19G108800 Glyma18G110700 Glyma19G108800 Zm00001d008399 Zm00001d008399 Zm00001d008399 Zm00001d007850 Zm00001d008399 Zm00001d028999 Zm00001d028999 Zm00001d028999 Zm00001d013151 Zm00001d028999 Zm00001d018773 Glyma12G221500 Glyma12G221500 Glyma12G221500 Glyma12G221500 Zm00001d031164 Glyma13G279900 Glyma13G279900 Glyma13G279900 Glyma13G279900 Zm00001d034277 Zm00001d005208 Zm00001d005208 Zm00001d005208 Zm00001d005208 Glyma16G043200 Glyma12G221400 Glyma12G221400 Glyma15G254000 Glyma12G221400 Glyma12G221400 Glyma15G254000 Glyma16G151500 Glyma13G280000 Glyma13G280000 Zm00001d002285 Glyma13G280000 Glyma13G280000 Zm00001d002285 Glyma19G108800 Zm00001d014405 Zm00001d014405 Zm00001d008399 Glyma03G164200 Glyma03G164200 Glyma03G164200 Glyma03G164200 Zm00001d024268 Zm00001d024268 Zm00001d028999 Glyma10G037700 Glyma10G037700 Glyma10G037700 Glyma10G037700 Zm00001d041472 Zm00001d041472 Glyma19G165600 Glyma19G165600 Glyma19G165600 Glyma19G165600 Glyma12G221500 Zm00001d003052 Zm00001d003052 Glyma02G284300 Zm00001d003052 Zm00001d003052 Glyma02G284300 Glyma13G279900 Zm00001d018435 Zm00001d018435 Glyma08G307100 Zm00001d018435 Zm00001d018435 Glyma08G307100 Zm00001d005208 Zm00001d025861 Zm00001d025861 Glyma14G030700 Zm00001d025861 Zm00001d025861 Glyma14G030700 Zm00001d051140 Zm00001d051140 Glyma18G110700 Zm00001d051140 Glyma12G221400 Zm00001d051140 Glyma18G110700 Zm00001d051956 Zm00001d051956 Zm00001d007850 Zm00001d051956 Glyma13G280000 Zm00001d051956 Zm00001d007850 Zm00001d013151 Zm00001d013151 Glyma16G051800 Glyma16G051800 Glyma16G051800 Glyma03G164200 Glyma16G051800 Zm00001d018773 Zm00001d018773 Glyma17G185000 Glyma17G185000 Glyma17G185000 Glyma10G037700 Glyma17G185000 Zm00001d031164 Zm00001d031164 Glyma18G301500 Glyma18G301500 Glyma18G301500 Glyma19G165600 Glyma18G301500 Zm00001d034277 Zm00001d034277 Zm00001d039004 Zm00001d039004 Zm00001d039004 Zm00001d003052 Zm00001d039004 Glyma16G043200 Zm00001d018435 Glyma16G043200 Glyma02G070600 Glyma02G070600 Glyma02G070600 Glyma02G070600 Glyma16G151500 Zm00001d025861 Glyma16G151500 Glyma16G042900 Glyma16G042900 Glyma16G042900 Glyma16G042900 Glyma19G108800 Zm00001d051140 Glyma19G108800 Glyma16G152100 Glyma16G152100 Glyma16G152100 Glyma16G152100 Zm00001d008399 Zm00001d051956 Zm00001d008399 Glyma19G109100 Glyma19G109100 Glyma19G109100 Glyma19G109100 Zm00001d028999 Zm00001d028999 Zm00001d005028 Zm00001d005028 Zm00001d005028 Glyma16G051800 Zm00001d005028 Zm00001d022517 Zm00001d022517 Zm00001d005208 Zm00001d022517 Glyma17G185000 Zm00001d022517 Glyma12G221500 Zm00001d028995 Zm00001d028995 Zm00001d028995 Glyma18G301500 Zm00001d028995 Glyma13G279900 Glyma12G221500 Zm00001d047554 Zm00001d047554 Zm00001d047554 Zm00001d039004 Zm00001d047554 Zm00001d005208 Glyma13G279900 Zm00001d052390 Zm00001d052390 Zm00001d052390 Zm00001d052390 Glyma12G221400 Glyma02G070600 Glyma12G221400 Glyma04G167200 Glyma13G280000 Glyma04G167200 Glyma04G167200 Glyma16G042900 Glyma04G167200 Glyma13G280000 Glyma06G195500 Glyma06G195500 Glyma06G195500 Glyma16G152100 Glyma06G195500 Glyma03G164200 Glyma09G235700 Glyma03G164200 Glyma09G235700 Glyma09G235700 Glyma19G109100 Glyma09G235700 Glyma10G037700 Zm00001d041886 Glyma10G037700 Zm00001d041886 Zm00001d041886 Zm00001d005028 Zm00001d041886 Glyma19G165600 Glyma19G165600 Zm00001d022517 Glyma05G192500 Zm00001d003052 Glyma05G192500 Glyma05G192500 Glyma05G192500 Zm00001d003052 Zm00001d028995 Zm00001d003626 Zm00001d018435 Zm00001d003626 Zm00001d003626 Zm00001d003626 Zm00001d018435 Zm00001d047554 Zm00001d016814 Zm00001d025861 Zm00001d016814 Zm00001d016814 Zm00001d016814 Zm00001d025861 Zm00001d052390 Zm00001d051140 Zm00001d051140 Glyma02G158700 Glyma02G158700 Glyma02G158700 Glyma02G158700 Zm00001d051956 Glyma04G167200 Zm00001d051956 Glyma13G203800 Glyma13G203800 Glyma13G203800 Glyma13G203800 Glyma06G195500 Glyma13G203900 Glyma16G051800 Glyma13G203900 Glyma13G203900 Glyma13G203900 Glyma16G051800 Glyma09G235700 Glyma17G252200 Glyma17G185000 Glyma17G252200 Glyma17G252200 Glyma17G252200 Glyma17G185000 Zm00001d041886 Zm00001d011438 Glyma18G301500 Zm00001d011438 Zm00001d011438 Zm00001d011438 Glyma18G301500 Zm00001d044122 Zm00001d039004 Zm00001d044122 Zm00001d044122 Glyma05G192500 Zm00001d044122 Zm00001d039004 Zm00001d003626 Glyma02G009800 Glyma02G070600 Glyma02G009800 Glyma02G009800 Glyma02G009800 Glyma02G070600 Zm00001d016814 Glyma10G010400 Glyma16G042900 Glyma10G010400 Glyma10G010400 Glyma10G010400 Glyma16G042900 Glyma19G218800 Glyma16G152100 Glyma19G218800 Glyma02G158700 Glyma19G218800 Glyma19G218800 Glyma16G152100 Zm00001d007191 Glyma19G109100 Zm00001d007191 Glyma13G203800 Zm00001d007191 Zm00001d007191 Glyma19G109100 Zm00001d010933 Zm00001d005028 Zm00001d010933 Glyma13G203900 Zm00001d010933 Zm00001d010933 Zm00001d005028 Zm00001d024726 Zm00001d022517 Zm00001d024726 Glyma17G252200 Zm00001d024726 Zm00001d024726 Zm00001d022517 Zm00001d028995 Zm00001d011438 Zm00001d028995 Glyma13G070900 Glyma13G070900 Glyma13G070900 Glyma13G070900 Zm00001d047554 Zm00001d044122 Zm00001d047554 Glyma19G011700 Glyma19G011700 Glyma19G011700 Glyma19G011700 Zm00001d052390 Zm00001d052390 Zm00001d035854 Zm00001d035854 Glyma02G009800 Zm00001d035854 Zm00001d035854 Senescence Associated Genes Glyma04G167200 Glyma10G010400 Glyma04G167200 Glyma01G137700 Glyma01G137700 Glyma01G137700 Glyma01G137700 Glyma06G195500 Glyma19G218800 Glyma06G195500 Glyma01G138000 Glyma01G138000 Glyma01G138000 Glyma01G138000 Glyma09G235700 Zm00001d007191 Glyma09G235700 Glyma03G029100 Glyma03G029100 Glyma03G029100 Glyma03G029100 Zm00001d041886 Zm00001d010933 Zm00001d041886 Zm00001d024726 Glyma02G132500 Glyma02G132500 Glyma02G132500 Glyma02G132500 Glyma05G192500 Glyma05G192500 Glyma07G212400 Glyma07G212400 Glyma07G212400 Glyma07G212400 Zm00001d003626 Glyma13G070900 Zm00001d003626 Zm00001d016814 Glyma19G011700 Glyma12G059200 Zm00001d016814 Glyma12G059200 Glyma12G059200 Glyma12G059200 Zm00001d035854 Glyma12G059300 Glyma12G059300 Glyma12G059300 Glyma12G059300 Glyma02G158700 Glyma02G158700 Glyma13G203800 Glyma01G137700 Zm00001d016664 Glyma13G203800 Zm00001d016664 Zm00001d016664 Zm00001d016664 Glyma13G203900 Glyma01G138000 Zm00001d018335 Glyma13G203900 Zm00001d018335 Zm00001d018335 Zm00001d018335 Glyma17G252200 Glyma03G029100 Glyma17G252200 Glyma18g022700 Zm00001d011438 Glyma18g022700 Glyma18g022700 Glyma18g022700 Zm00001d011438 Glyma02G132500 Glyma11g234300 Zm00001d044122 Glyma11g234300 Glyma11g234300 Glyma11g234300 Zm00001d044122 Glyma07G212400 Zm00001d028808 Zm00001d028808 Zm00001d028808 Zm00001d028808 Glyma02G009800 Zm00001d022569 Glyma02G009800 Zm00001d022569 Zm00001d022569 Zm00001d022569 Glyma12G059200 Glyma10G010400 Zm00001d047696 Glyma10G010400 Zm00001d047696 Zm00001d047696 Zm00001d047696 Glyma12G059300 Glyma19G218800 Glyma19G218800 Glyma03G253200 Zm00001d007191 Glyma03G253200 Glyma03G253200 Glyma03G253200 Zm00001d007191 Zm00001d016664 Zm00001d010933 Zm00001d010933 Zm00001d018335 Glyma20g250200 Glyma20g250200 Glyma20g250200 Glyma20g250200 Zm00001d024726 Zm00001d024726 Glyma03G066100 Glyma03G066100 Glyma03G066100 Glyma03G066100 Glyma18g022700 Zm00001d013777 Glyma13G070900 Zm00001d013777 Zm00001d013777 Zm00001d013777 Glyma13G070900 Glyma11g234300 Zm00001d033128 Glyma19G011700 Zm00001d033128 Zm00001d033128 Glyma19G011700 Zm00001d033128 Zm00001d028808 Zm00001d035854 Zm00001d035854 Zm00001d022569 Glyma01G056100 Glyma01G056100 Glyma01G056100 Glyma01G056100 Zm00001d047696 Glyma02G124700 Glyma01G137700 Glyma02G124700 Glyma02G124700 Glyma01G137700 Glyma02G124700 Glyma02G124900 Glyma01G138000 Glyma02G124900 Glyma02G124900 Glyma01G138000 Glyma02G124900 Glyma03G253200 Glyma05G137500 Glyma03G029100 Glyma05G137500 Glyma05G137500 Glyma03G029100 Glyma05G137500 Glyma05G137800 Glyma20g250200 Glyma05G137800 Glyma05G137800 Glyma05G137800 Glyma02G132500 Glyma08G092800 Glyma02G132500 Glyma03G066100 Glyma08G092800 Glyma08G092800 Glyma08G092800 Glyma07G212400 Glyma12G235300 Glyma07G212400 Zm00001d013777 Glyma12G235300 Glyma12G235300 Glyma12G235300 Glyma13G263900 Zm00001d033128 Glyma13G263900 Glyma13G263900 Glyma13G263900 Glyma12G059200 Glyma12G059200 Glyma15G090100 Glyma15G090100 Glyma15G090100 Glyma15G090100 Glyma12G059300 Glyma12G059300 Glyma01G056100 Zm00001d012819 Zm00001d012819 Zm00001d012819 Zm00001d012819 Glyma02G124700 Zm00001d029636 Zm00001d016664 Zm00001d029636 Zm00001d029636 Zm00001d016664 Zm00001d029636 Glyma02G124900 Zm00001d040597 Zm00001d018335 Zm00001d040597 Zm00001d040597 Zm00001d018335 Zm00001d040597 Glyma05G137500 Zm00001d048547 Zm00001d048547 Zm00001d048547 Zm00001d048547 Glyma18g022700 Glyma05G137800 Glyma18g022700 Glyma20G219000 Glyma11g234300 Glyma08G092800 Glyma20G219000 Glyma20G219000 Glyma11g234300 Glyma20G219000 Zm00001d034984 Zm00001d028808 Glyma12G235300 Zm00001d034984 Zm00001d034984 Zm00001d028808 Zm00001d034984 Zm00001d022569 Glyma13G263900 Zm00001d022569 Zm00001d028667 Zm00001d028667 Zm00001d028667 Zm00001d028667 Zm00001d047696 Glyma15G090100 Zm00001d047696 Glyma18G287200 Glyma18G287200 Glyma18G287200 Glyma18G287200 Zm00001d012819 Glyma02G094500 Glyma03G253200 Glyma02G094500 Glyma02G094500 Glyma03G253200 Glyma02G094500 Zm00001d029636 Glyma10G290200 Glyma10G290200 Glyma10G290200 Glyma10G290200 Zm00001d040597 Glyma20g250200 Glyma20g250200 Zm00001d048547 Glyma03G066100 Glyma03G066100 Zm00001d013777 Zm00001d013777 Glyma20G219000 Zm00001d033128 Zm00001d033128 Zm00001d034984 Glyma01G056100 Glyma01G056100 Zm00001d028667 Glyma02G124700 Glyma02G124700 Glyma18G287200 Glyma02G124900 Glyma02G124900 Glyma02G094500 Glyma05G137500 Glyma05G137500 Glyma10G290200 Glyma05G137800 Glyma05G137800 Glyma08G092800 Glyma08G092800 Glyma12G235300 Glyma12G235300 Glyma13G263900 Glyma13G263900 Glyma15G090100 Glyma15G090100 Zm00001d012819 Zm00001d012819 Zm00001d029636 Zm00001d029636 Zm00001d040597 Zm00001d040597 Zm00001d048547 Zm00001d048547 Glyma20G219000 Glyma20G219000 Zm00001d034984 Zm00001d034984 Zm00001d028667 Zm00001d028667 Glyma18G287200 Glyma18G287200 Glyma02G094500 Glyma02G094500 Glyma10G290200 Glyma10G290200 SRG1 ANAC022 SAG13 ANAC042 Plant_invertase MYB2 PADOX-1 ANAC047 ANAC055 ANAC05 SAG21 ANAC074 RRTF1 ANAC083 ANAC087 ANAC100 ANAC104 anthocyaninless1 SAG20 SAG24 ZAT11 VND7 SAG15 0 4 0 4 0 4 0 4 1 1 1 1 1 1 1 1 X X X X X X X X e e e e g g g g n n n n a a a a h h h h C C C C e g d d d d n l l l l o o o o a F F F h F C 2 2 2 2 g g g g d o o o o o L L L L F e e g 2 g g n n o a a h L h C C d d o o F F 2 2 g g o o L L SAG13 SAG15 SAG20 SAG21 SAG24 4 1 X 7 7 7 7 X X X X 0 0 0 0 X X X X 4 X D D D D l l ANAC042 Glyma15G254000 Zm00001d002285 Zm00001d014405 Zm00001d024268 Zm00001d041472 Glyma02G284300 Glyma08G307100 Glyma14G030700 Glyma18G110700 Zm00001d007850 Zm00001d013151 Zm00001d018773 Zm00001d031164 Zm00001d034277 Glyma16G043200 Glyma16G151500 Glyma19G108800 Zm00001d008399 Zm00001d028999 Zm00001d005208 Glyma12G221500 Glyma13G279900 Glyma12G221400 Glyma13G280000 Glyma03G164200 Glyma10G037700 Glyma19G165600 Zm00001d003052 Zm00001d018435 Zm00001d025861 Zm00001d051140 Zm00001d051956 Glyma16G051800 Glyma17G185000 Glyma18G301500 Zm00001d039004 Glyma02G070600 Glyma16G042900 Glyma16G152100 Glyma19G109100 Zm00001d005028 Zm00001d022517 Zm00001d028995 Zm00001d047554 Zm00001d052390 Glyma04G167200 Glyma06G195500 Glyma09G235700 Zm00001d041886 Glyma05G192500 Zm00001d003626 Zm00001d016814 Glyma02G158700 Glyma13G203800 Glyma13G203900 Glyma17G252200 Zm00001d011438 Zm00001d044122 Glyma02G009800 Glyma10G010400 Glyma19G218800 Zm00001d007191 Zm00001d010933 Zm00001d024726 Glyma13G070900 Glyma19G011700 Zm00001d035854 Glyma01G137700 Glyma01G138000 Glyma03G029100 Glyma02G132500 Glyma07G212400 Glyma12G059200 Glyma12G059300 Zm00001d016664 Zm00001d018335 Glyma18g022700 Glyma11g234300 Zm00001d028808 Zm00001d022569 Zm00001d047696 Glyma03G253200 Glyma20g250200 Glyma03G066100 Zm00001d013777 Zm00001d033128 Glyma01G056100 Glyma02G124700 Glyma02G124900 Glyma05G137500 Glyma05G137800 Glyma08G092800 Glyma12G235300 Glyma13G263900 Glyma15G090100 Zm00001d012819 Zm00001d029636 Zm00001d040597 Zm00001d048547 Glyma20G219000 Zm00001d034984 Zm00001d028667 Glyma18G287200 Glyma02G094500 Glyma10G290200 SRG1 Figure 6 Figure 20. Divergence of defense expression patterns of orthologous transcription factors. (A) Heatmap of log2 fold change >1 of significantly upregulated genes at a single timepoint in at least one host between mock and inoculated (n=215). Blue indicates orthologous genes from soybean and corn were significant, grey indicates orthologous genes from soybean were significant, blue, that corn orthologous genes were significantly differential expressed (DE) (B). Representation of reactive oxygen species driven no apical meristem (NAM), ATAF1/2, cup-shaped cotyledon2 (CUC2) (NAC) pathways that exhibited SRG1 ANAC022 SAG13 ANAC042 