MOLECULAR DIAGNOSTICS, EPIDEMIOLOGY, AND POPULATION GENETICS OF THE SOYBEAN SUDDEN DEATH SYNDROME PATHOGEN,  FUSARIUM VIRGULIFORME By Jie Wang           A DISSERTATION Submitted to Michigan State University in partial fulfillment of the requirements for the degree of  Plant Pathology - Doctor of Philosophy Ecology, Evolutionary Biology and Behavior  Dual Major  2016ABSTRACT  MOLECULAR DIAGNOSTICS, EPIDEMIOLOGY, AND POPULATION GENETICS OF THE SOYBEAN SUDDEN DEATH SYNDROME PATHOGEN,  FUSARIUM VIRGULIFORME By Jie Wang Soybean sudden death syndrome (SDS), caused by Fusarium virguliforme, is one of the most devastating diseases of soybean, responsible for yield losses in both North America and South America. In the United States, F. virguliforme is the predominant SDS causal pathogen, while four Fusarium species including F. virguliforme can cause SDS in South America. All four SDS-causing Fusarium species are located in clade2 of the Fusarium solani species complex (FSSC) along with three bean root rot (BRR) Fusarium pathogens. It is difficult to identify this group of fungi to species level based on morphological traits. In order to address this issue, we developed a specific and sensitive diagnostic real-time quantitative PCR assay (qPCR) for detection and quantification of F. virguliforme from plant or environmental samples. This assay was applied in characterization of temporal dynamics of F. virguliforme infection and colonization of soybean roots. The quantity of F. virguliforme increased over time and reached a plateau at the end of the season. The severity or appearance of SDS foliar symptoms was not associated with quantity of F. virguliforme infection, and cultivars with varied SDS resistance levels did not differ in their quantity of F. virguliforme in roots.  The fungicide fluopyram has been demonstrated to be effective in reducing SDS foliar symptoms in field trials as a seed treatment; however, in vitro evaluation of fungicide baseline sensitivity of F. virguliforme had not been determined. In this study, 185 F. virguliforme isolates collected from multiple locations in the United States were selected for estimation of fungicide  sensitivity to fluopyram. Overall, the US F. virguliforme population appears to be sensitive to fluopyram. The effective concentration to inhibit 50% growth for 95.1% of isolates was determined to be between 0.81 to 5 µg/ml, while only nine isolates were determined to be less sensitive. Since the first report of SDS in Arkansas in 1971, SDS had been reported in surrounding states with an apparent pattern of dispersal. To date, although SDS has been reported in most soybean producing areas in the United States, limited research has been conducted to date to study the population biology of F. virguliforme. We utilized 539 isolates from North and South America in a population genetics study to test the hypothesis that Arkansas was the center of introduction within the United States, and investigate possible intercontinental movement. The Arkansas population demonstrated the highest genotypic diversity and most diverse population structure. Coalescence based migration analysis also supported a directional migration model from Arkansas to Indiana and Michigan. Within the United States, there was a weak positive correlation (P = 0.08) between genetic dissimilarity and geographical distance, suggesting a mixed dispersal pattern of F. virguliforme in the United States. Although South America has been proposed as the center of origin in previous studies, this hypothesis was only supported in the migrate analysis, and the genotypic diversity and population structure compositions detected in the United States cannot be explained by this hypothesis. Therefore, Arkansas as the center of origin in the United States hypothesis is supported by the population genetic analyses, but the South America as the center of origin hypothesis does not have strong support in our analysis. iv I would like to dedicate this dissertation to my parents and to world peace. v ACKNOWLEDGEMENTS  I would like to acknowledge my advisor, Dr. Martin Chilvers, for his persistent support and guidance throughout my graduate study. I would like to thank my committee members Drs. Frances Trail, Dechun Wang, Jim Smith, and Jan Byrne for their support and encouragement throughout of research. I would also like to ll for giving suggestions and providing isolates. Finally, I would like to thank Dr. Tyre Proffer, Dr. George Sundin for their suggestions on my research project.  I would like to acknowledge my funding sources, project GREEEN, Michigan soybean promotion committee, North Central soybean research program, A. L. Rogers scholarship, student award. This research would not have been possible without their funding support. I would like to thank my lab mates for their help throughout the past years including Janette Jacobs, Alejandro Rojas, Adam Byrne, Devon Rossman, Zach Noel, Mitch Roth, and Olivia Stenzel.   vi PREFACE  This dissertation includes six chapters: one literature review and five research articles. The research topics in this dissertation covered qPCR diagnostic assay development, temporal dynamics of Fusarium virguliforme in root colonization, screening F. virguliforme fungicide sensitivity to fluopyram, development of microsatellite markers for F. virguliforme, and population genetics of F. virguliforme to predict the demographic history of this pathogen.     vii TABLE OF CONTENTS  LIST OF TABLES ....................................................................................................................... xi LIST OF FIGURES ................................................................................................................... xiii CHAPTER 1 LITERATURE REVIEW ..................................................................................... 1 Host Plant: Soybean .................................................................................................................. 2 Soybean Sudden Death Syndrome ............................................................................................ 2 Abiotic and biotic factors affecting SDS .................................................................................. 4 Distribution of Fusarium virguliforme ..................................................................................... 5 SDS in Michigan ....................................................................................................................... 7 Taxonomy ................................................................................................................................. 8 Other SDS causal pathogens ................................................................................................... 11 Interaction between F. virguliforme and SCN ........................................................................ 13 SDS field management strategies ........................................................................................... 14 Methods for detection of F. virguliforme ............................................................................... 15 Population genetic structure of F. virguliforme and mating type gene................................... 18 Summary ................................................................................................................................. 22 REFERENCES ....................................................................................................................... 23 CHAPTER 2 IMPROVED DIAGNOSES AND QUANTIFICATION OF FUSARIUM VIRGULIFORME, CAUSAL AGENT OF SOYBEAN SUDDEN DEATH SYNDROME .. 33 Abstract ................................................................................................................................... 34 Introduction ............................................................................................................................. 35 Materials and Methods............................................................................................................ 38 Fungal isolates and DNA extraction ................................................................................. 38 Real-time PCR primer and probe design for F. virguliforme ........................................... 40 qPCR amplification parameters ........................................................................................ 41 Real-time PCR specificity and sensitivity tests ................................................................ 42 Conventional PCR amplification parameters.................................................................... 42 DNA extraction from soil ................................................................................................. 43 DNA extraction from soybean roots ................................................................................. 43 Copy number of rDNA in F. virguliforme ........................................................................ 44 Real-time PCR assay cross-laboratory and platform validation ....................................... 45 Data analyses..................................................................................................................... 46 Results ..................................................................................................................................... 46 Assay design and in silico screening ................................................................................ 46 Specificity and sensitivity of real-time PCR..................................................................... 46 Conventional PCR assay ................................................................................................... 51 Validation of real-time PCR assay and conventional PCR assay ..................................... 51 rDNA IGS copy number variation between isolates......................................................... 52 qPCR assay cross-laboratory and platform validation ...................................................... 53 Discussion ............................................................................................................................... 58  viii Acknowledgement .................................................................................................................. 63 APPENDIX ............................................................................................................................. 64 REFERENCES ....................................................................................................................... 70 CHAPTER 3 TEMPORAL DYNAMICS OF FUSARIUM VIRGULIFORME IN SOYBEANS ................................................................................................................................. 75 Abstract ................................................................................................................................... 76 Introduction ............................................................................................................................. 77 Materials and Methods............................................................................................................ 81 Fusarium virguliforme inoculum preparation................................................................... 81 Greenhouse experimental design ...................................................................................... 81 Field experimental design ................................................................................................. 84 Disease evaluation  field trials ........................................................................................ 84 Sample collection and processing ..................................................................................... 85 Disease evaluation  greenhouse trials ............................................................................. 85 Fusarium virguliforme root quantification ....................................................................... 86 Data analysis ..................................................................................................................... 87 Results ..................................................................................................................................... 89 Greenhouse temporal dynamics of F. virguliforme colonization ..................................... 89 Fusarium virguliforme colonization of field grown soybean roots .................................. 92 Discussion ............................................................................................................................... 99 Greenhouse experiments ................................................................................................... 99 SDS foliar symptoms and F. virguliforme root infection/colonization .......................... 100 Serial sampling of soybean roots for quantifying F. virguliforme.................................. 102 Quantification methods ................................................................................................... 103 Acknowledgement ................................................................................................................ 107 APPENDIX ........................................................................................................................... 108 REFERENCES ..................................................................................................................... 112 CHAPTER 4 BASELINE SENSITIVITY OF FUSARIUM VIRGULIFORME TO FLUOPYRAM FUNGICIDE................................................................................................... 118 Abstract ................................................................................................................................. 119 Introduction ..................................................................................................................... 120 Materials and methods .......................................................................................................... 123 Fungal isolation collection and storage........................................................................... 123 Determination of baseline EC50 values  mycelial growth inhibition assay................... 124 Image analysis and model selection................................................................................ 125 Determine baseline EC50 values  conidia germination inhibition assay ....................... 126 Data analyses................................................................................................................... 127 Comparison of the mycelial and spore germination assays ............................................ 127 Results ................................................................................................................................... 128 Model selection and data validation for the mycelial growth inhibition assay............... 128 Mycelial growth sensitivity against fluopyram............................................................... 129 Mycelial growth hormetic effects ................................................................................... 130 Model selection and data validation for the conidia germination inhibition assay......... 134 Conidia germination sensitivity against fluopyram ........................................................ 134 Differences between two fungicide sensitivity testing methods ..................................... 135  ix Discussion ............................................................................................................................. 136 Acknowledgement ................................................................................................................ 140 APPENDICES ...................................................................................................................... 141 APPENDIX A Supplementary tables ............................................................................. 142 APPENDIX B Supplementary figures ............................................................................ 149 REFERENCES ..................................................................................................................... 151 CHAPTER 5 DEVELOPMENT AND CHARACTERIZATION OF MICROSATELLITE MARKERS FOR FUSARIUM VIRGULIFORME AND THEIR UTILITY WITHIN CLADE 2 OF THE FUSARIUM SOLANI SPECIES COMPLEX....................................... 156 Abstract ................................................................................................................................. 157 Introduction ........................................................................................................................... 158 Materials and methods .......................................................................................................... 160 Fungal material and DNA extraction .............................................................................. 160 Development of microsatellite markers and primer design ............................................ 164 Microsatellite marker screening for polymorphism........................................................ 164 Data analysis ................................................................................................................... 165 Results ................................................................................................................................... 166 Primer development and polymorphism screening......................................................... 166 Validation of microsatellite polymorphism .................................................................... 168 Genome location of microsatellites................................................................................. 169 Genetic diversity and structure ....................................................................................... 172 Cross-species transferability ........................................................................................... 174 Discussion ............................................................................................................................. 177 Acknowledgement ................................................................................................................ 180 Data accessibility .................................................................................................................. 181 APPENDICES ...................................................................................................................... 182 APPENDIX A Supplementary tables ............................................................................. 183 APPENDIX B Supplementary figures ............................................................................ 185 REFERENCES ..................................................................................................................... 186 CHAPTER 6 POPULATION GENETICS OF THE FILAMENTOUS FUNGUS FUSARIUM VIRGULIFORME CAUSING SUDDEN DEATH SYNDROME ON SOYBEAN ................................................................................................................................. 191 Abstract ................................................................................................................................. 192 Introduction ........................................................................................................................... 193 Materials and methods .......................................................................................................... 197 Sampling, fungal isolation, and DNA extraction ............................................................ 197 Haplotype identification.................................................................................................. 199 Data analyses................................................................................................................... 200 Genotypic diversity ......................................................................................................... 200 Test for population clonality ........................................................................................... 200 Analysis of molecular variance....................................................................................... 201 Index of population differentiation ................................................................................. 201 Mantel test and isolation by distance .............................................................................. 202 STRUCTURE analysis ................................................................................................... 202 Clustering based on individual genetic distances ........................................................... 203  x Multivariate analysis ....................................................................................................... 203 Multilocus inference of migration................................................................................... 203 Results ................................................................................................................................... 204 Genotyping results .......................................................................................................... 204 Clonality in populations .................................................................................................. 206 Population structure ........................................................................................................ 209 Genetic structure and SDS spread in the United States .................................................. 209 Bayesian method  STRUCTURE analysis.................................................................... 210 Relationships among genotypes...................................................................................... 213 Spatial correlations - isolation by distance ..................................................................... 216 Migrate analysis .............................................................................................................. 217 Discussion ............................................................................................................................. 220 Arkansas is center of origin in the US ............................................................................ 220 South America is the center of origin ............................................................................. 222 Means of pathogen dispersal........................................................................................... 224 Acknowledgement ................................................................................................................ 226 Data accessibility .................................................................................................................. 227 APPENDICES ...................................................................................................................... 228 APPENDIX A Supplementary tables ............................................................................. 229 APPENDIX B Supplementary figures ............................................................................ 231 REFERENCES ..................................................................................................................... 235 REFERENCES ..................................................................................................................... 236 CONCLUSION AND FUTURE DIRECTIONS .................................................................... 241 Summary of dissertation ....................................................................................................... 242    xi LIST OF TABLES  Table 1-1 Distribution and host specification of SDS-BRR clade Fusarium spp. ....................... 11 Table 2-1 Specificity test panel for qPCR assay validation. This panel includes the Fusarium species that are closely related to the SDS-causing Fusarium species and other commonly encountered soil fungal species. Ct values listed in the table indicate the specificity performance of the F. virguliforme qPCR assay when 100 pg genomic DNA were added to the reaction. ..... 38 Table 2-2 Primers and probes used in the qPCR quantification assays. ....................................... 41 Table 2-3 Primers for determining rDNA IGS copy number variation ........................................ 45 Table 2-4 Fusarium virguliforme rDNA IGS copy number estimation using three single copy reference genes.............................................................................................................................. 52 Table 2-5 Soybean and dry beans samples submitted to Michigan State University Diagnostic Services Laboratory for diagnosis assayed on a SmartCycler real-time PCR system. ................. 53 Table 2-6 Diagnostic results for commercial soybean samples on StepOnePlus real-time PCR system. Results include isolation on semi-selective media, conventional PCR, plant symptoms, and qPCR Ct values. ..................................................................................................................... 56 Table 3-1 Soybean cultivars used in this study. Soybean cultivars were selected based on seed industry SDS susceptibility rankings and maturity groups suitable for growing conditions in Michigan. ...................................................................................................................................... 83 Table 3-2 Primers and probes used in this study. ......................................................................... 88 Table 4-1 EC50 of F. virguliforme isolates used in the mycelial growth inhibition assay against the SDHI fungicide, fluopyram. A total of 130 isolates were collected from five states in the United States from 2009 to 2014. ............................................................................................... 123 Table 4-2 EC50 of F. virguliforme isolates used in the conidia germination inhibition assay testing against SDHI fungicide fluopyram. Isolates were collected from seven states in the United States from 2009 to 2014. ............................................................................................... 124 Table 4-3 Comparison of EC50 values calculated using 4-parameter log-logistic model (LL.4) and the Brain-Cousens model (BC.4) for the F. virguliforme isolates that showed hormetic effect in the mycelial growth inhibition assay. At model selection, the AIC values calculated for LL.4 and BC.4 models were -4148 and -3926 (lower is better), respectively. .................................... 132 Table S 4-1 EC50 estimations for all isolates that were tested in the mycelial growth inhibition assay. ........................................................................................................................................... 142  xii Table S 4-2 EC50 estimations for isolates that were tested in the conidia germination inhibition assay. ........................................................................................................................................... 146 Table 5-1 Details of the Fusarium species used in this study, including species name, isolate code, year of collection, geographic origin and host. ................................................................. 162 Table 5-2 Fusarium virguliforme microsatellite characteristics, including name, location within the F. virguliforme genome, repeat motif, forward and reverse primers, allele number, allele size range, gene location, reference genome location and primer pair combinations, note pigtails on ................................................................................................ 170 Table 5-3 Transferability of microsatellite markers developed for F. virguliforme across isolates in clade 2 of the Fusarium solani species complex .................................................................... 176 Table S 5-1 Microsatellite markers allele sizes (bp) detected for each of the multi-locus genotype..................................................................................................................................................... 183 Table 6-1 Genotypic and genetic diversity of F. virguliforme populations collected in both South and North America in this study. ................................................................................................ 207 Table 6-2 Analysis of molecular variance (AMOVA) of F. virguliforme within populations, among populations, and between continents. Sampling fields and states/provinces were labeled as populations and regions, respectively..................................................................................... 208 Table 6-3 Migration models selection using marginal log-likelihood calculated in the coalescence method Migrate-n.................................................................................................... 219 Table S 6-1 Shared genotypes between historical F. virguliforme isolates from Arkansas and current isolates. Of 13 historical F. virguliforme isolates, 13 unique MLGs were identified. In current populations, three MLGs were found to be identical with the historical isolates recovered from year 1985. MLG 110 was the most predominant shared genotype across a wide range of current geographic distributions.................................................................................................. 