ABSTRACT. Fusarium oxysporum f. sp. niveum (FON) is the fungal causal agent of Fusarium wilt of

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1 ABSTRACT MILLER, NATHAN FORREST. Characterization of Fungicide Sensitivity and Analysis of Microsatellites for Population Studies of Fusarium f. sp. niveum Causing Fusarium Wilt of Watermelon. (Under the direction of Dr. Lina M. Quesada-Ocampo). Fusarium f. sp. niveum (FON) is the fungal causal agent of Fusarium wilt of watermelon, an economically important disease of watermelon in the United States and worldwide. This vascular wilt disease has been difficult to manage because the host specific fungus forms chlamydospores that can survive for many years in the soil. It has been traditionally managed with host resistance, crop rotation, and soil fumigation. However, the development of new FON races and the increased environmental regulation regarding fumigants have limited the efficacy of host resistance and soil fumigation for control of Fusarium wilt. Currently, there is only one systemic fungicide, the triazole fungicide prothioconazole, labeled for control Fusarium wilt in the field. This study examines additional fungicides that might be efficacious against Fusarium wilt of watermelon. A mycelium growth assay was used to assess the ability of ten fungicides to reduce vegetative growth of diverse FON isolates. Prothioconazole and a new succinate dehydrogenase inhibitor fungicide, pydiflumetofen, were further examined against a panel of 98 Fusarium isolates to scout for intrinsic activity and potential resistance. The resistance to pydiflumetofen was studied by sequencing fungal genes associated with insensitivity to this fungicide class. The efficacy of pydiflumetofen and prothioconazole for Fusarium wilt control in watermelon was tested in field experiments over the summers of 2015 and 2016 in inoculated fields. Fungicides were applied as a drench at transplant, and some treatments received an additional spray treatment of the same fungicide. Both pydiflumetofen and prothioconazole

2 reduced disease incidence in 2015 with both a drench and an additional spray treatment. In 2016, disease incidence per plot and disease severity were reduced with both fungicides using the drench treatments and the additional spray treatments. Yield data was taken in 2016, and fruit count was reduced when prothioconazole was applied as a drench treatment, whereas pydiflumetofen had equal yield to the non-inoculated non-treated control treatments although there were no differences between the two products in terms of reducing disease. This study also developed microsatellite markers for FON with the aid of comparative genomics and bioinformatic tools. Twelve publicly available Fusarium genomes from isolates not associated with watermelon were scanned for predicted transcripts that contained microsatellites. The transcripts were compared with the other genomes to prioritize microsatellites in conserved transcripts, and 96 transcripts with homologs in all twelve F. genomes were identified. The predicted repeat number of the microsatellite motif from conserved transcripts was compared to identify polymorphisms. Primers were developed for 30 conserved microsatellites likely to be polymorphic in FON, and these were tested in the lab and used in a FON population study of 73 F. isolates, of which 64 were FON. Twenty-seven of these 30 markers amplified microsatellites in over 75% of isolates tested, and 28 of the 30 markers were polymorphic. In the population study, there was no population stratification based on host or location. In the future, these microsatellite markers may be useful in developing detailed population studies of FON to help inform resistance-breeding efforts and develop DNA fingerprints to track introductions of the pathogen.

3 Copyright 2017 by Nathan Forrest Miller All Rights Reserved

4 Characterization of Fungicide Sensitivity and Analysis of Microsatellites for Population Studies of Fusarium f. sp. niveum Causing Fusarium Wilt of Watermelon by Nathan Forrest Miller A thesis submitted to the Graduate Faculty of North Carolina State University in partial fulfillment of the requirements for the degree of Master of Science Plant Pathology Raleigh, North Carolina 2017 APPROVED BY: Dr. Lina M. Quesada-Ocampo Committee Chair Dr. James Kerns Dr. Tyler Harp

5 ii DEDICATION I dedicate this work to Audrey Miller, a passionate biologist who inspires me to explore and understand the natural world.

6 iii BIOGRAPHY Nathan Forrest Miller was born on June 4 th, 1992, in Occidental, California, where he lived for less than a year. The son of an oil company middle manager, he followed his parents from California to Springfield, Missouri, and then Ponca City, Oklahoma. He attended Ponca City High School, where he played tennis badly, ran cross country slowly, and marched out of step in the marching band. In August 2010, Nathan attended the University of Tulsa, where he majored in Biology with minors in Chemistry and Spanish. In summer of 2011, he worked in western Nebraska with Dr. Charles Brown as a field assistant in a cliff swallow mark-recapture study. From 2012 to 2014, he worked as a research assistant in the aerobiology lab of Dr. Estelle Levetin, where he validated Juniperus ashei pollen forecasts. In the same lab, Nathan began an independent project isolating endophytic and epiphytic fungi from Cercis canadensis leaves. After graduating from University of Tulsa in 2014, Nathan was awarded the North Carolina State University Syngenta Crop Protection Graduate Fellowship to pursue a Master s degree under the direction of Dr. Lina Quesada-Ocampo. He characterized fungicide sensitivity of Fusarium f. sp. niveum (FON), the causal agent of Fusarium wilt of watermelon, to registered and novel active ingredients. Nathan also analyzed microsatellites for population analysis of FON using a novel bioinformatics pipeline he developed. Through his degree, he learned both basic and applied aspects of plant pathology, and provided new resources to the FON community and disease management tools to growers. He received a travel award from the American Phytopathological Society in 2016 to present his research at the annual meeting in Tampa, Florida. Nathan hopes to continue working in agriculture research after graduation.

7 iv ACKNOWLEDGMENTS I would like to thank my committee members, Drs. Lina Quesada-Ocampo, Dr. Jim Kerns, and Dr. Tyler Harp for their continual feedback, understanding, and encouragement. I would like to especially thank Dr. Lina Quesada-Ocampo for pushing me to challenge myself in my work and encouraging me to interact with growers, extension agents, industry professionals, and fellow scientists. I would like to also thank Dr. Tyler Harp for encouraging me to visit the Vero Beach Research Center, where I got to learn how plant pathology research is done in an industry setting up close. I would like to acknowledge my funding from the Syngenta Crop Protection Graduate Fellowship, and I would like to thank them for the support. I would like to thank the current and past members of the Quesada lab, including Emma Wallace, Andrew Scruggs, Camilo Parada, Nick Noel, Madison Stahr, Kim D Arcangelo, Allie Druffel, Dr. Alamgir Rahman, Mike Adams, Hunter Collins, Saunia Withers, Dr. Elsa Góngora- Castillo, Dr. Liliana Cano, Emily Keller, Abel Walker, Zach Shea, Laura Williams, Kelsey Wynne, Lynde Ring, Christina Mara, Kayla Elswick, Aidan Shands, and Jesse Yamagata. Of this group, I would like to especially thank Mike Adams for teaching me about field experiments and Dr. Liliana Cano for mentoring me in bioinformatics. I would like to further thank Dr. Peter Ojiambo for his assistance with statistical analysis. Also, I would like to thank Dr. Estelle Levetin for inspiring me to love botany almost as much as she does. Finally, I would like to thank my family and friends for supporting me throughout my degree. To my parents, thank you for encouraging me from day one to now and for sending me pictures of all the diseased plants in the yard. I would like to thank my sister for laughing at my jokes and my Gramma for sending me the best postcards. I would like to thank my friends

8 v Charlie, Alayne, and Liz for always supporting me in my career and life. To my cats, Buckie and Judy, thank you for saving a thesis draft as aopijrfnkagllfffffff. I would like to thank my friends Emma Wallace, Megan Miller, Bri Hoge, Lindsey Becker, Michael Cannon, and Camilo Parada for bringing cheer wherever you go.

9 vi TABLE OF CONTENTS LIST OF TABLES... viii LIST OF FIGURES... xi CHAPTER I... 1 Literature Review... 1 Watermelon... 1 Fusarium wilt of watermelon... 2 Fusarium f. sp. niveum biology... 5 Disease cycle... 5 Characterization of isolates... 6 Genetics... 8 Genomics... 9 Microsatellites in Fusarium Succinate dehydrogenase inhibitor fungicides Fungal succinate dehydrogenase inhibitor fungicide resistance REFERENCES TABLES CHAPTER II Sensitivity of Fusarium f. sp. niveum to fungicides in vitro and in field experiments for management of Fusarium wilt of watermelon ABSTRACT INTRODUCTION METHODS Isolate collection and long-term storage Identification of fungicides with activity against FON isolates in vitro Evaluation of Fusarium isolates for fungicide sensitivity in vitro Confirmation of fungicide activity against FON in field experiments Genetic diversity and succinate dehydrogenase gene sequence analysis RESULTS Identification of fungicides with activity against FON isolates in vitro Evaluation of Fusarium isolates for fungicide sensitivity in vitro Confirmation of fungicide activity against FON in field experiments Genetic diversity and succinate dehydrogenase gene diversity DISCUSSION AKNOWLEDGEMENTS REFERENCES TABLES FIGURES CHAPTER III Analysis of microsatellites in Fusarium and their application for population analysis of Fusarium f. sp. niveum causing Fusarium wilt of watermelon ABSTRACT INTRODUCTION METHODS... 70

10 Identification and analysis of microsatellites in Fusarium genomes Isolate collection and DNA extraction Microsatellite amplification and genotyping Genetic diversity analysis of microsatellites and population study RESULTS Microsatellite analysis Population structure analysis DISCUSSION AKNOWLEDGEMENTS REFERENCES TABLES FIGURES vii

11 viii LIST OF TABLES Table 1: Watermelon genotypes used to differentiate races of Fusarium f. sp. niveum (FON). S refers to a genotype susceptible to that race whereas R refers to a genotype resistant to that FON race Table 2: Fusarium spp. isolates tested against both prothioiconazole and pydiflumetofen fungicides. The isolate name refers to the code in the culture collection, and the genus and species name refer to the nearest BLAST hit from the NCBI database. The country and state refer to where the fungus was first isolated, and the plant host refers to which plant host the fungus was first isolated. The isolate was kindly provided by the collaborator attributed in the final column. An asterisk (*) refers to isolates utilized in the expanded panel of fungicides. A plus sign (+) Refers to isolates that were included in the phylogenetic study Table 3: Fungicides tested including active ingredient, brand name, fungicide class, and FRAC code Table 4: Treatments for 2015 and 2016 field experiments. Drench treatments were applied at transplant and sprays were applied 14 days after transplant Table 5: Primers for the Internal transcribed spacer (ITS), Translation Elongation Factor 1α (Tef- 1α), and sdh genes. The sdhb and sdhc genes refer to the genes that code for the SdhB and SdhC subunits of the enzyme succinate dehydrogenase, associated with mutations that confer resistance to succinate dehydrogenase inhibitor fungicides Table 6: EC50 values in µg active ingredient/ml media for each fungicide tested with 10 isolates Table 7: Pairwise comparisons of EC50 values between fungicides. Values indicate differences in EC50 between two fungicides and an asterisk (*) indicates significant differences after Bonferroni correction Table 8: Analysis of variance (ANOVA) table for total fruit count, marketable fruit count, and weight by treatment in 2016 field experiments. There is no significant treatment effect for total fruit weight. SS stands for Sum of Squares and MS refers to Mean Sum of Squares. An asterisk (*) denotes a significant effect at the α=0.05 level Table 9: Isolate name, gene prefix code from sequenced isolates from the Broad Institute s Fusarium comparative genome project, host genus, forma specialis and culture collection number for each isolate from the ARS culture collection (NRRL)

12 ix Table 10: Microsatellite markers tested for amplification and polymorphism in the laboratory. GACGGCCAGT is the nucleotide sequence added to forward primers for fluorescent labeling and subsequent fragment analysis of PCR products Table 11: Isolates used for analysis including isolate code for the lab, isolate species, host from where the isolate was collected from, the state where the isolate was collected, and the source. Isolates marked with an asterisk (*) were evaluated with gel electrophoresis in addition to fragment analysis. Isolates marked with a plus sign (+) were included in the population analysis Table 12: Total number and size of sequences examined in Fusarium transcriptomes, as well as the total number of microsatellites identified. The number of microsatellite containing sequences refers to the number of transcripts per transcriptome Table 13: Microsatellite frequency by repeat length and predicted transcriptome Table 14: P-values from pairwise comparisons done with two sample T tests for microsatellite relative density (DNA in microsatellites (Mb) / total DNA examined (Mb)) in dinucleotide repeats. Green highlighted values are significant at the alpha = 0.05 level, and red highlighted values are significant at the alpha = 0.05 level with a Bonferroni correction.104 Table 15: P-values from pairwise comparisons done with two sample T tests for microsatellite relative density (DNA in microsatellites (Mb) / total DNA examined (Mb)) in trinucleotide repeats. Green highlighted values are significant at the alpha = 0.05 level, and red highlighted values are significant at the alpha = 0.05 level with a Bonferroni correction.105 Table 16: P-values from pairwise comparisons done with two sample T tests for microsatellite relative density (DNA in microsatellites (Mb) / total DNA examined (Mb)) in tetranucleotide repeats. Green highlighted values are significant at the alpha = 0.05 level, and red highlighted values are significant at the alpha = 0.05 level with a Bonferroni correction Table 17: P-values from pairwise comparisons done with two sample T tests for microsatellite relative density (DNA in microsatellites (Mb) / total DNA examined (Mb)) in pentanucleotide repeats. Green highlighted values are significant at the alpha = 0.05 level, and red highlighted values are significant at the alpha = 0.05 level with a Bonferroni correction Table 18: P-values from pairwise comparisons done with two sample T tests for microsatellite relative density (DNA in microsatellites (Mb) / total DNA examined (Mb)) in hexanucleotide repeats. Green highlighted values are significant at the alpha = 0.05 level, and red highlighted values are significant at the alpha = 0.05 level with a Bonferroni correction