Plant_invertase MYB2 PADOX-1 ANAC047 ANAC055 ANAC056 SAG21 ANAC074 RRTF1 ANAC083 ANAC087 ANAC100 ANAC104 anthocyaninless1 SAG20 SAG24 ZAT11 VND7 SAG15 e g n 2 2 2 2 a X X X X h C d 2 l o X F 2 g o L 0 0 1 1 X X 4 4 1 1 X X 0 1 X 4 1 X 4 1 X 4 1 X 0 1 X 0 1 X 4 1 X 4 4 4 4 X X X X 0 0 X X 2 2 X X 4 4 X X 7 7 X X 0 X 7 X 2 X 0 X 0 X 2 X 0 X 2 X 7 X 0 X 2 X 4 X 7 X D D D D D D I I I I I I l I I I I 145 0 X 2 X Log2 Fold Change Log2 Fold Change ID ID Log2 Fold Change 10 5 0 Samples -5 -10 ID Samples Samples SRG1 ANAC022 SRG1 SAG13 ANAC022 ANAC042 SAG13 Plant_invertase ANAC042 MYB2 Plant_invertase PADOX-1 MYB2 ANAC047 PADOX-1 ANAC055 ANAC047 ANAC056 ANAC055 SAG21 ANAC056 ANAC074 SAG21 RRTF1 ANAC074 ANAC083 RRTF1 ANAC087 ANAC083 ANAC100 SRG1 ANAC087 ANAC104 ANAC022 ANAC100 anthocyaninless1 SAG13 ANAC104 SAG20 ANAC042 anthocyaninless1 Glyma15G254000 SAG24 Plant_invertase Zm00001d002285 SAG20 Zm00001d014405 ZAT11 Zm00001d024268 MYB2 Zm00001d041472 SAG24 VND7 Glyma02G284300 PADOX-1 Glyma08G307100 ZAT11 Glyma14G030700 SAG15 Glyma18G110700 ANAC047 VND7 Zm00001d007850 Zm00001d013151 ANAC055 Zm00001d018773 SAG15 Zm00001d031164 Zm00001d034277 ANAC056 Glyma16G043200 Glyma16G151500 SAG21 Glyma19G108800 Zm00001d008399 ANAC074 Zm00001d028999 Glyma12G221500 RRTF1 Glyma13G279900 Zm00001d005208 ANAC083 Glyma12G221400 Glyma13G280000 ANAC087 Glyma03G164200 Glyma10G037700 Glyma19G165600 ANAC100 Zm00001d003052 Zm00001d018435 Zm00001d025861 ANAC104 Zm00001d051140 Zm00001d051956 anthocyaninless1 Glyma16G051800 Glyma17G185000 SAG20 Glyma18G301500 Zm00001d039004 SAG24 Glyma02G070600 Glyma16G042900 Glyma16G152100 ZAT11 Glyma19G109100 Zm00001d005028 Glyma15G254000 VND7 Zm00001d022517 Zm00001d028995 Zm00001d002285 Zm00001d047554 SAG15 Zm00001d052390 Zm00001d014405 Glyma04G167200 Glyma06G195500 Zm00001d024268 Glyma09G235700 Zm00001d041886 Zm00001d041472 Glyma05G192500 Zm00001d003626 Samples Glyma02G284300 Zm00001d016814 Glyma08G307100 Glyma02G158700 Glyma13G203800 Regulator of Glyma14G030700 Glyma13G203900 Glyma17G252200 Zm00001d011438 Glyma18G110700 Zm00001d044122 Zm00001d007850 Development SRG1 Glyma02G009800 SRG1 SRG1 SRG1 Glyma10G010400 Zm00001d013151 Glyma19G218800 ANAC022 ANAC022 ANAC022 ANAC022 Zm00001d007191 Zm00001d018773 Zm00001d010933 Zm00001d024726 SAG13 SAG13 SAG13 SAG13 Zm00001d031164 Glyma13G070900 Glyma19G011700 Zm00001d034277 ANAC042 ANAC083 ANAC042 ANAC042 ANAC042 Zm00001d035854 SRG1 Glyma01G137700 Plant_invertase Glyma16G043200 Plant_invertase Plant_invertase Plant_invertase Glyma01G138000 ANAC022 ANAC104 Glyma03G029100 Samples Glyma16G151500 MYB2 MYB2 MYB2 MYB2 Glyma02G132500 SAG13 Glyma19G108800 Glyma07G212400 PADOX-1 PADOX-1 PADOX-1 PADOX-1 Glyma12G059200 Zm00001d008399 ANAC042 ANAC022 Glyma12G059300 ANAC047 ANAC047 ANAC047 ANAC047 Zm00001d028999 Plant_invertase Zm00001d016664 Zm00001d018335 ANAC055 ANAC055 ANAC055 ANAC055 Glyma12G221500 MYB2 Glyma18g022700 Glyma11g234300 ANAC056 Glyma13G279900 ANAC056 ANAC056 ANAC056 Zm00001d028808 ANAC074 PADOX-1 SRG1 SRG1 Zm00001d022569 Zm00001d005208 Zm00001d047696 SAG21 SAG21 SAG21 SAG21 ANAC047 ANAC022 ANAC022 Glyma03G253200 Glyma12G221400 ANAC074 ANAC074 ANAC074 ANAC074 Glyma20g250200 ANAC055 SAG13 SAG13 Glyma03G066100 Glyma13G280000 Zm00001d013777 RRTF1 RRTF1 RRTF1 RRTF1 Zm00001d033128 ANAC056 ANAC042 ANAC042 Glyma03G164200 Glyma01G056100 ANAC083 ANAC083 ANAC083 ANAC083 Glyma02G124700 SAG21 Plant_invertase Glyma10G037700 Plant_invertase Glyma02G124900 ANAC087 ANAC087 ANAC087 Glyma05G137500 ANAC087 Glyma19G165600 ANAC074 MYB2 Glyma05G137800 MYB2 Glyma08G092800 ANAC100 Zm00001d003052 ANAC100 ANAC100 ANAC100 Glyma12G235300 RRTF1 PADOX-1 PADOX-1 Glyma13G263900 Zm00001d018435 ANAC104 Glyma15G090100 ANAC104 ANAC104 ANAC104 ANAC083 Zm00001d012819 ANAC047 ANAC047 Zm00001d025861 Zm00001d029636 anthocyaninless1 anthocyaninless1 anthocyaninless1 anthocyaninless1 Zm00001d040597 ANAC087 Zm00001d051140 ANAC055 ANAC055 Zm00001d048547 SAG20 SAG20 SAG20 SAG20 Glyma20G219000 Zm00001d051956 ANAC100 ANAC05 ANAC056 Zm00001d034984 SAG24 SAG24 SAG24 SAG24 Zm00001d028667 Glyma16G051800 ANAC104 SAG21 SAG21 Glyma18G287200 Glyma02G094500 ZAT11 Glyma17G185000 ZAT11 ZAT11 ZAT11 Glyma10G290200 anthocyaninless1 ANAC074 ANAC074 Glyma18G301500 VND7 VND7 VND7 VND7 SAG20 RRTF1 RRTF1 Zm00001d039004 D SAG15 SAG15 SAG15 SAG15 SAG24 ANAC083 ANAC083 Glyma02G070600 ZAT11 Glyma16G042900 ANAC087 ANAC087 Glyma16G152100 VND7 ANAC100 ANAC100 Glyma19G109100 SAG15 ANAC104 ANAC104 Zm00001d005028 Soybean anthocyaninless1 anthocyaninless1 Zm00001d022517 Zm00001d028995 SAG20 SAG20 Corn Zm00001d047554 SAG24 SAG24 Zm00001d052390 ZAT11 ZAT11 Glyma04G167200 VND7 VND7 Glyma06G195500 SAG15 SAG15 Glyma09G235700 Zm00001d041886 Glyma05G192500 Zm00001d003626 Zm00001d016814 Glyma02G158700 Glyma13G203800 Glyma13G203900 Glyma17G252200 Zm00001d011438 Zm00001d044122 Glyma02G009800 Glyma10G010400 Glyma19G218800 Zm00001d007191 Zm00001d010933 Zm00001d024726 Glyma13G070900 Glyma19G011700 Zm00001d035854 Glyma01G137700 Glyma01G138000 Glyma03G029100 Glyma02G132500 Glyma07G212400 Glyma12G059200 Glyma12G059300 Zm00001d016664 Zm00001d018335 Glyma18g022700 Glyma11g234300 Zm00001d028808 Zm00001d022569 Zm00001d047696 Glyma03G253200 Glyma20g250200 Glyma03G066100 Zm00001d013777 Zm00001d033128 Glyma01G056100 Glyma02G124700 Glyma02G124900 Glyma05G137500 Glyma15G254000 Zm00001d002285 Zm00001d014405 Zm00001d024268 Zm00001d041472 Glyma02G284300 Glyma08G307100 Glyma14G030700 Glyma18G110700 Zm00001d007850 Zm00001d013151 Zm00001d018773 Zm00001d031164 Zm00001d034277 Glyma15G254000 Zm00001d002285 Glyma16G043200 Zm00001d014405 Glyma16G151500 Zm00001d024268 Glyma19G108800 Zm00001d041472 Zm00001d008399 Zm00001d028999 Glyma02G284300 Glyma08G307100 Glyma12G221500 Glyma14G030700 Glyma13G279900 Glyma18G110700 Zm00001d005208 Zm00001d007850 Zm00001d013151 Glyma12G221400 Zm00001d018773 Glyma13G280000 Zm00001d031164 Glyma03G164200 Zm00001d034277 Glyma10G037700 Glyma16G043200 Glyma19G165600 Glyma16G151500 Zm00001d003052 Glyma19G108800 Zm00001d018435 Zm00001d008399 Zm00001d025861 Zm00001d028999 Zm00001d051140 Zm00001d051956 Glyma12G221500 Glyma13G279900 Glyma16G051800 Zm00001d005208 Glyma17G185000 Glyma15G254000 Glyma18G301500 Glyma12G221400 Zm00001d002285 Zm00001d039004 Glyma13G280000 Zm00001d014405 Glyma02G070600 Zm00001d024268 Glyma03G164200 Glyma16G042900 Zm00001d041472 Glyma10G037700 Glyma16G152100 Glyma19G165600 Glyma02G284300 Glyma19G109100 Zm00001d003052 Glyma08G307100 Zm00001d005028 Zm00001d018435 Glyma14G030700 Zm00001d022517 Zm00001d025861 Glyma18G110700 Zm00001d028995 Zm00001d051140 Zm00001d007850 Zm00001d047554 Zm00001d051956 Zm00001d013151 Zm00001d052390 Zm00001d018773 Glyma16G051800 Glyma04G167200 Zm00001d031164 Glyma17G185000 Glyma06G195500 Zm00001d034277 Glyma18G301500 Glyma09G235700 Zm00001d039004 Glyma16G043200 Zm00001d041886 Glyma16G151500 Glyma02G070600 Glyma05G192500 Glyma19G108800 Glyma16G042900 Zm00001d003626 Zm00001d008399 Glyma16G152100 Zm00001d016814 Zm00001d028999 Glyma19G109100 Zm00001d005028 Glyma02G158700 Glyma12G221500 Glyma15G254000 Zm00001d022517 Glyma13G203800 Glyma13G279900 Zm00001d002285 Zm00001d028995 Glyma13G203900 Zm00001d005208 Zm00001d014405 Zm00001d047554 Glyma17G252200 Zm00001d024268 Glyma12G221400 Zm00001d052390 Zm00001d011438 Zm00001d041472 Glyma13G280000 Zm00001d044122 Glyma04G167200 Glyma02G284300 Glyma03G164200 Glyma06G195500 Glyma02G009800 Glyma08G307100 Glyma10G037700 Glyma09G235700 Glyma10G010400 Glyma14G030700 Glyma19G165600 Zm00001d041886 Glyma19G218800 Glyma18G110700 Zm00001d003052 Zm00001d007191 Zm00001d007850 Glyma05G192500 Zm00001d018435 Zm00001d010933 Zm00001d013151 Zm00001d003626 Zm00001d025861 Zm00001d024726 Zm00001d018773 Zm00001d016814 Zm00001d051140 Zm00001d031164 Glyma13G070900 Zm00001d051956 Glyma02G158700 Zm00001d034277 Glyma19G011700 Glyma13G203800 Glyma16G051800 Zm00001d035854 Glyma16G043200 Glyma13G203900 Glyma17G185000 Glyma16G151500 Glyma17G252200 Glyma01G137700 Glyma18G301500 Glyma19G108800 Zm00001d011438 Glyma01G138000 Zm00001d039004 Zm00001d008399 Zm00001d044122 Glyma03G029100 Zm00001d028999 Glyma02G070600 Glyma02G009800 Glyma02G132500 Glyma16G042900 Zm00001d005208 Glyma10G010400 Glyma07G212400 Glyma16G152100 Glyma19G218800 Glyma19G109100 Glyma12G221500 Glyma12G059200 Zm00001d007191 Zm00001d005028 Glyma13G279900 Glyma12G059300 Zm00001d010933 Zm00001d022517 Glyma12G221400 Zm00001d024726 Zm00001d028995 Zm00001d016664 Glyma13G280000 Zm00001d047554 Zm00001d018335 Glyma13G070900 Glyma03G164200 Zm00001d052390 Glyma19G011700 Glyma18g022700 Glyma10G037700 Zm00001d035854 Glyma04G167200 Glyma11g234300 Glyma19G165600 Glyma06G195500 Zm00001d028808 Zm00001d003052 Glyma01G137700 Glyma09G235700 Zm00001d022569 Zm00001d018435 Glyma01G138000 Zm00001d041886 Zm00001d047696 Zm00001d025861 Glyma03G029100 Zm00001d051140 VND7 Glyma05G192500 Glyma03G253200 Glyma02G132500 Zm00001d051956 Zm00001d003626 Glyma07G212400 Glyma20g250200 Zm00001d016814 Glyma16G051800 Glyma03G066100 Glyma12G059200 Glyma17G185000 Glyma02G158700 Zm00001d013777 Glyma12G059300 Glyma18G301500 Glyma13G203800 Zm00001d033128 Zm00001d039004 Glyma13G203900 Zm00001d016664 Glyma01G056100 Glyma17G252200 Zm00001d018335 Glyma02G070600 Glyma02G124700 Zm00001d011438 Glyma16G042900 Glyma18g022700 Glyma02G124900 Zm00001d044122 Glyma16G152100 Glyma11g234300 Glyma05G137500 Glyma19G109100 Glyma02G009800 Zm00001d028808 Glyma05G137800 Zm00001d005028 Glyma10G010400 Zm00001d022569 Glyma08G092800 Zm00001d022517 Glyma19G218800 Zm00001d047696 Glyma12G235300 Zm00001d028995 Zm00001d007191 Glyma13G263900 Zm00001d047554 Glyma03G253200 Zm00001d010933 Glyma15G090100 Zm00001d052390 Zm00001d024726 Zm00001d012819 Glyma20g250200 Zm00001d029636 Glyma04G167200 Glyma03G066100 Glyma13G070900 Zm00001d040597 Glyma06G195500 Zm00001d013777 Glyma19G011700 Zm00001d048547 Glyma09G235700 Zm00001d033128 Zm00001d035854 Zm00001d041886 Glyma20G219000 Glyma01G056100 Glyma01G137700 Zm00001d034984 Glyma05G192500 Glyma02G124700 Glyma01G138000 Zm00001d003626 Glyma02G124900 Glyma03G029100 Zm00001d028667 Zm00001d016814 Glyma05G137500 Glyma18G287200 Glyma02G132500 Glyma05G137800 Glyma02G094500 Glyma02G158700 Glyma07G212400 Glyma08G092800 Glyma10G290200 Glyma13G203800 Glyma12G235300 Glyma12G059200 Glyma13G203900 Glyma13G263900 Glyma12G059300 Glyma17G252200 Glyma15G090100 Zm00001d011438 Zm00001d012819 Zm00001d016664 Zm00001d044122 Zm00001d029636 Zm00001d018335 Zm00001d040597 Glyma02G009800 Glyma18g022700 Zm00001d048547 Glyma10G010400 Glyma11g234300 Glyma19G218800 Glyma20G219000 Zm00001d028808 Zm00001d007191 Zm00001d034984 Zm00001d022569 Zm00001d010933 Zm00001d047696 Zm00001d024726 Zm00001d028667 Glyma18G287200 Glyma03G253200 Glyma13G070900 Glyma02G094500 Glyma19G011700 Glyma20g250200 Glyma10G290200 Zm00001d035854 Glyma03G066100 Zm00001d013777 Glyma01G137700 Zm00001d033128 Glyma01G138000 Glyma03G029100 Glyma01G056100 Glyma02G124700 Glyma02G132500 Glyma02G124900 Glyma07G212400 Glyma05G137500 Glyma05G137800 Glyma12G059200 Glyma08G092800 Glyma12G059300 Glyma12G235300 Zm00001d016664 Glyma13G263900 Zm00001d018335 Glyma15G090100 Zm00001d012819 Glyma18g022700 Zm00001d029636 Glyma11g234300 Zm00001d040597 Zm00001d028808 Zm00001d048547 Zm00001d022569 Zm00001d047696 Glyma20G219000 Zm00001d034984 Glyma03G253200 Zm00001d028667 Glyma20g250200 Glyma18G287200 Glyma03G066100 Glyma02G094500 Zm00001d013777 Glyma10G290200 Zm00001d033128 Glyma01G056100 Glyma02G124700 Glyma02G124900 Glyma05G137500 Glyma05G137800 Glyma08G092800 Glyma12G235300 Glyma13G263900 Glyma15G090100 Zm00001d012819 Zm00001d029636 Zm00001d040597 Zm00001d048547 Glyma20G219000 Zm00001d034984 Zm00001d028667 Glyma18G287200 Glyma02G094500 Glyma10G290200 10 5 0 -5 -10 Host 0 1 X 0 1 X 0 1 X 0 1 X 4 1 X 7 X 4 X 7 X 4 X 7 X 4 X 7 X 4 X D D I I I l l e g n a h C d o e F g n 2 a g h o C L d o F 2 g o L e g n a h C d o 0 F X 2 g o e L g n a h C d o F 2 g o L l 10 5 0 -5 -10 Root Vascular Associated Genes 10 5 0 -5 -10 ID Log2 Fold Change 4 1 X 4 X 2 X Log2 Fold Change Log2 Fold Change ID 10 5 0 -5 -10 Log2 Fold Change Log2 Fold Change 10 5 0 -5 -10 10 5 0 -5 -10 Log2 Fold Change SRG1 ANAC022 SAG13 ANAC042 Plant_invertase MYB2 PADOX-1 ID ANAC047 ANAC055 SRG1 ANAC056 ANAC022 SAG21 SAG13 ANAC074 ANAC042 RRTF1 Plant_invertase ID ANAC083 MYB2 SRG1 ANAC087 PADOX-1 ANAC022 ANAC100 ANAC047 SAG13 ANAC104 ANAC055 ANAC042 anthocyaninless1 ANAC056 Plant_invertase SAG20 SAG21 MYB2 SAG24 ANAC074 PADOX-1 ZAT11 RRTF1 ANAC047 VND7 ANAC083 ID ANAC055 SAG15 ANAC087 ANAC056 SRG1 ANAC100 SAG21 ANAC022 ANAC104 ANAC074 SAG13 anthocyaninless1 RRTF1 ANAC042 SAG20 ANAC083 Plant_invertase SAG24 ANAC087 MYB2 ZAT11 ANAC100 PADOX-1 VND7 ANAC104 ANAC047 SAG15 anthocyaninless1 ANAC055 SRG1 SAG20 ANAC056 ANAC022 SAG24 SAG21 SAG13 ZAT11 ANAC074 ANAC042 VND7 RRTF1 Plant_invertase SAG15 ANAC083 MYB2 ANAC087 PADOX-1 ANAC100 ANAC047 ANAC104 ANAC055 anthocyaninless1 ANAC05 SAG20 SAG21 SAG24 ANAC074 ZAT11 RRTF1 VND7 ANAC083 SAG15 ANAC087 ANAC100 ANAC104 anthocyaninless1 SAG20 SAG24 ZAT11 VND7 SAG15 ID differential expression patterns of log2(FC) > 1 between mock and inoculated of soybean, and corresponding orthologous or homologs in corn, affecting senesce genes, root vascular development and senescence regulation. Each gene heatmap contains temporal expression from 0-14 DPI for soybean (bar color blue) and corn (bar color yellow). Green gene names indicates orthologous genes. 