229 Table S 6-2 Simplified migration models selection using marginal log-likelihood calculated in the coalescence method Migrate-n.............................................................................................. 230   xiii LIST OF FIGURES  Figure 1-1 Foliar symptoms of soybean sudden death syndrome (SDS) shown in the field: (A) soybean premature defoliation, but petioles remain attached to the stem; (B-C) SDS foliar symptoms developed in the field; (D) asexual sporodochia reproductive structure form on the soybean root, when the infected roots are incubated on water agar for 7 days; (E) Fusarium virguliforme macroconidia, scale bar: 50 µm. Figures A-C photo credit to Dr. Martin Chilvers. . 4 Figure 1-2 SDS or F. virguliforme distribution in the world. SDS was first reported in 1970 in Arkansas, United States. In the 1990s, SDS was reported in most soybean producing countries in North and South Americas. In South Africa, SDS was reported and confirmed in 2013. In 2014 and 2016, F. virguliforme was isolated from soil collected in Malaysia and Iran.......................... 6 Figure 1-3 Distribution of SDS reported in the United States and Canada as the date of publication on the first report in Plant Disease, number in the map indicates the year SDS was reported to be present in the state.................................................................................................... 7 Figure 1-4 SDS distribution in Michigan as confirmed by qPCR assay or isolation of F. virguliforme..................................................................................................................................... 8 Figure 2-1 Standard curve for absolute quantification of F. virguliforme genomic DNA (fg). Genomic DNA samples were prepared from pure cultures grown in broth. The detection limit for pure culture genomic DNA was 100 fg. Two technical repeats for each F. virguliforme genomic DNA dilution level........................................................................................................................ 48 Figure 2-2 qPCR quantification of DNA samples isolated from artificially inoculated soil samples with serially diluted F. virguliforme macroconidia suspension. Detection limit was 100 macroconidia per 0.5 g soil. Total soil DNA were isolated from six soil sample replicates, and qPCR was run twice for each soil DNA sample. .......................................................................... 49 Figure 2-3 qPCR standard curve plotted with serially diluted genomic DNA (log transformed) in fg against Ct values with solid circles. Sensitivity of the assay was determined to be 100 fg of F. virguliforme genomic DNA. ......................................................................................................... 50 Figure S 2-1 Sequence self-dot plot of the IGS rDNA of F. virguliforme. Showing a repeat that may cause mis-binding in PCR assay. The plot was generated using the dottup package from EMBOSS....................................................................................................................................... 65 Figure S 2-2 Simulation of target amplicon secondary structure at annealing stage of the qPCR conducted using mfold .................................................................................................................. 66 Figure S 2-3 Assay sensitivity and specificity test, L: 1 kb+ DNA ladder, lane1  lane 6: 1 ng through 10 fg F. virguliforme genomic DNA, lane 7  lane 14: panel of F. virguliforme isolates at 100 pg genomic DNA, lane 15  lane 22: panel of other Fusarium spp. at 100 pg genomic  xiv DNA (F. tucumaniae, F. brasiliense, F. phaseoli, F. phaseoli, F. crassistipitatum, F. cuneirostrum, F. tucumaniae, and F. brasiliense) ........................................................................ 67 Figure S 2-4 Assay specificity test and validation, L: 1 kb+ DNA ladder, lane 1  lane 6, lane 8, and lane 11  lane 14: Other Fusarium spp.; lane 7, lane 9  lane 10, and lane 15: F. virguliforme isolates; lane 18  lane 21: SDS soybean root tissue DNA; lane 16, lane 17, and lane 22: NTC 68 Figure S 2-5 Interference of the F. virguliforme qPCR assay to HHIC exogenous assay in the serially diluted genomic DNA samples. Ct values of exogenous control assay (y axis) were plotted with the log transformed genomic DNA concentration (x axis). With the increase of the genomic DNA concentration, the Ct value of the exogenous control assay was affected............ 69 Figure 3-1 Temporal dynamics of F. virguliforme infection coefficient in soybean roots from two greenhouse experiments measured from 7 to 35 DAP. Four soybean cultivars were included in both greenhouse experiments, and resistance to SDS is indicated in the figure legend as susceptible (S) and moderately resistance (MR). Although significant root colonization levels were observed among cultivars at several sampling time points, no correlation between foliar symptoms and root colonization was detected. (A) Temporal dynamics of F. virguliforme infection coefficient at first experiment; (B) Temporal dynamics of F. virguliforme infection coefficient at second experiment................................................................................................... 90 Figure 3-2 (A) Boxplot of the area under Fusarium virguliforme infection coefficient curve (AUICC) calculated based on the temporal data of four soybean cultivars in both greenhouse experiments. No significant differences were detected among cultivars within each experiment, however the second greenhouse experiment had a higher AUICC. Dots within the figure are the data outliers in the boxplot. (B) Bar plot of SDS foliar disease rating index (DX in a scale 0-100, where 0 indicates healthy plant and 100 indicates most severe SDS foliar symptoms). Disease ratings were taken at 35 DAP in both greenhouse experiments as shown in black (first experiment) and gray (second experiment). The second greenhouse experiment showed more severe SDS foliar symptoms, which aligned with the increased F. virguliforme infection coefficient detected in soybean roots between two greenhouse experiments. .............................. 91 Figure 3-3 Temporal dynamics of the relative Fusarium virguliforme infection coefficient in soybean roots in 2012 field experiments in four soybean cultivars, two susceptible (S) and two moderately resistant (MR). Soybean cultivar susceptibility to SDS was based on industry rating on foliar symptom expression. Field locations: (A) an artificially inoculated field in East Lansing, MI; (B) a naturally infested field in Decatur, MI. The x-axis is represented by the days after planting (DAP) along with the corresponding soybean growth stage. The Y-axis is the F. virguliforme infection coefficient. F. virguliforme was detected in all soybean cultivars in both field locations from the first sampling point (V3 stage). By the end of the growing season at the post harvest stage (Post H), all cultivars in both field locations reached the same infection coefficient level............................................................................................................................. 94 Figure 3-4 Boxplot of area under relative quantity curve (AURQC) calculated using the temporal data of F. virguliforme DNA quantified in soybean roots at both field locations. The East Lansing site is represented by dark gray, while the Decatur site with light gray. (B) Bar plot of SDS foliar disease rating at R6 growth stage for four soybean cultivars at both locations based on  xv a plot scale disease rating. At both locations, F. virguliforme DNA was detected in considerable quantity across all soybean cultivars, however, not all soybean cultivars displayed SDS foliar symptoms. Soybean cultivar 92M82, showed the most severe foliar symptoms, but the AURQC was lower than the other three cultivars at both locations. ........................................................... 95 Figure 3-5 Infection coefficient of F. virguliforme (CtSoy/CtFv) in the five soybean cultivars measured at five growth stages during 2014 in a naturally infected field in Decatur MI. F. virguliforme DNA was detected in all soybean cultivars starting at the V3 time point. The relative quantity of F. virguliforme DNA detected only slightly increased from the V3 to R5 stage, but a significant increase of relative F. virguliforme DNA quantity was observed between the R5 and R7 growth stages. At the R7 growth stage, two cultivars showed significantly higher F. virguliforme relative quantities in roots. F. virguliforme DNA quantities were not associated with foliar disease symptom ratings taken at the R5 growth stage. .............................................. 96 Figure 3-6 Area under the relative Fusarium virguliforme DNA quantity curve (AURQC) and soybean SDS foliar disease rating index for individual plants sampled at the R5.5 growth stage for five soybean cultivars in 2014 Decatur field experiment. (A) Boxplot of AURQC calculated for the temporal dynamics data of F. virguliforme DNA quantified in soybean roots. AURQC were not significantly different among five cultivars (p=0.11). (B) Bar plot of SDS foliar disease rating index at R5.5 growth stage for five soybean cultivars. Significant difference was detected among the five soybean cultivars (p< 0.01) for SDS foliar disease rating index, however the quantified F. virguliforme in roots was at a similar level for all cultivars.................................... 97 Figure 3-7 Correlation between individually rated soybean plants for SDS symptoms and F. virguliforme infection coefficient detected in soybean roots. (A) Root rot severity rated on 15 individual plants for each of the 25 plots, based on the percentage of root surface discoloration correlated with the F. virguliforme infection coefficient in soybean roots. The y-axis is the root rot severity, and the x-axis is the relative quantity of F. virguliforme in roots. (B) SDS foliar symptom disease rating index on the same 15 individual plants from each of the 25 plots, based on disease severity rating scale from 0-9 and the disease incidence correlated with F. virguliforme relative quantities. There was no significant correlation between SDS root or foliar disease ratings and the amount of F. virguliforme quantified in soybean roots. .......................... 98 Figure S 3-1 Dry weight of four soybean cultivars in the greenhouse experiments. Each sampling time point represents the dry weight of six replicates of five individual plants per replicate. Root dry weights were collected to estimate the overall root health as affected by F. virguliforme under greenhouse conditions. (A) the first greenhouse experiment and (B) the second greenhouse experiment................................................................................................................................... 109 Figure S 3-2 Root dry weight of 15 plants collected from five replicated plots for each of the four soybean cultivars. (A) an artificially inoculated field in agronomy farm at East Lansing site. (B) a naturally infested field in Decatur site. .................................................................................... 110 Figure S 3-3 Throughout the growth season soybean root dry weight, soybean genomic DNA quantity, F. virguliforme DNA quantity, total DNA extracted from root tissue from V3 to Post (post harvest). Soybean root dry weight increased until R6, and started to decrease until the post harvest stage. As root dry weight decreased after R6, soybean genomic DNA also decreased  xvi dramatically. F. virguliforme DNA quantified from root increased gradually from R6 to Post harvest, but increase rapidly after R8. The amount of total DNA extracted from 100 mg of root tissues peaks at R5-R6 stages. .................................................................................................... 111 Figure 4-1 Dose response curve fitting for (A) mycelial growth and (B) conidia germination at different concentrations of fluopyram amended in the agar media for the F. virguliforme isolates: respectively. ................................................................................................................................ 128 Figure 4-2 Frequency distribution of effective fungicide concentration that inhibits growth by 50% for both (A) mycelial growth inhibition assay and (B) conidia germination inhibition assay. The mean EC50 value was indicated as the dotted vertical lines with the mean EC50 values: 3.35 and 2.28 µg/ml for the mycelial growth inhibition assay and conidia germination assay, respectively...................................................................................................................................................... 130 Figure 4-3 (A) Comparison of the difference in EC50 values estimated using mycelial growth inhibition assay and conidia germination inhibition assay. The differences in EC50 estimation between the mycelial growth inhibition and conidia germination assay were plotted for 20 F. virguliforme isolates. (B) Correlations between the EC50 estimations using mycelial growth inhibition assay and conidia germination inhibition assay. There was no significant correlation between those two methods (Spearman correlation P = 0.40).................................................... 135 Figure 4-4 Dose response curve fitting the isolates showed hormetic effect using (A) 4-parameter logistic model and (B) Brain-Cousens model. The hormetic effect isolates showed faster growth rate at 1 µg/ml concentration than the zero-control. The non-linear regression BC.4 model (AIC = -271) fits better than the LL.4 (AIC = -259) model for the isolates showed hormesis. .......... 136 Figure S 4-1 The reproducibility of EC50 estimation using mycelial growth inhibition assay. Twenty-two isolates were randomly selected from 11 sets of isolates to evaluate the assay reproducibility between batches. The bar filled with green color indicated a good reproducibility isolates, while the bar filled with red color indicated a poor reproducibility of the isolate. The error bars indicated the 95% confidence interval for the mean EC50 estimation. ....................... 149 Figure S 4-2 The reproducibility of EC50 estimation using conidia germination inhibition assay. Fifty-one isolates were replicated to evaluate reproducibility of this assay. The bar filled with green color indicated a good reproducibility isolates, while the bar filled with red color indicated a poor reproducibility of the isolate. The error bars indicated the 95% confidence interval for the mean EC50 estimation.................................................................................................................. 150 Figure 5-1 ----- ........................................................ 167 Figure 5-2  ...................................................................................................................................... 168  xvii Figure 5-3 ....................................................... 173 Figure 5-4 -..................................................................................................................................................... 174 Figure S 5-1 (A) Delta K method to determine the optimal K (assumed ancestors) based on likelihood method (Evanno et al. 2005). (B) Assignment of F. virguliforme isolates into clusters using the program STRUCTURE. Three ancestor memberships were labeled with three colors (blue, green, and red), the bar height indicates the proportion of ancestor membership compositions. Michigan: isolates were collected from Michigan in this study; others represent four F. virguliforme with NRRL codes: 34551, 31041, 22823, and 22292 (Mont1). ................ 185 Figure 6-1 Distribution of soybean sudden death syndrome (SDS) in the US and Canada based on the year of first report in journal articles. Since the first report of SDS in 1971 in Arkansas, SDS has been reported in the surrounding states in the following years with apparent pattern of spreading. By 2014, SDS has been confirmed in most soybean producing areas in the US and Canada, thus to continuing threat soybean production. .............................................................. 197 Figure 6-2 F. virguliforme values among the eight F. virguliforme populations by state or provinces. The heatmap color gradients and dendrogram delineate two main clusters of F. virguliforme populations, as the US and Argentina branches. Within the US branch, two subgroups were divided based on their relative geographical locations, except for the Kansas population. ............................................ 210 Figure 6-3 Population structure of F. virguliforme ancestry proportion from K=2 to K=7 clusters inferred from the STRUCTURE software. F. virguliforme isolates were grouped based on the source of origin to the hierarchical level of state or provinces. Each vertical bar represents an individual isolate that was partitioned into K segments indicating the proportion of assignment to the K clusters. For K=2, isolates from four provinces in Argentina, shown in blue, are distinct from most of the isolates collected in the United States, which are shown in red. For K=3, isolates from Argentina are mainly composed with two cluster, as shown in blue and green. In the US, Arkansas population has more similar population composition with the Argentinean populations, while the rest of the populations were primarily clustered into two clusters, shown as green and red. For K=4, which was the optimal K cluster as chosen based on deltaK method (Evano, 2005), isolates from Argentina still remained with two clusters composition, whereas the isolates from the US populations are mainly composed with three clusters, shown as green, red and purple. Admixed isolates are less common at K=4 clusters. With the increase of K clusters from K=5 to K=7, more admixed individuals started to appear in the US populations, but not in the Argentinean populations. ...................................................................................................... 212  xviii Figure 6-4 minimum spanning networks of F. virguliforme multilocus genotypes (MLG) based distance. Each node in the network represents a unique MLG. Line thickness indicates the genetic distance between MLGs, with thicker line represents closer distance, vice versa. MLG from different populations were labeled with different colors. .................................................. 214 Figure 6-5 Scatter plot of the discriminant analysis of principal components of F. virguliforme microsatellite multilocus genotypic data. Each point represents one individual, and individual points were colored based on their source of origin as states or provinces. ............................... 215 Figure 6-6 Correlation between pairwise population genetic distance (linearand geographic distance (km) with linear regression line fitting, and 95% confidence interval was plot in grey. No significant correlation between genetic distance and geographical distances (P=0.065). ................................................................................................................................... 216 Figure 6-7 Migrate analysis with three pooled populations: 1) Argentina, 2) Arkansas, and 3) Indiana and Michigan. Arrows connecting between locations showed directional migration model as supported in the Migrate-n analysis. The line thickness represents the number of migrants per generation as descried in the legends..................................................................... 218 Figure S 6-1 Shared multilocus genotypes (MLG) among populations by state/provinces within countries. ..................................................................................................................................... 231 Figure S 6-2 In STRUCTURE analysis, determination of optimal K for clustering individuals for each assigned populations. (A) log likelihood values of delta K against a range of K values. (B) The mean likelihood values calculated under varying K values, from K=2 to K=13. ............... 232 Figure S 6-3 is the mutation scaled population size and M is mutation scale migration rates (migrants per generation). ................................................................................................................................. 233 Figure S 6-4 Geographical distribution of F. virguliforme sample locations in Midwest - United States, Ontario - Canada, and Pampas area in Argentina.  Pie chart on the map represents the population composition based on STRUCTURE analysis ancestry membership assignment (K=4). Argentinean populations are mainly composed with blue and green clusters, while the US and Canadian populations were primarily composed of red and green clusters. The Arkansas population composed with four color-clusters, with each cluster contains at least 10%............ 234   1 CHAPTER 1 LITERATURE REVIEW    2 Host Plant: Soybean  Soybean (Glycine max (L.) Merr.), an annual legume of the Fabaceae family native to East Asia, is widely grown for its edible seeds. Soybean is one of the most important crops in the United States and worldwide. With its versatile uses and wide distribution, soybean has become an important food source for human beings and livestock. It has been estimated that soybean provides ca. 35% of total protein consumed by human directly and indirectly, and soybean is also an important source of oil (Ren and Michael, 2010), accounting for about 90% of oilseed production in the United States (USDA-ERS, 2012). In 2015, soybeans were the second-most planted field crop in the United States after corn, with 82 million acres planted and 81 million acres harvested (USDA-NASS, 2015). More than 80 percent of soybean acreage is concentrated in the Midwest, although significant amount of soybeans are still planted in southern states where soybean was historically grown (Doupnik, 1993; USDA-ERS, 2012). In Michigan, 2.03 million acres were planted in 2015 and 2.02 million acres were harvested with estimated yield of 98.98 million bushels, which is equivalent to $851 million farm-gate values (USDA-NASS, 2015).  Soybean Sudden Death Syndrome Soybean sudden death syndrome (SDS), caused by the hemibiotrophic fungus, Fusarium virguliforme, is responsible for devastating yield reductions in both North and South America (Aoki et al., 2005). A survey conducted from 2003 to 2014 demonstrated that SDS was estimated to be one of the top five most damaging soybean diseases in the United States (Wrather and Koenning, 2009; Bradley and Allen, 2014). From 2010 to 2014, the average annual yield loss caused by SDS was approximately 45 million bushels, which is equivalent to approximately  3 $450 million cash value (Wrather and Koenning, 2006; Koenning and Wrather, 2010; Bradley and Allen, 2014). SDS foliar symptoms usually start to develop in the early reproductive stage of soybean (Rupe, 1989; Roy et al., 1997). At this stage, the leaf symptoms begin as pale green to small chlorotic interveinal spots with a diameter of 1-3 millimeters scattered on leaves. With the development of disease, leaves may also demonstrate slight marginal cupping, and a wrinkled or puckered texture. The spots may enlarge or coalesce and eventually become necrotic, and only the region close to the leaf vein will remain green (Figure 1-1B-C). As the disease symptoms progress, severely diseased leaves may drop; only the petioles remained attached to the stem (Figure 1-1A). The most severe SDS symptoms may result in abortion of flowers and pods (Roy et al., 1989; Rupe, 1989). Although foliar symptoms are the most prominent, the causal pathogens have never been isolated from SDS symptomatic leaf tissues (Rupe, 1989). Additionally, molecular mechanism of the SDS pathogenicity proves that the toxins produced by F. virguliforme in the root and translocated to the leaves cause foliar symptoms (Ji et al., 2006). Root symptoms of SDS include severe taproot and lateral root necrosis, internal root discoloration and root biomass reduction. Soybean root infection by F. virguliforme occurrs as early as seed germination stage (Gao et al., 2006a). Infected taproots have brownish vascular discoloration expanding from the inner part of the taproot to the lower part of the stem, but the stem pith of the soybean plant remains white in color (Hartman et al., 1999; Hartman et al., 2015). The F. virguliforme infected soybean plants showed reduced number of lateral or hairy roots (Roy et al., 1997), so that the infected plants can be easily pulled from soil. Occasionally, the fungus produces blue sporulation structure (sporodochia, Figure 1-1D) on the surface of  4 taproots or lower part of the stem, which is one of the characteristic diagnostic features for SDS (Roy, 1997).  Figure 1-1 Foliar symptoms of soybean sudden death syndrome (SDS) shown in the field: (A) soybean premature defoliation, but petioles remain attached to the stem; (B-C) SDS foliar symptoms developed in the field; (D) asexual sporodochia reproductive structure form on the soybean root, when the infected roots are incubated on water agar for 7 days; (E) Fusarium virguliforme macroconidia, scale bar: 50 µm. Figures A-C photo credit to Dr. Martin Chilvers.  Abiotic and biotic factors affecting SDS SDS severity and incidence in the field can be affected by both abiotic and biotic factors including soil type (Rupe et al., 1993; Scherm and Yang, 1996; Scherm et al., 1998), interactions with soybean cyst nematodes (SCN, Heterodera glycines) (Roy et al., 1989; Roy et al., 1997; Roy et al., 2000), environmental conditions (Scherm and Yang, 1996), soybean cultivars (Rupe et al., 2000; Vick et al., 2006), and field management (Vick et al., 2003; Paulitz et al., 2010). Compact soil, low soil temperature at planting, and high soil moisture favors SDS occurrences in the field (Rupe, 1989; McLean and Lawrence, 1993b; Scherm and Yang, 1996; Scherm et al., 1998). Furthermore, severe SCN pressure along with these soil and environmental conditions in the same field will exacerbate the severity and incidence of SDS (Roy et al., 1997). SDS is often  5 associated with SCN presence, though F. virguliforme alone can also cause severe SDS (Hirrel, 1983; Roy et al., 1989; Roy et al., 1997). The role of SCN in the development of SDS is still not clear, results from field (Xing and Westphal, 2006; Westphal et al., 2014) and greenhouse studies (Gao et al., 2006b) are not always in agreement. Field experiments demonstrated a synergistic relationship between SCN and F. virguliforme in SDS disease development (Xing and Westphal, 2006; Westphal et al., 2014); however, results from the greenhouse experiment contradicted the findings in the field experiment (McLean and Lawrence, 1993a; Gao et al., 2006b). Additionally, high soil fertility and well-managed soybean fields with high yield potential tend to favor SDS occurrences (Rupe, 1989; Rupe et al., 1993; Scherm et al., 1998). Distribution of Fusarium virguliforme Fusarium virguliforme is widely distributed in North America with sporadic reports of F. virguliforme discovered in Argentina, South Africa, Iran, and Malaysia (Figure 1-2). In the United States, since the first report of SDS in Arkansas in 1971 (Hirrel, 1983), SDS has been reported in the surrounding states with a spreading trend from southern to the northern part of the United States (Figure 1-3). Currently, SDS has been reported in most of the soybean producing states (Roy et al., 1997; Hartman et al., 1999; Pennypacker, 1999; Kurle et al., 2003; Ziems et al., 2006; Bernstein et al., 2007; Chilvers and Brown-Rytlewski, 2010; Tande et al., 2014). The climate in the Midwest is conducive for F. virguliforme infection of soybeans, cold and moist weather at planting exacerbates the disease occurrence and severity (Scherm and Yang, 1999). In the early 1990s, SDS was reported in Brazil and Argentina (Nakajima et al., 1996; Scandiani et al., 2003; Scandiani et al., 2004), which are the two major soybeans producing countries in South America (USDA-FAS, 2015). In addition to the Americas, F. virguliforme was also isolated  6 from soybean and soil samples collected in Africa and Asia, respectively (Tewoldemedhin et al., 2013; Chehri et al., 2014; Chehri, 2015).    Figure 1-2 SDS or F. virguliforme distribution in the world. SDS was first reported in 1970 in Arkansas, United States. In the 1990s, SDS was reported in most soybean producing countries in North and South Americas. In South Africa, SDS was reported and confirmed in 2013. In 2014 and 2016, F. virguliforme was isolated from soil collected in Malaysia and Iran.  7  Figure 1-3 Distribution of SDS reported in the United States and Canada as the date of publication on the first report in Plant Disease, number in the map indicates the year SDS was reported to be present in the state. SDS in Michigan SDS was confirmed to be present in Michigan in 2009 (Chilvers and Brown-Rytlewski, 2010), but SDS-like symptoms were observed by local county agents and soybean growers in the early 2000s (Karen Zuaver and Martin Chilvers, personal communication). In 2009, SDS was confirmed in five counties located in the south west part of Michigan. Since then, SDS has been confirmed in an additional 21 Michigan countries (Figure 1-4). These diagnoses were confirmed by a PCR diagnostic assay (Wang et al., 2015).  