13 Table 19: P-values from pairwise comparisons done with two sample T tests for microsatellite relative density (DNA in microsatellites (Mb) / total DNA examined (Mb)) in total microsatellites for each transcriptome. Green highlighted values are significant at the alpha = 0.05 level, and red highlighted values are significant at the alpha = 0.05 level with a Bonferroni correction Table 20: P-values from pairwise comparisons done with two sample T tests for relative abundance (number of microsatellites divided by the number of transcripts examined) in the total microsatellites found in each transcriptome. The green symbolizes a significant difference at the alpha = 0.05 level, and red highlighted values are significant at the alpha = 0.05 level with a Bonferroni correction Table 21: Genetic diversity estimates for microsatellites tested for amplification and polymorphism in the laboratory. N is the number of isolates with alleles at that particular locus. Na is the number of distinct alleles per locus. Ne is the number of effective alleles. I is Shannon s Information index. h is gene diversity, and uh is unbiased gene diversity. PIC refers to polymorphism information content Table 22: Annotation and location of microsatellites tested for amplification and polymorphism in the laboratory obtained from the FOXG genome (F. f. sp. lycopersici 4275) Table 23: Analysis of molecular variance (AMOVA) table with host as grouping factor. There is no significant population differentiation between host groups either with or without clonecorrection. Significance values attained by a randomization test with 999 permutations.113 Table 24: Analysis of molecular variance (AMOVA) table with location for isolate as the grouping factor. There is no significant population differentiation between location groups either with or without clone-correction. Significance values attained by a randomization test with 999 permutations x

14 xi LIST OF FIGURES Figure 1: Percent disease incidence for each treatment was calculated per plot in The error bars represent the standard deviation for each treatment for each rating date. A in fungicide treatments on the horizontal axes refers to the drench application of the fungicide at transplant, and AB refers to both a drench treatment at transplant and a foliar spray application 14 days after transplant. Low and high refer to the rate of fungicide applied for each pydiflumetofen fungicide treatment (751.9 ml/ha and ml/ha, respectively).56 Figure 2: Percent disease incidence for each treatment was calculated per plot in The error bars represent the standard deviation for each treatment for each rating date. A in fungicide treatments on the horizontal axes refers to the drench application of the fungicide at transplant, and AB refers to both a drench treatment at transplant and a foliar spray application 14 days after transplant. Low and high refer to the rate of fungicide applied for each pydiflumetofen fungicide treatment (751.9 ml/ha and ml/ha, respectively).57 Figure 3: Photos from the final rating date (July 24) in A is a Non-treated plot, B is a plot with the pydiflumetofen treatment at a high rate applied as both a drench and a foliar spray, and C is a plot with the prothioconazole treatment applied as both a drench and a spray.58 Figure 4: Photos from the final rating date (July 14) in A is a Non-treated plot, B is a plot with the pydiflumetofen treatment at a high rate applied as both a drench and a foliar spray, and C is a plot with the prothioconazole treatment applied as a drench, and D is a nontreated, non-inoculated plot Figure 5: relative Area Under the Disease Progress Curve (AUDPC) was calculated for the incidence data, and was combined for the years 2015 and The relative AUDPC was plotted for each treatment and letters above bars indicate Tukey-Kramer groupings. A in fungicide treatments on the horizontal axes refers to the drench application of the fungicide at transplant, and AB refers to both a drench treatment at transplant and a foliar spray application 14 days after transplant. Low and high refer to the rate of fungicide applied for each pydiflumetofen fungicide treatment (751.9 ml/ha and ml/ha, respectively).60 Figure 6: relative Area Under the Disease Progress Curve (AUDPC) for severity data calculated for each fungicide treatment in The relative AUDPC was plotted for each treatment and letters above bars indicate Tukey-Kramer groupings. A in fungicide treatments on the horizontal axes refers to the drench application of the fungicide at transplant, and AB refers to both a drench treatment at transplant and a foliar spray application 14 days after transplant. Low and high refer to the rate of fungicide applied for each pydiflumetofen fungicide treatment (751.9 ml/ha and ml/ha, respectively).the non-treated control was inoculated, but the untreated non-inoculated non-treated control had no contact with FON and was used as a negative control

15 Figure 7: Total count for 2016 yield data in melons. Letters above bars indicate Tukey-Kramer groupings; the same groupings were obtained for marketable count. A in fungicide treatments on the horizontal axes refers to the drench application of the fungicide at transplant, and AB refers to both a drench treatment at transplant and a foliar spray application 14 days after transplant. Low and high refer to the rate of fungicide applied for each pydiflumetofen fungicide treatment (751.9 ml/ha and ml/ha, respectively). The non-treated control was inoculated, but the untreated non-inoculated non-treated control had no contact with FON and was used as a negative control Figure 8: A maximum likelihood phylogenetic tree paired with the EC50 values in ppm for prothioconazole (black) and pydiflumetofen (gray). The numbers at the nodes refer to the bootstrap support or the percentage of replicate trees where isolates cluster together Figure 9: WebLogo diagram of amino acid distribution for SdhC. The letters reflect the amino acid at that position in the polypeptide sequence, and the relative height of the letter reflects the percentage of isolates with that particular amino acid residue at that position. The Serine residue at position 222 and the Argenine residue at position 226 (starred) are not only conserved amongst Fusarium isolates examined in this study but also amongst Ascomycota fungi Figure 10: WebLogo diagram of amino acid distribution for SdhB. The letters reflect the amino acid at that position in the polypeptide sequence, and the relative height of the letter reflects the percentage of isolates with that particular amino acid residue at that position. The blue bar marks the conserved Iron-Sulfur subunit Figure 11: Minimum spanning network calculated with Bruvo s genetic distance for all isolates with host descriptors. The size and the number within each circle refer to the number of isolates of that particular multi-locus genotype (MLG). Thicker lines correspond to MLGs that are more closely related (smaller Bruvo s genetic distance value between MLGs).115 Figure 12: Minimum spanning network calculated with Bruvo s genetic distance for all isolates with location descriptors. The location refers to where the isolate was collected from. The size and the number within each circle refer to the number of isolates of that particular MLG. Thicker lines correspond to MLGs that are more closely related (smaller Bruvo s genetic distance value between MLGs) Figure 13: Minimum spanning network calculated with Bruvo s genetic distance for isolates collected from watermelon with location descriptors. The location refers to where the isolate was collected from. The size and the number within each circle refer to the number of isolates of that particular MLG. Thicker lines correspond to MLGs that are more closely related (smaller Bruvo s genetic distance value between MLGs) Figure 14: Neighbor-joining tree inferred from Provesti s genetic distance for the Fusarium dataset (N=73). There is both a resolved and an unresolved cluster xii

16 i Figure 15: Delta K plot for STRUCTURE output when total dataset analyzed from K=1 to K=10 with burnin of 300,000 and MCMC of 500,000 with 15 iterations at each K. Two clusters were selected for analysis Figure 16: STRUCTURE plot with K=2 for the total dataset (N=73). The colors refer to the genetic clusters predicted from STRUCTURE. Both clusters are represented in both the tomato and the watermelon isolates. Numbers refer to the host where the isolates were originally sampled: 1 is from ginger, 2 is muskmelon, 3 is sweetpotato, 4 is tomato, and 5 is watermelon Figure 17: Delta K plot for STRUCTURE output when total dataset analyzed from K=1 to K=10 with burnin of 300,000 and MCMC of 500,000 with 15 iterations at each K. Three clusters were selected for analysis Figure 18: STRUCTURE plot with K=3 for the FON dataset analyzed individually. Each color represents its own genetic cluster, and the number refers to the location of where the FON isolate originated: 1 is from CA, 2 is FL, 3 is GA, 4 is Germany, 5 is IN, 6 is Israel, 7 is MD, 8 is NC, 9 is OK, and 10 is SC

17 1 CHAPTER I Literature Review Watermelon Watermelon (Citrullus lanatus) is a fruit crop in the Cucurbitaceae plant family. Watermelon is an annual crop, and is planted in mid-april to June in North Carolina (Holmes et al. 2005). Transplants are grown for 4-6 weeks in the greenhouse before being transplanted in the field (Holmes et al. 2005). Watermelon growth is best in sandy and slightly acidic soils, and black plastic mulch with drip irrigation increases watermelon yield and decreases irrigation costs (Holmes et al. 2005). The most common cultivar grown from 2004 to 2006 in North Carolina was Liberty, a seedless triploid variety (USDA-NASS 2015). Watermelon is widely cultivated in the United States (US) and around the world. China is the largest producer and accounts for 62% of the world s watermelons (FAOSTAT 2017). In the US, North Carolina is the 7 th largest producer of watermelons in terms of acreage harvested in 2014, behind Florida, Georgia, California, Texas, South Carolina, and Indiana (USDA-NASS 2015). In that year, the value of watermelons produced in the state exceeded $34 million (USDA-NASS 2015). There are many cultivars of watermelon grown in the US. In the past, most watermelons produced were diploid seeded watermelon. However, triploid seedless varieties have become popular in recent years. While these varieties bring in more revenue, they also have higher production costs, different cultivation practices, and are often more susceptible to disease

18 2 (Bruton et al. 2007). For example, diploid cultivars are directly seeded whereas more expensive triploid and hybrid cultivars are often transplanted from a greenhouse (Bruton et al. 2007). From an economic study done in 2006, directly seeding an open-pollinating diploid cultivar costs $0.01 to $0.02 per plant, whereas a triploid variety costs $0.28 (Taylor et al. 2006). Fusarium wilt of watermelon Fusarium wilt is among the most economically damaging watermelon disease in the world and has become more of a problem in recent years in North Carolina with the planting of susceptible triploid/seedless watermelon and the increase in prevalence of race 2 and 3 isolates. Fusarium f. sp. niveum (FON), the causal agent of this disease, infects the xylem and plugs the tissue with both mycelium and extracellular polysaccharide (Kleczewski and Egel 2011). This blockage will manifest itself as a wilt, causing a characteristic unilateral wilting in the plant (Kleczewski and Egel 2011). As the disease progresses, the leaves will often become chlorotic and necrotic. The wilting can be associated with heat, and it has been noted that this symptom can seemingly recover with cooler weather at night and with watering because the plant s water requirement is lower under these conditions (Kleczewski and Egel 2011). In a cross section of the stem or root, the xylem will appear darker (Kleczewski and Egel 2011). There are currently very few management strategies for Fusarium wilt. Traditionally, this disease has been managed by planting resistant cultivars. Some watermelon cultivars are resistant to Fusarium f. sp. niveum race 0, and some are resistant to F. f. sp. niveum race 1. Other cultivars, including most hybrid and triploid watermelons, are susceptible to all F. f. sp. niveum races. It has been shown that a cover crop of hairy vetch (Vicia villosa) can suppress Fusarium wilt in the field, which has been attributed to the

19 3 production of ammonia from the organic soil amendment of the killed vetch (Zhou and Everts 2004). There is also evidence that soil solarization without organic soil amendments can delay Fusarium wilt of watermelon onset and reduce disease incidence in the field (Martyn and Hartz 1986). Another potential disease management tactic is to plant grafted plants with Fusarium wilt-resistant rootstocks. A study by Keinath and Hassell (2014) examined the efficacy of grafting resistant roots with susceptible scions, and found that there was reduced disease in the scion. The study concluded that not only was it efficacious, but that it was also cost effective if planted in a field with high disease pressure (Keinath and Hassell 2014). However, the cost of the grafted plants is very high, and there have been many different estimates. This study claimed that the cost of each grafted plant was $0.63 (Keinath and Hassell 2014). This is less than a 2006 economic model which estimated that the cost was approximately $0.75 per plant, and it has been estimated that the cost could be as high as $1.30 per grafted transplant (Taylor et al. 2006). While it is estimated that the costs will drop as this technology becomes increasingly available, it is still questionable whether grafted transplants are a financially viable option for growers. Chemical management of this disease is very limited. Several in vitro and greenhouse studies have looked at different fungicides or chemicals that limit Fusarium growth, but with only limited success. Amini and Sidovich (2010) demonstrated that carbendazim, prochloraz, benomyl, and bromuconazole all reduced mycelium growth of Fusarium f. sp. lycopersici at 10 µg/ml in vitro, while azoxystrobin and fludioxonil did not. Song et. al. (2004) also found that prochloraz and carbendazim reduced mycelium growth of F. f. sp. lycopersici. However, prochloraz is not labeled for use in the US, and carbendazim is only

20 4 labeled for tree trunk injection (EPA 2016). Other studies have found that difenoconazole is somewhat effective in controlling Fusarium avenaceum in that it slowed mycelia growth but did not succeed in killing the fungus (Kopacki and Wagner 2006). Everts et al (2014) tested fungicides throughout the country to look at chemicals not labeled for this pathosystem at the time. This study found that the most efficacious chemicals were prothioconazole (trade name Proline; Bayer CropScience, Research Triangle Park, NC), thiophanate-methyl (Topsin M; United Phosphorous, Inc, King of Prussia, PA), and acibenzolar- S-methyl (Actigard; Syngenta Crop Protection, Greensboro, NC). Prothioconazole is an ergosterol demethylation inhibitor (DMI). Thiophanate-methyl breaks down to form a betatubulin inhibitor, which effectively inhibits meiosis (Everts et al. 2014). Acibenzolar-S-methyl is a systemic acquired resistance-inducing chemical. After this study, Bayer CropScience included Fusarium wilt of watermelon management under a supplementary label for Proline (Everts et al. 2014). This study found prothioconazole to be effective with three drip applications, however, the supplementary label only allows one drip application per year (Everts et al. 2014). In addition, relying on a single active ingredient for chemical control of Fusarium wilt in watermelon significantly increases the risk of development of fungicide resistance in FON populations because there are no other labeled fungicides with which to rotate. Expanding chemical management solutions for Fusarium wilt of watermelon would greatly help watermelon production throughout the watermelon growing regions and ensure the sustainability of current control measures.