146 REFERENCES 147 REFERENCES Abdelsamad, N.A., MacIntosh, G.C., and Leandro, L.F.S. (2019). Induction of ethylene inhibits development of soybean sudden death syndrome by inducing defense- related genes and reducing Fusarium virguliforme growth. PLoS ONE 14, e0215653. Alexa A and Rahnenfuhrer J. (2018). Topgo: Enrichment analysis for gene ontology. R package version 2.36.0. Allardyce, J.A., Rookes, J.E., Hussain, H.I., and Cahill, D.M. (2013). Transcriptional profiling of Zea mays roots reveals roles for jasmonic acid and terpenoids in resistance against Phytophthora cinnamomi. Funct Integr Genom. 13, 217-228. 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XND1, a member of the NAC domain family in Arabidopsis thaliana, negatively regulates lignocellulose synthesis and programmed cell death in xylem. Plant J. 53, 425-436. 155 CHAPTER 5 Fusarium virguliforme Transcriptional Plasticity Revealed by Diverged Host Colonization This chapter is unpublished, with intention to submit to Plant Cell Baetsen-Young, A.M., Wai, C.M., VanBuren, R., Day, B. 20XX. Fusarium virguliforme transcriptional plasticity revealed by diverged host colonization. Plant Cell. XX: XX-XX 156 Abstract Comparative transcriptomics of fungal colonization of hosts have indicated infection programs vary by fungal lifestyle. However, individual fungi can colonize a broad range of hosts, and must contain and manipulate its genetic repertoire to enable colonization of hosts with distinct phenotypic outcomes. In the current study, we exploited the broad host range of Fusarium virguliforme as a comparative model to identify differentially fungal responses leading to an endophytic or pathogenic lifestyle when colonizing two different hosts, highlighting gene expression critical to pathogenicity upon soybean. A comparison of F. virguliforme transcriptomes colonizing soybean and corn in planta over a 14-day time course, uncovered a nearly complete network rewiring, with less than 8% average gene co- the different hosts. Divergence of expression module overlap upon colonizing transcriptomes originated from host specific temporal induction genes central to infection and colonization, including CAZymes and necrosis inducing effectors. Upregulation of Zn(2)- Cys6 transcription factors were uniquely induced in soybean, potentially aiding in virulence to soybean. Fungal regulation of host immune processes was unveiled by in planta induction of milRNAs targeting stress, defense and senescence process. Overall, this suggests that F. virguliforme modulates the infection program in at least two hosts through transcriptional plasticity. 157 Introduction Plant pathogens produce distinct phenotypes on susceptible hosts through molecular cross- talk enabling a compatible interaction. Fungal plant pathogens produce an array of symptoms during host colonization revealing diversity of infection programs through comparative exploration (Oliver and Ipcho, 2004; Horbach et al., 2011; Cordovez et al., 2017). While several studies have highlighted key processes and pathways critical to pathogenesis of fungi upon plants through single trait interactions (Derntl et al., 2017; Fang et al., 2017), genomic and transcriptomic exploration have revealed the elaborate infection program many fungi contain (Brown et al., 2017; Chowdhury et al., 2017). Comparative transcriptomics of fungal colonization have indicated infection programs vary by fungal lifestyle, suggesting induced pathways diverge within biotrophic, hemibiotrophic or necrotrophic interactions. The application of similar fungi yielding phenotypically diverse symptoms have also uncovered distinct processes employed to subvert host defenses (O'Connell et al., 2012; Haueisen et al., 2018). Transcriptomic approaches have highlighted that fungi regulate developmental programs to penetrate plants (Soanes et al., 2012; Vollmeister et al., 2012), secreted effectors (Yang et al., 2013; Haueisen et al., 2018), and express small RNAs (sRNAs) to modulate host defense response (Jiang et al., 2017; Lee Marzano et al., 2018). Yet these studies have often focused upon pinpointing candidates for virulence or aggressiveness upon single hosts by employing differences of the isolates genetic background. This approach has left gaps within our understanding of how individual pathogens regulate infection programs for successful colonization of different hosts. 158 Fungal plant pathogens can entail broad host ranges, causing substantial economic damage to agricultural crops. While most plant plants pathogens only colonize specific hosts, several fungi have broad pathogenic and non-pathogenic host ranges (Derbyshire et al., 2017). Verticillium dahliae causes diseases on more than 400 hosts, and while this fungus is considered adapted to specific hosts to cause disease, the broader question of the ecology of V. dahlia in agroecosystems has highlighted that this fungus has a much larger endophytic host range (Malcolm et al., 2013). This asymptomatic endophytic host range was more recently discovered for the causal agent of soybean sudden death syndrome, Fusarium virguliforme (Kolander et al., 2012). Therefore, individual fungi must contain and manipulate its genetic repertoire to enable colonization of hosts with distinct phenotypic outcomes. The application of single fungal species colonizing hosts with symptomatic and asymptomatic phenotypes provides an opportunity to understand transcriptional reprogramming to promote fungal colonization of hosts and disease development. Comparative systems have provided resolution distinguishing plant pathogen interactions in specific classifications of pathogenetic and endophytic (Laluk and Mengiste, 2010; Lofgren et al., 2018). Yet, fungal ecology often suggests that the host fungal interactions exhibit a continuum of molecular cross talk, presenting a gradation of pathogenic to mutualistic outcomes when interacting with diverse hosts, such as Botrytis spp., Verticillium spp., and Fusarium spp. (Malcolm et al., 2013; Demers et al., 2015; Shaw et al., 2016). Furthermore, this indicates these fungi are able to fulfill two unique ecological niches, potentially within the same community (Selosse et al., 2018). Exploring the genomes of fungi with broad host ranges has uncovered the genetic potential substantiating diverse ecological and pathogenic 159 niches (Ma et al., 2010; Seidl et al., 2014; Derbyshire et al., 2017). Comparing the underlying transcriptional processes regulating a pathogenic organism to exhibit an endophytic lifestyle, could yield novel genetic signatures promoting virulence within a susceptible host. To explore the fungal plant interaction continuum, Fusarium virguliforme, the causal agent for soybean sudden death syndrome, provides an exceptional model. This disease is a key limitation in reaching soybean yield potential, with an estimated annual economic impact of $330 million dollars in the United States, stemming from limited effective disease management practices (Koenning and Wrather, 2010; Hartman et al., 2015). This ascomycete fungus can colonize the roots of an array of leguminous dicots, stimulating the chlorosis, necrosis and eventual loss of above ground biomass (Kolander et al., 2012). However, on many monocots and weed species, this fungus colonizes roots without any observable negative implications to the host (Kolander et al., 2012; Kobayashi-Leonel et al., 2017). Within this array of F. virguliforme asymptomatic hosts is corn, a common rotational crop with soybean, indicating that this soybean pathogen is enacting as a potential endophyte in association with corn in the same agroecosystem. With the incomplete understanding of how F. virguliforme initializes molecular crosstalk with soybean and corn to enable distinct ecological niches, and with the limited exploration of genetic regulation of individual fungal colonization programs upon diverse hosts, we applied a systems comparison to resolve transcriptomic reprogramming aiding in pathogenic colonization of soybean, and asymptomatic interactions with corn roots. To highlight divergence in genetic pathways underlying fungal lifestyle, we investigated (1) early colonization strategies between F. virguliforme and soybean or corn through in planta assays, (2) what early 160 transcriptional responses of F. virguliforme colonizing corn or soybean are induced, (3) how distinct are fungal asymptomatic transcriptomes from symptomatic transcriptomes, and (4) does F. virguliforme modulate soybean defense responses through micro like RNA secretion. Material and Methods Genome Sequencing, Assembly, and Annotation of Fusarium virguliforme PacBio and Illumina sequencing were performed on high molecular weight DNA extracted from lyophilized (FreeZone 2.5, Labconco) F. virguliforme Mont-1 mycelia grown for four weeks in potato dextrose broth using a modified Ctab procedure to include 1% polyvinylpyrrolidone (Lade et al., 2014). A PacBio library was constructed at the University of Georgia Genomics and Bioinformatics Core and size selected for 15-20 KB fragments by the BluePippen system (Sage Scientific). The library was sequenced on a Sequel Platform. The single smart cell yielded 6.5 GB. For error correction, Illumina TruSeq Nano DNA Libraries were prepared and sequenced on an Illumina MiSeq v3 in a 2x300bp paired end format and HiSeq 4000 in a 2x150 bp format at Michigan State University Research Technology Support Facility. PacBio reads were assembled and error corrected by Canu (v1.8) (Koren et al., 2017) in default parameters. Genome size for assembly was taken from the previous F. virguliforme assembly at 50.9 MB (Srivastava et al., 2014). Bacterial DNA was present in the assembly, thus daft contigs were compared to the previous genome assembly with LAST (v912) (Kiełbasa et al., 2011), and novel contigs were validated of fungal origin by BLAST+ (v2.2.30) (Camacho et al., 2009) against the non-redundant NCBI database. The 161 parsed genome assembly was graphed in Bandage (Holt et al., 2015). Assembled contigs were error corrected by Pilon (v1.22) (Walker et al., 2014) under default settings, using 50x coverage of Illumina paired end 300 bp and 150 bp data for F. virguliforme. Paired end reads were adaptor and quality trimmed using Trimmomatic (v0.33) (Bolger et al., 2014), and then were aligned to the draft contigs by Bowtie2 (v2.2.6) (Langmead and Salzberg, 2012) in default settings. Pilon was run five times sequentially, till limited corrections were found. The new genome assembly was compared to the previous genome assembly by QUAST (v3.0) (Gurevich et al., 2013). Transcript evidence for gene predictions was acquired from the National Center for Biotechnology Information (NCBI) (SRA SRR1382101) and germinating macroconidia from the F. virguliforme RNA-Seq time course (see below). Single end reads were adaptor and quality trimmed using Trimmomatic (v0.33) (Bolger et al., 2014). These reads were then provided to FunGAP (v1.0) (Min et al., 2017) as transcript evidence. The parameters for running FunGAP were set as: --augustus_species fusarium_graminearum, with transcript reads provided as --trans_read_single. The final gene models from FunGAP contained 16,050. Single copy fungal orthologs from BUSCO (v3) (Kriventseva et al., 2015) assessed the completeness of the genome annotation. Functional annotation was completed using Trinotate (v3.1.1) (Bryant et al., 2017). Trinotate annotated gene models with evidence from several databases (NCBI non- redundant protein database, Swissprot-Uniprot database, Gene Ontology (GO) and InterpoScan) with BlastX finding single hit at an E-value threshold of 1E-5 (Altschul et al., --sister_proteome: Fusarium, 162 1990). This information was applied to predict protein domains with Hmmer (v3.1) (Clements et al., 2011), transmembrane proteins with TMHMM (v2.0) (Krogh et al., 2001), ribosomal RNA with RNAmmer (v1.2) (Lagesen et al., 2007), secreted proteins with SignalP (v4.1) (Petersen et al., 2011) and gene ontology with GOseq (Young et al., 2010). Additionally, EffectorP (v2.0) (Sperschneider et al., 2016) was used to predict fungal effectors within the secreted proteins, and dbCAN (Yin et al., 2012) found CAZymes and secondary metabolism genes. Comparative Genomics with Fusarium virguliforme MCSCAN toolkit (v1.1) (Wang et al., 2012) was used to find syntenic gene pairs between second version (v2) compared to the first version (v1) of the F. virguliforme genome. Conserved gene blocks were discovered through LAST alignment. Plots of macro and micro- synteny were created by the MCScan in python. To discover novel and retained genes within the v2 genome was compared to the v1 genome of the F. virguliforme genome. Coding sequences for gene models were extracted from the v1 genome by gffread (Trapnell et al., 2010). Then genes were reciprocally compared by BLAST (v2.2.26) (Altschul et al., 1990). Genes with an e-value below 1E-5, greater than 70% gene alignment, and 95% gene identity were classified as retained. If a gene was matched with 95% identity and an e-value below 1E-5, but less than 70% gene alignment, it was denoted as mis-assembled. 