8  Figure 1-4 SDS distribution in Michigan as confirmed by qPCR assay or isolation of F. virguliforme Taxonomy The species taxonomy in the Fusarium genus has been revised numerous times with additional sampling efforts and DNA sequence data. The morphological species Fusarium solani was first described by C.F.P. Von Martius in 1842 as Fusisporium solani from rotted potato tubers, Solanum tuberosum (Booth, 1975). This species was then reclassified into the Fusarium genus by Piers A. Saccardo in 1881 (Saccardo, 1901). The genus Fusarium (Family =  9 Nectriaceae, Order = Hypocreales, Division = Ascomycetes) was divided into 12 sections based on conidia and colony morphology. Fusarium solani (Mart.) Appel & Wollenweber (teleomorph = Nectria haematococca Berk. & Br.) was categorized within the section Martiella, which was first described by Wollenweber and Reinking (1935), comprised 5 species, 10 varieties, and 4 forms. Snyder and Hansen (1941) collapsed the species in section Martiella into a single species by deleting the other four species as synonyms of F. solani. Booth (1971) and then Gerlach and Nirenberg (1982) proposed the incorporation of four and six species in the section Martiella, respectively. Morphological identification of species in section Martiella is difficult, because most Martiella fusaria were usually reported as polytypic species: F. solani, f. sp. or mating populations of N. haematococca. Species found within the taxonomy of F. solani are now known as the Nectria haematococca-Fusarium solani species complex (O'Donnell, 2000). Using (2000) proposed that 26 phylogenetically distinct ingroup species are identified in the Nectria haematococca-Fusarium solani species complex, of which F. solani f. sp. glycines was reported as a putative mitosporic species. Fusarium solani species complex (FSSC) comprises species that are widely present in soil and responsible for many economically-important plant, animal, and human diseases (O'Donnell, 2000; Zhang et al., 2006), including the soybean SDS causal pathogen F. virguliforme (former name: F. solani f. sp. glycines) (Aoki et al., 2003). Currently, at least 60 phylogenetically distinct species have been documented or characterized within the Fusarium solani species complex (O'Donnell et al., 2013), so that the species name F. solani should be avoided when referring to a species within the Fusarium solani specie complex.  The SDS causing Fusarium species have been reclassified numerous times with additional sampling efforts and introduction of new phylogenetic tools. The SDS-causing pathogen was  10 first described as blue masses of Fusarium solani species formed on the surface of roots and lower stems of soybean, causing severe foliar chlorosis symptoms (Roy et al., 1988; Rupe, 1989). The Fusarium isolates recovered from soybean roots were then designated as two forms, FS-A and FS-B, based on distinct morphology and varied pathogenicity on soybean. Eventually, -A was the causal pathogen for SDS (Roy et al., 1989), and further characterizations of FS-A isolates supported the designation of forma specialis, F. solani (Mart.) Sacc. f. sp. glycines (Roy, 1997).  SDS-causing Fusarium species were first characterized in a systemic study using isolates collected from the United States and Argentina. Both morphology and phylogenetic analysis suggested that isolates collected from South America possessed characteristics that are distinct from the SDS-causing Fusarium species collected in North American (Aoki et al., 2003). Therefore, Aoki et al. (2003) proposed two SDS-causing Fusarium species, Fusarium tucumaniae Fusarium virguliforme & T. Aoki (syn. F. solani f. sp. glycines). Additional studies included isolates from Brazil, added two more new SDS-causal Fusarium species, Fusarium brasiliense Fusarium cuneirostrum -causing Fusarium species clustered within the clade2 Fusarium solani species complex (Aoki et al., 2005). Besides the phylogenetic and morphological evidences, Covert et al. (2007) also demonstrated biological species evidence for the distinction between F. tucumaniae and F. virguliforme using a fertility crossing test, which demonstrated that fertile crosses occurred within F. tucumaniae but no fertile crosses between F. tucumaniae and F. virguliforme. In addition to the SDS-causing species, clade2 Fusarium solani species complex also include species (F. cuneirostrum, F. phaseoli, F. azukicola) that cause bean root rot (BRR) on dry bean  11 (Phaseolus vulgaris L.), mung beans (Vigna radiata (L.) R. Wilczek), and azuki beans (Vigna angularis) in the U.S., Canada, and Japan (Aoki et al., 2005; O'Donnell et al., 2010), which has classified F. cuneirostrum as a pathogen that causes disease on both soybean and dry bean (Table1-1). However, one of the F. cuneirostrum isolates collected from Brazil possessed distinct morphological and phylogenetic differences to the other isolates of F. cuneirostrum recovered from dry bean and mung bean (Aoki et al., 2005). Multilocus genotyping (MLGT) (O'Donnell et al., 2010) combined with morphological evidence indicated that the divergent F. cuneirostrum isolate (NRRL 31949) is a phylogenetically and morphologically distinct species, which was then renamed as F. crassistipitatum (Aoki et al., 2012a). Table 1-1 Distribution and host specification of SDS-BRR clade Fusarium spp. Species Hosts Countries Causing Disease F. virguliforme Glycine max USA, Canada, and Argentina SDS F. tucumaniae G. max Argentina and Brazil SDS F. braziliense G. max Brazil and USA SDS F. crassistipitatum  G. max Brazil and Argentina SDS F. cuneirostrum Phaseolus vulgaris USA, Japan, Brazil, and Canada BRR F. phaseoli P. vulgaris USA  BRR F. azukicola Vigna angularis Japan BRR SA: South America; NA: North America; SDS: Sudden death syndrome; and BRR: Bean root rot  Other SDS causal pathogens Besides F. virguliforme, there are three additional Fusarium species (i.e., F. tucumaniae, F. brasiliense, and F. crassistipitatum) causing SDS on soybean in South America (Aoki et al., 2003; Aoki et al., 2005). The known reproductive cycle for F. virguliforme is asexual reproduction by forming asexual reproductive structure sporodochia on the taproot surface bearing asexual propagule conidia (Figure 1-1D, E). Genetic analysis of the mating type genes  12 within F. virguliforme indicated that only one mating type gene (MAT1-1) was present in the F. virguliforme populations with a survey of 138 isolates collected from both North and South America. Contrary to the strict asexual reproduction, the other two SDS causal pathogens, F. tucumaniae and F. brasiliense, have both mating type genes (MAT1-1 and MAT1-2) present in their populations (Hughes et al., 2014). Additionally, the sexual reproductive structure perithecia of F. tucumaniae were observed in both lab and field conditions (Covert et al., 2007; Scandiani et al., 2010), which demonstrated strong potential to generate higher genetic diversity in the field. Given the facts that four SDS causal species and both mating types are found in South America, it was hypothesized that South America was the center of origin for the SDS causing pathogens, and F. virguliforme diverged from South America or Mesoamerica and spread to North America (Covert et al., 2007; Hughes et al., 2014).   SDS foliar symptoms are not caused by Fusarium pathogens direct infection or colonization on soybean leaves, but by secreted toxins. One of the fungal toxins produced by SDS causing pathogens is FvTox1, which is a small protein with an approximate molecular weight of 13.5 kDa (Brar et al., 2011). FvTox1 was first discovered in  F. virguliforme culture filtrate and shown to induce chlorosis and necrosis in susceptible soybean leaves (Brar et al., 2011). Light is essential for developing SDS foliar symptoms (Ji et al., 2006). When the toxin is translocated to the foliage, it can cause the degradation of rubisco large subunit and subsequent accumulation of free radicals, triggering the programmed cell death causing SDS foliar symptoms (Ji et al., 2006). The FvTox1 gene is not only conservative within the SDS causing Fusarium pathogens, but also shares homologs with many clade2 Fusarium solani species complex species, and even some Fusarium species outside of Fusarium solani species complex (Mbofung 2011; Wang and Chilvers unpublished data). The wide presence of FvTox1 gene present in the non-SDS causing  13 Fusarium species may indicate that development of SDS foliar symptoms should be synergistic with multiple toxins or proteins. In addition, several other toxins have been found and verified to induce SDS foliar symptoms on soybean leaves (Brar et al., 2011; Chang et al., 2015). Therefore, there is a consortium of fungal toxins secreted by SDS-causing Fusarium species working synergistically to induce foliar chlorosis and necrosis. Interaction between F. virguliforme and SCN The relationship between soybean cyst nematodes (SCN) and F. virguliforme has been studied for decades; however, results from different studies are not always consistent with the effect of SCN on SDS development. In the early observation of SDS, Hirrel (1983) reported the dual presence of SDS symptoms and SCN in an SDS affected field. Also, he noted SCN was associated with 70% to 80% of plants displaying SDS symptoms. In 2000, Roy et al. isolated F. virguliforme from surface disinfested SCN cysts collected from the rhizosphere of SDS symptomatic soybean plants. They found the fungus can survive in SCN cysts for at least 8 months, and remain pathogenic (McLean and Lawrence, 1995; Roy et al., 2000). In addition, F. virguliforme can attach and get into the eggs and cysts of SCN which can possibly cause reduced SCN cysts or second stage juvenile during the field interactions (McLean and Lawrence, 1993b; Gao et al., 2006b). In greenhouse and micro plot studies, soil inoculated with both F. virguliforme and SCN can cause more severe SDS foliar symptoms than F. virguliforme inoculum alone, but SCN is not required to induce F. virguliforme root infections (Roy et al., 2000). A synergistic relationship between SCN and F. virguliforme has been observed to exacerbate the SDS foliar symptoms in some greenhouse and field trials (McLean and Lawrence, 1993a, b; Xing and Westphal, 2006; Westphal et al., 2014); however, non-significant or negative correlation  14 between SCN and SDS disease severities were reported in both greenhouse and field experiments (Gao et al., 2006b; Marburger et al., 2014). As both sides of the argument presented convincing evidences for their argument, the relationship between SDS and SCN is still controversial.  SDS field management strategies  Soybean SDS has been present in the United State for over 40 years, but effective disease managements for SDS are still lacking. Common disease management strategies for SDS include planting SDS resistance soybean cultivars, disease escape, managing SCN, and seed treatment with fungicides (Roy et al., 1997; Hartman et al., 2015). Among these management practices, no single tactic will completely control SDS; however, yield losses can be minimized if several management practices are combined (Roy et al., 1997). In the past, there were no fungicides that could be used to effectively manage SDS in the field, and agronomic practices were not always effective in reducing SDS impact (Rupe et al., 1997; Navi and Yang, 2016). Soybean SDS resistance is controlled by many genes and is highly heritable (Iqbal et al., 2001; Njiti et al., 2002). There are 58 quantitative trait loci (QTL) associated with reactions to F. virguliforme infection (http://www.soybase.org; access date: Apr. 2016). Many of those loci were identified based on SDS foliar symptoms, while only a small portion of them were associated with root disease ratings (Hnetkovsky et al., 1996; Prabhu et al., 1999; Kazi et al., 2008; Abdelmajid et al., 2012). Collecting soybean SDS root disease ratings is not as easy as collecting SDS foliar disease ratings; as root disease rating requires digging and processing roots to examine root symptom severity. Thus, soybean root resistance to F. virguliforme infection traits were seldom examined (Njiti et al., 1997; Kazi et al., 2008). The identified SDS resistance QTL were mostly identified in the linkage mapping with the bi-parental populations, which determined the  15 mapping resolution is not precise enough to find specific genes associated with SDS resistance. There was only one resistance gene (GmRLK18-1) tagged and cloned (Srour et al., 2012). A genome wide association study (GWAS) was used to seek genomic regions associated with SDS resistance, and 20 genomic regions, including GmRLK18-1, were found to be associated with SDS resistance (Wen et al., 2014). The genomic regions strongly associated with SDS resistance will serve as candidates for searching SDS resistance genes to improve SDS resistance cultivar breeding. Furthermore, disease ratings on SDS root symptoms or root susceptibility to F. virguliforme should be considered as phenotypes in future breeding projects, since F. virguliforme root infection is the initial and direct cause of SDS. Besides the breeding effort, seed treatments with chemical fungicides and bio-control microbes has been promising in reducing F. virguliforme root colonization and increasing yield in both greenhouse and field experiments. Seed treatment with fungicides for control SDS was first evaluated in the field, and then verified with greenhouse and seed germination assays in the lab (Mueller et al., 2011). Seed treatment with fungicide fluopyram was particularly effective in reducing F. virguliforme root colonization and SDS foliar symptoms.  Field experiments in multiple states also verified the effect of fluopyram seed treatment in managing SDS and benefiting yield (Wang et al., 2014; Kandel et al., 2016). In addition to fluopyram, a Syngenta fungicide A10466G controlling SDS by improving root health and reducing SDS foliar symptoms (Olaya et al., 2014); however, varied efficacies in SDS management were observed in additional field trials.  Methods for detection of F. virguliforme The success of field disease management relies on accurate and quick disease diagnostics, and thus a proper diagnostic assay for detection of F. virguliforme is vital for SDS management.  16 Numerous diagnostic assays have been developed for detection and quantification of F. virguliforme from plant and soil samples (Cho et al., 2001; Li and Hartman, 2003; Gao et al., 2006b; Li et al., 2008). Diagnostic assays for F. virguliforme can be categorized into two types: culture-based methods and DNA-based methods. The culture-based method includes plating root tissue or soil samples on the semi-enumerate the colonies formed on the surface of the agar media (Cho et al., 2001). The application of this detection method is limited by its poor sensitivity and specificity, as F. virguliforme colony growth may be out-competed by other fast growing fungi and the colony morphology of F. virguliforme is not distinct from the other closely related fungal species. Furthermore, the microscopic method was used to identify F. virguliforme from pure culture, but microscopic characteristics of F. virguliforme are not distinct from closely related species (Roy, 1997; Li et al., 1998). For example, F. virguliforme macroconidia sizes overlapp with its closely related clade2 Fusarium solani species complex species (Aoki et al., 2005; Aoki et al., 2012a). Therefore, culture-based diagnostic of F. virguliforme is feasible, but may not be the best option.  Given the drawbacks of the culture-based diagnostic methods, molecular diagnostic assays that take advantage of the PCR technique improve both specificity and sensitivity for pathogen detection. The first molecular diagnostic assay developed for F. virguliforme was a conventional PCR assay that targets the ribosomal DNA (rDNA), which could specifically differentiate F. virguliforme (then named as: F. solani f. sp. phaseoli) from the other Fusarium species (O'Donnell and Gray, 1995). Li and Hartman (2003) developed another conventional PCR assay targeting the mitochondrial small-subunit rDNA, which was applied to detect and semi-quantify F. virguliforme from plant or soil samples. To accurately quantify F. virguliforme from environmental samples, quantitative real-time PCR (qPCR) assays were developed for  17 quantification of F. virguliforme from plant samples. The developed qPCR assays were specific to F. virguliforme and their lower limit of detection sensitivity was reported to be as low as 90 fg of genomic DNA (Gao et al., 2006b; Li et al., 2008; Mbofung et al., 2011). Based on the revised taxonomy of clade-2 FSSC, two of the published assays (i.e., Gao et al. (2006) and Li et al, (2008) assays) were no longer only specific to F. virguliforme, as these two assays may cross amplify with non-SDS causing Fusarium species due to lack of primer specificity (O'Donnell et al., 2010; Aoki et al., 2012a; Aoki et al., 2012b). Another qPCR assay developed by Mbofung et al. (2011) targets the single copy fungal toxin gene (FvTox1), which results in poor detection sensitivity. The qPCR assay detection sensitivity was determined to be 25 pg of F. virguliforme genomic DNA, which is significantly lower than the other qPCR assays. Collectively, qPCR is the method of choice for detection and quantification of F. virguliforme from plant or soil samples, but an improved qPCR assay with better sensitivity and optimized specificity is needed.  The choices of loci for qPCR assay design are the keys to qPCR diagnostic assay specificity, sensitivity, and consistency. Specificity to the target pathogen is a must for a plant pathogen diagnostic assay; otherwise the false positive rate would be hard to estimate. One of the common ways to select genetic loci for specific assay design is from the most recent phylogenetic studies. For instance, taxonomy of the clade 2 Fusarium solani species complex has been revised several times in the past years with possible further delineation in the future (Aoki et al., 2003; Aoki et al., 2005; O'Donnell et al., 2010; Aoki et al., 2012a). The design of a diagnostic qPCR assay should take advantage of the knowledge in the phylogenetic studies to select proper genetic locus for assay design based on the scope of the detection targets. Other ways to search for genetic loci specific to the target include searching genome-wide orthologs and the alignment-free BLAST-based method (Satya et al., 2010; Pritchard et al., 2012). These methods have been successfully  18 utilized for human pathogen PCR assay development, and should be easily adopted to design diagnostic assays for plant pathogens (Pritchard et al., 2013). In addition, qPCR assay sensitivity is directly correlated with the gene copy numbers per haploid genome in one pathogen cell. For one pathogen cell, different genes have varied gene copy numbers that can range between zero to hundreds copies, this can make a huge discrepancy in detection sensitivities among qPCR assays. The F. virguliforme qPCR assay designed on a single copy gene was less sensitive than the assay developed on the multiple copy genes, and the differences in detection sensitivities can be as large as three orders of magnitude (Gao et al., 2004; Mbofung et al., 2011). To improve specificity, genes present on the dispensable part of the pathogen genome can be targeted for assay design, especially if dispensable gene is associated with pathogenicity. The disadvantage of this method is lack of consistency, since the dispensable genes are not always present in pathogen cells (Coleman et al., 2009). Assay validation is also an important facet to evaluate a qPCR assay. A multi-lab round robin experiment has been conducted on F. virguliforme qPCR assays by focusing on testing assay specificity, sensitivity, and consistency among assays. The best qPCR assay was suggested to be the qPCR assay designed on the multi-copy rDNA intergenic spacer (IGS) region, which demonstrated good specificity and sensitivity (Kandel et al., 2015).  Population genetic structure of F. virguliforme and mating type gene Selecting proper genetic markers is important to solve population biology questions. Molecular markers have been applied in many population genetic studies of plant pathogens for the purposes of detecting gene flow, identifying mating systems, locating the center of disease origin, and tracking pathogen adaptation to hosts (McDonald and Linde, 2002). To detect the genetic diversity of plant pathogens, there are three types of molecular markers as defined by the  19 nature of their molecular genetic marker: non-PCR based markers, PCR based markers that are not locus-specific, and locus specific markers (Milgroom, 2015). Before the advent of PCR technique, allozyme, restriction fragments length polymorphism (RFLP), and RFLP fingerprinting were the mainstream genetic markers used for population genetics of plant pathogens. After the advent of PCR, random amplified polymorphic DNA (RAPD), inter-simple sequence repeats (ISSR) and rep-PCR, and amplified fragment length polymorphism (AFLP) were developed and used widely in the population genetic studies of plant pathogens (Brown, 1996). Currently, the locus-specific molecular markers, such as microsatellite and single nucleotide polymorphism (SNP) markers are widely used for population genetic studies (Schlötterer, 2004). These two genetic markers were used for different population genetics analyses based on the topics of the biological questions, such as time of divergence, mating systems, and genes contributing to host adaptations. The microsatellite marker has a higher mutation rate than the SNP markers, which makes the microsatellite the marker of choice for studying a short time scale population genetic questions, such as a recent outbreak of plant disease or testing random mating of a local population. The SNP marker gained its popularity because of the advent of the next generation sequencing technology, which significantly decreased the cost and throughput for genotyping plant pathogen populations. Another benefit of SNP markers is the higher marker density than other markers, which can be not only used for population genetic study, but also can be utilized to identify genomic regions associated with the phenotypic traits (host specification, fungicide resistance, and thermal adaptation) of the pathogens using genome wide association study (GWAS).  To date, little genetic variability has been detected within F. virguliforme isolates collected in the United States. A survey based on 27 F. virguliforme isolates from northern US (i.e., IA, MN  20 and IL) suggested that DNA genetic loci including rDNA IGS, t(EF-F. virguliforme isolates (Malvick and Bussey, 2008; O'Donnell et al., 2010). These results may suggest F. virguliforme isolates obtained from the north central US appear to be genetically identical or perhaps part of US clonal population (Malvick and Bussey, 2008). However, a pathogenicity and virulence study on F. virguliforme isolates demonstrated varying levels of root rot or SDS foliar symptoms (Li S. et al., 2009). For example, in pathogenicity tests the FSG14 and FSG 13 isolates cause very severe root symptoms, but less severe foliar symptoms (Li S. et al., 2009). Given the variations in aggressiveness of F. virguliforme isolates on soybeans, it is believed that F. virguliforme is not a clonal lineage in the US, and proper genetic markers with higher mutation rate may be needed to detect genetic diversity among F. virguliforme isolates. Using both RFLP and RAPD genetic markers, Mbofung ed al (2012) identified 25 genotypes from a collection of 72 F. virguliforme isolates; all the genotypes can be further grouped into four subgroups in a cluster analysis. Unfortunately, the lack of reproducibility has made the RAPD marker difficult to use between labs (Penner et al., 1993; McDonald, 1997). Therefore, genetic markers with higher reproducibility rate and polymorphism among F. virguliforme isolates are needed to improve current F. virguliforme genotyping. Since the first report of SDS disease in Arkansas in 1970s, SDS has been reported in most soybean producing states in the United States in the past 40 years. If there was a spreading event from the one possible center of origin in Arkansas, the spreading event should be in a very short period of time scale. This implies that Sanger sequencing multi-locus sequencing typing genetic markers may not have enough evolutionary potential to create enough mutations to be detected in the previous sequencing genotyping efforts (Malvick and Bussey, 2008; O'Donnell et al., 2010).  21 The microsatellite markers with a higher mutation rate may become the ideal genetic marker for detecting genetic diversity for these types of population genetic questions in such a short time scale (Ellegren, 2004; Milgroom, 2015). Microsatellite genetic marker has been applied to multiple plant pathogens to study their population biology and demographic with narrow or wide geographical scales. It has been successfully used to predict plant pathogen center of origin, migration routes, and population structures (Berbegal et al., 2013; Ali et al., 2014; Goss et al., 2014; Everhart and Scherm, 2015; Kamvar et al., 2015). Therefore, to develop a set of microsatellite markers will facilitate the identification of population structure and demographic history for F. virguliforme in the United States. The release of F. virguliforme draft genome enabled the rapid development of microsatellite markers for F. virguliforme. Using a 454 next generation sequencing (NGS) platform, the draft genome of F. virguliforme has been assembled and annotated (Srivastava et al., 2014). The genome has been assembled into 1386 scaffold with average scaffold length of 36 kb, which estimated the genome size to be ~50 Mb. Though the genome assembly quality is not as good as other model fungal organisms, the assembly quality was long enough for mining microsatellite markers. There are several bioinformatics tools been developed for mining microsatellite marker patterns from genomic sequences, such MISA, SSRIT, and SciRoKo (Thiel et al., 2003; Kofler et al., 2007). Given the size of F. virguliforme genome, most of the available software tools will be able to process the sequence data in a reasonable computing time. Collectively, microsatellite markers of F. virguliforme is ideal for study the population genetic question in such a short time scale, and the development of F. virguliforme microsatellite markers is feasible based on the genomic sequence data and bioinformatics tools.  22 Summary Given the known knowledge about F. virguliforme and SDS, this review highlights the need for prioritized research in key areas. One critical need is for the development of improved qPCR assay for detect and quantify F. virguliforme from plant and soil samples. While the currently developed qPCR assays is specific or sensitive to F. virguliforme, qPCR assays that are both specific and sensitive are still not available. An improved qPCR assay can be used to study F. virguliforme temporal dynamics in soybean roots along the season, which greatly improve our understanding of soybean resistant to F. virguliforme root infections. Furthermore, F. virguliforme root colonization level is an index to represent trait that soybean root resistant to F. virguliforme infections, and these phenotypic data are quantitative and accurate than previous method. Besides the efforts for breeding SDS resistance cultivars, numerous fungicides showed potential to reduce F. virguliforme and SDS foliar symptoms, so that new research is also need to determine the baseline sensitivity of these fungicide chemicals and their performance in the field trials. At last, F. virguliforme has demonstrated diverse virulence on soybeans, and therefore molecular genetic markers with high mutation rate need to be developed for F. virguliforme. 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Interaction of Fusarium solani f. sp. glycines and Heterodera glycines in sudden death syndrome of soybean. Phytopathology 96:763-770. Zhang, N., O'Donnell, K., Sutton, D.A., Nalim, F.A., Summerbell, R.C., Padhye, A.A., and Geiser, D.M. 2006. Members of the Fusarium solani species complex that cause infections in both humans and plants are common in the environment. J Clin Microbiol 44:2186-2190. Ziems, A.D., Giesler, L.J., and Yuen, G.Y. 2006. First report of sudden death syndrome of soybean caused by Fusarium solani f. sp glycines in Nebraska. Plant Dis 90:109-109.  33 CHAPTER 2 IMPROVED DIAGNOSES AND QUANTIFICATION OF FUSARIUM VIRGULIFORME, CAUSAL AGENT OF SOYBEAN SUDDEN DEATH SYNDROME            This chapter was originally published in Phytopathology.    Wang, J., Jacobs, J.L., Byrne, J.M. and Chilvers, M.I., 2015. Improved diagnoses and quantification of Fusarium virguliforme, causal agent of soybean sudden death syndrome. Phytopathology, 105(3):378-387.    