21 5 Fusarium f. sp. niveum biology Fusarium f. sp. niveum is the causal agent of Fusarium wilt of watermelon. The pathogen is a filamentous ascomycete fungus that can produce three spore types. It can form straight and tapered septate macroconidia, as well as aseptate elliptical shaped microconidia (Leslie and Summerell 2006). These are produced on simple conidiophores singly or in sporodochia. It is thought that F. predominantly produces microconidia (Leslie and Summerell 2006). In adverse environments, such as a low carbon to nitrogen ratio in the surrounding environment, the fungus can produce thick walled chlamydospores that can survive in the soil for up to 5 or 10 years (Carlile 1956). These can be formed on either simple conidiophores or intercalarily within hyphae or macroconidia (Leslie and Summerell 2006). F. f. sp. niveum mycelium is generally fluffy and aerial, and range in color from white to purple in color, but is typically pink. The metabolites produced by the fungus can stain the media red, pink, or purple (Leslie and Summerell 2006). Disease cycle The fungus infects the plant tissue as a germinating spore and growing hyphae penetrate the plant tissue through wounds or openings near the site of elongation for a root hair (Agrios 2005). The fungal hyphae eventually penetrate the vascular tissue and produce microconidia (Agrios 2005). The microconidia are then released into the xylem, which travel upward with the water and then these begin to colonize the watermelon s vascular tissue further up in the plant (Di Pietro et al. 2003). However, recent experiments using green fluorescent protein (GFP) labeled F. demonstrated that microconidia were not formed in the plant tissue and that they were not required for pathogenicity (Michielse and Rep 2009). Nonetheless, the fungus

22 6 eventually colonizes all tissues of the plant, forming many spores when it reaches the plant surface that are spread by wind or splash transport (Agrios 2005). This disease is considered monocyclic, although there is debate with regards to the importance of conidia in the infection in other formae speciales (Kleczewski and Egel 2011). A report from Israeli tomato greenhouses demonstrated that the vascular wilt fungus F. f. sp. lycopersici forms airborne macroconidia on the stem surface of living tomatoes, although F. f. sp. lycopersici is traditionally a soil pathogen that does not form conidia on living tissues (Katan, Shlevin, and Katan 1997). This has not been otherwise been reported in F. f. sp. lycopersici, and has not been reported in F. f. sp. niveum, but this could have profound implications for the epidemiology of the disease. When the plant dies, the fungus forms conidia in sporodochia on the dead leaves as well as chlamydospores in the soil directly on the mycelia (Agrios 2005). The chlamydospores are long lived, and can survive for many years in the soil (Agrios 2005). The fungus can also colonize the seed and cause disease in the infected seedlings the following year (Bruton et al. 2007; Kleczewski and Egel 2011). Characterization of isolates Isolates of Fusarium have traditionally been classified into formae speciales (f. sp.), defined by the host that an isolate can infect. For example, f. sp. niveum infects watermelon whereas f. sp. lycopersici infects tomatoes. These distinctions are characterizations of convenience because they do not reflect evolutionary relationships (Gordon 1997). Nonetheless, these are still used to identify pathogens in the field before other characterizations can be used. In addition, many formae speciales have different races that describe pathogenicity or virulence between host cultivars. For example, there are four races of F. f. sp.

23 7 niveum (0, 1, 2, and 3). All races can infect susceptible cultivars, but race 0 cannot infect melons with race 0 resistance. Only races 2 and 3 can infect all commercially produced watermelon cultivars (Table 1) (Zhou, Everts, and Bruton 2010). Race 2 and race 3 differ in their aggressiveness towards the watermelon cultivar PI FR used for FON race testing, where race 3 more aggressive than race 2 (Zhou, Everts, and Bruton 2010). However, phylogenetic relatedness between races in a forme specialis is still unknown. What complicates matters is that races are often polyphyletic in origin, with only a few exceptions. For example, while race 3 in F. f. sp. niveum is monophyletic, isolates within the same clonal lineage can be either race 1 or race 2 (Zhou and Everts 2007). In Fusarium, vegetative incompatibility can be used functionally to characterize strains in heterogeneous populations. If two isolates are vegetatively compatible and can form a heterokaryon, then they are presumed to be part of the same clonal lineage (Aanen, Glass, and Saupe 2013). When isolates can form a heterokaryon, they are then placed in the same vegetative compatibility group. Robust population and evolutionary analysis with genetic markers are needed to clarify relatedness of races within a forma specialis of Fusarium. F. f. sp. niveum races 0 and 1 can be controlled with host resistance that can be found in some commercial watermelon cultivars. However, such resistance is not widely available for races 2 and 3, which have become more prevalent in the US, thus complicating disease management. F. f. sp. niveum race 2 was first discovered in Israel in 1973 and first discovered in the US in Texas in 1981 (Martyn 1987). FON Race 2 isolates have since been found in Oklahoma (1985) and Florida (1989) (Bruton, Patterson, and Martyn 1988; Martyn and Bruton 1989). Since 2000, it has also been identified in Maryland and Delaware

24 8 (2000), Indiana (2001), Georgia (2004), and South Carolina (Zhou, Everts, and Bruton 2010; Bruton, Fish, and Langston 2008; Egel, Harikrishnan, and Martyn 2005; Zhou and Everts 2001). Race 3 was discovered in Maryland and Delaware in 2000, although the report was not published until 2007 (Zhou, Everts, and Bruton 2010). Several resistance genes documented in tomato have been found that suggest a quantitative gene-for-gene relationship between host and pathogen (Michielse and Rep 2009). These host resistance genes and virulence genes in the pathogen define the race relationship between pathogen and host (Michielse and Rep 2009). Nonetheless, specific resistance genes recognizing pathogen virulence factors have not been described in F. f. sp. niveum and watermelon. Genetics Fusarium is a species complex of asexual fungi closely related to the Gibberella fujikuroi complex (Fourie et al. 2009). O Donnell et. al. (2009) created the Fusarium database that provides centralized information for research on the genus Fusarium. Within this website is the FUSARIUM-ID tool, which uses two gene loci, the Intergenic Spacer (IGS) and Elongation Factor region 1-α (EF-1α) to elucidate the phylogenetic relationships within the Fusarium species complex (O'Donnell et al. 2009). These organisms cannot reproduce sexually, yet these fungi exhibit exceptional diversity, which has been attributed to parasexuality and vegetative crosses with other fungi (Fourie et al. 2009; Puhalla 1985; Glass, Jacobson, and Shiu 2000). This occurs if two isolates are vegetatively compatible, and can fuse to form a heterokaryon and then undergo mitotic crossing over (Puhalla 1985). The ability to form a heterokaryon is defined by het or vic loci, of which there are an undetermined number in F. (Puhalla 1985). However, it has been calculated that there are probably at least

25 9 10 het loci, and so there should be approximately 1,024 vegetative compatibility groups (VCGs) (Puhalla 1985). If two isolates belong to the same VCG, then they should have compatible het loci and be able to form a heterokaryon and exchange genetic material (Fourie et al. 2009; Glass, Jacobson, and Shiu 2000). A forma specialis infecting a host can have different VCGs (Fourie et al. 2009; Elias and Schneider 1990). For example, F. f. sp. cubense (FOC), defined by the ability to infect banana, is comprised of 24 different VCGs, with many organisms that are not in FOC also in these VCGs (Fourie et al. 2009). This has also been found to hold true in both F. f. sp. lycopersici (Elias and Schneider 1990) and F. f. sp. niveum (Zhou and Everts 2007). As of 2009, there are 3 VCGs associated with F. f. sp. niveum (O'Donnell et al. 2009). One example of a study where this VCG characterization was used was the watermelon field F. f. sp. niveum survey done by Zhou and Everts (Zhou and Everts 2007). As there were several VCGs in the same field, the study concluded that there was much more genetic diversity than previously expected. The study also concluded that the VCGs in the field did not correspond with the race distinctions (Zhou and Everts 2007). Genomics Several Fusarium genomes have been sequenced thus far, including F. graminearum, F. f. sp. lycopersici, F. verticilloides, and F. solani f. sp. pisi (Ma et al. 2013). Genomic studies have revealed that F. genomes have more genes and noncoding DNA than other Fusarium genomes (Ma et al. 2013). It has also been found that there are supernumerary or conditionally dispensable chromosomes that can be transferred between organisms and can induce pathogenicity within a nonpathogenic organism (Ma et al. 2010). For

26 10 example, a chromosome transfer from a F. f. sp. lycopersici isolate to a nonpathogenic F. isolate allowed the nonpathogenic isolate to infect tomato (Ma et al. 2010). This phenomena has also been found in F. solani f. sp. pisi isolates that transfer chromosomes that allow formerly nonpathogenic isolates to infect pea (Ma et al. 2013). It is important to note that the Fusarium genome facilitates this horizontal chromosome transfer, as the housekeeping genes are centralized on separate chromosomes from those genes encoding pathogenicity and secondary metabolites (Ma et al. 2010; Ma et al. 2013). In addition, it was found that the pathogenicity chromosomes are preferably transferred, while the core chromosomes are not (Ma et al. 2010). Microsatellites in Fusarium Population analysis can be done with the help of microsatellites, or simple sequence repeats (SSRs) that are repeated oligonucleotides of 2-6 base pairs found throughout the genome. A study from India looked at SSRs that could be used in differentiation of F. pathogen lineages (Mahfooz et al. 2012). This study looked at published F. expressed sequence tags (ESTs) to mine for SSRs in F. f. sp. melonis (infects melon), cucumerium (cucumber), and lycopersici (tomato). Primers were then developed to test for these different gene regions and tested across different formae speciales (melonis, cucumerium, lycopersici, cubense, and cicero) to look at diversity (Mahfooz et al. 2012). Out of 30 gene candidates, eight genes showed polymorphism across all isolates (Mahfooz et al. 2012). In another study by the same authors, these same 30 gene candidates were tested for transferability to Fusarium udum, a different species of Fusarium that causes a vascular wilt (Kumar et al.