163 Plant and Fusarium virguliforme Assay Soybean c.v. Sloan (provided by Martin Chilvers, Michigan State University), and corn hybrid E13022S (Epley Brothers Hybrids Inc, Shell Rock, IA, provided by Martin Chilvers) were surface sterilized in 70% ethanol for 30 sec, 10% bleach solution for 20 min, and then triple rinsed in sterile water for 1 min. Soybeans seeds were placed between two sheets of sterile 100 mm Whatman filter paper with 5 mL of sterile water inside a petri dish. Seeds were incubated for five days in total darkness at 21°C. Corn seeds were incubated in sterile water for 24 hours in darkness and placed between two sheets of sterile 100 mm Whatman filter paper with 5 mL of sterile water inside a petri dish. Seeds were incubated for five days in total darkness at 21°C. Fusarium virguliforme Mont-1 isolate was propagated on potato dextrose agar (Difco, Fisher Scientific) for seven weeks. Asexual macroconidia spores were collected, diluted to 1 x 105 macroconidia mL-1 and sprayed onto five-day old corn or soybean seedlings with a 3 oz travel spray bottle. Twenty-five sprays were applied to the seedlings at angles of 0°, 90°, 180°, and 270° to ensure seeds were properly covered. For mock inoculated samples, water was sprayed onto the seedlings. Seedlings were incubated for 30 min with the inoculum, then excess inoculum was removed, and seedlings were incubated for an additional hour. Following incubation, three corn or soybean seedlings were placed into seed germination pouches (Mega International), containing 25 mL of sterile distilled water. Pouches containing seedlings were placed in a BioChambers Bigfoot Series Model AC-60 growth chamber with 140 µE m-2 sec -1 and 14:10 h light/dark cycle at 12°C for seven days and then 164 25°C for seven days. Plants were watered as needed with sterile water. Tap root from soybean or radical from corn root samples were taken at the same time (16:00 h) of day from the original 4 cm inoculation site throughout the time course. The two-week time course was repeated three independent times in the same growth chamber, with sampling of six plants for RNA isolation and three plants for DNA isolation at 0, 2, 4, 7, 10, and 14 days post inoculation (DPI) in each biological repeat. Time point 0 was sampled after completion of fungal or mock inoculation. Plant growth and disease symptomology was recorded at each timepoint by photography with a Nikon D50 camera. canon camera. Fungal Colonization Analysis To visualize fungal growth on samples, microscopic analyses of corn and soybean roots were conducted at each time point for all treatments. Roots were cleared in 100% ethanol, followed by staining in a 0.05% trypan blue solution containing equal parts of water, glycerol and lactic acid (Savory et al., 2012). Fungal structures were observed using MZ16 dissecting scope (Leica). DNA Extraction and Real Time PCR for Fusarium virguliforme DNA for real-time quantitative polymerase chain reaction (qPCR) was extracted from flash- frozen root tissue to determine the amount of fungal biomass present in samples throughout the time course. A total of 60 mg of ground root tissue from individual corn or soybean plants from each time point were extracted with a NucleoSpin Plant II Kit (Macherey-Nagel), with 165 an additional incubation on 1 hour at 65°C during lysis. Samples were prepared for F. virguliforme DNA detection by qPCR following (Wang et al., 2015). Analysis of variance (ANOVA) was calculated for DNA quantities using the “lme4” (Bates, 2015) and “Car” (Fox, 2011) package in R v3.4.1(R Development Core Team, 2010). Means were separated at P ≤ 0.05 using Tukey’s least significant different test using the “multicomp” package (Hothorn, 2008). RNA Extraction Total RNA was isolated from 200 mg of ground flash frozen germinating macroconidia and plant root samples for small RNA (sRNA) and messenger RNA (mRNA) sequencing with a miRNeasy Mini Kit (Qiagen). Contaminating DNA was removed with TURBO DNase Free (Invitrogen). RNA quality was determined by gel electrophoresis and the 2100 Bioanalyzer (Agilent) with the Agilent RNA 6000 Pico kit. Library Preparation and Sequencing The same extraction for each sample was used for mRNA and sRNA library preparation. Libraries were prepared using the Illumina TruSeq mRNA Library Preparation Kit from three biological repeats of each time point of F. virguliforme or mock inoculated corn or soybean or germinating macroconidia samples by the Michigan State Research Technology and Support Facility. Samples were pooled and sequenced on the Illumina HiSeq 4000 under single end 50 bp mode. Base calling was done by Illumina Real Time Analysis (RTA) v2.7.7 166 and output of RTA was demultiplexed and converted to FastQ format with Illumina Bcl2fastq v2.19.1 Samples for sRNA were prepared using the NEB Next Small RNA Sample Prep Kit at University of Illinois at Urbana-Champaign W.M Keck Center. Samples were pooled and sequenced on the Illumina HiSeq 4000 under single end 50 bp mode. Fastq files were generated and demultiplexed with the bcl2fastq v2.20 Conversion Software (Illumina). mRNA-Sequencing Processing and Differential Analysis Reads were trimmed for adapter presence and quality score by Trimmomatic (v0.33) (Bolger et al., 2014). The trimmed reads were uniquely mapped to the corresponding reference genome of F. virguliforme (Fv_v2) with HISAT2 (v 2.1.0) (Kim et al., 2015) with the following parameters --dta --rna-strandness F. Hits from HISAT2 were converted from SAM to BAM format by Picard (v 2.18.1) (http://broadinstitute.github.io/picard/). Alignments were then counted by HTSeq (v0.6.1) (Anders et al., 2014) with the following options: -- minaqual 50 -m intersection-strict -s reverse --idattr=gene_id. Gene counts were imported into DESeq2 (v1.22.2) (Love et al., 2014) conducted in R, normalized for library size and log2 transformed to determine correlation of biological replicates at each time point. To determine differential gene expression DESeq2 (v1.22.2) executed in R (R Development Core Team, 2010) with raw HTSeq counts. Counts were filtered for 90% of genes with less than 10 across all samples. DESeq2 was applied to determine significant genes with an adjusted p-valued ≤ 0.05. Pairwise comparison between each time point of corn or soybean F. virguliforme in planta samples and germinating F. virguliforme germinating macroconidia. 167 Within each host, pairwise comparisons were completed across the time course. Additionally, F. virguliforme was compared at each time point across hosts. Gene Co-Expression Network Analysis Genes were also filtered for weighted gene correlation network analysis (WGCNA) (Langfelder and Horvath, 2008) implemented in R (R Development Core Team, 2010) for 90% of genes with less than 10 across all samples. These 11,112 genes were variance stabilized transformed for importation and F. virguliforme expression on corn or soybean signed co-expression networks were constructed separately. A soft threshold power of 7 and tree cut height of 0.15 were applied to these analyses, with all other parameters unchanged. Overall gene expression was clustered into 22 modules for F. virguliforme colonization of corn and 20 modules when colonizing soybean. Modules were visualized in ggplot2 (v3.1.1) package (Wickham, 2016) in R. Gene Ontology Enrichment Analysis For each predicted protein from the F. virguliforme v2 genome, unique GO terms from InterPro Scan (Jones et al., 2014) were extracted with a custom script. Gene lists from either differential analysis or clusters from co-expression analysis were analyzed by TopGO (2.34.0) conducted (Alexa A and Rahnenfuhrer J, 2018) in R. Fishers Exact Test was conducted on each gene set with an adjusted P-value ≤ 0.05 to determine significance of enrichment. 168 Small RNA Read Processing and Identification Single end reads from F. virguliforme colonized soybean roots and germinating macroconidia were adaptor and quality trimmed by cutadapt (Martin, 2011) for reads 15-35 bp. Trimmed reads were aligned to the F. virguliforme v2 genome with the following parameters -v 0 -S -a -m 50 --best –strata with Bowtie (v1.1.2). Reads aligned to the F. virguliforme v2 genome were realigned with Bowtie to the soybean genome with the same parameters, and these reads that did not align to the soybean (Wm82.a2.v1) genome were utilized for downstream analysis. Reads that perfectly matched the F. virguliforme and did not align to soybean were collapsed and mapped in mirDeep2 (Friedländer et al., 2012). Next, mirDeep2 was employed to discover novel micro-like RNAs (milRNAs) and to quantify their abundance within each sample throughout the time course. milRNAs were identified through a BLASTn (Altschul et al., 1990) search with an E-value less than 1.0 against the miRBase database. A milRNA was considered expressed if present in two of three biological replicates and had a prediction score greater than one. Target Prediction and Differential Accumulation of milRNAs Targets of milRNAs were revealed by applying psRNATarget (Dai et al., 2018) with a minimum and maximum expectation value of 3 and 5, respectively. To validate targets were expressed within the in planta dataset, TAPIR was conducted to discover targets within the soybean transcripts. A gene considered a target of predicted milRNAs, if present in both 169 target prediction programs. The predicted milRNA targets were filtered for disease associated traits. Data were visualized by ggplot2 (Wickham, 2016) implemented in R. Results Generation of a Long-read Fusarium virguliforme Reference Genome To define and characterize the transcriptional landscape of the F. virguliforme-host interaction, we first sought to improve the pathogen reference genome using third generation sequencing technologies. To do this, we generated a high-quality PacBio single- molecule derived genome, based on 6.5 Gb of sequencing data, yield an approximate 17X coverage of the F. virguliforme genome. Filtered reads were assembled using the long-read optimized assembler Canu (Koren et al., 2017), and resultant contigs were error-corrected using 50x Illumina data by Pilon (Walker et al., 2014). In total the assembled F. virguliforme genome encompassed 52 mb with 96 contigs, with a N50 of 1.54 mb (Figure 21 and 22, Table 16). The resulting overall genome size was slightly larger than the first genome assembly version (Srivastava et al., 2014), which we posit stems from a more robust assembly of longer reads; indeed, the N50 was exponentially larger in this version of the genome from larger contigs (Table 16). Synteny of the two genome versions was highly fragmented (Figure 22A), an observation that stems from the over 3,000 contigs in the first version of the genome, which are further assembled together herein, version 2. Within these syntenic regions, micro-collinearity between the two genomes was observed to be very consistent (Figure 22B). 