34 Abstract Fusarium virguliforme (syn. F. solani f. sp. glycines) is the primary causal pathogen responsible for soybean sudden death syndrome (SDS) in North America. Diagnosis of SDS is difficult because symptoms can be inconsistent or similar to several soybean diseases and disorders. Additionally, quantification and identification of F. virguliforme by traditional dilution plating of soil or ground plant tissue is problematic due to the slow growth rate and plastic morphology of F. virguliforme. Although several real-time quantitative PCR (qPCR) based assays have been developed for F. virguliforme, the performance of those assays does not allow for accurate quantification of F. virguliforme due to the reclassification of the F. solani species complex. In this study, we developed a TaqMan qPCR assay based on the ribosomal DNA (rDNA) intergenic spacer (IGS) region of F. virguliforme. Specificity of the assay was demonstrated by challenging it with genomic DNA of closely related Fusarium species and commonly encountered soilborne fungal pathogens. The detection limit of this assay was determined to be 100 fg of pure F. virguliforme genomic DNA or 100 macroconidia in 0.5 g of soil. An exogenous control was multiplexed with the assay to evaluate for PCR inhibition. Target locus copy number variation had minimal impact, with a range of rDNA copy number from 138 to 233 copies per haploid genome, resulting in a minor variation of up to 0.76 Ct values between strains. The qPCR assay is transferable across platforms, as validated on the primary real-time PCR platform used in the North Central region of the National Plant Diagnostic Network. A conventional PCR assay for F. virguliforme detection was also developed and validated for use in situations where qPCR is not possible.     35 Introduction Within the United States, soybean (Glycine max (L.) Merr.) is the second most widely grown crop with more than 3 billion bushels produced per annum (USDA NASS, 2013). Numerous plant diseases threaten soybean production in the United States, including soybean sudden death syndrome (SDS) (Wrather and Koenning, 2010). In the past decade, SDS has ranked within the top five most yield damaging soybean diseases in the United States, with estimated yield losses of 70 million bushels in 2010 (Wrather and Koenning, 2011). In North America, the predominant causal agent of soybean SDS is the soilborne fungal pathogen, Fusarium virguliforme (Aoki et al., 2003). Fusarium virguliforme has been reported to colonize a wide range of host plant species (Kolander et al., 2012), and can survive in soil or debris by producing conidia or chlamydospores (Roy et al., 1997). Diagnosis of SDS in the field can be difficult as several other diseases can produce similar symptoms, such as brown stem rot caused by Phialophora gregata Cadophora gregata) or red crown rot caused by Cylindrocladium parasiticum (Roy et al., 1997).  Studies suggest that early infection of soybean plants by F. virguliforme is essential for foliar SDS symptom development (Navi and Yang, 2008), infection by F. virguliforme was detected as early as the seedling stage (Gao et al., 2006). As a soilborne pathogen, F. virguliforme only colonizes the root and the lower stem of soybean plants (Rupe, 1989), phytotoxins produced by the fungus are translocated through the xylem to the foliage, causing SDS foliar symptoms (Brar and Bhattacharyya, 2012). Accurate detection and quantification of F. virguliforme in root and soil samples are essential to study the epidemiology of F. virguliforme. Both culture-based and molecular PCR-based methods have been developed for the detection of F. virguliforme. Culture-based methods utilize dilution plating or isolation from infected tissue on semi-selective media (Roy et al., 1989; Rupe, 1989; Luo et al., 1999; Cho et al., 2001). However, these culture-  36 based methods are time consuming and can be difficult to implement given the slow growth rate and plastic morphology of F. virguliforme (Cho et al., 2001; Aoki et al., 2003). These limitations of sensitivity and specificity led to the development of DNA-based molecular detection tools. Multiple conventional PCR assays have been developed for the detection of F. virguliforme, but they are not specific due to the revised taxonomy of the F. solani species complex (O'Donnell and Gray, 1995; Achenbach et al., 1996; Li and Hartman, 2003). Therefore, no specific conventional PCR assay is currently available for diagnosis of F. virguliforme. Quantitative real-time PCR (qPCR) has been extensively applied in quantification and diagnosis of numerous plant pathogens. Compared to conventional PCR assay, qPCR assays can be more sensitive and specific and can detect multiple pathogens by multiplexing assays (Schaad and Frederick, 2002; Patrinos and Ansorge, 2010). Gao et al. (2004) and Li et al. (2008) reported two qPCR assays for quantification of F. virguliforme from plant samples using TaqMan probe assays designed against the mitochondrial SSU rDNA sequence. Due to the reclassification of the Fusarium solani species complex, the mtDNA region used for design of these two assays was too conserved to differentiate F. virguliforme from the dry bean root rot pathogens, F. cuneirostrum and F. phaseoli and other SDS causal agents such as F. tucumaniae, F. crassistipitatum, and F. brasiliense, which predominate in South America (O'Donnell et al., 2010; Aoki et al., 2012). Therefore, it is clear that qPCR assays for F. virguliforme need to be improved. O'Donnell et al. (2010) and Aoki et al. (2012) demonstrated that the intergenic spacer  (IGS) region of the rDNA is one of the genetic loci that can resolve F. virguliforme from the other closely related Fusarium species in their multilocus genotyping studies of clade 2 Fusarium solani species complex. The rDNA IGS region is a multi-copy genetic locus in the eukaryotic genome (Long and Dawid, 1980), therefore the sensitivity of the assay based on IGS   37 rDNA region is greater than a single copy gene assay, and has been used for detection and quantification of numerous plant pathogens (Chilvers et al., 2007; Bilodeau et al., 2012; Gramaje et al., 2013). Although the rDNA IGS region seems an ideal region for qPCR assay design, large rDNA copy number variations were reported in many fungal organisms (Herrera et al., 2009; Bilodeau et al., 2012), and accuracy of the quantification may be affected in plants infected by fungal strains with varied rDNA copy numbers. In addition, PCR inhibitors in DNA samples can cause false negative results or low PCR amplification efficiencies. Strategies to deal with PCR inhibitors include dilution of DNA samples (Malvick and Impullitti, 2007), additional DNA purification steps or the use of PCR additives such as polyvinylpolypyrrolidone (Jiang et al., 2005; Malvick and Grunden, 2005) and bovine serum albumin (Haudenshield and Hartman, 2011). Despite attempts to reduce or remove PCR inhibitors, an internal or exogenous control is needed to monitor each reaction for PCR inhibition. Soybean SDS is becoming one of the most devastating diseases threatening soybean production across most of the soybean production zones in the United States and its range continues to expand. Despite this, an accurate, robust and sensitive diagnostic and quantitative assay is not available. Therefore, development of such an assay will facilitate the diagnosis of SDS, and can be used to study the epidemiology of F. virguliforme, which will improve our understanding, and management of this pathogen. The objectives of this study were to: (i) develop a qPCR assay with the inclusion of an internal control for the sensitive and specific detection and quantification of F. virguliforme; (ii) determine the transferability of the qPCR assay between platforms; (iii) develop a complimentary conventional PCR assay for use in situations where qPCR is not possible; (iv) to thoroughly validate the qPCR and PCR assays with plant and soil samples.   38 Materials and Methods Fungal isolates and DNA extraction  Thirty-six isolates of Fusarium species and other genera commonly found associated with soybeans were used in this study (Table2-1). Cultures of Fusarium species were grown on potato dextrose agar (PDA) media (Acumedia, Burton, MI) at room temperature for 14 d, agar plugs with conidia were collected and stored at -80ºC in 20% glycerol. To obtain mycelia for DNA extraction, seven to eight 4-mm3 pieces of colonized PDA media were transferred to 50 mL potato dextrose broth (PDB) (Acumedia) in 250-mL Erlenmeyer flasks and shaken at room temperature on an orbital rotary shaker at 120 rpm for 3 to 4 d.  Mycelia were collected by vacuum filtration on Miracloth (Calbiochem, Darmstadt, Germany) with a Buchner funnel, frozen and lyophilized in sterile 2-mL micro centrifuge tubes overnight. Lyophilized mycelia (20 mg) were disrupted in 2-mL screw-cap tubes with one 6-mm ceramic bead and five 2-mm glass beads in a FastPrep FP120 Bio101 Savant machine (Qbiogene, Carlsbad, CA) at the speed setting 6 for 40 s. Mycelia were used for DNA extraction using the DNeasy Plant Mini kit (Qiagen, Germantown, MD).  DNA concentration was determined with a Quant-iT dsDNA high-sensitivity assay kit (Invitrogen, Carlsbad, CA) on a 96-well SAFIRE microplate reader (TECAN, Männedorf, Switzerland).  Table 2-1 Specificity test panel for qPCR assay validation. This panel includes the Fusarium species that are closely related to the SDS-causing Fusarium species and other commonly encountered soil fungal species. Ct values listed in the table indicate the specificity performance of the F. virguliforme qPCR assay when 100 pg genomic DNA were added to the reaction. Speciesa Ctb NRRLc  Hostd Geographic Origin Fusarium acuminatume 35.75 - Solanum tuberosum Michigan, USA F. avenaceume 35.07 - S. tuberosum Michigan, USA F. brasiliense  32.06 22678 Glycine max California, USA F. brasiliense  36.21 22743 G. max Brasilia, Distrito Federal, Brazil F. cerealise 34.11 - S. tuberosum Michigan, USA   39 F. crassistipitatum  36.04 31949 G. max Cristalina, Goias, Brazil F. cuneirostrum  33.05 31157 Phaseolus vulgaris Presque Isle, Michigan, USA F. equisetie 34.29 - S. tuberosum Michigan, USA Fusarium sp.* 32.6 22574 Coffea arabica Guatemala Fusarium sp.* 35.54 22412 Bark French Guiana F. graminearume 34.91 - S. tuberosum Michigan, USA Fusarium sp.* 31.35 22387 Bark  French Guian F. oxysporume 36.07 - S. tuberosum Michigan, USA F. phaseoli 37.18 22276 P. vulgaris USA  F. phaseoli 36.47 31156 P. vulgaris Michigan, USA F. sambucinume 37.36 - S. tuberosum Michigan, USA F. solani 37.19 22395 Bark Venezuela F. solanie 35.21 - S. tuberosum Michigan, USA F. torulosume 34.58 - S. tuberosum Michigan, USA F. tricinctume 35.31 - S. tuberosum Michigan, USA F. tucumaniae 38.57 31096 G. max San Agustin, Tucuman, Argentina F. tucumaniae 37.15 31773 G. max Brazil, PR, Ponta Grossa F. virguliforme 21.78 22823 G. max Indiana, USA F. virguliforme 20.88 31041 G. max Illinois, USA F. virguliforme 21.08 34551 G. max San Pedrom, Buenos Aures, Argentina F. virguliforme 20.53 22292 G. max Illinois, USA F. virguliforme  19.36 - G. max Counties across Michigan, USA P. gregata (B) 34.84 - G. max - Phialophora gregata (A) 33.82 - G. max - Phytophthora sansomeana 34.9 - G. max - Phytophthora sojae 35.98 - G. max - Pythium svlvaticum 34.21 - G. max - Pythium ultimum 34.93 - G. max - Rhizoctonia solani AG-2-2IIIB 34.93 - - - Rhizoctonia solani AG-4 34.99 - - - Sclerotinia sclerotiorum 35.36 - G. max - a  unnamed Fusarium species nested within clade two of the F. solani species complex (O'Donnell et al., 2008) b Ct values were determined by setting the threshold line at 0.1  c NRRL, The Agriculture Research Service Culture Collection, National Center for Agricultural Utilization Research, USDA/ARS d Host plants for the microorganisms e species isolates obtained from the study published by Gachango et al. (Gachango et al., 2012)  Table 2-   40 Real-time PCR primer and probe design for F. virguliforme  The multi-copy-number intergenic spacer (IGS) region of the ribosomal DNA (rDNA) was chosen to design primers and probes. IGS rDNA sequences of Fusarium species (Table2-1) were obtained from the National Center for Biotechnology Information (NCBI) GenBank database (FJ919498, FJ919499, FJ919507, FJ919510, FJ919511, FJ919512, FJ919515, and FJ919521).  Eight IGS rDNA sequences from the soybean sudden death syndrome and bean root rot (SDS-BRR) clade of Fusarium species (i.e., F. virguliforme, F. tucumaniae, F. cuneirostrum, F. crassistipitatum, F. brasiliense, and F. phaseoli) were chosen for multiple sequence alignment to find the unique polymorphic regions of F. virguliforme using the MUltiple Sequence Comparison by Log- Expectation (MUSCLE) method (Edgar, 2004). In addition, the sequence self-(Rice et al., 2000) to avoid cross amplification within the IGS rDNA region. Primers were designed to amplify PCR product sizes ranging from 50 to 150 bp. Polymorphic nucleotides unique to F. virguliforme elting temperatures for the primer oligos were predicted using the primer probe test tool in Primer Express 3.0 (Applied Biosystems, Carlsbad, CA). The secondary structure of the target amplicon was predicted with (Zuker, 2003) at the temperature of PCR annealing step and with the final [Mg2+] = 2 mM and [Na+] = 50 mM. The free energy (G) was used to evaluate the stability of the amplicon secondary structure, and the PCR product with a higher G was selected as test candidates for further evaluation. For the probe design, the following rules were  end of the TaqMan probe (Life Technologies) for F. virguliforme was labeled with 6FAM (fluorescein), -fluorescent   41 quencher). Other PrimeTime dual-labeled probes (IDT, Coralville, Iow-assay primers and probe were ordered from IDT as describe in Haudenshield and Hartman (2011). qPCR amplification parameters Real-time qPCR amplifications were performed on the ABI StepOnePlus thermocycler v2.3 (Applied Biosystems). Real-time qPCR was performed in a 20 µL total volume with at least two technical repeats. The qPCR mix consisted of 10 µL TaqMan Universal real-time PCR master mix (2X) (Applied Biosystems), 2 µL DNA template, 0.5 µL FvPrb-3 (10 µM), 0.5 µL for F6-3 and R6 primers (20 µM, Table2-2), 0.6 µL HHIC-F (20 µM) primer, 0.2 µL HHIC-R (20 µM), 0.4 µL HHIC-prb (10 µM), 0.5 µL linearized HHIC DNA plasmid (10 fg/ µL) (Haudenshield and Hartman, 2011), 0.4 µL of 10 mg/mL BSA and 4.4 µL distilled water (Gibco, Grand Island, NY). qPCR conditions were initialized with one cycle incubation at 50ºC for 2 min to activate uracil-N-glycosylase (UNG), one cycle at 95ºC for 10 min, 40 cycles at 95ºC for 15 s, 60ºC for 1 min, with fluorescent data collection in the annealing and extension step.  Table 2-2 Primers and probes used in the qPCR quantification assays. Names - Length (nt) Tm (°C)a F6-3bde GTAAGTGAGATTTAGTCTAGGGTAGGTGAC 30 57.8 R6be GGGACCACCTACCCTACACCTACT 24 59.6 FvPrb-3be 6FAM-TTTGGTCTAGGGTAGGCCG-MGBNFQ 19 70.0 R9bd CCATCCGTCTGGGAATTTTAACTA 24 59.3 HHIC-Fc CTAGGACGAGAACTCCCACAT 21 54.6 HHIC-Rc CAATCAGCGGGTGTTTCA 18 55.0 HHIC-prbc 5HEX-TCGGTGTTGATGTTTGCCATGGT-3IABkFQ 23 65.2 a Tm values were calculated using ABI PrimerExpress 3.0 - Primer Probe Test Tool b Primers and probes developed in this study c Primers and probes developed by Haudenshield and Hartman (2011) d Primers used for conventional PCR assay e Primers and probes used for qPCR assay   42 Real-time PCR specificity and sensitivity tests For the specificity tests, 100 pg of genomic DNA template of target and non-target species (listed in Table2-1) were tested against the designed assays. Only the assays with specificity to F. virguliforme were evaluated in the validation step. To determine the PCR efficiency and sensitivity, a ten-fold F. virguliforme genomic DNA serial dilution from 10 ng to 1 fg was tested with the specific qPCR assay. PCR efficiency was calculated with the formula: efficiency = 10(-1/slope) -1 (slope was calculated from the linear regression between DNA log10 (template DNA concentrations) and Ct values). Fusarium virguliforme genomic DNA serial dilutions were diluted with 1 ng/µL salmon sperm DNA (Invitrogen, Carlsbad, CA) to increase the stability of the low concentration DNA and protect against degradation or affinity to the plastic tubes.  Conventional PCR amplification parameters Multiple primer sets were designed for the conventional PCR assay. To ensure ease of PCR amplification and product visualization in an agarose gel, the amplicon size was designed to be between 300 to 400 bp. Conventional PCR assays were performed with an ABI 2720 Thermal Cycler (Applied Biosystems). Conventional PCR was performed in a total volume of 25 µL, which included 2.5 µL 10X DreamTaq DNA buffer (Thermo Fisher Scientific, Waltham, MA), 0.2 µL 25 µM dNTPs (Promega, Madison, WI), 1 µL of 10 µM primers (Sigma-Aldrich, St. Louis, MO, Table2-2), 0.2 µL (5U/µl) DreamTaq DNA polymerase (Thermo Fisher Scientific), 0.5 µL 10 mg/mL BSA, 2 µL DNA template and distilled water to a total volume of 25 µL. PCR cycling conditions were set at one cycle at 94ºC for 3 min, followed by 35 cycles of 30 s at 94ºC, 30 s at 65ºC, 30 s at 72ºC and a final extension for 5 min at 72ºC. PCR products were separated and visualized on a 1.5% agarose gel at 85 V for 45 min and stained with 1 µg/mL ethidium bromide. Genomic DNA of F. virguliforme was serially diluted 1:10 from 10 ng to 1 fg to test   43 the sensitivity of the conventional PCR assay. Also, 100 pg of genomic DNA from the specificity check panel (Table2-1) was tested against the conventional PCR assay. DNA extraction from soil To determine the applicability of the qPCR assay to soil samples, DNA was extracted from artificially inoculated and naturally infested soil samples. In artificially inoculated soil samples, 0.5 g of soil was inoculated with 100 µL 1:10 serially diluted F. virguliforme spore suspensions in sterile deionized water, with concentrations ranging from 108 spores/mL to 100 spores/mL. A half gram of moist Riddles-Hillsdale sandy loam soil was placed into a 2 mL lysing matrix E tube with the FastDNA SPIN kit for soil (MP Bio, Solon, OH). After adding 978 µL of sodium phosphate buffer and 122 µL of MT buffer, the tubes were placed in a FastPrep FP120 instrument (MP Bio) and homogenized twice at speed 6 for 40 s. Subsequent steps were performed according to the manufacvolume of 100 µL of DES buffer (MP Biomedicals). DNA extraction from soybean roots Soybean roots with SDS-like foliar or root rot symptoms were collected from commercial fields and from samples submitted to the MSU Diagnostic Services Laboratory. Roots were washed with tap water, rinsed with deionized water, and patted dry with paper towel. Lateral or tap root tissues with obvious discoloration or lesions were preferred for DNA extraction; two technical replicates were made for each processed sample. One hundred milligrams of root tissue was added to a 2-mL lysing tube with five 1-mm diameter glass beads and one 2-mm diameter glass bead (except for samples processed in the Diagnostic Service Laboratory, where Lysing matrix A with two ceramic beads instead of the glass beads). Four hundred microliters of AP1 buffer from the Qiagen Plant DNeasy mini kit (Qiagen) was added to each of the 2-mL lysing   44 tubes and the mixture was homogenized in a FastPrep FP120 instrument (MP bio) twice at speed setting 6 for 40 s. After homogenization AP2 buffer (130 µL) was added to each tube and tubes were cooled on ice for 5 min, then spun at 16,000 g for 6 min. Subsequent DNA purification using the Quant-iT dsDNA high-sensitivity assay kit (Invitrogen) on a 96-well SAFIRE microplate reader (TECAN).  Copy number of rDNA in F. virguliforme  Three single--tubulin (HM453328.1), G3PD (glyceraldehyde 3-phosphate dehydrogenase), and FvToxin-1 (JF440964.1)) were selected as single copy reference genes to determine the rDNA copy numbers among F. virguliforme isolates collected from multiple soybean fields. These genes were confirmed as single copy by BLASTn search against the F. virguliforme genome sequence (Srivastava et al., 2014). Primers specific to each of these three genes were designed using Primer Express 3.0 (Applied Biosystems) as listed in Table2-3. The PCR efficiency of these three primer sets was evaluated by running a five-fold serial dilution of F. virguliforme genomic DNA ranging from 10 ng to 1 pg. Quantification of the rDNA and the three single-copy genes was performed using SYBR green based qPCR, which consisted of 10 µL of 2X SYBR Green PCR Master Mix (Life Technologies), 0.5 µL of each primer (20 µM), 0.2 µL BSA (20 mg/mL) and distilled water (Gibco) to a total-volume of 20 µL in duplicate for each template DNA. Several genotypes were identified in a multilocus genotyping study of F. virguliforme (Wang and Chilvers, unpublished). Therefore, to maximize the genetic diversity among the tested F. virguliforme isolates, multiple isolates from different genotype groups were included in the rDNA copy number variation test. PCR cycling conditions were set at one cycle at 95ºC for 10 min, followed by 40 cycles of 15 s at 95ºC, 60 s at 60ºC (fluorescent signal was   45 collected at this stage).  After the qPCR cycling stage, the melt curve stage was performed to determine the specificity of each primer set. The melt curve stage parameter was set as: one step with 15 s at 95ºC for dissociation, 60 s at 60ºC for re-association, and increase the temperature from 60ºC to 95ºC with 0.3ºC increments using the step and hold method. Table 2-3 Primers for determining rDNA IGS copy number variation Names - Length (nt) Tm(°C)a PCR efficiency Amplicon size gpd_Fb CTTGATGGCCTGCTTGATCTC 21 58.5 94.72% 74 bp gpd_Rb ACGTCTCCGTTGTCGACCTTA 21 58.3 b-tub_Fc CTGCAGCAGCTTCATCATGAG 21 58.4 99.36% 67 bp b-tub_Rc CCGGTCTGGAGGTGAACCT 19 58.5 FvTox1_Fd CAACAACACCCATCGCTAACG 21 60.0 95.12% 68 bp FvTox1_Rd GGGAGGACCCCAGTTTTCC 19 59.2 a Tm values were calculated using ABI PrimerExpress 3.0 (ABI) - Primer Probe Test Tool b gpd: designed based on the F. virguliforme glyceraldehyde 3-phosphate dehydrogenase gene  c b-tub: designed based on the beta-tubulin gene d FvTox1: designed based on the FvTox1 gene, FvTox1: phytotoxin gene  Real-time PCR assay cross-laboratory and platform validation Specificity and sensitivity of the qPCR assay was evaluated on a different platform, in was performed on the Cepheid SmartCycler real-time PCR system (Cepheid, Sunnyvale, CA), in a 20 µL total volume with two technical repeats. The qPCR master mix and cycling conditions were the same as previously described and sensitivity was evaluated with serially diluted F. virguliforme genomic DNA from 10 ng to 10 fg with a 1:10 dilution factor. Specificity of this assay was tested against closely related Fusarium species (F. tucumaniae, F. brasiliense, F. phaseoli, F. crassistipitatum, and F. cuneirostrum). Additionally, soybean samples from commercial fields submitted for disease diagnosis were tested with this assay. DNA extraction from soybean samples was performed as described above.   46 Data analyses Real-time PCR data were collected and analyzed using StepOne Software v2.3. qPCR standard curve plots and DNA concentration correlations were plotted using the R ggplot2 package (Wickham, 2009). The limit of detection was determined by 100% amplification of the lowest amount of target genomic DNA(Bustin et al., 2009). Statistical analysis was performed with the R stats package. Results Assay design and in silico screening  Based on the multiple sequence alignment conducted with MUSCLE, two polymorphic regions were identified as candidates for design of F. virguliforme specific assays. One of the polymorphic regions (from 481 to 777 bp) has a self-repeat (from 1736 to 2034 bp) in the IGS rDNA region (Figure 2-4). Primers designed on the self-repeat region were determined to be less specific to F. virguliforme because of the ambiguous nucleotide mismatches with the self-repeat region. Instead, the other polymorphic region (between 1.2 kb and 1.4 kb) of the IGS rDNA with moderate polymorphism did not have any self-repeats and was chosen as the target to design primers and probe for the final assay. Multiple primers (n=35) and dual-labeled probes (n=5) were designed on the polymorphic sites. Before testing primer sets, the secondary structure of the designed PCR assay amplicons were predicted using the mfold (Zuker, 2003) web server under PCR annealing conditions. The best amplicon candidates had higher free energy and fewer stem-loop structures as demonstrated in the assay (Figure 2-5).  Specificity and sensitivity of real-time PCR  A total of 30 primers and 6 probes were tested during assay development. One primer set (F6-3 and R6, Table 2-2) amplifying a 76-bp product from F. virguliforme and one TaqMan   47 probe (FvPrb-3, Table 2-2) were determined to be the most specific and sensitive primer and probe combination. The qPCR assay detected all tested target F. virguliforme strains at 100 pg of DNA with a Ct value range between 20.53 and 21.78, while the Ct value for the other non-target species were all above the preset Ct values of the lower limit of detection (LOD-Ct = 30, Table2-1). There was a linear relationship (y = -3.3x + 36 R² = 0.996) between F. virguliforme genomic DNA serial dilutions (log transformed) and Ct values, and the corresponding PCR efficiency was 99% (Figure 2-1). The genomic DNA serial dilution standard curve indicated that the qPCR assay consistently detected 10 fg of F. virguliforme genomic DNA (Ct = 33.2). The cut-off Ct value threshold for positive amplification was set at 100 fg of F. virguliforme genomic DNA to remove non-specific background amplifications (Figure 2-2). In the artificially inoculated soil samples, our qPCR assay consistently detected 100 conidia artificially inoculated in 0.5 g soil samples (Figure 2-3).    48  Figure 2-1 Standard curve for absolute quantification of F. virguliforme genomic DNA (fg). Genomic DNA samples were prepared from pure cultures grown in broth. The detection limit for pure culture genomic DNA was 100 fg. Two technical repeats for each F. virguliforme genomic DNA dilution level.    49  Figure 2-2 qPCR quantification of DNA samples isolated from artificially inoculated soil samples with serially diluted F. virguliforme macroconidia suspension. Detection limit was 100 macroconidia per 0.5 g soil. Total soil DNA were isolated from six soil sample replicates, and qPCR was run twice for each soil DNA sample.    50  Figure 2-3 qPCR standard curve plotted with serially diluted genomic DNA (log transformed) in fg against Ct values with solid circles. Sensitivity of the assay was determined to be 100 fg of F. virguliforme genomic DNA.     51 Conventional PCR assay  The conventional PCR assay developed is specific to F. virguliforme, amplifying a 375 bp DNA fragment. The conventional PCR was specific to F. virguliforme except for weak amplification of two non-targets Fusarium species, F. crassistipitatum and F. brasiliense (product size = 375 bp). To overcome the nonspecific amplifications, the annealing temperature was raised from 60ºC to 65ºC, which successfully eliminated amplification of these non-target species (Figure S2-4). The detection limit of the conventional PCR was determined to be 1 pg of F. virguliforme genomic DNA (Figure S2-3).  Validation of real-time PCR assay and conventional PCR assay  Both conventional PCR and real-time PCR were used to diagnose soybean plants collected from commercial soybean fields in Michigan from 2011 to 2013. Based on the diagnostic results (Table 2-5), SDS-like symptomatic soybean plants were not always associated with the positive detection of F. virguliforme DNA from soybean root tissues. For example, samples collected from Midland county Michigan (Midland-12-02) in 2012 were detected to be SDS negative, but tested positive for Phialophora gregata the causal agent of Brown Stem Rot (BSR) with the BSR diagnostic qPCR assay (Malvick and Impullitti, 2007). However, three plant samples with non-typical SDS foliar interveinal symptoms (MISO2-3, GR-12-02, and SA-11-2a) were determined to have positive F. virguliforme infection (Ct values < 30). Using the conventional PCR assay, most plant samples (27 out of 38) were diagnosed with definitive results, as indicated with intense bands. However, the sensitivity of the conventional PCR assay was limited, potentially causing false negative results for 11 out of 38 samples. In contrast, the qPCR assay gave unambiguous diagnostic results by simultaneously utilizing quantitative Ct values and a reliable exogenous control assay. In diagnostic samples with abundant F. virguliforme DNA the   52 exogenous control may perform abnormally (e.g. no exponential amplification or early amplifications). The exogenous control assay began to be affected when the F. virguliforme genomic DNA reached 1 pg or more in the diagnostic samples (Figure S2-5). rDNA IGS copy number variation between isolates  Amplification efficiencies of the three single copy genes were all very similar at 95%, 99%, -tubulin, and FvTox1 assays, respectively. Less than 5% difference in efficiency exists between the qPCR assay and the single copy gene assays, indicating a valid rDNA copy number analysis. Melt curve analysis of the SYBR green PCR product, detected a single peak for each primer set, demonstrating amplification of a single product. The PCR efficiency of the qPCR assay was calculated using the formula: IGS copy = (1+0.994) deltaCt, (e.g. deltaCt is the difference between Ct values of two amplified genes) given the PCR efficiency of 99.36%. The IGS rDNA copy numbers of F. virguliforme ranged from 138 to 233 with an average of 208 per haploid genome (Table 2-4).  