27 ). Of these 30 markers, 21 produced amplicons in Fusarium udum, demonstrating the importance and application of SSR studies from ESTs and transcriptomes. Another study used the full Fusarium f. sp. lycopersici genome published by the Fusarium Comparative Database, operated by the Broad Institute of Harvard and MIT, to mine for microsatellites (Kumar et al. 2012). The study looked at the frequency and distribution of SSRs, and mapped them to chromosomes within the genome (Kumar et al. 2012). The findings concentrated on the length of the SSR motifs as well as the total length of the microsatellite sequence. However, there was no molecular confirmation of the in silico data to determine the usefulness of the marker for population analysis (Kumar et al. 2012). A third study looked at race diversity and evolution in Fusarium f. sp. ciceris, which infects chickpea. The work was done with conserved genes used in fungal identification, genes involved in plant pathogenicity, as well as microsatellites (Demers, Garzón, and Jiménez- Gasco 2014). The microsatellite loci were found with molecular labeling of genomic DNA (Demers, Garzón, and Jiménez-Gasco 2014). Findings revealed that this particular forma specialis is monophyletic, using microsatellites to differentiate between races of the pathogen. However, analyses yielded very few polymorphic microsatellites for F. f. sp. ciceris, which the authors attributed to the relatedness of the forma specialis (Demers, Garzón, and Jiménez-Gasco 2014). Succinate dehydrogenase inhibitor fungicides Succinate dehydrogenase inhibitors (SHDIs) are fungicides that block the ubiquinone reduction in the mitochondrial respiratory chain (Walter 2012). The succinate dehydrogenase protein is a four-domain protein that consists of two hydrophilic domains (SDHA and SDHB)

28 12 that are found on the mitochondrial membrane periphery, as well as two hydrophobic domains (SDHC and SDHD) found within the mitochondrial membrane (Walter 2012). The hydrophilic subunits are involved in the tricarboxylic acid cycle and provide the succinate dehydrogenase activity with a FAD cofactor in SDHA and iron-sulfur subunits in SDHB. The hydrophobic subunits comprise complex II of the respiratory chain and has heme groups that are involved with ubiquinone reduction (Walter 2012). The ubiquinone site is located between the B, C and D domains, near the heme group between the SDHC and SDHD domains and an iron-sulfur subunit in the SDHB domain (Walter 2012). The first SDHI released was carboxin, introduced in the 1960s, which was effective against basidiomycete plant pathogens, but had limited effects on ascomycete fungi (1970). However, boscalid, a pyridine carboxamide, was introduced in 2003 and had efficacy against ascomycete fungi, like Botrytis spp., Sclerotinia spp., and Alternaria spp. (Avenot et al. 2014). The SDHI fungicide class (Fungicide Resistance Action Committee code 7) has expanded since the market release of Boscalid, and now includes active ingredients of various chemical subclasses, including recent introductions of pyrazole carboxamides (Avenot et al. 2014). Fungal succinate dehydrogenase inhibitor fungicide resistance There is a medium to high risk for resistance development with SDHI fungicides. The Fungicide Resistance Action Committee (FRAC) has determined that SDHI fungicide compounds are at a medium to high risk for fungicide resistance development because these chemicals only affect one site in the target organism. All SDHI fungicides are grouped together by FRAC in a cross-resistance group, so that resistance to one fungicide will confer resistance to all the other fungicides within this class. However, many of these chemicals may have slightly

29 13 different binding, and, although they are in the same cross-resistance group, there is not complete cross-resistance between all chemicals in this fungicide class (Walter 2012). For example, within Alternaria alternata populations, boscalid resistance was correlated with fluxapyroxad resistance, but was not positively correlated with either fluopyram or penthiopyrad (Walter 2012). This is because the chemicals bind to slightly different sites within the enzyme, and different mutations within the fungal succinate dehydrogenase (SDH) enzyme alter the binding site so that the enzyme is functional yet not inhibited by the fungicide. Resistance is a concern with fungicides that bind to a single site. This is important because it can reduce the efficacy of a key disease management tool. Fungicide resistance can be managed by rotating fungicides of different modes of action or by applying fungicides with more than one mode of action at a time (van den Bosch et al. 2014). This reduces the selection for fungicide resistant alleles within a population, delaying the threshold where large populations with the resistant alleles have built up so that the fungicide is no longer effective in reducing the population of the plant pathogen and thus disease.

30 14 REFERENCES Aanen, D. K., N. L. Glass, and S. J. Saupe 'Biology and genetics of vegetative incompatibility in fungi.' in Alfons J. M. Debets (ed.), Cellular and Molecular Biology of Filamentous Fungi (ASM: Washington, D.C). Agrios, G. N Plant Pathology (Elsevier Academic Press: New York). Amini, J., and D. F. Sidovich 'The effects of fungicides on Fusarium f. sp. lycopersici associated with Fusarium wilt of tomato', Journal of Plant Protection Research, 50: Avenot, H. F., H. van den Biggelaar, D. P. Morgan, J. Moral, M. Joosten, and T. J. Michailides 'Sensitivities of baseline isolates and boscalid-resistant mutants of Alternaria alternata from pistachio to fluopyram, penthiopyrad, and fluxapyroxad', Plant Disease, 98: Bruton, B. D., W. W. Fish, and D. B. Langston 'First report of Fusarium wilt caused by Fusarium f. sp. niveum Race 2 in Georgia watermelons', Plant Disease, 92: Bruton, B. D., W. W. Fish, X. G. Zhou, K. L. Everts, and P. D. Roberts "Fusarium wilt in seedless watermelons." In Southeast Regional Vegetable Conference, edited by W;. T.Kelly, Savannah, Georgia. Bruton, B. D., C. L. Patterson, and R. D. Martyn 'Fusarium wilt (F. f. sp. niveum Race 2) of watermelon in Oklahoma', Plant Disease, 72: 734. Carlile, M. J 'A study of the factors influencing non-genetic variation in a strain of Fusarium ', Microbiology, 14: Demers, J. E., C. D. Garzón, and M. D. M. Jiménez-Gasco 'Striking genetic similarity between races of Fusarium f. sp. ciceris confirms a monophyletic origin and clonal evolution of the chickpea vascular wilt pathogen', European Journal of Plant Pathology, 139: Di Pietro, A., M. P. Madrid, Z. Caracuel, J. Delgado-Jarana, and M. I. G. Roncero 'Fusarium : exploring the molecular arsenal of a vascular wilt fungus', Molecular Plant Pathology, 4: Egel, D. S., R. Harikrishnan, and R. Martyn 'First report of Fusarium f. sp. niveum Race 2 as causal agent of Fusarium wilt of watermelon in Indiana', Plant Disease, 89: 108.

31 15 Elias, K. S., and R. W. Schneider 'Vegetative compatability groups in Fusarium f. sp. lycopersici', Phytopathology, 81: EPA, Environmental Protection Agency "Pesticide Product Label Search." In.: Environmental Protection Agency. Everts, K. L., D. S. Egel, D. Langston, and X-G. Zhou 'Chemical management of Fusarium wilt of watermelon', Crop Protection, 66: Fourie, G., E. T. Steenkamp, T. R. Gordon, and A. Viljoen 'Evolutionary relationships among the Fusarium f. sp. cubense vegetative compatibility groups', Applied and environmental microbiology, 75: Glass, N. Louise, David J. Jacobson, and Patrick K. T. Shiu 'The genetics of hyphal fusion and vegetative incompatability in filamentous ascomycete fungi', Annual Review of Genetics, 34: Gordon, T. R 'The evolutionary biology of Fusarium ', Annual review of phytopathology, 35: Holmes, Gerald J., David W. Monks, Jonathan R. Schultheis, Kenneth A. Sorenson, and Allan C. Thornton "Crop profile for watermelons in North Carolina." In, edited by Jr. Stephen J. Toth. Katan, Talma, E. Shlevin, and J. Katan 'Sporulation of Fusarium f. sp. lycopersici on Stem Surfaces of Tomato Plants and Aerial Dissemination of Inoculum', Phytopathology, 87: Keinath, A. P., and R. L. Hassell 'Control of Fusarium Wilt of Watermelon by Grafting onto Bottlegourd or Interspecific Hybrid Squash Despite Colonization of Rootstocks by Fusarium', Plant Disease, 98: Kleczewski, N. M., and D. S. Egel 'A diagnostic guide for Fusarium wilt of watermelon', Plant Health Progress. Kopacki, M., and A. Wagner 'Effect of some fungicides on mycelium growth of Fusarium avenaceum ( Fr.) Sacc. pathogenic to chrysanthemum (Dendranthema grandiflora Tzvelev )', Agronomy Research, 4: Kumar, S., D. Maurya, S. Rai, L. Kashyap, and A. K. Srivastava 'Computational mining and genome wide distribution of microsatellite in Fusarium f. sp. lycopersici', NOtulae Scientia Biologicae, 4:

32 16 Kumar, S., S. Rai, D. K. Maurya, P. L. Kashyap, A. K. Srivastava, and M. Anandaraj 'Cross-species transferability of microsatellite markers from Fusarium for the assessment of genetic diversity in Fusarium udum', Phytoparasitica, 41: Leslie, J. F., and B. A. Summerell The Fusarium laboratory manual (Blackwell Publishing: Ames, Iowa). Ma, L-J., D. M. Geiser, R. H. Proctor, A. P. Rooney, K. O'Donnell, F. Trail, D. M. Gardiner, J. M. Manners, and K. Kazan 'Fusarium pathogenomics', Annual review of microbiology, 67: Ma, L-J., H. C. van der Does, K. A. Borkovich, J. J. Coleman, M-J. Daboussi, A. Di Pietro, M. Dufresne, M. Freitag, M. Grabherr, B. Henrissat, P. M. Houterman, S. Kang, W-B. Shim, C. Woloshuk, X. Xie, J-R. Xu, J. Antoniw, S. E. Baker, B. H. Bluhm, A. Breakspear, D. W. Brown, R. A. E. Butchko, S. Chapman, R. Coulson, P. M. Coutinho, E. G. J. Danchin, A. Diener, L. R. Gale, D. M. Gardiner, S. Goff, K. E. Hammond-Kosack, K. Hilburn, A. Hua-Van, W. Jonkers, K. Kazan, C. D. Kodira, M. Koehrsen, L. Kumar, Y-H. Lee, L. Li, J. M. Manners, D. Miranda-Saavedra, M. Mukherjee, G. Park, J. Park, S-Y. Park, R. H. Proctor, A. Regev, M. C. Ruiz-Roldan, D. Sain, S. Sakthikumar, S. Sykes, D. C. Schwartz, B. G. Turgeon, I. Wapinski, O. Yoder, S. Young, Q. Zeng, S. Zhou, J. Galagan, C. A. Cuomo, H. C. Kistler, and M. Rep 'Comparative genomics reveals mobile pathogenicity chromosomes in Fusarium', Nature, 464: Mahfooz, S., D. K. Maurya, A. K. Srivastava, S. Kumar, and D. K. Arora 'A comparative in silico analysis on frequency and distribution of microsatellites in coding regions of three formae speciales of Fusarium and development of EST-SSR markers for polymorphism studies', FEMS Microbiology Letters, 328: Martyn, R. D 'Fusarium f. sp. niveum Race 2: A highly aggressive race new to the United States', Plant Disease, 71: Martyn, R. D., and B. D. Bruton 'An initial survey of the United States for races of Fusarium f. sp. niveum', HortScience, 24: Martyn, R. D., and T. K. Hartz 'Use of soil solarization to control Fusarium wilt of watermelon', Plant Disease, 70: Michielse, C. B., and M. Rep 'Pathogen profile update: Fusarium ', Molecular Plant Pathology, 10: Nations, Food and Agriculture Organization of the United "FAOSTAT database." In. Rome, Italy: FAO.

33 17 O'Donnell, K., C. Gueidan, S. Sink, P. R. Johnston, P. W. Crous, A. Glenn, R. Riley, N. C. Zitomer, P. Colyer, C. Waalwijk, T. Van Der Lee, A. Moretti, S. Kang, H-S. Kim, D. M. Geiser, J. H. Juba, R. P. Baayen, M. G. Cromey, S. Bithell, D. A. Sutton, K. Skovgaard, R. Ploetz, H. C. Kistler, M. Elliott, M. Davis, and B. A. J. Sarver 'A two-locus DNA sequence database for typing plant and human pathogens within the Fusarium species complex', Fungal Genetics and Biology, 46: Puhalla, J. E 'Classification of strains of Fusarium on the basis of vegetative compatibility', Canadian Journal of Botany, 63: Song, W., L. Zhou, C. Yang, X. Cao, L. Zhang, and X. Liu 'Tomato Fusarium wilt and its chemical control strategies in a hydroponic system', Crop Protection, 23: Taylor, M., B. Bruton, W. Fish, and W. Roberts 'Cost benefit analyses of using grafted watermelons for disease control and the fresh-cut market', Cucurbitaceae: USDA-NASS "Vegetables 2014 summary." In, edited by USDA-NASS. van den Bosch, F., R. Oliver, F. van den Berg, and N. Paveley 'Governing principles can guide fungicide-resistance management tactics', Annu Rev Phytopathol, 52: Walter, H Bioactive heterocyclic compound classes (Wiley-VCH Verlag GmbH & Co. KGaA: Weinheim, Germany). Zhou, X. G., and K. L. Everts 'First report of the occurrence of Fusarium f. sp. niveum Race 2 in commercial watermelon production areas of Maryland and Delaware', Plant Disease, 85: Zhou, X. G., and K. L. Everts 'Suppression of Fusarium wilt of watermelon by soil amendment with hairy vetch', Plant Disease, 88: Zhou, X. G., and K. L. Everts 'Characterization of a regional population of Fusarium f. sp. niveum by race, cross pathogenicity, and vegetative compatibility', Phytopathology, 97: Zhou, X. G., K. L. Everts, and B. D. Bruton 'Race 3, a new and highly virulent race of Fusarium f. sp. niveum causing Fusarium wilt in watermelon', Plant Disease, 94:

34 18 TABLES Table 1: Watermelon genotypes used to differentiate races of Fusarium f. sp. niveum (FON). S refers to a genotype susceptible to that race whereas R refers to a genotype resistant to that FON race. Disease response to FON Cultivar or genotype Race 0 Race 1 Race 2 Race 3 Sugar Baby, Black Diamond S S S S Charleston Gray R S S S Calhoun Gray R R S S PI FR R R R S

35 19 CHAPTER II Sensitivity of Fusarium f. sp. niveum to fungicides in vitro and in field experiments for management of Fusarium wilt of watermelon ABSTRACT Fusarium wilt of watermelon, caused by Fusarium f. sp. niveum (FON), is an economically important watermelon disease in the United States (US) and worldwide. This disease has traditionally been controlled in the US with resistant varieties, crop rotation, and soil fumigation. The development of new pathogen races and recent restrictions on fumigants have limited Fusarium wilt control. New fungicides that can be used in this system would benefit watermelon producers. This study examined the in vitro sensitivity of FON to fungicides and for control of Fusarium wilt of watermelon in the field. Fungicides prothioconazole, pydiflumetophen, tebuconazole, difenoconazole, and propiconazole reduced F. mycelia growth in vitro. Prothioconazole and pydiflumetofen reduced Fusarium wilt incidence and severity in field experiments. Isolates resistant to prothioconazole or pydiflumetofen were not identified in in vitro experiments. Sequencing of sdhb and sdhc, genes related to fungicide resistance to succinate dehydrogenase inhibitors, did not yield amino acid level variation in a subset of 71 F. isolates. Prothioconazole and pydiflumetofen could complement existing management practices to effectively reduce Fusarium wilt of watermelon incidence and the introduction of a new fungicide could slow development of fungicide resistance in FON populations.