170 The F. virguliforme genome generated in this study was annotated using FunGAP (Min et al., 2017), incorporating Breaker, MAKER, and Augustus gene model prediction algorithms. A total of 16,050 genes were identified in this version, representing an increase in 1,205 genes. Comparisons of the coding sequences between genome versions revealed 12,306 genes were conserved with a minimum 70% gene alignment rate and a 95% identity. However, 1,421 genes, including 564 genes that contained coding sequences of conserved genes, did not meet the threshold of similarity and thus were considered misassembled in one of the genomes. We suggest that this result arises from mis-assemblies generated using short read sequencing and repeat collapsing (Ardui et al., 2018). Overall, 2,889 genes did not have an alignment to the previous genome (Supplemental Dataset 2-1), and this gene set contained enrichments in protein ubiquitination, organic compound breakdown, and porphyrin compound biosynthesis (Table 17). Genome completion was assessed using Benchmarking Universal Single-Copy Orthologs, and from this, we observed an approximate 98% completion of the 1,438 groups searched. Through functional annotation, 10,162 of the 16,050 genes were found to have protein evidence. Further exploration of the genome found 232 genes were candidate effectors (Supplemental Dataset 2-2). As a hemibiotrophic pathogen, we also search the F. virguliforme genome for carbohydrate active enzymes (CAZymes) and discovered 365 genes with potential functions in this process. In total, these datasets provided a resource to explore the transcriptomic landscape of F. virguliforme across hosts. 171 Fungal Growth and Development Produced Differing Plant Root Phenotypes To understand F. virguliforme interactions with corn (asymptomatic) and soybean (symptomatic), we profiled F. virguliforme roots over a two-week time course and collected samples for RNA-seq analysis. In short, this time-frame (i.e., 2-to 14-days-post-inoculation) was selected as it enabled the discovery of time frames critical to fungal attachment, growth, penetration, differentiation, and symptom development. As illustrated in Figure 23A, by the end of the two-week time course, soybean roots showed signs of necrotrophy in both the tap and hypocotyl regions. Additionally, fungal-induced necrosis had also spread to developing lateral roots adjoining the tap root. This symptom development is consent with root disease progression of SDS, initializing as an asymptomatic biotroph, with the fungus depending on living plant tissue, then turning necrotrophic and killing host tissue (Ma et al., 2013). In the asymptomatic host, corn, we did not observe any striking evidence of fungal infection over the time-course of the experiment. To verify fungal growth, we used trypan blue staining to visualize fungal growth on both soybean and corn roots throughout the time-course. Fungal growth and colonization were apparent on both hosts, but developmental stage varied according to the host (Figure 23B). For example, following inoculation, fungal spore germination was apparent on roots of both hosts, and by 2 days post inoculation (DPI), fungal mycelia had expanded across the root surface. Interestingly, mycelia on corn roots parallel root epidermis cells, while mycelia growth on soybean roots did not have any apparent pattern of colonization. Also by 2 DPI, round and swollen mycelia structures were observed; these appear to be similar to 172 penetration structures (e.g., appressoria). Support for this classification comes from documented observations in Fusarium graminearum and F. virguliforme infection assays (Navi and Yang, 2008). Interestingly, these infection-like structures were observed, but not until ca. 7 DPI, indicating a slower infection process in corn. From 7 to 14 DPI, we continued to record fungal growth and development at the site of inoculation and observed an increase in mycelia colonization on both hosts. By 14 DPI however, masses of developing macroconidia were apparent on soybean roots, indicating asexual reproduction had initialized in the time course. The transition to necrotrophy was indicated by chlorosis on soybean roots at 7 DPI, followed by necrosis at 10 DPI (Figure 23B). On corn, no visible symptoms were observed throughout the time-course of the experiment. Once we confirmed the in planta growth of the pathogen on both hosts, we next conducted RNA-Seq at six-time points over the course of the inoculation experiment; we also included additional sampling of germinating (in vitro) F. virguliforme macroconidia spores. After trimming of lower quality reads and adaptors, resultant reads were mapped to the F. virguliforme genome. In our initial analysis, we identified low levels of fungal mRNA reads, representing 0.04-0.13% of total reads across 0-2 DPI (Figure 23C). While this was not unexpected, we generated a minimum of 200 million reads per sample (at 0-4 DPI) to generate read numbers greater than 80,000 reads per biological repeat (Table 18). As expected, the percent of mRNA reads aligning varied by host over the time course. For example, fungal reads from corn (non-host) increased in a linear fashion over the time course; ranging from 1.11-2.53% of the sample at 7-14 DPI. However, fungal reads from soybean (symptomatic host) did not increase substantially until 7 DPI (Figure 23C), 173 following which time the percent of fungal reads approached those observed from corn- inoculated samples. In soybean, this increase coincided with symptom development and potential shift from biotrophy to necrotrophy. Host-induced Gene Expression Profiles in Fusarium virguliforme To assess if the colonization program of F. virguliforme diverged in a manner consistent with the differing hosts, we next employed a comparative transcriptomic-based approach. We hypothesized this design would highlight transcriptional reprogramming specific to each host, and as a function of a single, common pathogen interaction, would yield insight into the influence of host of fungal gene expression. To ensure biological reproducibility, we compared gene expression across biological replicates. From this, we found a greater than 90% reproducibility of the fungal gene expression profiles for the last timepoint of the infection time course, indicating the biological response of the fungus within another organism was highly consistent (Figure 24). To understand if our treatments were globally distinct from one another, we next preformed principle coordinate analysis of all 39 samples across corn and soybean colonization, as well as from germinating macroconidia. Using this approach, we discovered that fungal response was primarily correlated with treatment (Figure 25), and that the germinating macroconidia formed a distinct cluster from samples colonizing hosts. While F. virguliforme response on hosts did form a single, large, cluster, all samples were distinctly separate within this cluster as a function of host. Intriguingly, gene expression from both hosts were separated by time as well, with the greatest separation identified at timepoints between 4 and 14 DPI. Additionally, a separation from the plant 174 samples was apparent in reads derived from 7-14 DPI samples from F. virguliforme colonizing soybean. This grouping of samples suggests hosts interactions greatly shape gene expression, and moreover, the temporal expression of those gene expression interactions. From the analysis above, we identified 11,112 F. virguliforme genes were expressed on corn, soybean, and germinating macroconidia that were expressed in at least one timepoint across the time course. To discover genes that were induced by host interaction, we next compared gene expression of F. virguliforme from soybean or corn with germinating (e.g., in vitro) macroconidia. Using this approach, we identified 4,192 and 4,072 unique genes that were upregulated (log2 fold change > 1) in fungal samples from soybean or corn, respectively, throughout the time course. As many genes were induced, we filtered the differentially expressed genes by |log2 fold change > 2| to discover processes relevant to host colonization. Of the significantly up-regulated genes from F. virguliforme on soybean, the vast majority of genes were induced at 0 and 7-14 DPI (Figure 26A). Similarly, the majority of induced F. virguliforme genes within corn roots were observed from samples derived at 7-14 DPI (Figure 26B). Surprisingly, while the read depth was less in corn samples at 0 DPI than soybean samples, an additional 127 gene were detected as significantly upregulated at log2 fold change > 2, highlighting an elevated response in F. virguliforme to corn than to soybean. The greatest changes of unique gene upregulation occurred between 4-7 DPI on corn, and at 10-14 DPI on soybean (Table 19). Only three genes were conserved as a function of expression between both hosts and timepoint. 175 To explore the function of the induced genes in F. virguliforme as they relate to host interactions, we undertook a functional gene ontology (GO) enrichment analysis of genes; the threshold for analysis was those genes that were upregulated greater than log2-fold > 2. Of the more than 50 biological process categories that were found to be enriched, several processes were consistent between both soybean and corn, among which included carboxylic acid, lipid or co-factor biosynthesis, as well as polysaccharide metabolism, protein dephosphorylation, and small molecule biosynthesis (Figure 26C and D, Supplemental Dataset 2-4 and 5). Overall expression patterns on both hosts were similar for lipid and co- factor biosynthesis, which is not surprising, as these processes are critical for fungal growth and signaling pathways (Schrettl et al., 2007; Lysøe et al., 2008). Carboxylic acid biosynthesis was induced across the colonization time course, which we hypothesize would a critical process for secondary metabolite production in support of fungal colonization, regardless of the host (Brown and Proctor, 2016). Interestingly, protein dephosphorylation and small molecule biosynthesis were enriched in fungal transcriptomic programs and elevated in expression when colonizing soybean roots at 7-10 DPI. The protein dephosphorylation of plant plasma membranes by fungi have been documented to prevent signaling cascades that normally simulate host defense responses (Yang et al., 2015). Also, small molecules secreted by plant pathogens may target host defense machinery and/or processes to module immune responses to the fungus (Jennings et al., 2000; Chang et al., 2016b). The upregulation of these processes in F. virguliforme during soybean colonization suggests an infection strategy to reduce host immune responses, more so, than when F. virguliforme colonizes corn roots. 176 Temporal Divergence of F. virguliforme in planta Gene Co-expression upon Host Colonization As specific genes related to broad processes of F. virguliforme colonization and development were differentially induced at distinct temporal stages during the time course, we were curious if global genes co-expression patterns had diverged during colonization of corn and soybean. To address this, we applied a weighted gene correlation network analysis (WGCNA) across the time course. As the results above suggest that F. virguliforme induces the expression of a large number of genetic processes during the shift from biotrophy to necrotrophy over the time course, we hypothesized this approach would allow us to separate the biotrophic from necrotrophic patterns. Gene co-expression networks have previously revealed induced developmental changes in Ustilago maydis (Lanver et al., 2018). Genes were filtered to remove low expression before co-expression network construction, leaving 11,112 genes remaining. Next, these genes were clustered into 22 modules for F. virguliforme colonization of corn and 20 modules for F. virguliforme colonization of soybean (Figure 27 Supplemental Datasets 2-6 and 7). As apparent temporal patterns existed within each co- expression network, we separated the modules into four large groups: 1) early induced expression at 2 DPI, but down regulated at 4DPI, 2) elevated expression at 4-7 DPI, 3) induced expression at 7-10 DPI and 4) down regulation of expression from 2-4 DPI, but induced at 10 DPI (Figure 27). While these temporal patterns of co-expression modules across F. virguliforme colonization of both hosts appear similar when placed in these four large groups, the gene enrichment of modules contained within these groups varied by host colonization by F. virguliforme. 