Table 2-4 Fusarium virguliforme rDNA IGS copy number estimation using three single copy reference genes   Mean Ct difference between IGS and single copy gene     Isolatesa Btube-IGSb FvTox1f-IGSb G3PDg-IGSb Mean Ct diffc Copy numberd INMO_A6 7.57 7.64 7.66 7.62 190 INMO_C5 7.69 7.53 7.76 7.66 195 KSSH_E5 7.83 7.54 7.84 7.74 205 MIBer_A6 7.59 7.51 7.71 7.60 187 MIBer_B7 7.76 7.56 7.83 7.72 202 MIBer_E1 7.99 7.76 8.02 7.92 233 MIIN_B7 7.89 7.67 7.93 7.83 219 MISA_C4 7.30 7.26 7.50 7.36 158 MISTJ_E4a 7.80 7.71 8.06 7.86 223 MISTJ_G5 8.02 7.66 8.04 7.91 231 MITU_B4 7.90 7.79 8.03 7.91 231   53 MIVB_A5 7.21 7.02 7.25 7.16 138 22292-Mont1 7.54 7.56 7.87 7.66 194 a Fusarium virguliforme isolates collected from Indiana (INMO), Kansas (KSSH), and Michigan (MIBer, MIIN, MISA. MISTJ, MITU and MIVB) b Ct differences between IGS rDNA assay and single copy gene assay c Average Ct for all three single copy genes d Copy number determined by mean Ct differences e Btub: beta tubulin gene f FvTox1: phytotoxin gene produced by F. virguliforme g G3PD:  glyceraldehyde 3-phosphate dehydrogenase gene  qPCR assay cross-laboratory and platform validation  The qPCR assay performed similarly on both platforms tested.  Assay sensitivity was determined to be 100 fg of F. virguliforme DNA and the assay was determined to be specific to this species. PCR efficiency of the F. virguliforme qPCR assay on the SmartCycler real-time PCR system and the StepOnePlus real-time PCR system was 99.8% and 99.0%, respectively. Sixteen soybean and one dry bean diagnostic samples were submitted from 14 counties in Michigan; 8 samples were diagnosed as positive for F. virguliforme with the qPCR assay Table 2-6. Of the 17 diagnostic samples tested on the SmartCycler system, 4 were also tested on the StepOnePlus system, producing comparable results. Ct values of DNA samples extracted from taproot versus lateral root were not significantly different (p > 0.05). Table 2-5 Soybean and dry beans samples submitted to Michigan State University Diagnostic Services Laboratory for diagnosis assayed on a SmartCycler real-time PCR system. Sample ID Root parts a County c Dilution d Ct Results 2192 Lateral Branch 1 19.32 positive Lateral Branch 1:10 22.74 Tap Branch 1 14.12 Tap Branch 1:10 18.48 2193 Lateral Branch 1 29.22 positive Lateral Branch 1:10 33.25 Tap Branch 1 26.31 Tap Branch 1:10 29.73 Table 2-   54 2198 Lateral Lenawee 1 26.42 positive Lateral Lenawee 1:10 30.19 Tap Lenawee 1 23.24 Tap Lenawee 1:10 27.11 2371 Lateral Hillsdale 1 19.44 positive Lateral Hillsdale 1:10 23.17 Tap Hillsdale 1 22.32 Tap Hillsdale 1:10 26.09 2905b Lateral Huron 1 37.99 negative Lateral Huron 1:10 UDe Tap Huron 1 UD Tap Huron 1:10 UD 3006 Lateral Allegan 1 24.41 positive Lateral Allegan 1:10 23.97 Tap Allegan 1 19.66 Tap Allegan 1:10 23.51 2603 Lateral Shiawasee 1 UD negative Lateral Shiawasee 1:10 UD Tap Shiawasee 1 UD Tap Shiawasee 1:10 UD 2591 Lateral Clinton 1 UD negative Lateral Clinton 1:10 UD Tap Clinton 1 UD Tap Clinton 1:10 UD 3059 Lateral Ingham 1 18.97 positive Lateral Ingham 1:10 23.07 Tap Ingham 1 19.36 Tap Ingham 1:10 22.81 3062 Lateral Van Buren 1 32.22 negative Lateral Van Buren 1:10 36.39 Tap Van Buren 1 37.13 Tap Van Buren 1:10 UD 3240 Lateral Tuscola 1 36.41 negative Lateral Tuscola 1:10 UD Tap Tuscola 1 34.37 Tap Tuscola 1:10 UD 3415 Lateral Tuscola 1 35.25 negative Lateral Tuscola 1:10 38.65 Tap Tuscola 1 37.56 Tap Tuscola 1:10 UD 3505 Lateral Van Buren 1 18.98 positive Lateral Van Buren 1:10 22.28 Tap Van Buren 1 17.48 Tap Van Buren 1:10 20.45 Table 2-   55 3525 Lateral Saginaw 1 UD negative Lateral Saginaw 1:10 UD Tap Saginaw 1 37.97 Tap Saginaw 1:10 UD 3851 Lateral Lapeer 1 31.08 negative Lateral Lapeer 1:10 35.28 Tap Lapeer 1 31.48 Tap Lapeer 1:10 34.21 3872 Lateral Arenac 1 UD negative Lateral Arenac 1:10 UD Tap Arenac 1 39.22 Tap Arenac 1:10 UD 4532 Lateral Lenawee 1 26.73 positive Lateral Lenawee 1:10 31.16 Tap Lenawee 1 36.86 Tap Lenawee 1:10 UD a Root portion used for DNA extraction;  b Dry bean samples;  c Michigan county;  d DNA dilution level from the original DNA extraction; and  e UD undetected  Table 2- Table 2-   56 Table 2-6 Diagnostic results for commercial soybean samples on StepOnePlus real-time PCR system. Results include isolation on semi-selective media, conventional PCR, plant symptoms, and qPCR Ct values. Sample ID Sourcesa (County) Isolationb CPCRc qPCR Fv Ct1 d Fv Ct2 HHIC Ct1e HHIC Ct2 Description of symptoms Coleman Midland NEGf POSg POS 15.64 14.90 UDf UD Foliar SDS symptoms, leaf chlorosis and necrosis Razjer Van Buren NEG NEG NEG UD UD UD UD Root rot Avery Van Buren NEG ?h POS 29.48 31.36 29.48 28.64 SDS-like foliar symptoms EA-13-1 Eaton POS POS POS 31.42 29.55 28.26 28.21 SDS-like foliar symptoms discoloration of taproot MtC-13-1 Montcalm POS POS POS 29.59 27.76 28.23 28.15 SDS-like foliar symptoms IN-13-1 Ingham NEG NEG NEG 32.78 31.31 28.10 28.09 White mold symptoms and chlorosis on leaves MISO2-7 Midland NEG NEG NEG 31.38 31.27 29.42 29.47 Foliar symptom on the lower part plant, possible herbicide  CL-12-01 Clinton NEG NEG NEG 34.97 33.44 29.04 29.31 Foliar SDS-like symptom, but not typical  IO-12-01 Ionia NEG NEG NEG 34.62 33.55 29.25 29.23 Foliar chlorosis symptom EA-12-01 Eaton NEG ? NEG 32.33 32.28 29.08 29.21 no SDS foliar symptoms TU-12-01 Tuscola NEG ? NEG 32.93 30.88 29.27 29.18 Manganese deficiency symptoms GR-12-01 Gratiot POS POS POS 31.34 24.15 29.34 28.93 Typical SDS-like symptoms, wilting IS-12-01 Isabella NEG NEG NEG 34.67 34.52 29.16 28.99 no SDS foliar symptoms MISO2-3 Allegan NEG POS POS 29.68 26.78 29.22 29.06 Both oomycete and fungal wilting symptom IN-12-01 Ingham NEG NEG NEG 33.18 35.22 29.20 29.39 Interveinal chlorosis, lack of hairy roots, may be BSR pith dark brown, but BSR assay negative GR-12-02 Gratiot NEG POS POS 33.09 27.96 29.22 28.95 Wilting and leaves chlorosis Midland-12-02 Midland NEG ? NEG 35.52 34.88 29.29 29.13 SDS-like foliar symptoms, brown pith, BSR assay positive DL201203875 Monroe POS POS POS 18.59 -i UD - Brown stem, leaves discoloration  EA-12-02 Eaton NEG ? NEG 32.71 31.7 29.13 29.236 Phytoplasma symptoms, but no SDS symptoms    57 SA-11-2a Saginaw POS POS POS 20.55 - *23.24 - Plant wilting, defoliation, and Dead SA-11-2b Saginaw NEG POS POS 21.16 - UD - Foliar SDS-like symptom HU-11-1b Huron POS POS POS 21.50 - UD - Foliar SDS-like symptom HU-11-1 (DL-6) Huron POS POS POS 21.89 - *23.90 - Foliar SDS-like symptom DL-5 Lenawee POS POS POS 20.21 20.83 UD UD Foliar SDS-like symptom SA-11-1 Saginaw POS POS POS 24.32 25.84 28.18 27.766 Foliar SDS-like symptom CL-11-1 Clinton POS POS POS 24.00 - *26.97 - N/A GR-11-2b Gratiot POS POS POS 28.22 - 28.2 - Foliar SDS-like symptom CL-11-2 Clinton NEG POS POS 28.23 - 28.09 - N/A GR-11-2a Gratiot NEG POS POS 28.03 - 28.31 - Plant Defoliation and Dead IN-11-1 Ingham NEG POS POS 29.75 - 29.29 - Foliar SDS-like symptom TUS-11-2a Tuscola NEG ? NEG 33.58 - 28.43 - Leaf burn/ herbicide/desiccant damage TUS-11-2b Tuscola NEG ? NEG 33.38 - 28.09 - Foliar SDS-like symptom TUS-11-1b Tuscola NEG ? NEG 33.85 - 28.08 - Stunted beans/yellowing/root rot HU-11-2 Huron NEG ? NEG 32.55 - 28.34 - SDS-like symptoms TUS-11-1a Tuscola NEG ? NEG 33.79 - 28.18 - N/A DL-4 Kalamazoo NEG ? NEG 34.71 33.94 28.32 28.476 Foliar SDS-like symptom TUS-11-3 Tuscola NEG NEG NEG 34.35  - 28.14  - Foliar SDS-like symptom  a County in Michigan where samples were collected from;  b Positive isolation was identified by colony morphology; c CPCR, conventional PCR assay results;  d Two DNA extractions were made for qPCR detection;  e Internal control to assess the potential PCR inhibitors, *abnormal Ct values affected by F. virguliforme assay competition;  f NEG: negative results;  g POS: positive results;  h ? unclear results, i.e. weak band i not applicableTable 2-   58 Discussion Soybean SDS is a major threat to soybean production in the United States. Specific and sensitive molecular diagnostic assays are lacking, which hampers diagnosis, and limits epidemiological studies of the disease. To address this need, we developed a specific and sensitive qPCR assay to quantify F. virguliforme in soybean tissue, soil, and environmental samples. The assay was specific to F. virguliforme when tested against closely related Fusarium species and other commonly encountered fungi in soybean fields. The assay is very sensitive and can detect as little as 100 fg of F. virguliforme genomic DNA or 100 macroconidia per half gram of soil. An exogenous control was multiplexed with the F. virguliforme assay to detect false negatives and monitor for the presence of PCR inhibitors. In addition, the assay was validated with soybean samples collected from commercial soybean fields. The qPCR assay is transferable and successfully performed on a Cepheid SmarterCycler real-time PCR platform at the North  The choice of genetic locus and in silico sequence analysis plays a pivotal role in development and performance of a qPCR assay. Revision of the taxonomy of Fusarium species that cause SDS or bean root rot has resulted in the obsolescence of previously developed assays. However, the taxonomic revision and loci used for phylogeny construction also provided an opportunity for the development of more specific diagnostic assays. The phylogenetic tree constructed with IGS rDNA sequences explicitly separated the Fusarium species in the SDS-BRR (bean root rot) clade with high branch support (O'Donnell et al., 2010). Therefore, the IGS rDNA was determined to be a suitable target locus to design an assay for the specific detection of F. virguliforme. The multi-copy nature of the IGS rDNA target was also desirable from the standpoint of assay specificity.    59 An optimized multiple sequence alignment is essential for identifying suitable polymorphic regions within a locus for species-specific primer and probe design. Different sequence alignment methods (MUSCLE, MAFFT, ClustalW, and T-Coffee) were used to align the IGS rDNA sequence of SDS-BRR clade Fusarium species. The multiple sequence alignments of the IGS rDNA loci required the insertion of a considerable number of gaps, which increased the complexity of the alignment. Alignments with ClustalW (Larkin et al., 2007) and T-Coffee (Notredame et al., 2000) resulted in alignments that were not optimal for locating polymorphic loci. However, the use of methods such as MUSCLE (Edgar, 2004) and MAFFT (Katoh and Standley, 2013) resulted in the identification and validation of polymorphisms suitable for specific species discrimination.  Sensitivity is also a significant factor that determines the applicability of a qPCR assay. For the previous qPCR assays designed for F. virguliforme, two targeted the small subunit of the mitochondrial DNA and the other was designed on the FvTox1 toxin-coding gene (Gao et al., 2004; Li et al., 2008; Mbofung et al., 2011). Although the limit of detection for the mtDNA assays was 90 fg genomic DNA, these two assays are not specific to F. virguliforme based on the discovery of four SDS and three BRR Fusarium pathogens (Aoki et al., 2005; Aoki et al., 2012). The assay designed to the FvTox1 is specific, however the assay is not as sensitive, because it requires 25 pg of genomic DNA. We designed our qPCR assay to the IGS rDNA region, and the limit of detection was determined to be 100 fg of genomic DNA (approximately equivalent to the DNA quantity of four haploid genomes of F. virguliforme), which is 100 times more sensitive than the single copy FvTox1 gene assay.  Ribosomal DNA copy number variation and IGS rDNA sequence variation among isolates have been reported in several fungal species (Herrera et al., 2009; Akamatsu et al., 2012;   60 Bilodeau et al., 2012). Variation in rDNA copy number within species may cause variability in qPCR assay sensitivity. IGS rDNA sequence variation within species may affect the specificity of the qPCR assay as well. In this study, we tested our qPCR assay with numerous isolates of F. virguliforme obtained from isolates representing the primary genetic groups (Wang and Chilvers, unpublished) from geographically distant locations from within the United States. Although the rDNA copy number ranged from 138 to 233 with an average number of 208 copies per haploid genome, the largest difference in Ct value was 0.76 cycles, which is within the same order of magnitude. Therefore, the differences in rDNA IGS copy numbers did not cause significant differences in qPCR quantification or diagnostic detection. Although improved sensitivity and specificity are ideal, consistent performance in dealing - diagnostic assay results are strong evidence in determining a diagnostic conclusion, especially when the submitted plant samples lack other specific symptoms or signs. Because of PCR inhibitors in plant DNA or plant tissue sampling bias, the molecular diagnostic assay detection results can be inconsistent (Table 2-5). To avoid the potential inconsistent detection of F. virguliforme during diagnostics, we implemented PCR additives (e.g., BSA), which alleviated the PCR inhibition effects. Multiple (n>=2) biological and technical repeats are recommended to improve consistency in the diagnostic assay. Normally, without competition or inhibition, the HHIC exogenous control assay will produce a Ct value of 29. However, inhibitors in the DNA samples can prevent PCR amplification for the F. virguliforme assay and exogenous control assay. To overcome the PCR inhibition issues, magnetic bead based DNA sample purification can be used to reduce PCR inhibitors in DNA samples. Furthermore, PCR inhibitions are not the only cause for lack of amplification in the exogenous control assay. Too much F. virguliforme genomic   61 DNA in the sample can also cause no amplification or malfunction of the HHIC exogenous control assay. When the F. virguliforme genomic DNA reaches 1 pg in the reaction, the HHIC exogenous control assay is affected. Therefore, interpretations of the HHIC exogenous control results help evaluate the performance of the F. virguliforme assay. Stable performance across labs is one of the most important criteria of a qPCR diagnostic assay. The performance of a qPCR assay can be vulnerable to changes in the PCR reagents or real-time PCR systems (Hayden et al., 2008). It may require efforts to optimize the qPCR conditions to perform a robust qPCR assay with the desired performance. Use of a qPCR assay on a new platform, change in PCR master mix, or use in a new lab should always be implemented with the appropriate validation. To explore the applicability of our qPCR assay on another real-time PCR systems, we collaborated with the Michigan State University Diagnostic Services Laboratory. Assay performance was tested on a Cepheid SmartCycler real-time PCR system, which is the most popular system in the North Central region of the National Plant Diagnostic Network. Without changing the reagents or cycling parameters, this assay successfully achieved the desired performance (specificity, sensitivity and PCR efficiency), demonstrating high transferability of this assay between platforms. In addition, the Michigan State University Diagnostic Services Laboratory used this assay for the diagnostic evaluation of soybean and dry bean plants with SDS and/or root rot symptoms (Table 2-6). Although both taproot and lateral root tissues were used for DNA extractions, the detection results (Ct values) were not significantly different. The F. virguliforme-specific qPCR assay presented here allows for the rapid and accurate quantification of F. virguliforme, which will facilitate epidemiological studies and diagnosis of this pathogen in plant, soil and environmental samples. The assay may be used to screen for root   62 rot resistance where visual estimation or culture-based methods are difficult or not practical. In addition, the assay may be used to potentially predict the pre-plant risk of SDS development if F. virguliforme inoculum thresholds can be established.    63 Acknowledgement We are grateful to Dr. James Haudenshield and Dr. Glen Hartman of the USDA-ARS at the University of Illinois at Urbana Champaign for generously providing the HHIC plasmid. We thank Esther Gachango and Dr. Willie Kirk for sharing Fusarium strains. This work was supported by grants from Project GREEEN (#GR10-113), the Michigan Soybean Promotion Committee, and the A.L. Rogers Endowed Research Scholarship.  64 APPENDIX   65 Supplementary figures  Figure S 2-1 Sequence self-dot plot of the IGS rDNA of F. virguliforme. Showing a repeat that may cause mis-binding in PCR assay. The plot was generated using the dottup package from EMBOSS.    66  Figure S 2-2 Simulation of target amplicon secondary structure at annealing stage of the qPCR conducted using mfold    67  Figure S 2-3 Assay sensitivity and specificity test, L: 1 kb+ DNA ladder, lane1  lane 6: 1 ng through 10 fg F. virguliforme genomic DNA, lane 7  lane 14: panel of F. virguliforme isolates at 100 pg genomic DNA, lane 15  lane 22: panel of other Fusarium spp. at 100 pg genomic DNA (F. tucumaniae, F. brasiliense, F. phaseoli, F. phaseoli, F. crassistipitatum, F. cuneirostrum, F. tucumaniae, and F. brasiliense)     68  Figure S 2-4 Assay specificity test and validation, L: 1 kb+ DNA ladder, lane 1  lane 6, lane 8, and lane 11  lane 14: Other Fusarium spp.; lane 7, lane 9  lane 10, and lane 15: F. virguliforme isolates; lane 18  lane 21: SDS soybean root tissue DNA; lane 16, lane 17, and lane 22: NTC    69  Figure S 2-5 Interference of the F. virguliforme qPCR assay to HHIC exogenous assay in the serially diluted genomic DNA samples. 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The genome sequence of the fungal pathogen Fusarium virguliforme that causes sudden death syndrome in soybean. PloS one 9:e81832. Wickham, H. 2009. ggplot2: elegant graphics for data analysis. Springer Science & Business Media. Wrather, J., and Koenning, S. 2010. Suppression of soybean yield potential in the continental United States by plant diseases from 2006 to 2009. Plant Health Progress HP-2010:1122-1101. Wrather, J., and Koenning, S. (2011). Soybean disease loss estimates for the United States, 1996-2010. (http://aes.missouri.edu/delta/research/soyloss.stm ) Access date: Dec 2013  2013 Zuker, M. 2003. Mfold web server for nucleic acid folding and hybridization prediction. Nucleic Acids Res 31:3406-3415.     75 CHAPTER 3 TEMPORAL DYNAMICS OF FUSARIUM VIRGULIFORME IN SOYBEANS     76 Abstract Sudden death syndrome (SDS) is one of the most yield limiting soybean diseases in the United States. SDS symptoms include two major components: root rot caused by Fusarium virguliforme infection and foliar symptoms induced by fungal toxins. Planting SDS resistant soybean cultivars is one of the most effective disease management options, but no cultivar displays full resistance. In breeding programs, SDS foliar symptoms are used as the major phenotype in screening for SDS resistant soybean germplasm. Disease severity rating for resistance to F. virguliforme seldom includes examination of root infection or colonization, partly due to the laborious nature of methods for quantification of F. virguliforme. In this study, two greenhouse and three field experiments were conducted to determine the temporal dynamics of F. virguliforme colonization of soybean roots using quantitative real-time PCR (qPCR). In addition, soybean cultivars with varied levels of SDS resistance were selected to identify their responses to F. virguliforme infection and colonization. In the greenhouse experiments, all soybean cultivars developed SDS foliar symptoms, but the F. virguliforme root colonization levels did not significantly correlate with SDS foliar symptoms. Fusarium virguliforme in soybean roots was detected as early as 7 days after planting (DAP) and quantification peaked at 14 DAP, and maintained a relatively stable quantity until 35 DAP. In the field experiments, varied levels of SDS foliar symptoms developed among soybean cultivars; however, the F. virguliforme infection coefficients across the cultivars were not significantly different between SDS foliar symptomatic and asymptomatic cultivars. In the 2014 field experiment where individual plants were rated, there was no significant correlation between SDS foliar symptoms and the F. virguliforme infection coefficient detected in roots. Starting from the initial V3 stage (25 DAP) sampling, F. virguliforme was detected in all soybean cultivars, with F. virguliforme infection coefficient increasing in roots over time, reaching a maximum level for all soybean   77 cultivars at the post-harvest stage (153 DAP). The area under the F. virguliforme infection coefficient curves was not significantly correlated with the SDS foliar symptoms in both greenhouse and field experiments. Collectively, appearance and disease severity ratings of SDS foliar symptoms were not associated with F. virguliforme infection coefficient in roots, thus it suggests a need to include F. virguliforme root colonization as a breeding trait to screen soybean germplasm for resistance to F. virguliforme. Keywords:  cultivar resistance, Fusarium virguliforme, root colonization, soybean sudden death syndrome, temporal dynamics  Introduction The causal pathogen of sudden death syndrome (SDS) of soybean in North America is the soilborne fungus, F. virguliforme (Aoki et al., 2003). Soybean resistance to SDS is composed of two major components: resistance to foliar chlorosis and necrosis as a result of phytotoxins produced by F. virguliforme, and resistance to root infection by F. virguliforme (Kazi et al., 2008). SDS foliar symptoms have been extensively used as a phenotype in soybean breeding programs to evaluate soybean germplasm for resistance (Njiti et al., 1996; Clark et al., 2013; Wen et al., 2014), while soybean resistance to F. virguliforme root infection has seldom been evaluated (Njiti et al., 1997; Luckew et al., 2013). Evaluation of soybean root resistance to F. virguliforme has primarily relied on visual rating of percent root surface discoloration (Ortiz-Ribbing and Eastburn, 2004; Srour et al., 2012) or the number of F. virguliforme colonies formed on taproot slices (Njiti et al., 1997; Luo et al., 1999). These methods, are either irreproducible across labs or inaccurate in quantification of F. virguliforme. With increased interest in breeding soybeans that are resistant to F. virguliforme root infection, there is a critical   78 need to develop an accurate and reproducible disease rating method for evaluating soybean resistance to F. virguliforme root infection. SDS disease symptoms include both root rot caused by fungal infection and interveinal leaf chlorosis and necrosis as a result of toxins produced by F. virguliforme (Ji et al., 2006; Brar et al., 2011; Chang et al., 2015). SDS root infections and foliar symptoms are not simultaneous. Root rot symptoms can be detected as early as the seedling stage (VC-V1), while the earliest foliar symptoms develop at the V3-V4 vegetative growth stage. The onset of full SDS foliar symptom development usually occurs at the early reproductive stage (R1) (Roy et al., 1997). Although the foliar symptoms are the key identification feature for SDS disease diagnosis, the pathogen has never been recovered from above ground tissues (Rupe, 1989). Early in the growing season, F. virguliforme conidia germinate and form germ tubes to infect soybean taproots, and infections can be detected as early as 18 days after planting in field conditions (Njiti et al., 1997; Gao et al., 2006). In a greenhouse experiment, Huang and Hartman (1998) were able to recover F. virguliforme within 10 days after planting. The xylem plays an integral role in translocating fungal toxins from the roots to the leaves, and causing SDS foliar symptoms (Abeysekara and Bhattacharyya, 2014). Therefore, it was proposed that early infection at the seedling stage is necessary for F. virguliforme penetration into xylem tissues in order to induce foliar phytotoxic symptoms (Navi and Yang, 2008; Gongora-Canul and Leandro, 2011a). Unfavorable conditions for soybean seedling germination, such as low temperature, high precipitation, high soil moisture, and compacted soils may increase the risk of infection by F. virguliforme, (Scherm and Yang, 1996; Gongora-Canul and Leandro, 2011b). To avoid these unfavorable conditions during germination or emergence, delayed planting has been suggested as a possible SDS management strategy   79 (Hershman et al., 1990; Roy et al., 1997), however there are often yield penalties associated with late planted soybeans, and the short window for field operations mean that this is often not a viable management option (Kandel et al., 2016). Soybean resistance to SDS is primarily determined by visual evaluation of foliar symptom development (Njiti et al., 1996; Clark et al., 2013). The examination of the roots for discoloration or rotting symptoms has been rarely used to resolve soybean resistance to F. virguliforme root infection (Kazi et al., 2008; Gongora-Canul et al., 2012). The relationship between SDS foliar symptom severity and F. virguliforme infection levels is not clear, as root infection by F. virguliforme does not necessarily cause SDS foliar symptoms. SDS foliar and root rot phenotypes have been mapped on separate quantitative trait loci (Kazi et al., 2008). In addition, yield loss due to F. virguliforme infection is difficult to estimate, due to the lack of effective tools for quantifying F. virguliforme colonized in soybean roots. Currently, two major root disease-rating methods (i.e., visual rating of percent root discoloration and counting colony forming units (CFU) of F. virguliforme from root tissues) have been utilized for screening soybean root resistance to F. virguliforme infection (Njiti et al., 1997; Gongora-Canul et al., 2012). The culture-based quantification methods provide a good estimation of viable F. virguliforme in soybean roots or soil samples, but it can be highly inaccurate, as F. virguliforme colony growth may be out-competed by other fast growing fungi and the F. virguliforme colony morphology is not distinct from other closely related fungal species (Njiti et al., 1997; Cho et al., 2001). Also, the visual estimation of root surface discoloration can be unspecific and subjective, as root surface discoloration may be caused by other soil-borne soybean pathogens, such as other Fusarium spp., Rhizoctonia solani, or Phytophthora spp. (Dorrance et al., 2003; Arias et al., 2013), which makes this method difficult to reproduce between labs. The low sensitivity and   80 non-specific nature of the culture-based or visual rating methods may limit the interpretation of the root disease ratings, so that a method that can effectively quantify F. virguliforme in root is needed.  Quantitative real-time PCR (qPCR) provides an alternative method to the culture-based or visual rating methods. qPCR has been widely used for diagnosing and quantifying plant pathogens from soil or plant tissues (Chilvers et al., 2007; Hughes et al., 2009; Bilodeau et al., 2012). There are several qPCR assays that have been developed for the quantification of F. virguliforme in plant tissues or soil (Gao et al., 2004; Li et al., 2008; Westphal et al., 2014; Wang et al., 2015). The performance of the F. virguliforme specific assays have been compared, and each assay showed distinct variations in regard to specificity and sensitivity, with assays developed based on the rDNA intergenic spacer region demonstrating the greatest potential for F. virguliforme quantification (Kandel et al., 2015). Based on the benchmark performance related to specificity, sensitivity, and consistency, the qPCR assay developed by Wang et al. (2015) was used in this study. We set out to improve our understanding of the temporal dynamics of F. virguliforme in soybean roots and the variation of colonization across soybean cultivars in greenhouse and field conditions. The objectives of this study were to i) evaluate the colonization dynamics of F. virguliforme with qPCR in soybean root tissues throughout the growing season; ii) detect colonization differences among soybean cultivars; iii) determine the relationship between SDS foliar symptoms and root colonization by F. virguliforme.   81 Materials and Methods Fusarium virguliforme inoculum preparation Fusarium virguliforme isolates (VB2a and Mont-1) were transferred onto Nash Snyder (NS) medium (Leslie et al., 2008) and incubated at room temperature for 2-3 weeks to allow full plate colonization. Sorghum (milo maize) seeds were used as a growth substrate for F. virguliforme inoculum. Sorghum seeds were soaked overnight in deionized water, were drained to remove any excess water, and 1.8 kg of seeds were weighed out into mushroom spawn bags (Fungi Perfecti, Olympia, WA). Seeds were autoclaved at 121°C, 18 psi for 8 h and cooled at room temperature for 24 h. Five F. virguliforme colonized NS medium plates, five non-colonized NS medium plates, and 500 mL sterile deionized water were placed in a stainless steel sterile blender carafe and homogenized at low speed for 30 s. The homogenized inoculum slurry was evenly distributed across five mushroom bags containing autoclaved sorghum seeds, and bags were sealed with a heat sealer. Inoculum bags were incubated at room temperature with ambient light for 30 d and shaken every other day. After sorghum grains were completely colonized, the inoculum grains were spread onto kraft paper in trays and air dried at room temperature with the aid of a fan for 3-4 d. Greenhouse experimental design  To study the temporal dynamics of F. virguliforme colonization on roots and determine the potential variability of root colonization across soybean cultivars with varied SDS susceptibility ratings, a five-week greenhouse experiment was conducted from planting to early vegetative growth stages and replicated twice. Plants were destructively sampled at each of the five time points to quantify F. virguliforme DNA. Four soybean cultivars (AG2107, AG2002, 92M82, and 92Y53) were selected by their commercial susceptibility rating to SDS based on foliar disease   82 symptom expression (Table 3-1). The greenhouse experimental protocol was modified from the SDS inoculated-layer technique developed by R. Bowen and G. Hartman (2011, unpublished). A soil mix was prepared by homogenizing pasturized sandy loam soil and SUREMIX (Sure, Galesburg, MI) at a ratio of 1:2. Trays (10.2 × 35.5 × 50.8 cm, catalog number: 14-3401 Hummert, Earth City, MO) with 16, 5 mm diameter holes drilled into the tray bottom for drainage were used to assemble the SDS inoculated-layer screening protocol. To assemble the trays, two paper towels were placed on the bottom of the tray to prevent soil mix loss, and 3.2 L of soil mix was placed over the paper towels and gently leveled. A second layer consisting of homogenized F. virguliforme inoculum (336 mL) and 1.26 L soil mix was added evenly across the tray. An additional 3.2 L of soil mix was spread over the inoculum layer. Seven furrows were created across the width of each tray using a form constructed with seven equally spaced 6.35 mm wood strips attached to a piece of plywood cut to fit the inside dimension of the tray.  