36 20 INTRODUCTION Fusarium wilt of watermelon is an economically important disease of watermelon in the United States (US) and worldwide. Watermelon is a major cucurbit crop in the southern US, where Florida, Texas, California, Georgia, Indiana, South Carolina, and North Carolina are the most productive states (USDA-NASS 2015). Fusarium wilt of watermelon presents itself in the field as a complete or unilateral wilt due to obstruction of the vascular system by the soilborne fungal pathogen Fusarium f. sp. niveum (FON), which prevents the free passage of water in the xylem (Kleczewski and Egel 2011). The fungus produces thick walled survival propagules called chlamydospores that can survive for up to 20 years in the soil (Martyn 2014). The long-term survival of the pathogen makes the disease difficult to manage, and resistant watermelon cultivars and soil fumigants such as methyl bromide have been the main methods of disease control (Everts and Himmelstein 2015). However, methyl bromide was phased out in accordance with the Montreal Agreements due to damage to the ozone layer and fungicides for Fusarium wilt control are limited (Everts et al. 2014; King et al. 2008). Fusarium wilt resistance in watermelon was first developed in the 1890s, and these cultivars offered resistance to FON Race 0. FON Race 1, which was virulent on FON Race 0 resistant cultivars, soon reached prominence, but could also be managed with host resistance (Martyn 2014). In 1985, FON Race 2 was first reported in the US, and has since been reported in Texas, Oklahoma, Indiana, Maryland, and Delaware, South Carolina, and Georgia (Bruton, Fish, and Langston 2008; Bruton, Patterson, and Martyn 1988; Egel, Harikrishnan, and Martyn 2005; Keinath and DuBose 2009; Martyn 1987; Martyn and Bruton 1989; Zhou and Everts 2001). FON Race 2 is virulent on FON Race 1 resistant cultivars, and there are no watermelon

37 21 cultivars in production that are resistant to this race (Everts and Himmelstein 2015). In 2010, FON Race 3 was reported in Maryland and Delaware, and is virulent towards PI FR, which is resistant to FON Races 0, 1, and 2 (Zhou, Everts, and Bruton 2010). Thus, PI FR has been investigated as a source of resistance genes for resistance to FON Race 2, but is not produced commercially, as it produces small fruit without flavor (Martyn and Netzer 1991). Currently, no sources of resistance to FON Race 3 have been reported. A field and greenhouse study conducted in 2014 found that only two out of eight fungicides tested reduced Fusarium wilt incidence. These fungicide active ingredients were prothioconazole and thiophanate-methyl (Everts et al. 2014). Of these fungicides, prothioconazole, under the trade name Proline, is the only fungicide that received a label for controlling Fusarium wilt of watermelon. Prothioconazole (FRAC 2016) is a demethylase inhibitor fungicide that interferes with ergosterol synthesis in fungi, disrupting the normal growth and function of the fungal cell wall (Parker et al. 2011). Although the study examined applying the fungicides three times through a drip tape, the Proline label only allows for up to one soil application and up to two direct-banded sprays aimed at the soil line (Everts et al. 2014). These additional sprays may be less effective for disease management because the foliar spray would not maximize contact between the fungicide and the soilborne pathogen. Demethylase inhibitor fungicides (DMIs) are often triazole or imidazole compounds. These compounds act as a ligand that binds to the fungal enzyme CYP51, and inhibit the 14αdemethylation step of sterol biosynthesis, which is necessary to produce ergosterol (Parker et al. 2011). All DMIs have been classified into the FRAC code 3, although there are subdivisions within this fungicide class. Interestingly, it appears that although the effect of depleted

38 22 ergosterol is found across many different fungi, Mycosphaerella graminicola has been reported to have different effects under triazole treatment than yeasts (Parker et al. 2011). In addition, prothioconazole is demonstrated to bind in a competitive manner to CYP51 in M. graminicola, whereas other triazoles bind noncompetitively to the same enzyme (Parker et al. 2011). Everts et. al. (2014) did not include carboxamide succinate dehydrogenase inhibitors (SDHIs) in their experiments, which act by inhibiting the ubiquinone-pocket of succinate dehydrogenase, also known as complex II, in the mitochondrial respiratory chain (Sierotzki and Scalliet 2013). Succinate dehydrogenase is an enzyme comprised of four nuclear encoded subunits, A, B, C, and D; mutations in subunits B, C, and D have been associated with resistance to SDHI fungicides in ascomycete fungi, including Alternaria alternata, Botrytis cinerea, and Corynespora cassiicola (Avenot and Michailides 2010). Initially, SDHI fungicides were mostly active against basidiomycete fungi; nonetheless, in 2003, the SDHI boscalid showed activity against ascomycete fungi by inhibiting growth of Botrytis spp., Sclerotinia spp., and Alternaria spp (Avenot and Michailides 2007). Many other SDHI fungicides have since been registered with activity against economically important ascomycete fungi. Interestingly, the SDHI fluopyram has been reported to also have activity against nematodes (Faske and Hurd 2015). SDHI fungicides have all been so far classified into FRAC code 7. Chemically, these fungicides contain an amide bond, and are classified by the moiety on the carbonyl side of the bond, although the linker and hydrophobic rest are also variable (Sierotzki and Scalliet 2013). Although SDHIs are classified together by their mode of action, there are different crossresistance patterns between different SDHI fungicides (Scalliet et al. 2012). For example, Alternaria alternata isolates with moderate frequency of boscalid resistance were not fully

39 23 resistant to fluopyram, fluxapyroxad, or penthiopyrad (Avenot et al. 2014). There are currently no SDHIs labeled for Fusarium in the US (EPA 2016). One study examined the response of both Fusarium graminearum and Zymoseptoria tritici to isopyrazam and found several mutations that could explain differential fungicide responses in Zymoseptoria tritici (Dubos et al. 2013). However, all F. graminearum isolates were insensitive to isopyrazam, and no amino acid substitutions among 40 isolates sequenced explained differential fungicide response (Dubos et al. 2013). Disease control options for watermelon Fusarium wilt in the US are limited, only one fungicide is labeled, and there is little knowledge of fungicide efficacy and the occurrence of fungicide resistance in FON populations. To address this knowledge gap and provide insight regarding fungicide resistance in FON we aimed to: i) evaluate fungicides in vitro for their ability to inhibit mycelium growth of Fusarium f. sp. niveum; ii) test the DMI prothioconazole and the SDHI pydiflumetofen against a diverse panel of F, isolates from the US for inhibition of mycelium growth; iii) confirm the efficacy of prothioconazole and pydiflumetofen on watermelon Fusarium wilt in field experiments; and iv) determine the genetic diversity and occurrence of mutations conferring fungicide resistance in the succinate dehydrogenase B and C genes of Fusarium isolates. METHODS Isolate collection and long-term storage Fusarium isolates were collected from fields in North Carolina (NC) from symptomatic watermelon plants. Infected crowns were excised from the plant and surface-

40 24 sterilized in a 1.85% NaClO solution for 5 minutes. The tissue was rinsed in sterile distilled water (SDW) and stem cross-sections were plated on Fusarium spp. selective Nash and Snyder media (Nash and Snyder 1962). Grown agar plugs were transferred to potato dextrose agar (PDA) (39 g PDA per 1 L of water, HiMedia, Mumbai, India) amended with 100 µg/ml ampicillin and 100 µg/ml rifampicin to remove bacterial contaminants. Isolates were singlespored by flooding plates with 1 ml of SDW, spreading 10 µl of the conidia suspension mixed with 90 µl of SDWon a water agar plate (10 g Select Agar per 1 L of water, BD Diagnostics, Sparks, MD), and transferring single germinating conidia after 12 hours onto PDA plates. Single-spored isolates were stored with two methods. In the first method, 2-4 agar plugs of monoconidial isolates were placed in 2 ml microcentrifuge tubes with 1mL of SDW and 2-3 sterilized hemp seeds, and these were stored in a refrigerator at 4 C. These isolates were used as temporary stocks, and were used in the experiments described here. In the other method, 2 agar plugs were placed in 2 ml cryotubes (Corning, NY) with 500 µl of potato dextrose broth (PDB, HiMedia). These were set at room temperature on an Advanced Digital Shaker (VWR International, Radnor, PA) at 100 rpm for 2 days/48 hours, and then 500 µl sterile 1:1 glycerol: SDW was added to the tube, and these were stored at -80 C for long term preservation of the isolates. Other isolates were kindly provided by collaborators (Table 2). These were singlespored and stored in the same conditions described. Identification of fungicides with activity against FON isolates in vitro This experiment examined the efficacy of 10 formulated fungicides against 10 FON isolates to quickly identify chemistries with activity for further examination (Table 2). Single active ingredient fungicides included in this experiment were ADEPIDYN TM fungicide (active

41 25 ingredient (a. i.) pydiflumetophen, Syngenta Crop Protection, Greensboro, NC), Endura (boscalid; BASF Corporation, Research Triangle Park, NC), Folicur (a.i. tebuconazole; Bayer CropScience LP, Research Triangle Park, NC), Fontelis (a.i. penthiopyrad; Dupont Crop Protection, Wilmington, DE), Inspire (a.i. difenoconazole; Syngenta Crop Protection), Luna Privilege (a.i. fluopyram; Bayer CropScience), Mertect (a.i. thiabendazole; Syngenta Crop Protection), Quadris (a.i. azoxystrobin; Syngenta Crop Protection), Proline (a.i. prothioconazole; Bayer CropScience), and Tilt (a.i. propiconazole; Syngenta Crop Protection) (Table 3). Isolate plugs were taken from temporary storage and plated on PDA. After 7 days, 6 cm plugs were taken from the growing colony edge and plated on media amended with a single fungicide. Fungicide formulated products were diluted in water and added to autoclaved PDA media cooled to approximately 50 C at 1 ml per 1 L volume to yield a final concentration of 10, 1, 0.1, and 0.01 µg active ingredient /ml media. SDW was added in the same volume to control plates. After 7 days of growing at 25 C with a 12-hour light/dark cycle, mycelium growth of 10 FON isolates was measured twice at a perpendicular angle. Three technical replicates were used and the experiment was repeated once per isolate. In vitro fungicide efficacy data for each concentration was divided by the average control treatment for that isolate and replication to yield relative growth at each fungicide concentration. Relative growth for each fungicide was analyzed as a four parameter log-logistic model with the R package drc (R Core Team 2016; Ritz and Streibig 2005). The model utilized was y = min + (max-min)/(1+10 (Hillslope(log(x)-log(EC50))) ), where y is the mycelium growth at x fungicide concentration, max is the maximum relative growth, min is the minimum relative growth, EC50 is the concentration where growth is inhibited by 50%, and Hillslope is the slope

42 26 parameter about the EC50 (Ritz and Streibig 2005). EC50s for each fungicide were analyzed by ANOVA and compared pairwise with a Bonferroni correction in Microsoft Excel (Excel for Mac 2011 Version ). Evaluation of Fusarium isolates for fungicide sensitivity in vitro Since pydiflumetofen and prothioconazole showed mycelium growth inhibition in the first experiment, they were selected for evaluation in an expanded panel of 98 Fusarium spp. isolates, including 68 FON, 21 F. f. sp. lycopersici (FOL), 4 F. f. sp. radicis-lycopersici (FORL), and 3 F. isolates. There was one Fusarium proliferatum isolate and one F. solani isolate also included in this panel. The fungicide concentrations in this study were 10, 1, 0.1, and 0.01 µg/ml active ingredient, and the experiment was conducted as described in the previous section. Calculations were done as previously described, but fungicide*isolate combination factor was used as the grouping variable. EC50 values of prothioconazole and pydiflumetofen were plotted for each isolate, and a linear regression was calculated in Microsoft Excel. Confirmation of fungicide activity against FON in field experiments Field experiments were conducted in 2015 and 2016 at the Piedmont Research Station in Salisbury, (NC). Plots were raised beds with white plastic mulch, and each plot had 10 plants. Plots were 30-ft long with 10-ft centers and had 9-ft fallow borders between each plot. Fields were set up in a randomized complete block design with four replications. The watermelon cultivar Black Diamond was seeded in transplant trays in the greenhouse with Fafard 2 mix and transplanted after 5 weeks. FON inoculum using local isolates was grown in 500 ml quarter

43 27 strength potato dextrose broth in 1L plastic flasks. Mycelium plugs were added to the broth, and they were grown in the dark on an Advanced Digital Shaker set at 200 rpm for 14 days. The solutions were filtered through four layers of sterile cheesecloth to remove mycelia, and spore concentration was calculated using a hemocytometer. Conidia suspensions were diluted in water to standard concentrations that differed for individual inoculation procedures. Plants were inoculated immediately before transplant by dipping the tray in a 10 7 conidia per ml solution for 20 minutes. Treatments and rates used are described in Table 4, and drench applications were performed by pouring 100 ml fungicide solution to the base of each plant. Non-treated control plots were inoculated with FON, and water was used in place of the fungicide. Additional FON inoculations were done 10 days after transplant by drenching the base of seedlings with a 100 ml suspension of 10 6 conidia/ml. Fungicide sprays were applied 14 days after transplant with a CO2 pressurized backpack sprayer equipped with a single nozzle handheld boom with hollow cone nozzles (TXVS-26) delivering L/ha at 310 kpa with one pass per plot. Incidence and severity ratings were taken weekly for both trials, and yield ratings were taken on the final rating date in 2016 by counting and weighing the fruit per plot. Area under the disease progress curve (AUDPC) was calculated with trapezoidal integration for the 2015 and 2016 field disease incidence data and for severity data for 2016 using the R package agricolae (de Mendiburu 2016) and was normalized by dividing the AUDPC by the number of rating dates. These were analyzed as a generalized linear mixed model with PROC GLIMMIX in SAS with Tukey s HSD (P=0.05) multiple comparison adjustment (SAS Institute, Cary, NC). Exponential distributions were utilized for the incidence and 2016 severity data. Yield data was analyzed with an ANOVA procedure after checking for normality assumptions in R (R Core Team 2016).