177 Gene co-expressing modules in group one while F. virguliforme was colonizing corn roots, were enriched for negative regulation of many functional processes, inducing cellular metabolism, macromolecule production, and expression of primary metabolism. Indicating F. virguliforme was repressing secondary metabolism and utilizing self-derived energy at early interactions with corn roots. However, processes upregulated in F. virguliforme colonizing soybean roots at 2 DPI, were enriched for reactive oxygen species (ROS) generation and oxalic acid production. Both of these processes are associated with early hemibiotrophic and necrotrophic plant fungal interactions at early time points in colonization. Reactive oxygen species in fungal hyphae supports the differentiation of cells for infection structures like appressoria (Heller and Tudzynski, 2011). We observed the develop of appressoria like structures at 2 DPI (Figure 23B), suggesting that this fungal as already penetrating host tissues within 48 hours of contact. Also, oxalic acid biosynthesis enrichment indicates a potential down regulation of host cell death through autophagy processes, prevent a massive necrotic response to kill the fungus by the host, as with Sclerotinia sclerotium (Veloso and van Kan, 2018). Together, the processes suggest that F. virguliforme is infecting and manipulation soybean host responses by 2 DPI. It is not surprising that numerous co-expression modules were upregulated from 2-4 DPI in F. virguliforme within corn and 2-7 DPI within soybean. Most of these modules were highly expressed for the remainder of the colonization time course. Modules generated from F. virguliforme derived from corn (Figure 27A and 28) also contained processes enriched for primary metabolism, similar to group 1; however, processes associated with response to the defenses were also enriched. For example, at 4 DPI, carboxylic acid biosynthesis-associated 178 genes and related processes were upregulated, suggesting that toxin production was occurring (Supplemental Dataset 2-6). Conversely, the same processes from F. virguliforme- associated soybean highlighted a faster colonization program; indeed, processes were enriched for cellular catabolic processes of cellulose, pectin and polysaccharides in soybean. Of interest to the nature of the host-association and symptomatic nature of F. virguliforme- soybean, we also observe an enrichment in small molecule biosynthesis following soybean colonization at ca; 4 DPI (Supplemental Dataset 2-7). These processes highlight the initial transition from a biotrophic to necrotrophic lifestyle of modifying and breaking down host tissue for further proliferation (Laluk and Mengiste, 2010). One striking observation of this analysis is that numerous diverse processes were enriched during module upregulation at 7-10 DPI in F. virguliforme-corn samples. Of these, the upregulation of NADP stood out, as this process has been previously associated with hyphal differentiation initiation for infection structures (Heller and Tudzynski, 2011); this is consistent with our phenotypic observations shown in Figure 23B, 7 DPI. The upregulation of gene process in F. virguliforme associated with amino acid sugar catabolism also suggests access to plant derived compounds, likely via direct penetration of the host tissue by the fungus. Concomitant with this, upregulation of processes associated with chemical and stimulus likely indicates F. virguliforme was sensing host defense response to the production and secretion of anti-microbial compounds. During this same timeframe, while F. virguliforme from corn was activating nutrient access-associated processes, F. virguliforme interaction with soybean revealed an upregulation of protection-associated mechanisms, 179 among which included antibiotic catabolism, response to ROS and chemical to host defense activation. By 14 DPI, processes that are associated with group-4 (Figure 27) were found to be expressed as a function of host colonization. Also at 14 DPI, primarily amino sugar and nitrate acquisition were induced in samples derived from corn, while at the same timepoint in samples from soybean, necrotrophic processes had ensued, and we observed an enrichment in functions associated with cell killing, organic acid transport, and self- protection form host induced ROS by cell redox homeostasis. This process enrichment is supported by the necrotrophic envelopment of the soybean tap root at 14 DPI (Figure 23B). In total, the enrichment of biological processes during the colonization of corn and soybean roots suggest two distinct infection programs are occurring within the same fungal organism. For example, the infection program during the colonization of corn appears to be delayed, with fungal penetration of host cells occurring at a time much later than observed in soybean. This slower rate of infection and pathogen progress may stem either from the activation of host defenses, an absence of the appropriate host signals which are required to simulate the initiation of rapid infection-associated processes, or both (Elliott, 2016). Additionally, toxin related processes were enriched throughout the sampled timepoints during soybean colonization, a process that we hypothesize may further assist colonization, nutrient acquisition, and eventual host cell death (Chang et al., 2016b; Chang et al., 2016a; Ngaki et al., 2016). The lack of temporal conservation of enriched processes between colonization of these two hosts highlights plasticity of the F. virguliforme transcriptome. 180 Overall only few genes were co-expressed in a similar manner upon these two hosts (Figure 29). On average, only 8% of genes were conserved within module gene co-expression between F. virguliforme expression on soybean and corn. Comparison of gene overlap highlights that processes enriched in group three of co-expression in corn contain more genes from the temporal of F. virguliforme across all soybean co-expression groups. This indicates that enrichment of processes related to nutrient access and host infection from 7- 10 DPI in corn were expressed across a much longer timeframe in soybean. Interestingly, module 14 in group 2 of F. virguliforme within soybean contained the greatest overlap with several early (2-4 DPI) induced corn modules. Module 14 was the largest co-expression module, with 3,503 genes, which may contain many genes relevant to basic cellular functions needed for viability and growth (Figure 30 and Supplemental Dataset 2-7). Host-specific Gene Expression Patterns during Root Colonization As noted above, we observed a temporal divergence of biological processes enriched by respective host colonization. To further explore this, we next asked if these induction responses were host specific. Previous work comparing the infection programs of phytopathogenic Zymoseptoria tritici discovered temporal variation of isolate infection (Haueisen et al., 2018). To determine if this is also the case in F. virguliforme, we directly compared F. virguliforme gene expression from each host at each time point (Figure 31A). The majority of genes in the F. virguliforme transcriptome were not differentially regulated by cross species colonization; this analysis represented 81% of the genes expressed. Of the proportion of genes that were differentially induced, 43% were uniquely upregulated during 181 corn root colonization, 56% were upregulated upon colonization of soybean only and 0.1% were conserved in upregulation at different time points, during the infection time course. Of these differentially induced genes at |log2 fold change > 1|, the vast majority were induced at 10 and 14 DPI (Table 21, Supplemental Dataset 2-8). While fewer genes were differentially upregulated at early time points, these genes highlight specific processes underlying fungal temporal colonization. Interestingly, genes highly (>20 log2) upregulated at 0 DPI within F. virguliforme colonizing soybean roots, were related to DNA methylation, suggesting this process did not occur during early interactions on corn roots. No processes were uniquely induced to corn root colonization at 0 DPI. Fusarium virguliforme began to respond to host induced anti-microbial metabolites at 2 DPI by upregulating ABC transporters (Gupta and Chattoo, 2008), and initializing toxin secretion with terpene synthases, as the fungus grew on the corn roots. Based on the unique upregulated genes during soybean colonization at 2 DPI, F. virguliforme was penetrating roots through reactive oxygen species production, and regulation through Zn(II)-Cys6 fungal transcription factors (Brown et al., 2007). Fusarium virguliforme colonization of soybean roots resulted in the activation of marked defense signals, ca. 4 DPI, as indicated by the rapid upregulation (>10 log2) of various cytochrome oxidase genes. Interestingly, these genes were not upregulated at the same time in samples derived from F. virguliforme-corn colonization, suggesting that either the fungus had not penetrated the root, and/or a lack of anti-microbial metabolite accumulation. However, at 7 DPI, cytochrome oxidases and NDAP was upregulated in F. virguliforme corn interactions, thus indicating cellular differentiation of hyphal penetration structures (Heller and Tudzynski, 2011). At the same time, cellular degradation and nutrient access-associated 182 processes were highly (>10 log2) upregulated in F. virguliforme-colonized soybean samples, as indicated by the expression of glycoside hydrolases and pectinases, as well as various nutrient transporters. A larger set of genes were differentially induced within F. virguliforme across hosts at 10 and 14 DPI. At these timepoints, samples from corn showed induced genes enriched in secretion catabolic processes (Supplemental Dataset 2-9), while those from soybean revealed a shift to processes indicative of fungal growth (e.g., glycerolipid and lipoprotein biosynthesis; (Takahashi et al., 2009). This is in support of our observation of asexual production at 14 DPI (i.e., Figure 23B). Toxin and secreted proteins were induced in a temporally-dependent manner, as well as in differential quantities, when compared across hosts (Figure 31B and C). While nearly triple the number of predicted effector-encoding mRNAs were upregulated at 0-2 DPI in soybean, none of the effector-encoding mRNAs were differentially induced between hosts at these time points. By 4 DPI, almost four times as many effectors were upregulated in soybean roots (Figure 32), including those with predicted functional domains associated with pectin lyases, glycoside hydrolases, and necrosis-inducing proteins. However, similar to the above, all but three genes were not considered differentially expressed when compared to F. virguliforme colonization of corn (Supplemental Dataset 2-10). Until this timepoint in the infection process, CAZymes expression was observed to be induced in similar patterns (i.e., no DE genes) (Figure 33). However, at 4 DPI pectin lyases and glycoside hydrolases were expressed much higher (>10 log2) by F. virguliforme in soybean roots (Supplemental Dataset 2-11). This trend was exacerbated by 7 DPI with many CAZymes and effectors related to pectin lysates being uniquely upregulated in F. virguliforme colonization of soybean roots. This suggests 183 divergence in fungal colonization programs between corn and soybean at 7 DPI, potentially stemming from the shift of biotrophic to necrotrophic, as visible symptoms started to initialize at 7 DPI. A necrotrophic lifestyle was evident by 10 and 14 DPI with 14 and 28 effectors, and 37 and 114 CAZymes, respectively targeting cellular breakdown of soybean roots by F. virguliforme. Across the time course, few pectin lyases were expressed while F. virguliforme was colonizing corn roots. This may stem from the differential physiology of monocot roots containing only 10% pectin, whereas dicots contain up to 30% pectin (Caffall and Mohnen, 2009). The effectors that were uniquely induced between 7-10 DPI in corn roots are currently putative candidates with no known function. Regulation of Soybean Defenses by milRNAs Modulation of fungal pathogenesis has recently expanded to include micro-like RNAs (milRNAs). These small regulatory RNA modules of 18-22 nt in length were first observed in Neurospora crassa (Romano and Macino, 1992). Since initial discovery, milRNA have been observed to be involved in processes related to fungal development (Shao et al., 2019), including in S. sclerotiorum (Zhou et al., 2012) and F. oxysporum (Chen et al., 2014). To build upon the demonstrated roles for milRNAs in fungi, we next asked if plant pathogenic fungal milRNAs regulate the expression of host targets, as previously observed with small RNAs (Weiberg et al., 2013). To test this, we conducted a comprehensive microRNA sequencing analysis of our time-course of infection to 1) inventory the suite of F. virguliforme-derived milRNAs, and 2) determine with these molecules affect the expression of host target transcripts. Reads from F. virguliforme-infected soybean samples, as well as from in