Two trays were planted for each of the five time points. Each of the seven furrows was planted with three cultivar replicates, which consisted of five seeds per replicate, each cultivar was replicated three times within a tray and planted in a completely randomized design. Finally, the seeds were uniformly covered with 1.6 L soil mix. Foliar SDS disease assessment at the 35 days after planting (DAP) followed the disease severity rating system prepared by R. Bowen and G. indicate developed symptoms of defoliation and premature death. Root rot SDS symptoms were assessed by estimating the percentage of discoloration and rot on the plant root system with a rating scale from 0 to 100%.   83 Table 3-1 Soybean cultivars used in this study. Soybean cultivars were selected based on seed industry SDS susceptibility rankings and maturity groups suitable for growing conditions in Michigan.  Varieties Relative Maturity Group SCN Resistance SDS Resistance Experiments* AG2107 2.1 PI88788 Susceptible Greenhouse and field 2012  AG2002 2.0 PI88788 Moderately resistant Greenhouse and field 2012 92M82 2.8 No Susceptible Greenhouse and field 2012  92Y53/ P92Y53 2.5 Peking (PI548402) Moderately resistant Greenhouse, field 2012, and field 2014 P92Y11 2.1 Peking  Susceptible Field 2014 P92Y51 2.5 PI88788 Moderately resistant Field 2014 P92Y60 2.6 PI88788 Susceptible Field 2014 P93M11 3.1 none Moderately resistant Field 2014 *: Soybean cultivars used for different research experiments.    84 Field experimental design  Field experiments were conducted at two locations: 1) research field site in Decatur, MI (42°7'35.86"N, 86°1'28.60"W) with sandy loam soil naturally infested with F. virguliforme and soybean cyst nematode, and 2) research field site at the Michigan State University (MSU) Agronomy Farm (42°42'40.08"N, 84°28'7.80"W) in East Lansing, MI with loam sandy soil artificially inoculated with F. virguliforme. Four soybean cultivars (AG2107, AG2002, 92M82, and 92Y53) as in the greenhouse study were planted at both locations in a randomized complete block design with five replicated plots. At the Decatur site, soybean plots were planted in six rows, 6.1 m (20 ft) long spaced 0.38 m (15 in) apart. At the East Lansing site, soybean plots were planted in two rows, 9.1 m (30 ft) long and spaced 0.76 m (30 in) apart. Planting dates were May 7 and May 11 2012 at Decatur and East Lansing, respectively. In 2014, a field experiment was conducted at the Decatur site with five soybean cultivars, P92Y53, P92Y11, P92Y51, P92Y60, and P93M11 (Table3-1). Each cultivar was replicated five times across 6-row plots in a randomized complete block design with 6.1 m (20 ft) long rows spaced 0.38 m (15 in) apart. The outer rows (i.e., first and sixth) were used for destructive root sampling, while the inner four rows were kept for harvest to estimate yield.  Disease evaluation  field trials  SDS foliar symptoms for each plot were rated for disease severity (DS) and disease incidence (DI) based on the SDS scoring method developed by C. Schmidt (2007) at Southern Illinois University. The DS scale ranges from 0 to 9, 0 representing a healthy plant and 9 representing premature defoliation of the whole plant. DI was estimated by estimating the percentage (0-100%) of SDS symptomatic plants in a plot. Disease index (DX) is a metric combining both DS and DI parameters to indicate the soybean SDS foliar symptoms present in a soybean plot; DX   85 was calculated using the formula: DX= (DI×DS)/9, where 9 represents the highest SDS disease severity rating possible. In 2014, to gain a better understanding of the relationship between F.virguliforme colonization and foliar symptom expression, the severity of SDS foliar symptoms and root rot were rated on individual plants. Fifteen whole soybean plants were sampled at four time points Each plant was rated for SDS foliar symptom severity and root rot discoloration (percentage). Root tissue was processed for subsequent DNA extraction and qPCR quantification. Sample collection and processing  In 2012, 15 roots were collected from each field plot five times from June 4 to October 9, 2014. Roots were dug with a shovel, and above ground tissues were removed. Roots were transported to the lab, rinsed under tap water to remove attached soil and debris, and oven dried per plot were measured. Dried soybean roots were ground in a Wiley mill (Thomas Scientific, Swedesboro, NJ) with a 2-mm pore size mesh screen.  Disease evaluation  greenhouse trials  Five soybean roots were collected and washed under tap water to remove the attached soil . Stems were removed by cutting at the soil line, and roots were root dry weight was collected. Soybean roots taken at V2 growth stage were dried and ground using FASTPREP tubes (MPBIO, Solon, OH) with a ceramic sphere (MPBIO) and five 2-mm glass beads. Roots collected after the V2 stage were ground using a coffee grinder (KRUPS). To avoid cross contamination between samples, the inside of the coffee grinder was rinsed with 70% ethanol and wiped dried with paper towel between samples.    86 For both field and greenhouse root tissues, an automated DNA extraction protocol was used in an AutoGenprep 850 Alpha system (AutoGen, Holliston, MA). One hundred milligrams of dried root tissue and 1 mL plant lysis solution (AutoGen) were added to a sample tube. Tubes were sealed with aluminum foil and incubated in a water bath for 1.5 h at 7samples were submitted to the Michigan State University genomics core for automated high-throughput phenol:chloroform DNA purification. The precipitated DNA pellet was dissolved in 150 µL 1X Tris-EDTA buffer. DNA was quantified using the Quant-iT dsDNA high-sensitivity assay kit (Invitrogen, Carlsbad, CA) on a 96-well SAFIRE microplate reader (TECAN, Männedorf, Switzerland).  Fusarium virguliforme root quantification qPCR was performed on the ABI StepOnePlus thermo cycler (Applied Biosystem, Carlsbad, CA) with a 20 µL total reaction volume and two technical repeats. The qPCR master mix for F. virguliforme consisted of 10 µL TaqMan Universal real-time PCR master mix (2X) (Applied Biosystems), 0.5 µL TaqMan dual labeled MGB probe FvProb3 (10 µM), 0.5 µL of each primer (20 µM, F6-3 and R6), 0.4 µL bovine serum albumin (10 mg/mL), 0.6 µL of HHIC-F primer (20 µM), 0.2 of HHIC-R primer (20 µM), 0.4 µL of HHIC-prb probe (10 µM), 0.5 µL linearized HHIC DNA plasmid (10 fg/µL) (Table 3-2) (Haudenshield and Hartman, 2011) , 4.4 µL molecular grade water (Gibco, Carlsbad, CA) and 2 µL DNA template (1:10 dilution with dH2O). qPCR cycling conditions were initialized with one cycle incubation at 50ºC for 2 min, one cycle at 95ºC for 10 min for denaturing, 40 cycles at 95 ºC for 15 s, 60ºC for 1 min, with fluorescent data collection at 60ºC step. A qPCR assay for quantification of soybean genomic DNA was designed on a single-copy beta tubulin gene from the soybean genome. The qPCR master mix for the soybean quantification assay consisted of 10 µL TaqMan Universal real-time PCR master   87 mix (2X) (Applied Biosystems), 0.5 µL TaqMan dual labeled MGB probe TUB-Prb-1 (10 µM), 0.5 µL for each primer (20 µM, TUB-F1 and TUB-R1), 0.4 µL bovine serum albumin (10 mg/mL), 6.1 µL of molecular grade dH2O, and 2 µL DNA template (1:100 dilution with dH2O). PCR efficiency for both quantification assays was determined by running a five-point standard curve ranging from 1 ng to 100 fg of genomic DNA. The qPCR conditions for the soybean genomic DNA quantification assay were optimized to be the same as the F. virguliforme qPCR assay, which makes it possible to run both assays on the same 96-well plate. Infection coefficient of F. virguliforme was calculated using the formula: CTSoy/CTFv, where CTSoy is the CT value of soybean real-time PCR assay and CTFv is the CT value of F. virguliforme real-time PCR assay. The infection coefficient (IC) is a measurement ratio between F. virguliforme and soybean, which is a normalized F. virguliforme infection value of soybean root tissue (Valsesia et al., 2005). Data analysis Real-time qPCR quantification data were collected from the real-time PCR thermal cycler using StepOnePlus software version 2.3 (Applied Biosystems). Analysis of variance (ANOVA) (R Core Team, 2015). Figures were generated  (Wickham, 2009). The areas under infection coefficient (De Mendiburu, 2014) in R.   88 Table 3-2 Primers and probes used in this study.  Primer names Sequences (w/ modifications) Final concentration Target Source F6-3 GTAAGTGAGATTTAGTCTAGGGTAGGTGAC 500nM rDNA-IGS Wang et al. 2015 R6 GGGACCACCTACCCTACACCTACT 500nM rDNA-IGS Wang et al. 2015 FvPrb3 6FAM-TTTGGTCTAGGGTAGGCCG-MGBNFQ 250nM rDNA-IGS Wang et al. 2015 TUB-F1 GCGGTGCTCATGGATCTAGAG 500nM beta-tubulin This study TUB-R1 TGACCGTAGGGACCAGATCTG 500nM beta-tubulin This study TUB-Prb-1 NED-AGGGACCATGGACAGC-MGBNFQ 250nM beta-tubulin This study HHIC-F CTAGGACGAGAACTCCCACAT 600 nM pJSH-B14 Haudenshield et al. 2011 HHIC-R CAATCAGCGGGTGTTTCA 200 nM pJSH-B14 Haudenshield et al. 2011 HHIC-Prb 5HEX-TCGGTGTTGATGTTTGCCATGGT-3IABkFQ 200 nM pJSH-B14 Haudenshield et al. 2011    89 Results Greenhouse temporal dynamics of F. virguliforme colonization In the first greenhouse experiment, F. virguliforme was detected at the initial sampling time point, 7 days after planting (DAP) in all soybean cultivars with an average infection coefficient (IC) ranging between 0.96 and 1.12 (Figure 3-1). The F. virguliforme root IC reached a maximum level of 1.19 at the second sampling time point (14 DAP). After 14 DAP, F. virguliforme relative quantities decreased for the subsequent sampling time points (21, 28, and 35 DAP), with the average IC ranged between 0.99 and 1.14. The IC of F. virguliforme colonized roots were significantly different among the four cultivars at two sampling time points (28, and 35 DAP, P < 0.01), but F. virguliforme relative quantities in the susceptible cultivars (AG2107 and 92M82) were not always significantly higher than the moderately resistant cultivars. All soybean cultivars developed foliar symptoms by 35 DAP (Figure 3-2), and foliar disease indices were not significantly different among the four cultivars (P = 0.73).    90  Figure 3-1 Temporal dynamics of F. virguliforme infection coefficient in soybean roots from two greenhouse experiments measured from 7 to 35 DAP. Four soybean cultivars were included in both greenhouse experiments, and resistance to SDS is indicated in the figure legend as susceptible (S) and moderately resistance (MR). Although significant root colonization levels were observed among cultivars at several sampling time points, no correlation between foliar symptoms and root colonization was detected. (A) Temporal dynamics of F. virguliforme infection coefficient at first experiment; (B) Temporal dynamics of F. virguliforme infection coefficient at second experiment.     91  Figure 3-2 (A) Boxplot of the area under Fusarium virguliforme infection coefficient curve (AUICC) calculated based on the temporal data of four soybean cultivars in both greenhouse experiments. No significant differences were detected among cultivars within each experiment, however the second greenhouse experiment had a higher AUICC. Dots within the figure are the data outliers in the boxplot. (B) Bar plot of SDS foliar disease rating index (DX in a scale 0-100, where 0 indicates healthy plant and 100 indicates most severe SDS foliar symptoms). Disease ratings were taken at 35 DAP in both greenhouse experiments as shown in black (first experiment) and gray (second experiment). The second greenhouse experiment showed more severe SDS foliar symptoms, which aligned with the increased F. virguliforme infection coefficient detected in soybean roots between two greenhouse experiments.   In the second greenhouse experiment, the ICs of F. virguliforme colonized in roots were higher than the relative quantities of F. virguliforme detected in the first greenhouse experiment. At the first time point (7 DAP), F. virguliforme was detected in all cultivars with average IC ranging between 1.42 and 1.46. From 7 to 14 DAP, the mean of F. virguliforme relative quantities increased from 1.43 to 2.02 with a relative colonization rate of 0.08 IC per day. For the subsequent sampling time points (21, 28, and 35 DAP), the F. virguliforme IC fluctuated between 1.78 and 2.15. In the first four sampling time points, the F. virguliforme IC did not show significant differences among four soybean cultivars (P > 0.05). At the fifth sampling time point (35 DAP), the F. virguliforme IC were significantly higher in susceptible cultivar 92M82 than the moderately resistant cultivars 92M53 and AG2002 (P < 0.01), but IC in AG2107 was not significantly higher than IC in AG2107 (P > 0.05). All four soybean cultivars developed SDS   92 foliar symptoms at 35 DAP, and SDS foliar symptom disease indices were not significantly different among cultivars (P = 0.87, Figure 3-2B). The overall F. virguliforme root colonization was estimated by calculating the area under infection coefficient curves (AUICC) for each cultivar. The AUICC in the second greenhouse experiment were greater than the AUICC in the first greenhouse experiment (P < 0.01) for all cultivars. Within each greenhouse experiment, there were no significant differences among cultivars (P > 0.7, Figure 3-2A). In the first greenhouse experiment, root dry weight increased over time and reached an average weight of 0.08 g/plant at 35 DAP, except for cultivar 92M82 (Figure S 3-1). Significant root dry weight differences among cultivars were observed at 28 and 35 DAP, with cultivar 92M82 root dry weight being significantly lower than the other three cultivars (P < 0.05). In the second greenhouse experiment, root dry weight increased over time was around 0.03 g/plant. Significant root dry weight differences were observed among the four cultivars at 14, 21, and 28 DAP, but there were no significant root dry weight differences among cultivars at time point 35 DAP.  Fusarium virguliforme colonization of field grown soybean roots In 2012, F. virguliforme root colonization was similar at both field locations. Variation in F. virguliforme IC among soybean cultivars was greater at the East Lansing field location than the Decatur site (Figure 3-3). Fusarium virguliforme was detected in root tissue with qPCR at the V3 stage of all soybean cultivars, the average F. virguliforme IC ranged between 0.91 and 1.03. The F. virguliforme IC in soybean roots was significantly higher at the East Lansing site than the Decatur site (P < 0.01) for the first three sampling time points (25, 28, 56 DAP). From the V5 to R5 stage, the F. virguliforme IC increased over time at both locations, but the Decatur site had a   93 higher root colonization rate (0.007 IC/day) than at the East Lansing site (0.003 IC/day). At 96 and 99 DAP, the F. virguliforme IC decreased by 0.060 and 0.079 at the East Lansing site and Decatur site, respectively. For the later reproductive stages (R7, R8, and Post Harvest), F. virguliforme IC started to increase again and reached their maximum colonization level at either 124 DAP or 153 DAP (post-harvest). At the Decatur site, the first appearance of SDS foliar symptoms were recorded between 40 DAP, and gradually developed into most severe foliar symptoms at 96 DAP. At the Decatur site, the average SDS foliar disease indices were 3.33, 0.56, 63.9, and 0 for soybean cultivars AG2107, AG2002, 92M82, and 92Y53, respectively. Despite the significant differences in SDS foliar symptoms among cultivars, F. virguliforme IC in roots were not significantly different among cultivars over time, except 127 DAP at the Decatur site (P < 0.05, Figure3-3B). No SDS foliar symptoms were observed at the artificially inoculated East Lansing site.    94  Figure 3-3 Temporal dynamics of the relative Fusarium virguliforme infection coefficient in soybean roots in 2012 field experiments in four soybean cultivars, two susceptible (S) and two moderately resistant (MR). Soybean cultivar susceptibility to SDS was based on industry rating on foliar symptom expression. Field locations: (A) an artificially inoculated field in East Lansing, MI; (B) a naturally infested field in Decatur, MI. The x-axis is represented by the days after planting (DAP) along with the corresponding soybean growth stage. The Y-axis is the F. virguliforme infection coefficient. F. virguliforme was detected in all soybean cultivars in both field locations from the first sampling point (V3 stage). By the end of the growing season at the post harvest stage (Post H), all cultivars in both field locations reached the same infection coefficient level.  The areas under infection coefficient curves (AUICC) was calculated to evaluate the season-long trends of F. virguliforme root colonization. In 2012, AUICCs were not significantly different among the four cultivars at both Decatur (P = 0.08) and the East Lansing site (P = 0.70) (Figure 3-4A). The AUICCs at the Decatur site were significantly higher than the East Lansing site (P = 0.02), and SDS foliar symptoms were only observed at the Decatur site (Figure 3-4B). The most severe SDS foliar symptoms (DX = 66.7) were detected on cultivar 92M82 at the Decatur site, its root colonization level was significantly lower than the other three soybean cultivars (P = 0.046, Figure 3-4A). On the contrary, the cultivars AG2002 and AG2107, that showed less severe SDS foliar symptoms (i.e., DX: 0.56 and 3.33), showed higher F. virguliforme AUICC over the season.   95   Figure 3-4 Boxplot of area under relative quantity curve (AURQC) calculated using the temporal data of F. virguliforme DNA quantified in soybean roots at both field locations. The East Lansing site is represented by dark gray, while the Decatur site with light gray. (B) Bar plot of SDS foliar disease rating at R6 growth stage for four soybean cultivars at both locations based on a plot scale disease rating. At both locations, F. virguliforme DNA was detected in considerable quantity across all soybean cultivars, however, not all soybean cultivars displayed SDS foliar symptoms. Soybean cultivar 92M82, showed the most severe foliar symptoms, but the AURQC was lower than the other three cultivars at both locations.   In 2014, F. virguliforme was detected in all five soybean cultivars with an average IC ranging between 0.92 and 1.00 at the first sampling time point at Decatur site. The F. virguliforme IC in roots slightly increased at 62 DAP, and remained stable until 93 DAP with the average IC ranging between 0.92 and 1.09 (Figure 3-5). Fusarium virguliforme root colonization was greatest at 107 DAP for all five cultivars. Three out of five cultivars developed SDS foliar symptoms at 93 DAP with disease indices: 23.0, 9.1, 0.0, 18.1, and 0.0, for cultivar P92Y11, P92Y51, P92Y53, P92Y60, and P93M11, respectively (Figure 3-6B). Although SDS foliar symptom disease indices were significantly different among cultivars (P = 0.0062), the F. virguliforme IC in soybean roots did not significantly correlate with plot SDS foliar symptoms (Spearman correlation test, P = 0.143). The AUICC did not align with the foliar symptoms at R6 growth stage (Figure 3-6).   96  Figure 3-5 Infection coefficient of F. virguliforme (CtSoy/CtFv) in the five soybean cultivars measured at five growth stages during 2014 in a naturally infected field in Decatur MI. F. virguliforme DNA was detected in all soybean cultivars starting at the V3 time point. The relative quantity of F. virguliforme DNA detected only slightly increased from the V3 to R5 stage, but a significant increase of relative F. virguliforme DNA quantity was observed between the R5 and R7 growth stages. At the R7 growth stage, two cultivars showed significantly higher F. virguliforme relative quantities in roots. F. virguliforme DNA quantities were not associated with foliar disease symptom ratings taken at the R5 growth stage.     97  Figure 3-6 Area under the relative Fusarium virguliforme DNA quantity curve (AURQC) and soybean SDS foliar disease rating index for individual plants sampled at the R5.5 growth stage for five soybean cultivars in 2014 Decatur field experiment. (A) Boxplot of AURQC calculated for the temporal dynamics data of F. virguliforme DNA quantified in soybean roots. AURQC were not significantly different among five cultivars (p=0.11). (B) Bar plot of SDS foliar disease rating index at R5.5 growth stage for five soybean cultivars. Significant difference was detected among the five soybean cultivars (p< 0.01) for SDS foliar disease rating index, however the quantified F. virguliforme in roots was at a similar level for all cultivars.   In 2014, SDS foliar and root ratings were performed on individual plants to improve the precision of disease-rating compared to whole plot foliar disease ratings. Individual plant SDS root severity indices demonstrated a positive, but non-significant correlation with the F. virguliforme relative quantities in roots (r2 = 0.02, Spearman correlation p = 0.21, Figure 3-7A). There was no correlation between individual plant SDS foliar disease indices with F. virguliforme relative quantities in roots (r2 < 0.001, Spearman correlation p = 0.80, Figure 3-7B).     98  Figure 3-7 Correlation between individually rated soybean plants for SDS symptoms and F. virguliforme infection coefficient detected in soybean roots. (A) Root rot severity rated on 15 individual plants for each of the 25 plots, based on the percentage of root surface discoloration correlated with the F. virguliforme infection coefficient in soybean roots. The y-axis is the root rot severity, and the x-axis is the relative quantity of F. virguliforme in roots. (B) SDS foliar symptom disease rating index on the same 15 individual plants from each of the 25 plots, based on disease severity rating scale from 0-9 and the disease incidence correlated with F. virguliforme relative quantities. There was no significant correlation between SDS root or foliar disease ratings and the amount of F. virguliforme quantified in soybean roots.     99 Discussion In this study, both field and greenhouse experiments were conducted to examine F. virguliforme colonization of soybean roots throughout the growing season using a qPCR based quantification method. Based on two years of field experiments, the quantities of F. virguliforme present in soybean roots were not significantly correlated with SDS foliar symptoms. Soybean cultivars with varied SDS disease susceptibility ratings based on foliar symptom development, were grown in field trials in the quantity of F. virguliforme in the roots. Therefore, screening soybean cultivars resistant to SDS based on SDS foliar symptom severity ranking does not equate to resistance to F. virguliforme root infection or colonization. Quantifying the amount of F. virguliforme present in soybean roots as infection coefficient provides a quantitative measurement to score soybean cultivars for resistance to F. virguliforme root infection or colonization. Compared to the previous culture-based methods, the qPCR method provides a fast and accurate means to quantify F. virguliforme in soybean roots. In the field experiments, the R5-R7 stages were determined to be the optimal sampling time point for F. virguliforme quantification in roots, this coincides with the full development of the foliar symptoms on soybean plants. Based on results of greenhouse experiment, the early time points are not recommended for quantification of F. virguliforme. Soybean plants grown in the greenhouse start developing SDS foliar symptoms at the V3/V4 stage or approximately 30 DAP, thereby providing an optimal time point for collecting roots for F. virguliforme quantification. Greenhouse experiments   100 ----- SDS foliar symptoms and F. virguliforme root infection/colonization SDS foliar symptom development requires root infection by F. virguliforme, but SDS foliar symptoms do not always develop after F. virguliforme root infection, and SDS foliar symptom severity is not correlated with the F. virguliforme IC detected in roots (Njiti et al., 1997; Prabhu et al., 1999, this study). SDS foliar symptoms are caused by the toxins produced by F. virguliforme that colonizes the roots; the severity or appearance of foliar symptoms should depend on the amounts of F. virguliforme toxins translocated to the leaves. Infected but SDS asymptomatic soybean plants and symptomatic plants had similar amount of F. virguliforme present in roots (Njiti et al., 1997, this study). There were no significant differences in F.   101 virguliforme quantities among plants that showed varied levels of SDS foliar symptoms (-). Based on these results, soybean cultivars may have different susceptibility to foliar symptoms, but do not differ in resistances to F. virguliforme root infections. SDS foliar symptoms were consistently developed at the naturally infested site in this study, but not at the artificially infested site. At both sites, F. virguliforme was detected at all sampling points throughout the season, but the F. virguliforme IC detected between R5 to R7 stages were significantly higher (P < 0.01) at the naturally infested field than the artificially inoculated field. At R5 to R7 stages, soybean plants usually had fully developed SDS foliar symptoms (Roy et al., 1997). The significantly higher amount of F. virguliforme detected in the naturally infested field (Decatur site) than the artificially infested field may indicate the causing of SDS foliar symptoms on the susceptible soybean cultivars associated with amount of F. virguliforme colonized in roots. After R7 growth stage, when soybean starts to senesce, F. virguliforme root IC reached similar level at both sites (Figure 3-3). Because senescent soybean roots have less live tissue (Figure S3-3), the pathogen transitions from necrotrophic to saprophytic growth, possibly explaining the fast colonization of F. virguliforme in soybean roots after R7 growth stage.  It was proposed that soybean radical roots infected with F. virguliforme at the seedling stage were more likely to develop SDS foliar symptoms (Navi and Yang, 2008; Gongora-Canul and Leandro, 2011a). Early infection by F. virguliforme allowed the pathogen to grow into the root into the vascular systems causing SDS foliar symptoms (Navi and Yang, 2008). In our study, soybean plants at the artificially inoculated site had significantly higher F. virguliforme IC than the naturally infested site at the first sampling time points (25-28 DAP), however, only plants at naturally infested site developed foliar symptoms. Therefore, the amount of F. virguliforme quantified at the early vegetative growth   102 stage may not associate with later season SDS foliar symptoms development.  Serial sampling of soybean roots for quantifying F. virguliforme Serial root sampling is not practical for soybean breeding projects, thus to select a proper time for root sampling is important to study the infection and colonization of F. virguliforme in soybean roots. In previous studies, there was no agreement on a certain time point for root sampling. For the initial root infection evaluation, Luo et al. (1999) arbitrarily used 45 DAP time point for comparing root infection levels, since they found some locations were difficult to obtain data at 30 DAP. Njiti et al. (1997) started root sampling at 8 DAP, both SDS susceptible and resistant cultivars were not detected with F. virguliforme infection until 15 or 24 DAP. In these previous studies, the inconsistent detection of F. virguliforme from soybean roots at the initial stage may have been caused by limited detection sensitivity of the culture-based methods. In this study, F. virguliforme was consistently detected in all soybean cultivars since the first sampling time point (25, 28 DAP in the field and 7 DAP in the greenhouse) using the qPCR based quantification method. Though no significant differences were detected in F. virguliforme quantity among soybean cultivars, detection sensitivity of the qPCR quantification method provided the ability to compare F. virguliforme infection at the early growth stage. In addition, Luo et al. (1999) were able to distinguish resistant and susceptible cultivars in the field study using area under population curve (AUPC) measurements. The AUPC method was also used in our study to obtain an overall estimation for F. virguliforme root colonization, because soybean cultivars showed inconsistent colonization levels at different sampling time points (Figure 3-1, Figure 3-3, and Figure 3-5). In our study, AUICCs were calculated for different soybean cultivars, AUICCs were not significantly different among soybean cultivars in the ANOVA test, thought varied levels of SDS foliar symptoms were developed on each cultivar. For example,   103 cultivar 92M82 had the most severe SDS foliar symptoms in the naturally infested field, but it it had the lowest AUICC of the soybean cultivars evaluated (Figure 3-4). On the other hand, AUICCs may not be practical measurement for soybean cultivars resistant to F. virguliforme root infections, especially in a breeding project, because of the multiple root samplings and processing are labor intensive.  