44 28 Genetic diversity and succinate dehydrogenase gene sequence analysis Monoconidial Fusarium cultures were grown from an agar plug in Potato Dextrose Broth on an Advanced Digital Shaker set at 150 rpm. The agar plug was removed, and mycelium was washed in SDW and vacuum filtrated to remove the excess media. The mycelium was placed in 2 ml microcentrifuge tubes with 2-3 2mm glass beads (Sigma Aldrich, St. Louis, MO) and lyophilized for 48 hours in a FreeZone 1 lyophilizer (LabConco, Kansas City, MO). The lyophilized mycelium was broken down with a Bead Ruptor 24 (Omni International, Kennesaw, GA) for 5 seconds on speed 6 for two cycles with a rest period of 5 seconds between cycles. DNA was extracted with a modified published protocol where ethanol washes were added to purify the DNA (Ahmed et al. 2009). DNA samples were quantified with a NanoDrop Lite (Thermo Scientific, Waltham, MA, USA) and diluted to 50 ng/µl. DNA samples were stored at -20 C. Polymerase chain reactions (PCR) were performed in a T100 Thermal Cycler (Bio-Rad, Hercules, CA). Each reaction well contained 5 µl 2x GoTaq green master mix (Promega, Durham, NC), 3.5 µl filter-sterilized double distilled water, 0.5 µl DNA, and 0.5µL each forward and reverse primer for each locus diluted to 10µM. Primers ITS4 and ITS5 were used for the internal transcribed spacer (ITS) PCR reaction (White et al. 1990). The thermal cycler was set to 94 C for 3 minutes, and there were 30 cycles of denaturation at 94 C for 30 seconds, annealing at 50 C for 30 seconds, and then elongation at 72 C for 60 seconds. The final extension was set for 5 minutes at 72 C. The translation elongation factor EF-1α (TEF) was also amplified with previously reported primers (O'Donnell et al. 1998) and the same PCR reaction

45 29 and thermal cycling protocol, except the annealing temperature was set to 53 C. For both PCR protocols, the samples were held at 4 C until the samples were removed and stored at -20 C. Primer sequences from Fusarium graminearum for the sdhb and sdhc genes were obtained from previous literature (Dubos et al. 2013). The gene sequence and annotation for sdhb and sdhc from F. graminearum and Zymoseptoria tritici were downloaded from GenBank. Additional sequences from Alternaria alternata, Botrytis cinerea, and Mycosphaerella graminicola were utilized as reference sequences (Scalliet et al. 2012; Avenot, Sellam, and Michailides 2009; Staats and van Kan 2012; Yin, Kim, and Xiao 2011). These were compared with publicly available F. genomic data from the Broad Institute with BLASTN alignments (E=1e-10) (Ma et al. 2010; Cuomo et al. 2007). Primers were designed from the F. f. sp. lycopersici genome with Primer3 to flank the Iron-Sulfur binding units of sdhb and the ubiquinone-binding domain of sdhc (Untergasser et al. 2012; Ma et al. 2010). The primers used in this study are in Table 5. The PCR reagents were the same as in ITS for amplification of sdhb and sdhc, except for the respective primers for each reaction. For amplification of sdhc, the initial temperature of the PCR was set to 95 C for 3 minutes. There were 35 cycles of denaturation at 95 C for 30 seconds, annealing at 50 C for 30 seconds, and extension at 72 C for 60 seconds. The thermal cycler had a final extension at 72 C for five minutes and then an infinite hold at 4 C. The PCR products were stored at -20 C until further use. A touchdown PCR protocol was used for amplification of sdhb. The samples were heated to 95 C for 3 minutes, followed by a cycle of denaturation at 95 C, annealing at 65 C, and extension at 72 C. Each successive cycle lowered the annealing temperature by 1 C, while keeping the denaturation and extension temperatures

46 30 constant, until the annealing temperature reached 53 C. The cycle with the annealing temperature of 53 C was repeated 25 times. There was a final extension of 5 minutes at 72 C, followed by an infinite hold at 4 C. PCR products were run on a 1% agarose gel amended with Ethidium Bromide at 90 volts, and bands were analyzed with Quantity One 1-D Analysis software (Bio-Rad, Hercules, CA, USA). Five µl of PCR product was combined with 2 µl of ExoSAP-IT (Affymetrix, Santa Clara, CA). Samples were placed in a thermal cycler, which was set to 37 C for 15 minutes to degrade excess nucleotides and primers, and then set to 80 C for 15 minutes to inactivate the ExoSAP-IT reagent. 1 µl of the purified PCR product was combined with 1 µl 10µM forward primer and 10 ml filter sterilized double distilled water for sequencing at the Genomic Sciences Laboratory at North Carolina State University (Raleigh, NC). Sequencing reactions were done with a LifeTech 3730xl DNA Analyzer (Life Technologies, Carlsbad, CA). Electropherograms were visualized using FinchTV software (Geospiza, Inc. Seattle, WA), and files were trimmed manually to remove low quality base calls. ITS and TEF PCR product sequences were compared with GenBank using BLAST for identity. ITS sequences for 71 isolates were aligned with a MUSCLE alignment in Geneious version 8.1.6, and the alignments were trimmed so that they were of equal length (Kearse et al. 2012). The same was done for the TEF sequence, and ITS and TEF sequences were concatenated for each isolate. A Maximum Likelihood consensus tree was inferred utilizing Tamura-Nei s genetic distance in MEGA7 (Kumar, Stecher, and Tamura 2016; Tamura and Nei 1993). The tree was bootstrapped 1,000 times. The sdhb and sdhc sequences were compared to the downloaded sequences and previous reports of fungicide resistance. These nucleotide sequences were translated in Geneious version

47 with the 6-frame translation function (Kearse et al. 2012). The translations of were aligned with MUSCLE v3.8.31, and the sequences that matched the reference amino acid sequences were selected for analysis (Edgar 2004; Dubos et al. 2013). FON amino acid sequences were aligned with reference strains used to develop primers, and amino acid substitutions were noted for sdhb and sdhc. Amino acid distributions were visualized with WebLogo (Doerks et al. 2002). RESULTS Identification of fungicides with activity against FON isolates in vitro There were significant differences for the EC50 values after the Bonferroni correction between penthiopyrad and every other fungicide, and boscalid and every other fungicide (Table 6, Table 7). These two fungicides had higher EC50 values than other fungicides, and did not inhibit mycelium growth for the FON isolates. Fluopyram also had an EC50 value above 1 µg/ml. Thiabendazole exhibited an EC50 value above 1 µg/ml with no significant restriction of mycelium growth. All other fungicides yielded EC50 values between 0.1 and 1 µg/ml of a. i., and there were no significant differences between these values after the Bonferroni correction. The FRAC 3 fungicides examined in this study had low EC50 values, but only prothioconazole is labeled in this pathosystem and was further evaluated in the diversity panel and field trials. Of the FRAC group 7 fungicides, only pydiflumetofen was able to restrict growth and thus further examined alongside prothioconazole. There were no significant differences in the upper limit value term, which models the relative growth at low concentrations of fungicides, between any pair of fungicides. However,

48 32 there were differences between fungicides in the lower limit term, which models the relative growth at higher concentrations of fungicides. There were significant differences after the Bonferroni correction between penthiopyrad and each of prothioconazole, tebuconazole, and difenoconazole. There were also significant differences between azoxystrobin and prothioconazole and tebuconazole. The other fungicides were not statistically different after the Bonferroni correction in terms of how much they were able to restrict growth at the highest concentrations. Evaluation of Fusarium isolates for fungicide sensitivity in vitro Out of 98 isolates tested, there were no isolates that were insensitive to prothioconazole at 10 µg/ml. Sensitivity, intermediate sensitivity, and resistance are defined here as <30%, 30-90%, and >90% relative growth compared to the control at a given fungicide concentration, respectively (Petkar et al. 2017). However, there were 25 isolates that were intermediately sensitive to pydiflumetofen at 10 µg/ml, and the rest were sensitive to pydiflumetofen at that concentration. The average EC50 response for pydiflumetofen was 0.353± µg/ml and for prothioconazole was 0.279± µg/ml. There was a range of to 9.41 µg/ml of EC50s for prothioconazole. For pydiflumetofen, the EC50 value ranged between to 3.47 µg/ml. At 10 µg/ml, there was often very little growth of the isolates, and they would colonize the plug but not grow into the fungicide-amended media. There was low correlation between EC50 values for prothioconazole and pydiflumetofen among isolates (R 2 = ).

49 33 Confirmation of fungicide activity against FON in field experiments Fusarium wilt incidence progressed quickly in the control treatments in both 2015 and 2016 (Figure 1 and 2). In 2015, disease progressed faster in the treatments with pydiflumetofen initially, but the final treatments were significantly lower for the treatments with both a drench and a follow-up foliar spray treatment for both prothioconazole and pydiflumetofen by the end of the season (Figure 3). In 2016, there was initial disease pressure in all treatments, but there was not a large increase in disease incidence over the course of the season for any treatment (Figure 4). Incidence data for 2015 and 2016 was combined because there was no significant effect for year (P=0.4361) or interaction between treatment and year (P=0.2965). There was a significant effect for treatment (P=0.0204). The prothioconazole treatment that was applied as a drench at transplant and then followed with a foliar spray fourteen days after transplant and the pydiflumetofen treatment at a high rate applied as both a drench and foliar spray experienced significantly less disease in terms of incidence than the non-treated control ( Figure 5). For the 2016 AUDPC value of severity, there was a significant effect for treatment (P=0.0026), but not for block (P>0.05). The treatments that had significantly less disease severity were the pydiflumetofen treatment applied at a high rate of concentration as both a drench at transplant and up to 14 days later as a foliar spray, as well as the non-inoculated nontreated positive control plots (Figure 6). Final harvest yield data was taken in the 2016 season. There was no significant difference for the treatment effects for the total fruit weight (P=0.112), although there was a significant effect for block (P=0.004, Table 8). There was a significant fungicide treatment

50 34 effect on both marketable and total fruit count (P=0.023 and P=0.031, respectively). All pydiflumetofen treatments regardless of application scheme and rate performed as well as the non-inoculated non-treated positive control in terms of marketable and total fruit count. There was a significant reduction in both marketable and total fruit counts in the prothioconazole treatment with only the drench application at transplant compared to the positive control ( Figure 7). Genetic diversity and succinate dehydrogenase gene diversity A subset of 71 isolates was selected for genetic distance analysis in this study, marked with a star in Table 2. The consensus sequence of the concatenated ITS and TEF sequences was 811 nucleotides in length, with 413 nucleotides in the ITS consensus sequence and 400 nucleotides in the TEF consensus sequence. Of these, 715 total sites were included in the analysis with 185 variable sites. The resulting phylogenetic tree has one grouping with 37 isolates with bootstrap values over 70%, while the other isolates fit into a polytomy with very little support (Figure 8). The maximum likelihood tree had high bootstrap support for the node with NM211 and NM438, It is interesting that the largest EC50 reported in this study was for NM229, an isolate of Fusarium originally isolated from muskmelon. This isolate clustered together with NM211, a Fusarium solani isolate, and NM438, a F. isolate. Primers for the sdhb and sdhc genes for the SdhB and SdhC subunits of the F. succinate dehydrogenase enzyme were successfully amplified and sequenced. All 72 isolates examined in this study contained the sdhb amino acid motif SCREGICGSCAMNINCQNTL, which is associated with the 2Fe-2S subunit of the succinate dehydrogenase enzyme in Fusarium graminearum (Figure 10) (Dubos et al. 2013). This study also examined the ubiquinone-binding