vitro 184 germinating macroconidia, were aligned to both F. virguliforme and soybean genomes to remove reads that matched both genomes (Weiberg et al., 2013). Following this, samples of germinating macroconidia were found to contain approximately 74% mapped reads (Table 21). Mixed samples of soybean root and F. virguliforme ranged from 1.5-5.6% for 0-7 DPI, and 7.5-13.5% for 10 and 14 DPI read alignment after filtering. We posit that the increase in read alignment corresponds to increased fungal growth (Figure 23B). These unique reads shared a similar read length distribution to other fungi, with two peaks of read alignment at 21-23 nt and 26 nt (Figure 34A) (Zhou et al., 2012; Chen et al., 2014). milRNA derived from F. virguliforme were predicted using mirDEEP2 (Friedländer et al., 2012), with no reference option to form pooled and collapsed F. virguliforme uniquely aligned reads. In total, 22 candidate milRNA were annotated, and of these, 20 passed the randfold p-value test. Of these 20 candidates, two putative milRNA were consistently induced (expressed in all 3 biological replicates) in planta (Supplemental Dataset 2-12). One candidate, Fvm1-novel-1, was highly expressed from 0-4 DPI only in planta. The second candidate was expressed from 10-14 DPI, at an elevated level from germinating macroconidia. Using Fvm1-novel-1 and Fvm1-novel-2, we next predicted soybean targets of these two candidates using psRNATraget (Dai et al., 2018) and TAPIR (Bonnet et al., 2010). The individual resultant target lists were filtered to only include candidates that were cross- validated in both programs. While no targets were repressed in their expression by Fvm1- novel-2, six soybean target genes were repressed in expression by Fvm1-novel-1; identified 185 Glyma.11g185700 In brief, Fvm1-novel-1 was predicted cytochrome targets all were associated with processes corresponding to defense and susceptibility pathways (Figure 34B). target root an (Glyma.02g181500), Glyma.12g087200), which are known to be induced by jasmonate acid and wounding (Heitz et al., 2012). Fvm1-novel-1 also was predicted to target a regulator of stress responsive alternative splicing (Glyma.08g334100, Glyma.18g067300) (Gulledge et al., 2012). Intriguingly, one target (Glyma.14g022100) of Fvm1-novel-1 was a dihydrolipoamide succinyltransferase, a component of oxoglutarate dehydrogenase complex. Previous work demonstrated that a knock-down of this pathway leads to enhanced senescence in tomato (Araújo et al., 2012). Based on this, we hypothesize that F. virguliforme may promote senescence in host tissue to advance necrotrophy and nutrient acquisition. In total, the repression in expression of these soybean targets suggests F. virguliforme regulates host immunity through milRNAs. Comparative systems-based approaches of pathogenic and nonpathogenic fungal isolates have led to the identification of genetic signatures relevant to pathogenicity and compatible host interactions on a single host. This approach can be manipulated to explore the continuum of pathogenic and endophytic niches of two hosts from the genetic repertoire of a single fungal organism. Herein, we applied a similar design of two distinct phenotypic outcomes generated by a single isolate to explore how fungal pathogens transcriptionally rewire infection programs to promote a symptomatic or asymptomatic host reaction. To Discussion P450s (CYP94C1, to 186 accomplish this goal, we de novo assembled an annotated the F. virguliforme genome, generated 39 mRNA transcriptomes across in vitro and in planta conditions to identify infection program modulation across two hosts, and assessed the soybean in planta milRNAome of F. virguliforme over a two-week time course. Fusarium virguliforme colonization yields distinct changes in root phenotype, dependent upon host. Corn roots when colonized by F. virguliforme remain asymptomatic and soybean roots turn chlorotic and eventually necrotic. Underlying these phenotypic developments are host dependent transcriptional programs. Temporal shifts within the transcriptome of F. virguliforme colonizing corn roots were gradual during the infection time course. Whereas, F. virguliforme colonization program drastically shifted from 4-7 DPI. These large shifts in the in planta transcriptome of hemibiotrophic plant pathogens has been observed within single hosts (O'Connell et al., 2012). Interestingly, the identified differences in transcriptomes appears to not be solely derived by gene content, but rather the temporal induction of genes with respect to host colonization. Gene co-expression networks highlighted temporal processes unique to each host through varying stages of fungal growth, infection and proliferation through nutrient access. The quick growth and infection of F. virguliforme on soybean roots by 2 DPI, indicates a rapid recognition of the host surface and initiation of the early infection program (Elliott, 2016). However, F. virguliforme gene expression on corn roots through the early time course were enriched for negative regulation of biological processes and primary metabolism, suggesting that this fungal was not immediately stimulated to infect the corn host. Upregulation of 187 processes indicative of host infection did not occur till 7 DPI, suggesting a delaying in fungal- host recognition (Giovannetti et al., 1994). By the timeframe F. virguliforme had penetrated corn roots, the same fungus on soybean roots had started to transition in lifestyle form biotrophic to necrotrophic. Upregulation of small protein secretion and fungal derived toxins signaled that F. virguliforme was promoting cell modification of host tissues for nutrient access (Sahu et al., 2017). As F. virguliforme within soybean roots induced process for cell killing and pathogenicity through the remainder of the time course, gene activity on corn highlighted fewer catabolic processes to acquire nutrients. These host derived temporal shifts in the F. virguliforme transcriptome reveals gene expression plasticity. While the vast majority of the transcriptome was expressed during colonization of corn and soybean roots, the induction of genes enabling the processes were temporally diverged. Temporal changes of gene expression have been observed between colonization of hosts of the same fungus exhibiting different lifestyles (Lahrmann et al., 2013; Lorrain et al., 2018), and may suggest the shift to necrotrophy on corn was limited. The transcriptomic plasticity of F. virguliforme between soybean and corn colonization provides a unique angle to view processes critical to necrotrophy on soybean. For example, the upregulation of effectors and CAZymes initializing at 4 DPI, highlights the start of the transition from biotrophic activity to necrotrophic (Ngaki et al., 2016). CAZymes expressed within soybean roots had a greater upregulation of pectin lyase expression, more than corn roots. Also the Necrosis Inducing Protein, was highly upregulated during soybean colonization across the time course, suggesting dicot specific upregulation (Bae et al., 2006). 188 Effectors and CAZymes within soybean roots were secreted in temporal waves at 2, 4 and 7 DPI. Each temporal wave increased in intensity of gene expression as well as diversity of enzymatic activity. This upregulation of diverse array of cell degrading and necrosis inducing peptides, demonstrates the shift to necrotrophy (Kleemann et al., 2012; Haueisen et al., 2018). While a hemibiotrophic infection program ensued in soybean roots, infection was delayed, and catabolic activities were lower in corn by F. virguliforme. A lack of host recognition (Giovannetti et al., 1994) or an upregulation of host defenses from pattern triggered immunity (Bagnaresi et al., 2012; Zhang et al., 2018), could slow fungal growth and down regulate development. Once inside the host fewer effectors and CAZymes were uniquely expressed in corn roots and were, more often than not, down-regulated after induction. The lower level of expression along with the decrease in CAZyme diversity, suggest that the cellular environment within corn roots did not stimulate prolific upregulation of necrotic inducing peptides. This may stem from the physiological differences in cell structure between monocots and dicots (Caffall and Mohnen, 2009). Additionally, as the primary hosts for F. virguliforme is legumes, potentially F. virguliforme may not be as adapted to colonization of monocots (Zhao et al., 2013). Regulation of biotrophy to necrotrophy during the hemibiotrophic lifestyle of fungi has intrigued many plant pathologists (Rai and Agarkar, 2016; Chowdhury et al., 2017; Haueisen et al., 2018). Transcription factors may pay a critical role in shifting fungal gene expression from biotrophic to necrotrophic lifestyle. Several transcription factors from the Zn(II)-Cys6 189 and C2H2 Zinc Finger family alter pathogenicity and growth (Chen et al., 2017; Sang et al., 2019). We discovered several Zn(II)-Cys6 were uniquely upregulated during soybean colonization, which may initiate pathogenicity of this fungus to soybean. Additionally, we explored host defense regulation by F. virguliforme and discovered a putative milRNA targeting stress, defense and plant maturation pathways in soybean. Collectively these gene targets indicate F. virguliforme is secreting milRNAs to modulate host defenses, as discovered with other filamentous pathogens (Weiberg et al., 2013; Burkhardt and Day, 2016). The targeting of plant senescence by a milRNA secreted for a hemibiotrophic pathogen may indicate an induction of early senescence to promote tissue maturation and susceptibility (Haffner et al., 2015). In total, these milRNA targets should be further explored to understand their role in pathogenesis by F. virguliforme. The transition from biotrophic to necrotrophic on soybean, while colonizing corn roots as a lower subdued infection program, highlights the transcriptional plasticity of a single fungal isolate. The diverse transcriptomic rewiring aids in the phenotypic development of an asymptomatic and symptomatic hosts over a two-week time course. The visual manifestation of these interactions suggests a potential endophytic role for F. virguliforme in corn roots, while also enacting as a pathogen on soybean (Rai and Agarkar, 2016), constituting two distinct roles F. virguliforme may play in agroecosystems. The ability of F. virguliforme to enact in these two distinct roles suggests the need to consider the genomic potential for plant pathogens to be expressing a gradation of transcriptional programs enabling lifestyle plasticity on a broad range of hosts. 190 Acknowledgments We would like to recognize Michigan State University (MSU) project GREEEN and the C.S. Mott Foundation for funding of ABY and research. We would like to thank Kevin Childs and John Johnston for sever access and computational assistance, Marty Chilvers for providing the Fusarium virguliforme mont-1 isolate. We would also like to recognize the support staff at the MSU Institute for Cyber enabled Research High Performance Computing Cluster for assistance in software optimization. This research was supported by funding from the MSU Plant Resilience Institute. Author Contributions Designed framework: B.D., R.B., C.M.W, A.B.Y.; Conducted Experiments: A.B.Y; Analyzed data: A.B.Y. B.D., R.B., C.M.W, A.B.Y.; Wrote the manuscript: B.D., R.B., C.M.W, A.B.Y.; 191 Table 16. Genome assembly metrics of Fusarium virguliforme versions 1 and 2. V2 Metric Number of Contigs 96 Contig N50 (bp) 1,547,076 49.21 GC % Total Length (bp) 52,079,485 Total Coverage 17x Predicted Genes 16,050 ZMetrics were taken from Srivastava et al., 2014 for the first version of the F. virguliforme genome. V1Z 3,089 72,747 49.38 50,448,805 20x 14,845 192 Table 17. GO enrichment of genes only contained in Fv_v2 genome. Term GO.ID porphyrin-containing compound biosynthesis GO:0006779 chloride transport GO:0006821 protein ubiquitination GO:0016567 transcription, DNA-templated GO:0006351 protein neddylation GO:0045116 Ontology P P P P P Classic Fisher 0.0039 0.0127 0.0166 0.0288 0.0409 193 Table 18. Number of quality trimmed reads uniquely aligning to the Fusarium virguliforme v2 genome. Days Post Inoculation 14 2 0 10 7 4 Sample Number of Uniquely Aligned Reads ID 820,196 129,458 112,045 611,916 EFv1 448,448 460,305 84,485 195,637 2,059,296 1,026,275 2,232,654 1,549,577 EFv2 891,445 153,871 640,340 113,274 189,543 719,899 EFv3 193,270 127,706 665,846 1,154,194 SFv1 138,772 99,069 227,823 1,112,727 1,860,529 167,226 91,268 SFv2 87,332 SFv3 324,521 159,529 114,317 323,611 1,137,281 4,575,746 Replicate Plant Biological Type Inoculated Corn 1 Corn 2 3 Corn Soybean F. virguliforme 1 Soybean F. virguliforme 2 3 Soybean F. virguliforme F. virguliforme F. virguliforme F. virguliforme 194 Table 19. Host specific differential gene expression across timepoints of Fusarium virguliforme colonization of soybean or corn. Shared expression change Differing Matching Timepoint Soybean direction direction change total 0 vs 2 0 0 22 0 0 2 vs 4 1 0 0 1 4 vs 7 7 vs 10 30 1 0 10 vs 14 128 1 1 Soybean compared to corn total Uniquely Corn Uniquely down up 27 13 7 27 0 1 1 0 94 10 24 5 13 95 19 Corn compared to soybean Uniquely Uniquely down up 26 1 0 27 0 94 7 2 8 3 195 Table 20. Host specific differential gene expression within timepoints of Fusarium virguliforme colonization of soybean or corn at log2 fold change > 1. Number of Differentially Expressed Genes Corn Total 11 0 50 11 0 55 143 37 615 293 781 1858 Timepoint 0 2 4 7 10 14 Soybean 11 39 55 106 322 1077 196 Table 21. Number of quality trimmed milRNA reads uniquely aligning to the Fusarium virguliforme v2 genome. SFv indicates in planta sample, and GM indicates geminating macroconidia. Days Post Inoculation 4 7 14 2 0 10 Sample Number of Uniquely Aligned milRNA Reads ID 542,446 2,456,820 1,705,182 453,556 428,635 482,138 SFv1 844,800 2,078,877 3,268,441 445,743 SFv2 273,748 371,088 937,109 1,654,041 3,031,727 567,939 352,295 527,232 SFv3 GM-1 9,985,673 GM-2 14,937,775 GM-3 18,689,308 Replicate Plant Biological Inoculated Type Soybean F. virguliforme 1 Soybean F. virguliforme 2 Soybean F. virguliforme 3 N/A 1 N/A 2 3 N/A F. virguliforme F. virguliforme F. virguliforme 197 Figure 21. Genome assembly of Fusarium virguliforme. Individual lines represent a single contig and relative length. Color was assigned randomly, with lines connecting contigs indicating uncertainty in the genome assembly. Supplemental Figure 1. 