Root samples collected at R6 and R8 stages were used for F. virguliforme quantification, and different QTL were associated with root quantification data collected at R6 and R8 growth stages (Kazi et al., 2008). In this study, significant differences in F. virguliforme root IC were observed among cultivars at R8 stages, but no significant differences were detected at R6 stage. At R8 stage, more than 95% of the plants reached full maturity, the differences in F. virguliforme relative quantities could possibly cause by different soybean maturity groups (Table 3-1). Early mature group cultivars tend to senescence earlier and root dry weights started to decrease earlier than the later maturity groups (Figure S3-2), so that F. virguliforme colonized well in the early matured soybean cultivar. When the soybean roots started to degrade, the life style of F. virguliforme may switch from necrotrophic stage into saprophytic stage for fast colonization, as has been observed in the Colletotrichum fungus (O'Connell et al., 2012). Therefore, root sampling between R5 to R7 stages is recommended to detect F. virguliforme as a measurement for soybean resistance to F. virguliforme root infection/colonization, but root samples from R8 or post-harvest stages are not recommended.  Quantification methods ---  104 --(Cho et al., 2001)   105 -----   106     107 Acknowledgement We would like to thank Adam Byrne and John Boyse for their assistance in fieldwork.  This research was funded by the Michigan Soybean Promotion Committee, the Carter Harrison Endowed Graduate Student Fund, and Pioneer Crop Sciences.     108 APPENDIX  109 Supplementary figures  Figure S 3-1 Dry weight of four soybean cultivars in the greenhouse experiments. Each sampling time point represents the dry weight of six replicates of five individual plants per replicate. Root dry weights were collected to estimate the overall root health as affected by F. virguliforme under greenhouse conditions. (A) the first greenhouse experiment and (B) the second greenhouse experiment.        110  Figure S 3-2 Root dry weight of 15 plants collected from five replicated plots for each of the four soybean cultivars. (A) an artificially inoculated field in agronomy farm at East Lansing site. (B) a naturally infested field in Decatur site.      111  Figure S 3-3 Throughout the growth season soybean root dry weight, soybean genomic DNA quantity, F. virguliforme DNA quantity, total DNA extracted from root tissue from V3 to Post (post harvest). Soybean root dry weight increased until R6, and started to decrease until the post harvest stage. As root dry weight decreased after R6, soybean genomic DNA also decreased dramatically. F. virguliforme DNA quantified from root increased gradually from R6 to Post harvest, but increase rapidly after R8. The amount of total DNA extracted from 100 mg of root tissues peaks at R5-R6 stages.     112 REFERENCES  113 REFERENCES  Abeysekara, N.S., and Bhattacharyya, M.K. 2014. Analyses of the xylem sap proteomes identified candidate Fusarium virguliforme proteinacious toxins. PloS one 9:e93667. Aoki, T., O'Donnell, K., Homma, Y., and Lattanzi, A.R. 2003. 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In this study, the baseline sensitivity of F. virguliforme to fluopyram was determined using mycelium growth and conidia germination with two collections of F. virguliforme isolates. A total of 130 and 75 F. virguliforme isolates were tested using the mycelial growth and conidia germination assays, respectively, including a core set of isolates that were tested with both assays. In the mycelial growth inhibition assay, 113 out of 130 isolates (86.9%) were determined to have effective concentrations that inhibited 50% of growth (EC50) at less than 5 µg/ml with a mean EC50 of 3.35 µg/ml. For the conidia germination assay, 73 out of 75 isolates (97%) were determined to have an estimated EC50 of less than 5 µg/ml with a mean EC50 value of 2.28 µg/ml. In a subset of 20 common isolates that were phenotyped with both assays, conidia germination of F. virguliforme was also determined to be more sensitive to fluopyram (mean EC50 = 2.28 µg/ml) than mycelial growth (mean EC50 = 3.35 µg/ml). Hormetic effects were observed in the mycelial growth inhibition assay, 19.2% of the isolates demonstrated more growth on medium amended with the lowest fluopyram concentration (1 µg/ml), as compared to the non-fluopyram amended control. Nine out of 185 isolates (4.8%) were less sensitive to fluopyram, as those isolates did not reach 50% growth or conidia germination inhibition at the highest fluopyram concentrations (100 µg/ml and 20 µg/ml for mycelial growth and conidia germination inhibition assays, respectively) tested. On the whole, the F. virguliforme population appears to be sensitive to fluopyram, and this study enables future monitoring of fungicide sensitivity.     120 Introduction Sudden death syndrome (SDS) is a major yield limiting disease in soybean (Glycine max) production. SDS is primarily caused by Fusarium virguliforme within North America. Crop rotation with common field crops, especially corn, is ineffective in reducing SDS, due to the broad host range of F. virguliforme (Kolander et al. 2012; Navi and Yang 2016). Increased soil porosity or reduced soil moisture through tillage have been reported to reduce SDS severity (Vick et al. 2003); however, consistency of response, long term benefit, and associated costs are still not clear (Hartman et al. 2015). Planting partially resistant soybean cultivars can significantly reduce foliar SDS severity (Njiti et al. 1997). However, the F. virguliforme colonization of partially resistant soybean cultivars may not be significantly different to those of susceptible cultivars (Wang et al. 2013), possibly resulting in hidden yield loss and enabling maintenance of F. virguliforme inoculum in the soil. Thus, there is a critical need to develop additional SDS disease management tools, including protection from root rot and root colonization.      Soybean fungicide seed treatments are primarily used to manage soilborne pathogens that cause seed rot, damping-off, seedling blight, and root rot (Mueller et al. 2013; Munkvold 2009). Although SDS is known for its typical foliar symptoms late in the season, root infections by F. virguliforme are essential for the development of SDS symptoms. Fusarium virguliforme can infect soybean roots shortly after germination (Gao et al. 2006). Early infection events also appear to be essential for severe SDS symptom development (Navi and Yang 2008). Therefore, an effective method to inhibit early infection may assist in SDS management. Fungicides seed treatments have been examined for their management of soybean SDS, however to date only fluopyram appears to have any efficacy (Kandel et al. 2016; Mueller et al. 2011; Wang et al.   121 2014; Weems et al. 2015). However, the baseline sensitivity of F. virguliforme to fluopyram has not yet been determined. Fluopyram is a succinate dehydrogenase inhibitor (SDHI) that targets the ubiquinone binding site to block energy metabolism in mitochondria (Kuhn 1984). SDHI fungicides have been successfully utilized to manage a broad range of plant pathogens causing diseases on various fruits and vegetables (Avenot et al. 2008; Vega and Dewdney 2015). SDHI fungicides are categorized by the Fungicide Resistance Action Committee (FRAC) to have a medium to high risk for the selection of SDHI-resistant fungal isolates (FRAC 2015). This medium to high risk is due to the single-site mode of action of SDHI fungicides, and many registered SDHI fungicide are in the same cross-resistance group (Avenot and Michailides 2010; 2015). Examples of resistance to SDHI fungicides (carboxin, fluotolanil, and boscalid) were reported shortly after their introduction to the market, and have been linked to point mutations in one subunit of succinate dehydrogenase coding genes (Avenot et al. 2008; Broomfield and Hargreaves 1992; Gunatilleke et al. 1975). The recently developed SDHI fungicide, fluopyram, showed a lack of cross-resistance to the other SDHI fungicide resistant fungal strains (Amiri et al. 2014; Ishii et al. 2011). The low cross-resistance feature of fluopyram was demonstrated at the molecular level, by showing that the fluopyram molecule binds to a different cavity on the succinate dehydrogenase molecule that also requires a lower binding energy than other SDHI fungicides (Fraaije et al. 2012).  The fungicide testing method will affect the estimation of fungal sensitivity (Vega and Dewdney 2015). Fungicide-amended agar medium for the characterization of fungal mycelial growth inhibition is one of the most common methods to determine fungicide sensitivity (Liang et al. 2015; Saville et al. 2015). Spore germination rates on fungicide amended agar medium   122 have also been used to calculate fungicide sensitivities (Bradley and Pedersen 2011; Chiocchio et al. 2000). SDHI fungicides have been reported to have species-specific differential inhibition effects on mycelial growth and conidia germination (Vega and Dewdney 2015). Therefore, both mycelial growth and spore germination assays were included in this study to obtain a complete evaluation of F. virguliforme sensitivity to fluopyram. To determine the efficacy of fluopyram in the field, and to prolong the product life of fluopyram, it is essential to monitor for shifts in sensitivity by conducting a fungicide baseline sensitivity study. Thus the objectives of this study were: 1) to determine the baseline sensitivity of F. virguliforme against the SDHI fungicide, fluopyram; 2) to compare the inhibition efficacy of fluopyram on F. virguliforme mycelia growth and conidia germination rate.   123 Materials and methods Fungal isolation collection and storage A total of 130 F. virguliforme isolates were tested in mycelial growth inhibition assay at Michigan State University (MSU). Isolates were recovered from soybean roots showing typical SDS foliar and root symptoms from 2009 to 2014 from five states (Table 4-1): 8 Arkansas isolates, 13 Illinois isolates, 11 Kansas isolates, 80 Michigan isolates, and 18 Indiana isolates. The conidia germination fungicide sensitivity assay was conducted on a collection of 75 F. virguliforme isolates at the University of Illinois. These isolates were collected from seven states (Table 4-2): 2 Arkansas isolates, 47 Illinois isolates, 6 Iowa isolates,1 Kansas isolate, 1 Kentucky isolate, 10 Michigan isolates, 1 Minnesota isolate, and 7 unknown sources. Table 4-1 EC50 of F. virguliforme isolates used in the mycelial growth inhibition assay against the SDHI fungicide, fluopyram. A total of 130 isolates were collected from five states in the United States from 2009 to 2014.  State N Years EC50 a (µg/ml) Arkansas 8 2012 2.34 - 4.07* Illinois 13 2013 - 2014 2.15 - 6.92 Kansas 11 2012 1.76 - 3.73* Michigan 80 2009 - 2013 1.53 - 9.28* Indiana 18 2012 2.22 - 4.47* Total 130   a: range of EC50 estimation for the isolates from each state * Does not include the isolates from Arkansas (n=1), Kansas (n=2), Michigan (n=10), and Indiana (n=1) that did not reach 50% growth inhibition at the highest concentration of fluopyram (100 µg/ml) or non-significant EC50 estimation at non-linear regression.     124 Table 4-2 EC50 of F. virguliforme isolates used in the conidia germination inhibition assay testing against SDHI fungicide fluopyram. Isolates were collected from seven states in the United States from 2009 to 2014.  State N Years EC50b (µg/ml) Arkansas 2 2012 2.41 - 2.94 Illinois 47 2010 - 2014 0.81 - 5.57 Iowa 6 2013 2.18 - 4.07 Kansas 1 2012 3.31 Kentucky 1 2014 2.54 Michigan 10 2009 - 2012 1.34 - 4.08 Minnesota 1 Unknown 3.41 Unknown a 7 2012 - 2013 1.24 - 2.38* Total 75   a: indicates isolates with unknown source of origin b: range of EC50 estimation for the isolates from each state * not include the isolates with unknown source that did not reach 50% growth inhibition at the highest concentration of fluopyram (100 µg/ml)  All isolates were derived from a single-conidium culture. Isolates at MSU were selected to ensure genetic diversity by genotyping with microsatellite markers (Wang and Chilvers 2016; Wang and Chilvers, unpublished). Isolates were stored by allowing mycelia to colonize pieces of sterile Whatman #1 qualitative filter paper, which was placed on the surface of potato dextrose agar (PDA) (Acumedia, Lansing, MI) in a petri plate. Once filter papers were fully colonized with mycelia, filter papers were taken from the media, and air dried for storage at -20ºC. Additionally, macroconidia suspensions were added to equal volume of 30% glycerol for long term storage at -80ºC. Isolates used at MSU were identified to be F. virguliforme using a species specific PCR assay (Wang et al. 2015). Determination of baseline EC50 values  mycelial growth inhibition assay The inhibition effect of fluopyram on F. virguliforme mycelia growth was evaluated using fungicide amended half-strength PDA (Acumedia). Formulated fluopyram (Luna Privilege; Bayer CropScience, Research Triangle Park, NC) (containing 43% active ingredient) was used to   125 prepare stock solutions at concentrations of 1, 5, 10, 25, 50, and 100 mg/ml in sterilized water. One liter of culture media included 12 g of potato dextrose broth powder (Acumedia), 15 g of agar (Sigma Aldrich, St. Louis, MO), and 1 liter of deionized water (dH2O). After the media was autoclaved and cooled to 55ºC, 1 ml of fluopyram stock solution was added to the media to make a final concentration of 0, 1, 5, 10, 25, 50, and 100 µg/ml (ppm) fluopyram. Isolates were divided into 11 sets tested in different experimental runs. To validate the reproducibility of this experiment, two isolates from each set of the experiment were randomly selected to verify the reproducibility of this assay (Figure S4-1).  Mycelial plugs (2 mm3 cubes) cut from the edge of 10 day-old fungal colonies were placed mycelia side down on the center of each petri dish plate. Three replicate plates were used for each fungicide concentration. Plates were incubated at 24ºC for 10 days in the dark, and a culture of each isolate was scanned at 3 and 10 days after inoculation (DAI) with 300 dpi image quality, using a Perfection V600 scanner (EPSON, Long Beach, CA). On each scanned image, a photographic reference scale (http://web.ncf.ca/jim/scale/) was included to calibrate the ratio between image digital pixel and physical length for the subsequent image analysis. Dark blue background card (Hobby Lobby, hex color code: #1E2E4D) was used at the scanning step to create strong contrast to facilitate image analysis. Image analysis and model selection All images were analyzed using Assess v2.0 software (American Phytopathological Society, St. Paul, MN) using the manual panel option, and the HSV color space was used to analyze the selected colony area using hue value between 0 and 195. Relative mycelial growth rate was calculated by subtracting the 3 DAI colony areas from the 10 DAI colony area and divided by seven days to get the colony area increase per day. The growth rate of the colony area was   126 converted to calculate the mycelial growth rate (mm/d). The R (R Core Team 2015) (Ritz and Streibig 2005) was used to select the best fitting non-linear dose-response curve model. -linear model for the fungal mycelial growth inhibition dose response curves. The tested non-linear models include: the three-parameter log-logistic model (LL.3), the four-parameter log-logistic model (LL.4), the four-parameter Weibull models (W2.4), the Cedergreen-Ritz-Streibig model (CRS.4a), and the Brain-Cousens hormesis models (BC.4). The best fitting model was determined based on the lowest Akaike's Information Criterion (AIC) value. Determine baseline EC50 values  conidia germination inhibition assay Macroconidia were plated on fungicide amended agar media to determine the inhibitory effect of fluopyram on F. virguliforme spore germination. A conidia spore suspension was prepared from a 6-day old F. virguliforme colony grown on PDA by collecting conidia from F. virguliforme sporodochia. Sterilized water (5 µl) was added to a sporodochium and a pipette was used to homogenize the water and conidia by pipetting up and down, and a 5 µl condial suspension was collected and diluted into 1 ml sterilized water in a 1.5-ml eppendorf tube. Technical grade fluopyram (Bayer CropScience) was dissolved in dimethyl sulfoxide (DMSO) to make a stock solution for making medium. Based on a preliminary experiment, five fluopyram final concentrations (0.1, 1, 5, 10, and 20 µg/ml including a 0 µg/ml control) were used in the conidia germination experiment. A conidia suspension (20 µl) was transferred and evenly spread with a bent glass rod onto PDA amended with different concentrations of fluopyram. Cultures were incubated in ambient light at 20ºC for 20 h. After incubation, 50 conidia on two replicates plates were evaluated under a microscope to determine the conidia germination rate. A conidium in which the germination tube grew to the length of the conidium was counted as germinated.    127 Data analyses The effective concentration to reduce mycelial growth or conidia germination rate by 50% (EC50) for the isolates were calculated by fitting the mycelial relative growth rate against the log transformed fungicide concentrations using the best fitting mov2.5 (Ritz and Streibig 2005). The calculated EC50 values were filtered through two thresholds: 1) relative growth inhibition at highest fluopyram concentration was more than 50% of the zero-control; 2) P value of the EC50 parameter estimation less than 0.05. ANOVA was performed to the sources of variances in EC50 package (Wickham 2009) in R. The estimated EC50 values all the isolates were listed in Table S4-1 and Table S4-2. The R scripts used for data analysis was made to be accessible through Zenodo (DOI: 10.5281/zenodo.53939) Comparison of the mycelial and spore germination assays To compare the fungicide sensitivity testing methods, 20 isolates were selected for testing with both methods (). To determine the differences between these two methods, EC50 values calculated for each of these methods were compared using a paired t-test, and the EC50 mean contrasts were plotted in a forest plot (Sarkar 2008). The EC50 values calculated using these two methods were regressed over each other in a linear model, and the Spearman correlation was used to determine the level of correlation significance using base R (R Core Team 2015).     128 Results Model selection and data validation for the mycelial growth inhibition assay The 4-parameter-log-logistic model (LL.4) was determined to be the best fitting model based on the lowest AIC for most of isolates (78%) tested in the mycelial growth inhibition assay (Figure 4-1). The remaining 22% of the isolates in the mycelial growth assay showed a hormetic effect, where mycelial growth rate was higher in the low concentration fungicide treatment (1 µg/ml) than the non---Cousins modified log-logistic model (BC.4) was determined to be the best fitting model for the hormetic effect isolates. To keep a parsimonious summary for EC50 calculation, the 4-parameter log-logistic model was used consistently for all the mycelial growth inhibition assay data.    Figure 4-1 Dose response curve fitting for (A) mycelial growth and (B) conidia germination at different concentrations of fluopyram amended in the agar media for the F. virguliforme isolates: 14Fv1, 14Fv21, Mo1b, and respectively.    129 To validate the robustness of the mycelial growth inhibition assays, two F. virguliforme isolates were randomly selected from each of the 11 batch runs to repeat the mycelial growth inhibition assay. The estimation of EC50 using mycelial growth inhibition was reproducible, as EC50 value replicates calculated for the 21 out of 22 isolates overlapped with their means confidence interval (Figure 4-5). Mycelial growth sensitivity against fluopyram In the mycelial growth inhibition assay, 116 out of 130 F. virguliforme isolates demonstrated mycelial growth inhibition by 50% on the media amended with fluopyram, while the other eight isolates (6.1%) did not reach a 50% mycelial growth inhibition even at the highest fluopyram concentration (100 µg/ml), where a reduction in growth rate by 0 and 43.8% was observed; five isolates were filtered at non-linear regression step; and one isolate failed at image analysis step possibly due to heavily pigmented background. The calculated EC50 values for 116 isolates ranged from 1.53 to 9.28 µg/ml, with mean and median EC50 of 3.35 and 3.25 µg/ml, respectively (Figure 4-2 and Table 4-4). EC50 values for most isolates (78.5%) were within a range between 2 and 4 µg/ml. There were no significant differences in EC50 values among the isolates collected from five states (P = 0.41) in the US. The frequency distribution of EC50 values for the 116 isolates was a unimodal curve, with a right tail distribution indicating the presence of few less sensitive F. virguliforme isolates to fluopyram, not including those 14 isolates for which we could not calculate EC50.    130  Figure 4-2 Frequency distribution of effective fungicide concentration that inhibits growth by 50% for both (A) mycelial growth inhibition assay and (B) conidia germination inhibition assay. The mean EC50 value was indicated as the dotted vertical lines with the mean EC50 values: 3.35 and 2.28 µg/ml for the mycelial growth inhibition assay and conidia germination assay, respectively.  Mycelial growth hormetic effects A hormetic effect was solely observed in the mycelial growth inhibition assay at the concentrations of 1 µg/ml and 0.5 µg/ml (data not shown) amended in the media. Twenty-two percent (n=29) of the isolates tested in the mycelial growth inhibition assay demonstrated a hormetic effect. The best fitting model for these isolates was determined to be the Brain-Cousens hormesis model (BC.4) based on the lowest AIC values (Figure 4-4 and Table 4-3). The calculated EC50 values with the BC.4 model were significantly higher than the EC50 values calculated using the LL.4 model (P = 0.006, Table 4-3). On average, the BC.4 model estimated EC50 values were higher than the LL.4 model estimated EC50 values by 10 µg/ml. Between these two models, 22 out of 29 EC50 values estimated using the BC.4 model fell within the concentration range where the 50% growth inhibition was reached, while 3 of the 29 EC50 values estimated using the LL.4 model fell the concentration range where the 50% growth inhibition was reached. Neither of these two models fell within the concentration range where 50% growth   131 inhibition was reach for the rest of the four isolates (Table 4-3). Overall, the LL.4 model underestimated the EC50 for the isolates showed hermetic effect, and BC.4 had a more accurate estimation of EC50 for the isolates showed hormetic effect.   132 Table 4-3 Comparison of EC50 values calculated using 4-parameter log-logistic model (LL.4) and the Brain-Cousens model (BC.4) for the F. virguliforme isolates that showed hormetic effect in the mycelial growth inhibition assay. At model selection, the AIC values calculated for LL.4 and BC.4 models were -4148 and -3926 (lower is better), respectively. Strain Set a LL.4 BC.4 Difference  in EC50 Relative growth ratioe Better Model d EC50 StdErr b EC50 StdErr 5 µg/ml 10 µg/ml 50 µg/ml 100 µg/ml AL1a 2 3.10 0.49 5.14 0.60 2.04 0.45 0.38 0.22 - f  LL.4 ARLE-A1 1 3.73 0.98 5.77 1.13 2.04 0.47 0.44 0.18 - LL.4 Mo4a 1 2.91 0.74 5.17 0.97 2.26 0.45 0.38 0.22 - LL.4 MIVB-A5 2 1.21 0.11 3.67 0.41 2.47 0.51 0.35 0.25 0.30 None MIVB-C1 10 3.79 0.50 9.14 1.47 5.35 0.53 0.43 0.29 0.29 BC.4 MITU-B1 12 4.01 1.46 9.98 2.46 5.97 0.54 0.49 0.32 0.33 BC.4 MISTJ-A1 3 4.12 1.03 10.44 4.13 6.32 0.55 0.43 0.26 0.38 None STJ-7P2ss 1 3.59 1.18 9.93 3.66 6.33 0.54 0.47 0.33 - BC.4 MIBer-E6 3 3.74 0.79 11.13 3.78 7.39 0.49 0.43 0.23 0.34 None Mo1a-2 10 3.71 0.50 12.21 2.18 8.50 0.56 0.51 0.30 0.33 BC.4 MIBer-F3 10 3.75 0.55 13.38 2.78 9.63 0.57 0.52 0.30 0.38 BC.4 KSSH-G2 10 3.73 0.61 14.66 3.03 10.93 0.58 0.52 0.36 0.36 BC.4 MISA-A1 7 4.46 1.12 20.16 5.09 15.69 0.64 0.58 0.36 0.38 BC.4 Mo3a 9 4.38 0.92 23.83 6.94 19.45 0.69 0.59 0.38 0.42 BC.4 INMO-A6 10 4.47 0.82 26.09 5.92 21.62 0.70 0.60 0.39 0.43 BC.4 MIVB-A6 1 4.61 1.58 27.38 11.10 22.77 0.79 0.68 0.47 - BC.4 Mont1 14 9.34 2.00 34.10 7.64 24.76 0.77 0.75 0.43 0.45 BC.4 MISTJ-F6 11 4.50 1.06 30.45 8.89 25.96 0.75 0.67 0.43 0.49 BC.4 14Fv1 13 5.24 0.87 33.39 6.49 28.16 0.74 0.66 0.49 - BC.4 MISTJ-E4 11 4.00 1.48 36.19 13.25 32.19 0.59 0.54 0.42 0.42 BC.4 Mont1 7 4.51 1.39 42.01 11.40 37.51 0.77 0.75 0.43 0.45 BC.4 Ca2a 4 4.30 1.66 117.88 96.28 113.58 0.87 0.70 0.64 0.72 BC.4 g MIBer-E4 8 7.70 3.00 231.42 170.19 223.72 1.11 1.00 0.69 0.81 BC.4 g ARLE-C3a 11 NA c NA 270.18 187.66 NA 1.09 1.00 0.70 0.62 BC.4 g    133 KSSH-A4 1 NA c NA 11.46 4.18 NA 0.53 0.51 0.38 - BC.4 LE-11-1 3 NA c NA 154.55 130.28 NA 1.01 0.92 0.59 0.76 BC.4 g MIBer-A5 11 NA c NA 22.29 9.16 NA 0.61 0.51 0.43 0.39 BC.4 MIBer-D1 2 NA c NA 7.78 1.37 NA NA 0.43 NA - BC.4 MITU-B1 2 NA c NA 11.85 3.76 NA 0.54 0.49 0.32 0.33 None a: batch number for different sets of testing isolates b: standard error c: the EC50 parameter estimation in the LL.4 model does not have a significant P value. d: EC50 estimated using either LL.4 or BC.4 falls within the range of relative growth inhibition by 50% e: relative germination ratio at these concentrations to the 0 µg/ml control, the percentage values lower than 50% are in bold font f: missing data g: EC50 estimation out of tested concentration range Table 4-   134 Model selection and data validation for the conidia germination inhibition assay For the conidia germination assay, conidia germination rate decreased as fluopyram concentrations increased. The best fitting model selected for the conidia germination assay dose response data was the 4-parameter-log-logistic model, less than 10% of the isolates showed a better fit with other non-linear models (e.g., Weibull, Cedergreen-Ritz-Streibig model, or Brain-Cousens hormesis models). To keep a parsimonious summary for EC50 and to be able to compare amongst isolates, the 4-parameter-log-logistic model was used for all isolates (Figure 4-1).  To validate the reproducibility of the conidia germination inhibition assay, 51 isolates were tested in two separate runs to compare their EC50 estimations. The confidence intervals of the EC50 estimation were calculated for the replicated isolates, and 40 out of 51 replicated isolates were determined to be not significantly different between two replicates, as indicated by overlapped EC50 mean confidence interval (Figure S4-2). Conidia germination sensitivity against fluopyram Most of the F. virguliforme isolates were sensitive to fluopyram in the conidia germination inhibition assay. In total, 74 out of 75 F. virguliforme isolates showed a conidia germination rate reduction by 50% on medium amended with fluopyram, with one isolate not reaching a 50% conidia germination rate reduction even at the highest fluopyram concentration (20 µg/ml). The calculated EC50 values for the 74 isolates ranged between 0.81 and 5.57 µg/ml, with mean and median EC50 of 2.28 and 2.24 µg/ml, respectively (Figure 4-2 and Table S4-2). EC50 values for most of the isolates (89%) fell within a range of 1 and 3.5 µg/ml with a right-tailed unimodal distribution indicating that most isolates were sensitive to fluopyram, not including one isolate for which we could not calculate EC50.    135 Differences between two fungicide sensitivity testing methods There is a fundamental difference in fungicide sensitivity tests between the mycelial growth inhibition assay and the conidia germination inhibition assay, including starting fungal materials and experiment durations. The EC50 values estimated using the mycelial growth inhibition assay were significantly higher than the EC50 values calculated with the conidia germination assay (P < 0.01). Of the 20 cross-validated F. virguliforme isolates, 15 isolates showed higher EC50 estimates for the mycelial growth inhibition assay than the conidia germination inhibition assay (Figure 4-3, P < 0.01). Between the EC50 values calculated from both of the fungicide sensitivity testing methods, there was no statistically significant correlation between those two sets of EC50 estimation data (Spearman correlation was non-significant at P = 0.4, Figure 4-3).     Figure 4-3 (A) Comparison of the difference in EC50 values estimated using mycelial growth inhibition assay and conidia germination inhibition assay. The differences in EC50 estimation between the mycelial growth inhibition and conidia germination assay were plotted for 20 F. virguliforme isolates. (B) Correlations between the EC50 estimations using mycelial growth inhibition assay and conidia germination inhibition assay. There was no significant correlation between those two methods (Spearman correlation P = 0.40).    