51 35 domain of Fusarium from the sdhc gene. The reference motif is EQTWFGSSAWNRITG from Fusarium graminearum (Dubos et al. 2013). There was variation among individual amino acids, but each sequence, including reference genes from ascomycete fungi other than Fusarium spp., had the motif SxxxR (Figure 9). These conserved amino acids are important because these are thought to directly bind ubiquinone in the succinate dehydrogenase enzyme (Dubos et al. 2013). Our analysis did not find amino acid level variation associated with fungicide resistance in the sdhb and sdhc genes. DISCUSSION Our experiments revealed that there is a range of efficacy for fungicides against FON. Prothioconazole and pydiflumetofen, as well as tebuconazole, difenoconazole, and propiconazole were all effective at reducing mycelium growth in vitro. Fungicides in the FRAC code 3 group (triazoles) were all capable of reducing growth; however, fungicides that interfere with the respiratory chain, FRAC groups 7 and 11 (SDHI and QoI fungicides), had less success restricting mycelium growth in vitro. Interestingly, pydiflumetofen can reduce mycelium growth and it was the only FRAC 7 fungicide successful in that regard. This study found that pydiflumetofen is equally efficacious to prothioconazole in inhibiting the growth of F. in a mycelium growth assay. The EC50 values for both pydiflumetofen and prothioconazole were in between 0.1 and 1 µg/ml in in vitro experiments. This is higher than reported EC50 values of other fungicides tested against F. f. sp. lycopersici, where the EC50 values were found to be µg/ml for prochloraz and µg/ml for carbendazim (Amini and Sidovich 2010). However, the EC50 values from our study

52 36 are lower than a previously reported EC50 value, 1.62 µg/ml, for prothioconazole in FON (Petkar et al. 2017). Prothioconazole and pydiflumetofen equally reduced Fusarium wilt incidence in field trials in 2015 and There was a reduction in disease incidence in 2015 when fungicides were applied as a drench at transplant with an additional spray treatment 14 days after transplant. This effect was demonstrated with prothioconazole and both high and low rates of pydiflumetofen. This was surprising because the pathogen survives in the soil and infects via the root tissue (Lü et al. 2014), and we expect that a foliar spray would not efficiently expose the fungicide to the roots. Because FON is a soilborne pathogen, the foliar treatments were not expected to have a significant effect, and thus were not tested by themselves. Further studies should examine the effect of a foliar spray without a soil application. A different trend was observed in 2016, when the drench treatment at transplant was sufficient to significantly reduce disease incidence and severity. There was no significant difference between the drench treatments alone and the drench treatment with the additional spray. There were lower temperatures and higher rainfall in 2016 compared to 2015, which could explain the higher disease pressure in the control treatments in 2016 (data not shown). During 2016 there was a significant yield difference among treatments. A significant yield reduction was associated with a drench treatment of prothioconazole without the follow-up foliar spray. There was a nonsignificant reduction in yield for the prothioconazole treatment applied as a drench and spray. Previous literature suggests that triazole fungicides, such as prothioconazole, may negatively impact the production of gibberellins, which could reduce yield (Fletcher 1985; Köller 1987). This study was only able to examine one year of yield data, and

53 37 further studies would be needed to determine yield effects of fungicide programs for Fusarium wilt control in watermelon. The addition of a new fungicide active ingredient is important for sustainable Fusarium wilt management. Resistance to methyl benzimidazole carbamate (MBC) fungicides has been reported in Taiwan for F. ff. spp. lilii and gladioli, the causal agents of Fusarium corm rot and yellowing of lily and gladiolus (Chung et al. 2009). This has reduced the efficacy of benomyl, a MBC fungicide commonly applied for control of these diseases in Taiwan (Chung et al. 2009). Prothioconazole is limited to three total applications within a single growing season, and because this is the only fungicide currently labeled for Fusarium wilt in watermelon, there are only three chances to control Fusarium wilt. The drench application is highly effective but growers would not likely apply preventively due to cost unless there is Fusarium wilt history in the field being planted. Because FON colonizes the vascular tissue and causes systemic disease by blocking water transport in the xylem (Lü et al. 2014), when symptoms present themselves in the field, it is too late for preventative control. If enough fungicide applications can be made quickly once symptoms are seen in the field, they may be able to reduce disease in asymptomatic plants that have been partially colonized by FON to prevent further disease progression and total wilt of plants and prevent new infections in the field. A new active ingredient labeled in watermelon for Fusarium wilt control will improve disease management by allowing for a greater number of applications in a growing season, and provide an alternate mode of action to help manage fungicide resistance.

54 38 Our experiments did not find any isolates that were resistant to either prothioconazole or pydiflumetofen. A previous study that examined fungicide resistance in FON found no resistance to prothioconazole (Petkar et al. 2017). Prothioconazole is a single-site demethylation inhibitor fungicide, which presents a medium risk of resistance development (FRAC 2016). Pydiflumetofen is also a fungicide that affects a single site, and it presents a medium to high risk of resistance development (FRAC 2016). There are currently no multisite fungicides labeled for Fusarium wilt of watermelon that could be used in a fungicide rotation or tank mix with these single-site fungicides that could reduce the risk of resistance. If pydiflumetofen is later labeled for use in controlling Fusarium wilt of watermelon, this would reduce the risk of fungicide resistance development in this pathogen. It is important that no baseline resistance to pydiflumetofen or prothioconazole was found in FON populations from the southeastern US in this study. Because FON is a soilborne asexual fungus, it is categorized as a pathogen with low evolutionary potential and thus has a lower risk of fungicide resistance development and proliferation of alleles conferring fungicide resistance (McDonald and Linde 2002). However, because Fusarium wilt of watermelon management relies on integrating fungicide control with resistant watermelon varieties where applicable and crop rotation (Everts and Himmelstein 2015), the development of fungicide resistance would reduce the efficacy of the only tool available for reducing disease pressure within a growing season. A recent study discovered that there was a high rate of insensitivity to thiophanate-methyl at both 10 and 100 µg/ml, a fungicide shown to reduce Fusarium wilt of watermelon incidence in the field (Petkar et al. 2017). Thiophanate-methyl is a methyl benzimidazole carbamate (MBC) fungicide class, which inhibits the polymerization of tubulin and thereby disrupts mitosis.

55 39 We did not examine thiophanate-methyl, however we did test thiabendazole, another fungicide of the same chemical class and FRAC designation was tested in our experiments (FRAC 2016). There was no insensitivity to thiabendazole at 10 µg/ml found in the ten isolates examined; however, there was no reduction in relative growth at 1 µg/ml for thiabendazole either. Azoxystrobin was not found to be effective in in vitro or in planta experiments in F. f. sp. lycopercisi (Amini and Sidovich 2010). The maximum likelihood phylogenetic tree generated in our analysis lacked resolution at the tips. Many isolates clustered together with low resolution in the phylogenetic tree, yet there was some variation between isolates within this polytomy. Although there were numeric differences in the EC50 values, all isolates in this polytomy were sensitive to both fungicides at the highest concentration of 10 µg/ml. The highest EC50 value reported for prothioconazole was NM211, a F. solani isolate from sweetpotato. It would be interesting to examine the efficacy of prothioconazole and other demethylation inhibitor fungicides across the genus Fusarium to determine whether this trend continues. Because there was no true resistance to pydiflumetofen within the F. sampled, we were not able to associate resistance phenotypes to genotypic variation within succinate dehydrogenase subunits B and C. We found significant differences in EC50 values between pydiflumetofen and boscalid, but there was low within fungicide variation in response to pydiflumetofen. A previous study in F. graminearum found a similar result, where low phenotypic variation yielded little information with regards to resistant genotypes (Dubos et al. 2013). Because SDHI fungicides have not previously been labeled for managing watermelon Fusarium wilt, there is low exposure and selection pressure of FON to this type of fungicide.

56 40 Our findings revealed that prothioconazole, pydiflumetofen, tebuconazole, difenoconazole, and propiconazole are all effective at reducing FON mycelium growth in vitro. In addition, prothioconazole and pydiflumetofen reduced watermelon Fusarium wilt incidence and severity in a field setting. If labeled in watermelon for Fusarium wilt control, the new active ingredient pydiflumetofen could improve disease management by expanding the number of crop protection applications in a growing season and helping to manage the development of fungicide resistance if used in a program with prothioconazole. AKNOWLEDGEMENTS We thank all the members of the Quesada lab for their valuable help, especially Mike Adams, Emily Keller, and Zachary Shea for technical assistance. We also thank Dr. Peter Ojiambo for assistance with field data analysis. This work was supported by funds from the North Carolina State University Hatch Project No. NC02418 and the United States Department of Agriculture Specialty Crops Research Initiative Award No We thank colleagues that contributed isolates used in the study.

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62 46 TABLES Table 2: Fusarium spp. isolates tested against both prothioiconazole and pydiflumetofen fungicides. The isolate name refers to the code in the culture collection, and the genus and species name refer to the nearest BLAST hit from the NCBI database. The country and state refer to where the fungus was first isolated, and the plant host refers to which plant host the fungus was first isolated. The isolate was kindly provided by the collaborator attributed in the final column. An asterisk (*) refers to isolates utilized in the expanded panel of fungicides. A plus sign (+) Refers to isolates that were included in the phylogenetic study. Isolate Genus Species Country State Plant host Source AS087 Fusarium USA NC sweetpotato NCSU-PDIC AS124* Fusarium USA SC watermelon A. Keinath AS125* Fusarium USA SC watermelon A. Keinath AS148 Fusarium USA LA sweetpotato C. Clark NM174* Fusarium USA IN watermelon D. Egel + NM189* Fusarium Israel - watermelon USDA-NRRL NM190* Fusarium Germany - watermelon USDA-NRRL + NM192 Fusarium USA GA watermelon P. Ji NM193* Fusarium Israel - watermelon USDA-NRRL NM194* Fusarium USA GA watermelon P. Ji + NM206+ Fusarium USA NC tomato NCSU-PDIC NM207+ Fusarium USA NC tomato NCSU-PDIC NM208+ Fusarium USA NC tomato NCSU-PDIC NM209+ Fusarium USA FL tomato G. Vallad NM210+ Fusarium USA NC tomato NCSU-PDIC NM211+ Fusarium solani USA NC sweetpotato NCSU-PDIC NM212+ Fusarium USA FL tomato G. Vallad NM213+ Fusarium USA FL tomato G. Vallad NM214+ Fusarium USA FL tomato G. Vallad NM215* Fusarium USA GA watermelon H. Sanders NM227+ Fusarium USA FL tomato G. Vallad NM228+ Fusarium USA FL tomato Fusarium Research Center NM229+ Fusarium USA PA Muskmelon/t omato Fusarium Research Center NM230+ Fusarium - - Passiflora Fusarium

63 47 Research Center Table 2 (continued) NM231+ Fusarium ITALY - tomato Fusarium Research Center NM232+ Fusarium USA FL tomato Fusarium Research Center NM233+ Fusarium Venezuel a - tomato Fusarium Research Center NM234+ Fusarium SPAIN - tomato Fusarium Research Center NM235+ Fusarium udum USA RI tomato Fusarium Research Center NM284 Fusarium USA MD watermelon K. Everts NM285+ Fusarium USA FL watermelon Fusarium Research Center NM286+ Fusarium USA IN watermelon D. Egel NM287 Fusarium USA OH muskmelon Fusarium Research Center NM377+ Fusarium USA IN watermelon D. Egel NM378+ Fusarium USA MD watermelon K. Everts NM427+ Fusarium USA NC tomato L. Quesada NM428+ Fusarium USA NC tomato L. Quesada NM429+ Fusarium USA NC tomato L. Quesada NM430 Fusarium USA NC tomato L. Quesada NM431 Fusarium USA NC watermelon L. Quesada NM432+ Fusarium USA NC watermelon L. Quesada NM433+ Fusarium USA NC watermelon L. Quesada NM434+ Fusarium USA NC watermelon L. Quesada NM435+ Fusarium USA NC watermelon L. Quesada NM436+ Fusarium USA NC tomato L. Quesada NM437+ Fusarium USA NC watermelon L. Quesada NM438+ Fusarium USA NC watermelon L. Quesada NM439+ Fusarium USA NC watermelon L. Quesada NM440+ Fusarium USA NC watermelon L. Quesada NM441 Fusarium USA NC watermelon L. Quesada NM442 Fusarium USA NC watermelon L. Quesada NM443+ Fusarium USA NC watermelon L. Quesada NM444 Fusarium USA NC watermelon L. Quesada NM445 Fusarium USA NC watermelon L. Quesada NM446 Fusarium USA NC watermelon L. Quesada