198 Inter-genomiccomparison:v1vsv2(14,266genepairs) Sca1 Sca2 Sca3Sca4Sca5Sca6Sca7Sca8Sca9Sca10Sca11Sca12Sca13Sca14Sca15Sca16Sca17Sca18Sca20Sca21Sca22Sca23Sca24Sca25Sca26Sca28 A ) 2 v ( 1 - t n o M n i a r t s e m r o f i l u g r i v m u i r a s u F Fusarium virguliforme strain Mont-1 (v1) B Ctg1 Ctg2 Ctg3Ctg4 Ctg5Ctg6 Ctg7Ctg8Ctg9Ctg10 Ctg11 Ctg12 Ctg13 Ctg14 Ctg17 Ctg18 Ctg19 Ctg20 Ctg21 Ctg23 Ctg24 Ctg25 Ctg26 Ctg27 Ctg28 Ctg29 Ctg31 Ctg33 Ctg36 Figure 22. Syntenic regions between genome versions of Fusarium virguliforme. (A) Dot plot of syntenic regions retained between genome version 1 (v1) and genome version 2 (v2). (B) Micro-collinearity between scaffold 1 of v1 genome and contig 1 of v2 connected by shaded grey areas. Regions containing genes are highlighted in green or blue, for forward or reverse orientation, respectively. Figure 1. 199 A B I n a k ) e c % b o y ( M o e S m r o d f i n e l a u t a e g l b u r y i c v o o S n . F o t d g e t n n a i r l n o u c C g o i l n A s d a k n e c r R o o C M I C 0 DPI 2 DPI 4 DPI 7 DPI 10 DPI 14 DPI 6 4 2 0 0 2 4 7 Days Post Inoculation 10 14 Trt Corn Inoculated Corn Mock Soybean Inoculated Soybean Mock 6 4 2 0 ) % ( e m r o f i l u g r i v . F o t g n n g i i l A s d a e R 0 2 4 7 10 14 Days Post Inoculation Trt Corn Mock Soybean Mock Corn Inoculated Soybean Inoculated Figure 23. Fusarium virguliforme growth chamber assay of soybean and corn. (A) Plant growth and development after 14 days post inoculation with F. virguliforme. (B) Trypan blue staining of inoculation site on inoculated with F. virguliforme or mock inoculated soybean cv. Sloan and corn cv. E13022S roots. (DPI = Days post inoculation). Black bar represents 16 mm. (C) The percent of unique reads aligning F. virguliforme within corresponding treatments of corn or soybean inoculated with F. virguliforme or mock (water) inoculated soybean or corn, across 0-14 days post inoculation. Reads were trimmed by Trimmomatic/0.33, aligned to Fusarium_virguliforme_V2 genome assembly with HISAT2/2.1.0. Each sample is indicated by a colored dot and lines are the average of three biological repeats. Grey shade indicates SEM. Figure 2. 200 R2=0.903636 5 1 0 1 5 0 0 5 10 15 ] " 3 v F _ 4 1 D _ S 2 2 0 3 1 E " , [ s t n u o c g o l 2 1 8 6 4 2 0 R2=0.9033802 0 5 10 15 logcounts[, "E13022S_D14_Fv2"] logcounts[, "E13022S_D14_Fv2"] 2 1 R2=0.9409087 8 6 4 2 ] " 3 v F _ 4 1 D _ n a o S " , [ s t n u o c g o l l 6 0 0 0 2 2 4 8 14 12 10 logcounts[, "Sloan_D14_Fv2"] Figure 24. Biological consistency of samples from different time courses. Correlation of gene expression values in three biological replicates from 14 DPI samples of Fusarium virguliforme colonizing corn (A), and Fusarium virguliforme colonizing soybean (B). 10 logcounts[, "Sloan_D14_Fv2"] 4 6 10 logcounts[, "Sloan_D14_Fv3"] 8 14 12 12 14 2 4 6 8 0 0 A ] " 1 v F _ 4 1 D _ S 2 2 0 3 1 E " , [ s t n u o c g o l 5 1 0 1 5 0 B R2=0.9115649 0 5 10 15 logcounts[, "E13022S_D14_Fv2"] 2 1 R2=0.94601 8 6 4 2 ] " 1 v F _ 4 1 D _ n a o S " , [ s t l n u o c g o l ] " 1 v F _ 4 1 D _ S 2 2 0 3 1 E " , [ s t n u o c g o l ] " 1 v F _ 4 1 D _ n a o S " , [ s t l n u o c g o l 2 1 R2=0.9206135 8 6 4 2 0 Supplemental Figure 3. 201 20 0 −20 −40 e c n a i r a v % 1 2 : 2 C P TimePoint 0dpi 10dpi 14dpi 2dpi 4dpi 7dpi Trt Corn GM Soybean −30 0 30 PC1: 33% variance Figure 25. Samples of fungal plant colonization cluster by host. Principle component analysis (PCA) of gene expression values of F. virguliforme colonizing soybean or corn, or germinating macroconidia (GM). Supplemental Figure 3. 202 A 5000 B 5000 4000 s e n e G E D f o r e b m u N 3000 Regulation Up Down 2000 1000 0 s e n e G E D f o r e b m u N 4000 3000 2000 1000 0 0 2 C 0 2 e g n a h C d o F 2 g o L l Regulation Up Down 14 7 10 4 Days Post Inoculation Soybean Samples 4 7 14 10 14 0 2 0 2 D 4 7 10 Days Post Inoculation Corn Samples 4 7 10 14 Process carboxylic acid biosynthetic process cofactor biosynthetic process lipid biosynthetic process polysaccharide catabolic process protein dephosphorylation small molecule biosynthetic process Log2 Fold Change e g n a h C d o F 2 g o L l 15 10 5 0 -5 Process carbohydrate catabolic process carboxylic acid biosynthetic process cofactor biosynthetic process lipid biosynthetic process polysaccharide catabolic process protein dephosphorylation small molecule biosynthetic process Log2 Fold Change 15 10 5 0 -5 0 X 2 X 7 X 2 X 4 X 0 X 7 X 4 1 X 0 1 X s s e c o r P s s e c o r P Figure 3. Figure 26. Temporal expression patterns of Fusarium virguliforme response genes within soybean and corn hosts in comparison to germinating macroconidia. (A) and (B) Number of significant differentially regulated genes with |log2(FC) > 2| between F. virguliforme in planta compared to geminating macroconidia within corn or soybean roots over six timepoints. (C) and (D) Heatmap of significant gene ontology enrichment of |log2(FC) > 2| upregulated genes across pooled time points for F. virguliforme colonization soybean (n=233) and corn (n=165). 0 1 X 4 1 X 4 X 203 A 6 1 2 3 4 l e g n a h C d o F 1 + 2 g o L 4 2 0 0 2 4 7 10 1 B 6 7 10 Days Post Inoculation 3 14 0 2 4 14 0 2 4 14 7 10 4 14 0 2 4 7 10 2 Grouping 1 2 3 4 l e g n a h C d o F 1 + 2 g o L 4 2 0 0 2 4 7 10 14 0 2 4 7 10 7 10 Days Post Inoculation 14 0 2 4 14 0 2 4 7 10 14 2 1 Grouping Figure 27. Distinct gene co-expression groups of host induced Fusarium virguliforme response. (A) and (B) Mean expression of genes within co-expression modules from F. virguliforme colonization of corn or soybean roots, respectively across the time course. Modules grouped into four distinct expression patterns of 1) upregulation at 2 DPI, 2) upregulation at 4-7 DPI, 3) induction at 7-10 DPI, and 4) increase in expression at 10-14 DPI. 3 4 Figure 4. 204 3 8 13 18 4 9 14 19 5 10 15 20 0 2 4 7 10 14 0 2 4 7 10 14 0 2 4 7 10 14 2 7 12 17 22 1 6 11 16 21 10 5 0 10 5 0 10 5 0 10 5 0 10 5 0 l e g n a h C d o F 1 + 2 g o L 0 2 4 7 10 14 0 2 4 7 10 14 Days Post Inoculation Figure 28. Weighted gene co-expression network modules from F. virguliforme temporal colonization of corn. Log2 plus one transformed expression for all genes in the co-expression module is plotted in green over the time course. The mean expression for all genes in that modules is plotted in black. Supplemental Figure 4. 205 n r o C o t e s n o p s e R e m r o f i l u g r i v . F G4_8 G4_7 G4_18 G4_17 G4_16 G4_15 G4_12 G3_21 G3_2 G3_19 G3_14 G3_13 G3_11 G3_10 G2_9 G2_5 G2_4 G2_3 G2_22 G2_1 G1_6 G1_20 Percent 50 40 30 20 10 G1_6 G1_18 G4_8 G2_1 G4_4 G3_9 G2_3 G4_17 G4_16 G4_13 G4_20 G2_14 G2_12 G2_11 G2_10 G2_19 F. virguliforme Response to Soybean G3_2 G2_7 G3_15 G3_5 Figure 29. Symptomatic and asymptomatic hosts uncover Fusarium virguliforme transcriptome plasticity. Genes from weighted gene correlation network analysis modules were calculated for percent overlap from gene count shared divided by the smaller module between F. virguliforme expression on each host. Modules are annotated with grouping assignment from Figure 27. Figure 5. 206 1 6 2 7 3 8 4 9 5 10 11 12 13 14 15 16 17 18 19 20 10 5 0 10 5 0 10 5 0 10 5 0 l e g n a h C d o F 1 + 2 g o L 7 10 7 10 14 7 10 0 2 4 7 10 14 0 2 4 14 0 2 4 14 0 2 4 14 0 2 4 Days Post Inoculation 7 10 Supplemental Figure 5. Figure 30. Weighted gene co-expression network modules from F. virguliforme temporal colonization of soybean. Log2 plus one transformed expression for all genes in the co-expression module is plotted in green over the time course. The mean expression for all genes in that modules is plotted in black. 207 A 0 2 4 7 Samples 10 14 e g n a h C d o F 2 g o L l B Corn Soybean e g n a h C d o F 2 g o L l 10 5 0 -5 0 2 4 7 10 Days Post Inoculation 14 0 2 4 7 10 14 Corn Soybean Host Soybean Corn Log2 Fold Change C 15 10 5 0 −5 −10 10 5 0 -5 e g n a h C d o F 2 g o L l Host Corn Soybean Host Corn Soybean 0 2 4 7 10 14 0 2 4 7 10 14 0 X 2 X 7 X 4 X 0 1 X 4 1 X t s o H Days Post Inoculation Figure 31. Host unique genes induced within Fusarium virguliforme highlight disease development. (A) Heatmap of log2(FC) > 1 of significantly upregulated genes at a single timepoint in F. virguliforme between soybean and corn (n=2,099). Yellow indicates differentially upregulated genes from F. virguliforme colonization of corn and blue indicates, grey indicates differentially upregulated genes from F. virguliforme colonization of soybean. (B) Expression patterns of log2(FC) > 1 comparing F. virguliforme host expression, of significantly upregulated candidate effector genes at a single timepoint in in corn or soybean over the infection time course. (C) Expression patterns of log2(FC) > 1 comparing F. virguliforme host expression, of significantly upregulated candidate carbohydrate active enzymes at a single timepoint in in corn or soybean over the infection time course. Color line indicates mean of all genes in the plot. Grey lines represent individual genes. Figure 6. 208 Corn Soybean Regulation Up Down 30 20 10 0 s e n e G E D f o r e b m u N 0 2 4 7 0 2 14 10 4 Days Post Inoculation Figure 32. Temporal expression patterns of Fusarium virguliforme candidate effector genes within soybean and corn hosts. Number of significant differentially regulated effector genes with |log2(FC) > 1| in at least a single timepoint between F. virguliforme in each corn or soybean compared soybean or corn, respectively over the time course. 10 14 7 Supplemental Figure 6. 209 Corn Soybean Regulation Up Down 120 90 60 30 0 s e n e G E D f o r e b m u N 0 2 4 7 7 10 0 2 14 10 4 Days Post Inoculation Figure 33. Temporal expression patterns of Fusarium virguliforme carbohydrate active enzyme related genes within soybean and corn hosts. Number of significant differentially regulated carbohydrate active enzyme related genes with |log2(FC) > 1| in at a single timepoint between F. virguliforme in each corn or soybean compared soybean or corn, respectively over the time course. 14 Supplemental Figure 7. 210 A 2000000 s d a e R f o r e b m u N 1500000 1000000 500000 B e r o c s - Z 2 1 0 15 20 25 Read Length 30 35 Gene Fvm1-novel-1 Glyma.02G181500 Glyma.08G334100 Glyma.11G185700 Glyma.12G087200 Glyma.14G022100 Glyma.18G067300 0 2 14 4 10 Days Post Inoculation 7 Figure 34. Expression of micro-like RNAs (milRNAs)in Fusarium virguliforme colonization of soybean host. (A) Read length distribution of reads mapping to F. virguliforme. (B) Z-score plots of transcripts per million (TPM) of fungal milRNA Fvm1-novel- 1 and targeted soybean genes across temporal time course. Figure 7. 211 REFERENCES 212 Araújo, W.L., Tohge, T., Osorio, S., Lohse, M., Balbo, I., Krahnert, I., Sienkiewicz- Alexa A and Rahnenfuhrer J. (2018). 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