64 Table 2 (continued) NM454+ Fusarium USA NC watermelon L. Quesada NM455 Fusarium USA NC watermelon L. Quesada NM456+ Fusarium USA NC watermelon L. Quesada NM457+ Fusarium USA NC watermelon L. Quesada NM458+ Fusarium USA NC watermelon L. Quesada NM459+ Fusarium USA NC tomato L. Quesada NM460+ Fusarium USA NC watermelon L. Quesada NM461+ Fusarium USA NC watermelon L. Quesada NM462+ Fusarium USA NC tomato L. Quesada NM463+ Fusarium USA NC watermelon L. Quesada NM464 Fusarium USA NC watermelon L. Quesada NM465 Fusarium USA NC watermelon L. Quesada NM466 Fusarium USA NC watermelon L. Quesada NM467+ Fusarium USA NC watermelon L. Quesada NM468+ Fusarium USA NC watermelon L. Quesada NM469+ Fusarium USA NC watermelon L. Quesada NM470+ Fusarium USA NC tomato NCSU-PDIC NM471+ Fusarium USA NC watermelon L. Quesada NM472+ Fusarium USA NC tomato L. Quesada NM473+ Fusarium USA NC tomato NCSU-PDIC NM474+ Fusarium USA NC watermelon L. Quesada NM475+ Fusarium USA NC watermelon L. Quesada NM476 Fusarium USA NC watermelon L. Quesada NM477+ Fusarium USA NC watermelon L. Quesada NM478+ Fusarium USA NC watermelon L. Quesada NM479 Fusarium USA NC watermelon L. Quesada NM480+ Fusarium USA NC watermelon L. Quesada NM481 Fusarium USA NC watermelon L. Quesada NM482+ Fusarium USA NC watermelon L. Quesada NM483+ Fusarium USA NC watermelon L. Quesada NM484 Fusarium USA NC watermelon L. Quesada NM485 Fusarium USA NC watermelon L. Quesada NM486+ Fusarium USA NC watermelon L. Quesada NM487 Fusarium USA NC watermelon L. Quesada NM488+ Fusarium USA NC watermelon L. Quesada NM489+ Fusarium USA NC watermelon L. Quesada NM490+ Fusarium USA NC watermelon L. Quesada NM491+ Fusarium USA NC watermelon L. Quesada 48

65 NM492+ Fusarium USA NC watermelon L. Quesada NM497+ Fusarium USA OK watermelon J. Damicone NM498+ Fusarium USA OK watermelon J. Damicone NM499 Fusarium USA OK watermelon J. Damicone NM500+ Fusarium USA OK watermelon J. Damicone NM502+ Fusarium USA FL watermelon M. Paret SW008* Fusarium USA NC watermelon NCSU-PDIC 49

66 50 Table 3: Fungicides tested including active ingredient, brand name, fungicide class, and FRAC code. Active Ingredient Brand Name Fungicide Class FRAC code Prothioconazole Proline Demethylation Inhibitor (DMI) 3 Propiconazole Tilt DMI 3 Difenoconazole Inspire DMI 3 Tebuconazole Folicur DMI 3 Thiabendazole Mertect Methyl Benzimidazole Carbamates (MBC) 1 Azoxystrobin Quadris Quinone outside inhibitor (Complex III inhibitor) (QoI) Boscalid Endura Succinate Dehydrogenase Inhibitor (Complex II inhibitor) (SDHI) Fluopyram Luna Privilege SDHI Penthiopyrad Fontelis SDHI 7 Pydiflumetofen ADEPIDYN TM fungicide SDHI 7

67 51 Table 4: Treatments for 2015 and 2016 field experiments. Drench treatments were applied at transplant and sprays were applied 14 days after transplant. Treatment Number Product Application Rate Application Method 1 Non-treated, inoculated Pydiflumetofen ml/ha Drench 3 Pydiflumetofen ml/ha Drench 4 Pydiflumetofen ml/ha Drench, Spray 5 Pydiflumetofen ml/ha Drench, Spray 6 Prothioconazole ml/ha Drench 7 Prothioconazole ml/ha Drench, Spray 8 Non-inoculated, Non-treated - -

68 52 Table 5: Primers for the Internal transcribed spacer (ITS), Translation Elongation Factor 1α (Tef-1α), and sdh genes. The sdhb and sdhc genes refer to the genes that code for the SdhB and SdhC subunits of the enzyme succinate dehydrogenase, associated with mutations that confer resistance to succinate dehydrogenase inhibitor fungicides. Locus Forward Reverse primer primer ITS ITS 5 (F) ITS 4 (R) Tef- 1α Forward primer sequence (5 to 3 ) Reverse primer sequence (5 to 3 ) Citation GGAAGTAAAAGTCGTAACAAGG TCCTCCGCTTATTGATATGC (White et al. 1990) ef1 ef2 ATGGGTAAGGA(A/G)GACAAGAC GGA(G/A)GTACCAGT(G/C)ATCATGTT (O'Donnell et al. 1998) sdhb sdhb_fp sdhb_rp GCCAACCTCAAGTCTTTCCA TCTAGTTGCCGAAAGCCATC This study shdc sdhc_fp sdhc_rp GCATCAATTGAGCTTCAGCA GAGGTAGGCGAGGGAGAAAC This study O'Donnell, K., H. C. Kistler, E. Cigelnik, and R. C. Ploetz 'Multiple evolutionary origins of the fungus causing Panama disease of banana: concordant evidence from nuclear and mitochondrial gene genealogies', Proceedings of the National Academy of Sciences of the United States of America, 95: White, T. J., T. Bruns, S. Lee, and J. Taylor 'Amplification and direct sequencing of fungal ribosomal RNA genes for phylogenetics.' in Michael A. Innis, David H. Gelfand, John J. Sninsky and Thomas J. White (eds.), PCR Protocols: a Guide to Methods and Application (Academic Press: San Diego, CA).

69 53 Table 6: EC50 values in µg active ingredient/ml media for each fungicide tested with 10 isolates. Active ingredient Trade name Fungicide class FRAC code EC50 value Prothioconazole Proline Demethylation Inhibitor (DMI) Propiconazole Tilt DMI Difenoconazole Inspire DMI Tebuconazole Folicur DMI Thiabendazole Mertect Methyl Benzimidazole Carbamates (MBC) Azoxystrobin Quadris Quinone outside inhibitor (Complex III inhibitor) (QoI) Boscalid Endura Succinate Dehydrogenase Inhibitor (Complex II inhibitor) (SDHI) Fluopyram Luna Privilege SDHI Penthiopyrad Fontelis SDHI Pydiflumetofen ADEPIDYN TM fungicide SDHI

70 54 Table 7: Pairwise comparisons of EC50 values between fungicides. Values indicate differences in EC50 between two fungicides and an asterisk (*) indicates significant differences after Bonferroni correction. Boscalid Tebuconazole Penthiopyrad Difenoconazole Fluopyram Thiabendazole Pydiflumetofen Prothioconazole Azoxystrobin Propiconazole FRAC 7 FRAC 3 FRAC 7 FRAC 3 FRAC 7 FRAC 1 FRAC 7 FRAC 3 FRAC 11 FRAC 3 Tebuconazole 5.54E+06* Penthiopyrad 3.86E E-01 Difenoconazole 1.28E E E+00 Fluopyram 3.45E E-04* 8.94E-04* 2.70E-04* Thiabendazole 9.27E E-01* 2.40E E-02* 2.69E+02 Pydiflumetofen 7.17E+06 * 1.29E E E E E+00 Prothioconazole 3.49E+06* 6.30E E E-01* 1.01E E E-01* Azoxystrobin 3.28E+06* 5.93E E E-01* 9.52E E E-01* 9.40E-01 Propiconazole 3.85E+06* 6.95E E E-01* 1.12E E E-01* 1.10E E+00

71 55 Table 8: Analysis of variance (ANOVA) table for total fruit count, marketable fruit count, and weight by treatment in 2016 field experiments. There is no significant treatment effect for total fruit weight. SS stands for Sum of Squares and MS refers to Mean Sum of Squares. An asterisk (*) denotes a significant effect at the α=0.05 level. Total fruit count Df SS MS F value P value Treatment * Block Residuals Marketable fruit count Df SS MS F value P value Treatment * Block Residuals Total fruit weight Df SS MS F value P value Treatment Block * Residuals

72 Fusarium wilt incidence per plot (%) FIGURES Jun 25-Jun 30-Jun 3-Jul 7-Jul 10-Jul 14-Jul 17-Jul 21-Jul 24-Jul Rating Date Non-treated, inoculated Pydiflumetofen (low) A Pydiflumetofen (high) A Pydiflumetofen (low) AB Pydiflumetofen (high) AB Prothioconazole A Prothioconazole AB Figure 1: Percent disease incidence for each treatment was calculated per plot in The error bars represent the standard deviation for each treatment for each rating date. A in fungicide treatments on the horizontal axes refers to the drench application of the fungicide at transplant, and AB refers to both a drench treatment at transplant and a foliar spray application 14 days after transplant. Low and high refer to the rate of fungicide applied for each pydiflumetofen fungicide treatment (751.9 ml/ha and ml/ha, respectively).

73 Fusarium wilt incidence per plot (%) Non-treated, inoculated Pydiflumetofen (low) A Pydiflumetofen (high) A Pydiflumetofen (low) AB Pydiflumetofen (high) AB Prothioconazole A Prothioconazole AB Non-treated, non-inoculated 0 30-May 6-Jun 13-Jun 20-Jun 28-Jun 5-Jul 11-Jul 14-Jul Rating Date Figure 2: Percent disease incidence for each treatment was calculated per plot in The error bars represent the standard deviation for each treatment for each rating date. A in fungicide treatments on the horizontal axes refers to the drench application of the fungicide at transplant, and AB refers to both a drench treatment at transplant and a foliar spray application 14 days after transplant. Low and high refer to the rate of fungicide applied for each pydiflumetofen fungicide treatment (751.9 ml/ha and ml/ha, respectively).

74 58 A B C Figure 3: Photos from the final rating date (July 24) in A is a Non-treated plot, B is a plot with the pydiflumetofen treatment at a high rate applied as both a drench and a foliar spray, and C is a plot with the prothioconazole treatment applied as both a drench and a spray.

75 59 A B C D A B C D Figure 4: Photos from the final rating date (July 14) in A is a Non-treated plot, B is a plot with the pydiflumetofen treatment at a high rate applied as both a drench and a foliar spray, and C is a plot with the prothioconazole treatment applied as a drench, and D is a non-treated, noninoculated plot.

76 AUDPC value for incidence A AB AB AB B AB B Figure 5: relative Area Under the Disease Progress Curve (AUDPC) was calculated for the incidence data, and was combined for the years 2015 and The relative AUDPC was plotted for each treatment and letters above bars indicate Tukey-Kramer groupings. A in fungicide treatments on the horizontal axes refers to the drench application of the fungicide at transplant, and AB refers to both a drench treatment at transplant and a foliar spray application 14 days after transplant. Low and high refer to the rate of fungicide applied for each pydiflumetofen fungicide treatment (751.9 ml/ha and ml/ha, respectively).

77 AUDPC severity A 8 7 AB AB AB AB AB 6 5 B B Figure 6: relative Area Under the Disease Progress Curve (AUDPC) for severity data calculated for each fungicide treatment in The relative AUDPC was plotted for each treatment and letters above bars indicate Tukey-Kramer groupings. A in fungicide treatments on the horizontal axes refers to the drench application of the fungicide at transplant, and AB refers to both a drench treatment at transplant and a foliar spray application 14 days after transplant. Low and high refer to the rate of fungicide applied for each pydiflumetofen fungicide treatment (751.9 ml/ha and ml/ha, respectively).the non-treated control was inoculated, but the untreated non-inoculated non-treated control had no contact with FON and was used as a negative control.

78 Number of watermelons C AB AB AB AB BC ABC A Figure 7: Total count for 2016 yield data in melons. Letters above bars indicate Tukey-Kramer groupings; the same groupings were obtained for marketable count. A in fungicide treatments on the horizontal axes refers to the drench application of the fungicide at transplant, and AB refers to both a drench treatment at transplant and a foliar spray application 14 days after transplant. Low and high refer to the rate of fungicide applied for each pydiflumetofen fungicide treatment (751.9 ml/ha and ml/ha, respectively). The non-treated control was inoculated, but the untreated non-inoculated non-treated control had no contact with FON and was used as a negative control.

79 63 Prothioconazole Pydiflumetofen EC50 value (µg/ml) Figure 8: A maximum likelihood phylogenetic tree paired with the EC50 values in µg/ml for prothioconazole (black) and pydiflumetofen (gray). The numbers at the nodes refer to the bootstrap support or the percentage of replicate trees where isolates cluster together.

80 Figure 9: WebLogo diagram of amino acid distribution for SdhC. The letters reflect the amino acid at that position in the polypeptide sequence, and the relative height of the letter reflects the percentage of isolates with that particular amino acid residue at that position. The Serine residue at position 222 and the Argenine residue at position 226 (starred) are not only conserved amongst Fusarium isolates examined in this study but also amongst Ascomycota fungi. 64

81 Figure 10: WebLogo diagram of amino acid distribution for SdhB. The letters reflect the amino acid at that position in the polypeptide sequence, and the relative height of the letter reflects the percentage of isolates with that particular amino acid residue at that position. The blue bar marks the conserved Iron-Sulfur subunit. 65