136  Figure 4-4 Dose response curve fitting the isolates showed hormetic effect using (A) 4-parameter logistic model and (B) Brain-Cousens model. The hormetic effect isolates showed faster growth rate at 1 µg/ml concentration than the zero-control. The non-linear regression BC.4 model (AIC = -271) fits better than the LL.4 (AIC = -259) model for the isolates showed hormesis.  Discussion Most of the isolates tested in this study were sensitive to fluopyram, but a small fraction of isolates (~5%) were insensitive to fluopyram treatment. The presence of fluopyram insensitive F. virguliforme isolates may imply the risk to accumulate less sensitive isolates in the field populations, and result in possible disease management failure. Based on this survey, there is no significant difference in EC50 values among sampling locations or years of collections, which is perhaps expected since this pathogen was most likely not previously exposed to fluopyram. Although one of the major fluopyram product, Luna Privilege, is registered on horticultural crops that could have been in rotation with soybean, most of the F. virguliforme isolates in this study were collected from the fields, which were predominantly in corn-soybean rotation. Additionally, other SDHI fungicides may have been applied in soybean seed treatments, but fluopyram binds -resistance (Fraaije et al. 2012). Fungicide sensitivity EC50s evaluated using the mycelial growth inhibition assay was significantly higher than the fungicide sensitivity EC50s calculated using the conidia germination   137 inhibition assay by 1 µg/ml. The difference in EC50 estimation using different methods was reported in a previous study (Vega and Dewdney 2015) with Alternaria alternata isolates tested against another SDHI fungicide, boscalid. Contrary to their findings that mycelial growth of A. alternata was more sensitive to boscalid than conidia germination, fluopyram was more effective in inhibiting conidia germination for F. virguliforme than mycelia growth. The difference in EC50 estimations from these two methods could be explained by a fundamental difference in transferring starting materials: conidia and mycelia. Before transferring to the fungicide amended media agar, the conidia were in a actively growing part of a colony (Gougouli and Koutsoumanis 2013). The different onset of metabolic levels (Cochrane and Cochrane 1966; Liu et al. 2015) may affect the dose response to fungicide treatment. Thus, it may not be surprising to observe the difference in EC50 estimations using these two methods. A combination of these two testing methods provided a relatively complete estimation of F. virguliforme sensitivity against the fluopyram fungicide. At the lower fungicide concentration treatment, a hormetic effect was observed for 20% of the isolates solely in the mycelial growth inhibition assay. The presence of a hormetic effect on isolates in the field indicate that exposure to sublethal doses of fungicide can possibly result in more severe disease symptoms (Garzon et al. 2011). In this study, the hormetic effect was observed at the concentrations of 0.5 and 1 µg/ml (concentration 0.5 µg/ml was tested in the preliminary study, data not shown), so that the application of fluopyram on the seed treatment should ensure an effective concentration above 1 µg/ml.  Multiple non-linear models were selected to fit the fungi growth dose-response curve. The four-parameter log-logistic model (LL.4) was determined to be the best fitting model based on   138 for the isolates showed hormetic effect was the BC.4 model, which was specifically designed for the hormetic effect dose response curve (Cedergreen et al. 2005). The BC.4 model showed a higher estimation of EC50 than the LL.4 model, because the hormesis model will lead to higher EC50 levels as hormetic effect could possibly delay the onset of toxicity. In reality, most EC50 values (22 out of 29) estimated using the BC.4 fell within the concentration range where 50% growth inhibition was reached (Table 4-3). The LL.4 model underestimated the EC50 for the isolates demonstrating hormesis. Although the BC.4 performs better in predicting EC50 for the isolates showed hermetic effect, to achieve the most parsimonious interpretation of the EC50 estimations (C. Ritz, personal communication), the LL.4 model was used consistently for all the isolates.  A small fraction (4.8%) of F. virguliforme isolates were determined to be less sensitive to fluopyram treatment in vitro. Based on the FRAC definition, EC50 is the dose that provides 50% inhibition of the isolates as compared to a non-fungicide-amended control, which is also known as absolute EC50. To calculate the EC50, isolates that showed less than 50% growth inhibition at the highest fungicide concentration treatment were filtered out from the data set before using the data for subsequent analysis. In this study, the less sensitive isolates tend to be the slow-growing isolates and secreted melanin-like pigments in the media, even in absence of fluopyram in the medium. Similar results, reduced fungicide sensitivity coupled with slow growth rate and secretion of melanin, were also reported on other plant or human pathogens, such as Zymoseptoria tritici (Lendenmann et al. 2015) and Paracoccidioides brasiliensis (Taborda et al. 2008). The presence of less sensitive isolates does not always indicate a rapid accumulation of less sensitive isolates in the field. Currently, fluopyram has been mainly applied in seed treatment, which is only applied once per season. Therefore, the selection pressure to accumulate   139 resistant isolates is not as strong as the foliar fungal disease management, which may require multiple applications of foliar fungicide sprays per season. Besides F. virguliforme, SDS can be caused by three additional Fusarium species (F. tucumaniae, F. brasiliense, and F. crassistipitatum) in South America, which are phylogenetically clustered within the clade-2 Fusarium solani species complex (FSSC) with F. virguliforme (Aoki et al. 2005; Aoki et al. 2012a). Also, there are additional Fusarium species within the clade-2 FSSC causing root rots on dry bean, and these Fusarium species are phylogenetically close to F. virguliforme (Aoki et al. 2012b). Therefore, it is possible that fluopyram also will be effective in inhibiting mycelial growth or conidia germination for those Fusarium species.     140 Acknowledgement We thank Dr. Tyre Proffer, Alejandro Rojas, and Janette Jacobs for their suggestions on the design of this experiment. We also thank Zach Noel for his technical suggestions for data analysis. The set of NRRL F. virguliforme isolates was kindly provided by ARS culture collection, USDA, Peoria IL. We also thank Dr. Leonor Leandro for sharing some of the F. virguliforme isolates. This work was supported by Bayer CropSciences, the North Central Soybean Research Program (NCSRP), and the Michigan Soybean Promotion Committee. Collection of some of the F. virguliforme isolates was supported by a grant from the United States Department of Agriculture  National Institute of Food and Agriculture.     141 APPENDICES  142 APPENDIX A Supplementary tables   Table S 4-1 EC50 estimations for all isolates that were tested in the mycelial growth inhibition assay. Strain State County Year EC50 StdErr a Relative growth ratio b 5ppm 10ppm 50ppm 13Fv163 Illinois Jackson 2013 2.47 0.17 0.50 0.45 0.38 13Fv182 Illinois Pope 2013 2.30 0.29 0.46 0.44 0.36 13Fv185 Illinois Edwards 2013 3.52 0.37 0.61 0.45 0.38 13Fv186 Illinois Pulaski 2013 2.15 0.23 0.50 0.47 0.41 13Fv193 Illinois Livingston 2013 3.71 0.48 0.62 0.46 0.41 13Fv196 Illinois DeKalb 2013 3.18 0.29 0.57 0.50 0.43 14Fv1 Illinois Clinton 2014 5.24 0.87 0.74 0.66 0.49 14Fv12 Illinois Adams 2014 2.81 0.23 0.54 0.48 0.41 14Fv14 Illinois McDonough 2014 2.24 0.17 0.52 0.44 0.41 14Fv21 Illinois Pike 2014 2.81 0.22 0.54 0.45 0.42 14Fv26 Illinois Logan 2014 2.62 0.25 0.51 0.44 0.38 14Fv9 Illinois Mason 2014 2.61 0.26 0.52 0.48 0.42 AL1a Michigan Allegan 2009 3.10 0.49 0.45 0.38 0.22 AL1b Michigan Allegan 2009 2.48 0.43 0.40 0.34 0.21 ARLE-A1 Arkansas Lee 2012 3.73 0.98 0.47 0.44 0.18 ARLE-B2 Arkansas Lee 2012 3.68 0.54 0.50 0.42 0.28 ARLE-B3 Arkansas Lee 2012 4.07 1.56 0.60 0.47 0.51 ARLE-C1 Arkansas Lee 2012 3.18 0.82 0.64 0.55 0.37 ARLE-C2 Arkansas Lee 2012 2.34 0.31 0.39 0.38 0.24 ARLE-C3a Arkansas Lee 2012 NA f NA 1.09 1.00 0.70 ARLE-G4 Arkansas Lee 2012 3.70 1.02 0.59 0.52 0.39 ARLE-G7 Arkansas Lee 2012 3.22 0.70 0.49 0.37 0.31 Ber1-5 Michigan Berrien 2009 2.45 0.72 0.35 0.34 0.22 Ber1-9 Michigan Berrien 2009 3.44 0.39 0.49 0.41 0.26 Ca1a Michigan Cass 2009 NA e NA 1.02 0.79 0.81 Ca2a Michigan Cass 2009 4.30 1.66 0.87 0.70 0.64 Ca2b Michigan Cass 2009 NA e NA 0.87 1.10 0.69 CL-11-1 Michigan Clinton 2011 3.45 0.64 0.46 0.37 0.27 DBP28R13 Michigan Van Buren 2013 1.69 0.16 0.38 0.33 0.22 DBP30R5 Michigan Van Buren 2013 3.02 0.57 0.48 0.44 0.35 DBP30R7 Michigan Van Buren 2013 1.84 0.27 0.41 0.34 0.29   143 Hu-11-1 Michigan Huron 2011 2.37 0.29 0.43 0.42 0.34 INMO-A1 Indiana White 2012 3.06 0.48 0.49 0.40 0.29 INMO-A2 Indiana White 2012 3.41 1.44 0.41 0.41 0.29 INMO-A3 Indiana White 2012 2.90 0.40 0.48 0.42 0.32 INMO-A5 Indiana White 2012 2.22 0.19 0.44 0.37 0.29 INMO-A6 Indiana White 2012 4.47 0.82 0.70 0.60 0.39 INMO-B2 Indiana White 2012 2.75 0.48 0.43 0.38 0.31 INMO-B5 Indiana White 2012 2.37 0.24 0.48 0.44 0.38 INMO-B6 Indiana White 2012 2.24 0.31 0.39 0.35 0.24 INMO-C1 Indiana White 2012 3.71 1.07 0.60 0.54 0.54 INMO-C3 Indiana White 2012 2.48 0.39 0.40 0.38 0.24 INMO-C4 Indiana White 2012 NA d NA NA NA NA INMO-D1 Indiana White 2012 3.25 0.39 0.50 0.42 0.33 INMO-E1 Indiana White 2012 3.16 0.41 0.47 0.44 0.30 INMO-E5 Indiana White 2012 3.78 1.22 0.64 0.58 0.51 INMO-F1 Indiana White 2012 3.62 0.73 0.54 0.32 0.31 INMO-F2 Indiana White 2012 4.03 0.34 0.57 0.42 0.32 INMO-G3 Indiana White 2012 3.88 0.50 0.60 0.50 0.36 INMO-G6 Indiana White 2012 2.62 0.30 0.46 0.37 0.31 KA-11-1 Michigan Kalamazoo 2011 4.90 0.54 0.83 0.60 0.58 KSSH-A1 Kansas Shawnee 2012 3.53 0.47 0.53 0.45 0.34 KSSH-A2 Kansas Shawnee 2012 2.50 0.29 0.42 0.34 0.23 KSSH-A4 Kansas Shawnee 2012 NA f NA 0.53 0.51 0.38 KSSH-A7 Kansas Shawnee 2012 1.76 0.40 0.36 0.47 0.35 KSSH-C2 Kansas Shawnee 2012 1.98 0.19 0.41 0.35 0.27 KSSH-C3 Kansas Shawnee 2012 3.47 1.28 0.65 0.57 0.40 KSSH-C4 Kansas Shawnee 2012 3.67 0.85 0.52 0.43 0.34 KSSH-C5 Kansas Shawnee 2012 3.43 0.53 0.49 0.43 0.25 KSSH-E4 Kansas Shawnee 2012 3.60 1.00 0.55 0.49 0.35 KSSH-F4 Kansas Shawnee 2012 NA f NA 0.42 0.59 0.24 KSSH-G2 Kansas Shawnee 2012 3.73 0.61 0.58 0.52 0.36 LE-11-1 Michigan Lenawee 2011 NA e NA 1.01 0.92 0.59 MIBer-A1 Michigan Berrien 2012 3.79 0.47 0.54 0.46 0.30 MIBer-A2 Michigan Berrien 2012 1.94 0.26 0.46 0.42 0.41 MIBer-A3 Michigan Berrien 2012 3.68 1.21 0.61 0.56 0.58 MIBer-A5 Michigan Berrien 2012 NA f NA 0.61 0.51 0.43 MIBer-A6 Michigan Berrien 2012 3.80 0.81 0.79 0.71 0.52 MIBer-B1 Michigan Berrien 2012 3.10 1.10 0.62 0.69 0.55 MIBer-B2 Michigan Berrien 2012 NA e NA 0.88 0.62 0.73 MIBer-B3 Michigan Berrien 2012 3.83 0.58 0.55 0.45 0.29 Table S4-   144 MIBer-B4 Michigan Berrien 2012 3.74 0.58 0.50 0.40 0.26 MIBer-B5 Michigan Berrien 2012 1.75 0.27 0.44 0.41 0.44 MIBer-D1 Michigan Berrien 2012 NA f NA NA c 0.43 NA c MIBer-E4 Michigan Berrien 2012 7.70 3.00 1.11 1.00 0.69 MIBer-E6 Michigan Berrien 2012 3.46 0.62 0.49 0.43 0.23 MIBer-F3 Michigan Berrien 2012 3.75 0.55 0.57 0.52 0.30 MIBer-F4 Michigan Berrien 2012 3.49 0.43 0.51 0.44 0.37 MIBer-F6 Michigan Berrien 2012 3.24 0.59 0.53 0.42 0.22 MIBer-F7 Michigan Berrien 2012 4.55 1.30 0.56 0.48 0.42 MIBer-out Michigan Berrien 2012 NA e NA 1.26 1.19 0.88 MIIN-B7 Michigan Ingham 2012 2.18 0.20 0.38 0.32 0.20 MISA-A1 Michigan Saginaw 2012 4.46 1.12 0.64 0.58 0.36 MISA-A3 Michigan Saginaw 2012 2.96 0.60 0.53 0.49 0.47 MISA-B6 Michigan Saginaw 2012 3.66 1.63 0.51 0.46 0.33 MISTJ-A1 Michigan St. Joseph 2012 3.83 0.73 0.55 0.43 0.26 MISTJ-A2 Michigan St. Joseph 2012 3.55 0.56 0.48 0.39 0.28 MISTJ-A3 Michigan St. Joseph 2012 2.88 0.38 0.47 0.43 0.30 MISTJ-C6 Michigan St. Joseph 2012 2.99 0.47 0.49 0.39 0.29 MISTJ-C7 Michigan St. Joseph 2012 2.71 0.35 0.49 0.47 0.41 MISTJ-D5 Michigan St. Joseph 2012 7.00 1.97 0.64 0.49 0.27 MISTJ-D6 Michigan St. Joseph 2012 9.28 2.16 0.38 0.33 0.08 MISTJ-E4 Michigan St. Joseph 2012 3.15 0.85 0.59 0.54 0.42 MISTJ-E4a Michigan St. Joseph 2012 2.27 0.33 0.42 0.39 0.28 MISTJ-F6 Michigan St. Joseph 2012 4.50 1.06 0.75 0.67 0.43 MITU-A1 Michigan Tuscola 2012 2.72 0.52 0.47 0.40 0.33 MITU-A2 Michigan Tuscola 2012 3.69 1.06 0.57 0.52 0.49 MITU-A3 Michigan Tuscola 2012 2.92 0.41 0.42 0.33 0.21 MITU-A4 Michigan Tuscola 2012 3.46 0.52 0.49 0.40 0.27 MITU-A5 Michigan Tuscola 2012 3.41 0.42 0.49 0.42 0.27 MITU-B1 Michigan Tuscola 2012 4.01 1.46 0.54 0.49 0.32 MITU-C1 Michigan Tuscola 2012 NA e NA 0.93 0.77 0.59 MITU-C2 Michigan Tuscola 2012 2.36 0.26 0.45 0.39 0.32 MITU-C3-b Michigan Tuscola 2012 5.15 0.86 0.69 0.49 0.35 MITU-C3a Michigan Tuscola 2012 3.33 0.91 0.61 0.53 0.41 MIVB-A1 Michigan Van Buren 2012 3.20 0.37 0.48 0.43 0.25 MIVB-A5 Michigan Van Buren 2012 2.50 0.28 0.51 0.35 0.25 MIVB-A6 Michigan Van Buren 2012 4.61 1.58 0.79 0.68 0.47 MIVB-A7 Michigan Van Buren 2012 3.66 0.41 0.54 0.45 0.29 MIVB-B3 Michigan Van Buren 2012 2.50 0.39 0.35 0.30 0.19 MIVB-B4 Michigan Van Buren 2012 2.12   0.613 NA c 0.29 NA c Table S4-    145 MIVB-B5 Michigan Van Buren 2012 NA e NA 0.73 0.71 0.62 MIVB-C1 Michigan Van Buren 2012 3.79 0.50 0.53 0.43 0.29 MIVB-D5 Michigan Van Buren 2012 2.05 0.38 0.35 0.32 0.24 MIVB-G7 Michigan Van Buren 2012 NA e NA 1.26 1.07 0.66 Mo1a Michigan Monroe 2009 3.37 0.49 0.46 0.38 0.28 Mo1a-2 Michigan Monroe 2009 3.71 0.50 0.56 0.51 0.30 Mo1b Michigan Monroe 2009 4.33 0.57 0.72 0.54 0.40 Mo2a Michigan Monroe 2009 2.43 0.29 0.42 0.34 0.25 Mo3a Michigan Monroe 2009 4.38 0.92 0.69 0.59 0.38 Mo4a Michigan Monroe 2009 2.91 0.74 0.45 0.38 0.22 Mo4c-2 Michigan Monroe 2009 3.19 0.47 0.43 0.38 0.26 Mo5a Michigan Monroe 2009 2.68 0.37 0.47 0.36 0.23 Mont1 Illinois Piatt 1991 6.92 1.70 0.77 0.75 0.43 SA-11-2b Michigan Saginaw 2011 3.05 0.37 0.68 0.63 0.47 ST-11-1 Michigan St. Clair 2011 2.54 0.27 0.41 0.37 0.24 STJ-7P2ss Michigan St. Joseph 2009 3.59 1.18 0.54 0.47 0.33 STJ3a Michigan St. Joseph 2009 3.55 1.00 0.48 0.44 0.30 STJ3b Michigan St. Joseph 2009 1.53 0.11 0.38 0.33 0.30 VB1 Michigan Van Buren 2009 2.98 0.40 0.42 0.38 0.26 a: standard error b: relative germination ratio at these concentrations to the 0 µg/ml control c: missing data at these concentrations d: failed at image analysis step possibly due to heavy pigment in the media e: EC50 cannot be calculated due to no absolute EC50 f: EC50 cannot be calculated due to failed at non-linear regression parameter estimation.  Table S4-    146 Table S 4-2 EC50 estimations for isolates that were tested in the conidia germination inhibition assay. Strain State County Year EC50 StdErr a Relative growth ratio c 5ppm 10ppm 20ppm 10Fv680 Illinois Piatt 2010 2.49 0.49 0.27 0.09 0.01 13C13#1 Unknown Unknown 2013 1.57 0.14 0.13 0.00 0.00 13C1N Unknown Unknown 2013 2.38 0.16 0.20 0.02 0.00 13Clark3#1 Unknown Unknown 2013 1.30 0.08 0.17 0.06 0.01 13Clinton-N Iowa Clinton 2013 2.18 0.20 0.21 0.04 0.00 13Fv163 Illinois Jackson 2013 1.86 0.18 0.21 0.09 0.02 13Fv165 Illinois White 2013 2.17 0.16 0.20 0.05 0.00 13Fv167 Illinois Jasper 2013 3.37 0.62 0.42 0.19 NA 13Fv169 Illinois Johnson 2013 1.69 0.21 0.28 0.13 NA 13Fv171 Illinois Union 2013 1.74 0.50 0.36 0.24 0.10 13Fv173 Illinois Alexander 2013 2.13 0.34 0.38 0.16 NA 13Fv176 Illinois Saline 2013 2.30 0.34 0.23 0.06 0.09 13Fv179 Illinois Massac 2013 2.66 0.80 0.26 0.08 0.04 13Fv180 Illinois Massac 2013 0.81 0.11 0.28 0.10 NA 13Fv182 Illinois Pope 2013 2.59 0.24 0.26 0.08 0.02 13Fv185 Illinois Edwards 2013 2.28 0.32 0.25 0.07 0.02 13Fv186 Illinois Pulaski 2013 1.57 0.15 0.18 0.07 0.00 13Fv188 Illinois Pulaski 2013 5.58 1.40 0.41 0.15 0.03 13Fv190 Unknown Unknown Unknown b NA d NA 0.96 1.00 0.98 13Fv193 Illinois Livingston 2013 2.57 0.75 0.33 0.08 0.01 13Fv195 Illinois Grundy 2013 2.44 0.27 0.26 0.12 0.00 13Fv196 Illinois DeKalb 2013 2.38 0.45 0.30 0.07 0.00 13Fv199 Illinois Jersey 2013 0.84 0.05 0.00 0.00 0.00 13Fv201 Illinois Champaign 2013 2.09 0.14 0.20 0.09 0.03 13Fv202 Illinois Perry 2013 2.25 0.23 0.23 0.06 0.01 13Fv204 Illinois Monroe 2013 2.35 0.43 0.28 0.12 0.02 13FV206 Illinois Livingston 2013 1.48 0.28 0.22 0.03 0.00 13Fv207 Illinois Macoupin 2013 2.31 0.24 0.26 0.07 0.01 13Fv209 Illinois Warren 2013 4.57 1.38 0.37 0.10 0.02 13Pike1 Unknown Unknown 2013 1.84 0.15 0.21 0.05 0.00 14Fv1 Illinois Clinton 2014 2.26 0.15 0.32 0.17 0.02 14Fv12 Illinois Adams 2014 1.25 0.08 0.10 0.05 0.01 14Fv14 Illinois McDonough 2014 1.14 0.08 0.08 0.04 0.00 14Fv15 Illinois Brown 2014 1.16 0.07 0.10 0.02 0.00 14Fv16 Illinois Brown 2014 2.16 0.12 0.21 0.08 0.00 14Fv17 Illinois Hancock 2014  0.26 0.18 0.03 0.02   147 14Fv19 Illinois Henderson 2014 2.30 0.36 0.21 0.04 0.00 14Fv21 Illinois Pike 2014 1.91 0.41 0.21 0.09 0.02 14Fv22 Illinois Pike 2014 2.49 0.48 0.28 0.06 0.01 14Fv24 Illinois Fulton 2014 2.16 0.18 0.20 0.08 0.02 14Fv26 Illinois Logan 2014 1.41 0.14 0.14 0.07 0.01 14Fv28 Kentucky Paducah 2014 2.54 0.61 0.23 0.06 0.01 14Fv29 Illinois LaSalle 2014 1.61 0.20 0.19 0.05 0.01 14Fv3 Illinois Schuyler 2014 1.84 0.28 0.20 0.12 0.04 14Fv30 Illinois LaSalle 2014 1.77 0.22 0.24 0.11 0.01 14Fv31 Illinois Williamson 2014 1.61 0.16 0.14 0.06 0.01 14Fv32 Illinois Williamson 2014 1.51 0.24 0.19 0.04 0.00 14Fv34 Illinois Kendall 2014 1.53 0.29 0.22 0.07 0.01 14Fv36 Illinois Kane 2014 1.67 0.36 0.33 0.09 0.05 14Fv37 Illinois Lee 2014 4.19 0.54 0.39 0.12 0.02 14Fv6 Illinois Calhoun 2014 1.62 0.16 0.19 0.05 0.01 14Fv7 Illinois McLean 2014 1.21 0.09 0.12 0.03 0.00 14Fv9 Illinois Mason 2014 1.71 0.26 0.17 0.04 0.01 AL1b Michigan Allegan 2009 2.28 0.36 0.26 0.08 0.01 AR171 Arkansas Unknown Unknown b 2.94 0.56 0.40 0.09 0.03 ARLE-C2 Arkansas Lee 2012 2.41 0.31 0.26 0.07 0.00 Ca1a Michigan Cass 2009 4.08 1.12 0.38 0.06 0.01 FAV13 Unknown Unknown 2013 2.31 0.27 0.22 0.00 0.00 FAYSDS13 Unknown Unknown 2013 1.24 0.18 0.18 0.04 0.02 FV-LS1-ss1 Minnesota Unknown Unknown b 3.42 0.77 0.37 0.15 0.04 HU-11-1 Michigan Huron 2011 1.93 0.16 0.20 0.08 0.00 KSSH-A7 Kansas Shawnee 2012 3.31 0.71 0.34 0.08 0.01 LE-11-1 Michigan Lenawee 2011 3.97 1.16 0.39 0.14 0.04 LL0023 Iowa Unknown Unknown b 2.26 0.34 0.25 0.06 0.01 LL0036 Iowa Unknown Unknown b 2.49 0.44 0.30 0.09 0.02 LL0039 Iowa Unknown Unknown b 3.41 0.32 0.35 0.09 0.02 LL0059 Iowa Unknown Unknown b 4.07 1.03 0.37 0.15 0.01 LL0094 Iowa Unknown Unknown b 3.49 1.26 0.32 0.10 0.01 MIBer-A5 Michigan Berrien 2012 2.24 0.41 0.26 0.10 0.01 MIBer-A6 Michigan Berrien 2012 3.35 0.72 0.30 0.11 0.02 MIBer-F7 Michigan Berrien 2012 1.34 0.06 0.03 0.00 0.00 Mo1b Michigan Monroe 2009 2.35 0.22 0.22 0.05 0.00 Mont-1 Illinois Piatt 1991 2.02 0.09 0.17 0.03 0.00 VB-2a Michigan Van Buren 2009 2.24 0.31 0.27 0.06 0.01 VB1 Michigan Van Buren 2009 2.81 0.67 0.35 0.12 0.01  Table S4-   148 a: indicates standard error b: indicates isolates with unknown year of isolation record c: relative germination ratio at these concentrations to the 0 µg/ml control d: EC50 cannot be calculated either due to no absolute EC50 or failed at non-linear regression parameter estimation   Table S4-   149 APPENDIX B Supplementary figures    Figure S 4-1 The reproducibility of EC50 estimation using mycelial growth inhibition assay. Twenty-two isolates were randomly selected from 11 sets of isolates to evaluate the assay reproducibility between batches. The bar filled with green color indicated a good reproducibility isolates, while the bar filled with red color indicated a poor reproducibility of the isolate. The error bars indicated the 95% confidence interval for the mean EC50 estimation.     150  Figure S 4-2 The reproducibility of EC50 estimation using conidia germination inhibition assay. Fifty-one isolates were replicated to evaluate reproducibility of this assay. The bar filled with green color indicated a good reproducibility isolates, while the bar filled with red color indicated a poor reproducibility of the isolate. The error bars indicated the 95% confidence interval for the mean EC50 estimation.    151 REFERENCES  152 REFERENCES  Amiri, A., Heath, S. M., and Peres, N. A. 2014. Resistance to fluopyram, fluxapyroxad, and penthiopyrad in Botrytis cinerea from strawberry. Plant Dis 98:532-539. 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Effect of fungicide seed treatments on Fusarium virguliforme infection of soybean and development of sudden death syndrome. Canadian Journal of Plant Pathology 37:435-447. Wickham, H. 2009. ggplot2: elegant graphics for data analysis. Springer Science & Business Media.     156 CHAPTER 5 DEVELOPMENT AND CHARACTERIZATION OF MICROSATELLITE MARKERS FOR FUSARIUM VIRGULIFORME AND THEIR UTILITY WITHIN CLADE 2 OF THE FUSARIUM SOLANI SPECIES COMPLEX              This chapter was originally published in Fungal Ecology.  Wang, J. and Chilvers, M.I., 2016. Development and characterization of microsatellite markers for Fusarium virguliforme and their utility within clade 2 of the Fusarium solani species complex Fungal Ecology, 20:7-14.      157 Abstract Clade 2 of the Fusarium solani species complex contains plant pathogens including F. virguliforme and closely related species F. brasiliense, F. crassistipitatum, F. tucumaniae, which are the primary causal agents of soybean sudden death syndrome (SDS), a significant threat to soybean production. In this study, we developed microsatellite markers from a F. virguliforme genome sequence and applied them to a F. virguliforme population collection of 38 isolates from Michigan and four reference strains from other locations. Of the 225 detected microsatellite loci, 108 loci were suitable for primer design, and 12 of the microsatellite markers were determined to be highly polymorphic, amplifying on average 5.7 alleles per locus. Using these markers, F. virguliforme isolates were partitioned into three distinct clusters, but isolates were not grouped based on relatedness of sampling sites. In addition, 11 out of 12 markers were demonstrated to be highly transferrable to other closely related species.   Keywords: Fusarium virguliforme, marker development, microsatellite, simple sequence repeat (SSR), soybean sudden death syndrome    158 Introduction Fusarium species are ascomyceteus filamentous fungi (Class: Sordariomycetes Order: Hypocreales Family: Nectriaceae), including animal and plant pathogens, toxin producers, and debris decomposers. The F. solani species complex is one of four major plant pathogenic clades within the genus . The Fusarium solani species complex contains more than 50 phylogenetic species, which comprises three major clades .-Fusarium solani species complexPhaseolus vulgaris)(Aoki et al. 2012a; Aoki et al. 2012b) -(Roy et al. 1997; Chilvers & Brown-Rytlewski 2010; Tande et al. 2014; Hartman et al. 2015)   159  In 2009, F. virguliforme was documented in six Michigan counties, and has since been confirmed in a total of 22.   160  Materials and methods Fungal material and DNA extraction ---  161 -----DNA was eluted from the binding matrix column with 100 µL of AE buffer (), and 1:10 dilutions were used for PCR. -(Wang et al. 2015)-fusALPHA and fusHMG     162 Table 5-1 Details of the Fusarium species used in this study, including species name, isolate code, year of collection, geographic origin and host.   Species MLGa Isolate codeb Year Origin Host/source Fusarium virguliforme 23 DB_P27R11 2012 Van Buren Co. Michigan, USA Phaseolus  vulgaris F. virguliforme 23 DB_P27R13 2012 Van Buren Co. Michigan, USA P. vulgaris F. virguliforme 21 DB_P28R13 2012 Van Buren Co. Michigan USA P. vulgaris F. virguliforme 23 DB_P29R9 2012 Van Buren Co. Michigan USA P. vulgaris F. virguliforme 19 DB_P30_R3 2012 Van Buren Co. Michigan USA P. vulgaris F. virguliforme 30 DB_P30_R4 2012 Van Buren Co. Michigan USA P. vulgaris F. virguliforme 18 DB_P30_R5 2012 Van Buren Co. Michigan USA P. vulgaris F. virguliforme 24 DB_P30_R6 2012 Van Buren Co. Michigan USA P. vulgaris F. virguliforme 20 DB_P30R7 2012 Van Buren Co. Michigan USA P. vulgaris F. virguliforme 23 AL1a# 2009 Allegan Co. Michigan USA Glycine max F. virguliforme 23 AL1b# 2009 Allegan Co. Michigan USA G. max F. virguliforme 22 Ber1-5# 2009 Berrien Co. Michigan USA G. max F. virguliforme 25 Ber1-9# 2009 Berrien Co. Michigan USA G. max F. virguliforme 17 Ca-1a# 2009 Cass Co. Michigan USA G. max F. virguliforme 10 Ca-2a# 2009 Cass Co. Michigan USA G. max F. virguliforme 8 Ca-2b 2009 Cass Co. Michigan USA G. max F. virguliforme 27 CL-11-1# 2011 Clinton Co. Michigan USA G. max F. virguliforme 25 Hu-11-1# 2011 Huron Co. Michigan USA G. max F. virguliforme 31 KA-11-1 2011 Kalamazoo Co. Michigan USA G. max F. virguliforme 2 LE-11-1# 2011 Lenawee Co. Michigan USA G. max F. virguliforme 6 Mo1a_2# 2009 Monroe Co. Michigan USA G. max F. virguliforme 29 Mo1a# 2009 Monroe Co. Michigan USA G. max F. virguliforme 13 Mo1b 2009 Monroe Co. Michigan USA G. max F. virguliforme 26 Mo2a# 2009 Monroe Co. Michigan USA G. max F. virguliforme 15 Mo3a# 2009 Monroe Co. Michigan USA G. max F. virguliforme 23 Mo4a# 2009 Monroe Co. Michigan USA G. max F. virguliforme 23 Mo4c_2 2009 Monroe Co. Michigan USA G. max F. virguliforme 21 Mo4c# 2009 Monroe Co. Michigan USA G. max F. virguliforme 12 Mo5a# 2009 Monroe Co. Michigan USA G. max F. virguliforme 23 SA-11-2b 2011 Saginaw Co. Michigan USA G. max F. virguliforme 26 ST-11-1# 2011 St. Joseph Co. Michigan USA G. max F. virguliforme 14 STJ1-7Ps# 2009 St. Joseph Co. Michigan USA G. max F. virguliforme 16 STJ3a# 2009 St. Joseph Co. Michigan USA G. max F. virguliforme 5 STJ3b# 2009 St. Joseph Co. Michigan USA G. max F. virguliforme 7 STJ-7P2ss 2009 St. Joseph Co. Michigan USA G. max F. virguliforme 28 VB1# 2009 VanBuren Co. Michigan USA G. max F. virguliforme 1 VB-2a# 2009 VanBuren Co. Michigan USA G. max F. virguliforme 23 MIIN-B7 2012 Ingham Co. Michigan USA G. max F. virguliforme 4 34551* - San Pedro Buenos Aires Argentina G. max   163 F. virguliforme 9 31041* 1998 Illinois, USA G. max F. virguliforme 11 22823* - Indiana, USA G. max F. virguliforme 3 Mont1 1991 Monticello, Illinois, USA G. max F. azukicola NA 54362* 1996 Obihiro, Hokkaido, Japan Vigna angularis F. brasiliense NA 22678* 1993 California, USA G. max F. brasiliense NA 22743* 1992 Brasilia, Distrito Federal, Brazil G. max F. brasiliense NA 34938* 2003 Brazil, Rio Grande do Sul, Passo Fundo  G. max F. crassistipitatum NA 31949* 2000  G. max F. cuneirostrum NA 31157* 1992 Michigan, USA Phaseolus vulgaris  Fusarium sp. NA 22574* - Guatemala  Coffea arabica Fusarium sp. NA 22412* - French Guiana bark Fusarium sp. NA 22387* - French Guiana bark F. phaseoli  NA 31156* - Michigan, USA Phaseolus vulgaris  F. phaseoli  NA 22276* - USA Phaseolus vulgaris  F. phaseoli  NA 22411* - California, USA Phaseolus vulgaris  Fusarium sp. NA 22395* - Venezuela bark F. tucumaniae NA 31773* 2000 Ponta Grossa, PR, Brazil G. max F. tucumaniae NA 31096* 2001 Argentina  G. max F. tucumaniae NA 34549* 2000 Argentina G. max -       Table 5-   164 Development of microsatellite markers and primer design -------Figure 5-2 Microsatellite marker screening for polymorphism ---  165 ---- Data analysis --  166 -(Kamvar et al. 2014)(Agapow & Burt 2001)(Kamvar et al. 2014)the Bayesian clustering software STRUCTURE v2.3 (Pritchard et al. 2000). The parameters for each run include 20,000 generations burn-in period and 100,000 iterations for data collection for each K (K = 1-10) replicated for five runs. The lambda was set to 1.0, and an admixture model was assumed. The optimal K was chosen by evaluating dealta K (Evanno et al. 2005) calculated with the web-based STRUCTURE-HARVESTER program (Earl & Vonholdt 2012). The five replicated runs of the optimal K were combined using CLUMPP v1.1.2 (Jakobsson & Rosenberg 2007) to generate a single output for direct graphic visualization using DISTRUCT v1.1 (Rosenberg 2004).  Results Primer development and polymorphism screening ---  167 -Figure 5-1   Figure 5-1 -----  050100150Mono-Di-Tri-Tetra-Penta-Hexa-Size (bp) / CountsAverage LengthSSR countsA020406080100120AATACAGAACACGAAGAAGCAAATACGTATCountsB  168  Figure 5-2   Validation of microsatellite polymorphism ±02468101214161820Counts N. haematococca chromosomes  169  Genome location of microsatellites ---  170 Table 5-2 Fusarium virguliforme microsatellite characteristics, including name, location within the F. virguliforme genome, repeat motif, forward and reverse primers, allele number, allele size range, gene location, reference genome location and primer pair combinations, n IDa Locusb Repeat motif Forward Primers Reverse Primers Allele No. Allele Size range: Gene diversity PICc Location Ref. genomed Multiplex sete 43 Scaffold1_17 (AAG)13 FAM-GGGCCGTAAGTCGACAGTAA GTTTATCTTTGGCTTCGCATCATT 5 113-224 0.60 0.58 Intergenic Chr1 1 12 Scaffold9 (AC)37 HEX-CCTCCGTCATCAAAGATGGT GTTTTTGCTCTGTGAACCTTGCC 7 173-225 0.35 0.34 Intergenic Chr2 1 93 Scaffold4_9 (AAC)18 NED-GGGCGAGTCTTCTTCTCTCA GTTTAAGGCGTTGGTAATGTGGAG 3 166-193 0.22 0.21 Exon No hit 1 83 Scaffold14 (AACAGC)12 FAM-TACCTACGAGGCCCAGAGAA GTTTGCCATGATTGCTGAAGTGTT 2 201-219 0.09 0.09 Exon/intron Chr8 2 4 Scaffold1_22 (AC)22 HEX-TGTGTTGGAGCTGAGGACTG GTTTTCGTTCGCTACTCCGACTTT 4 185-217 0.56 0.54 Intergenic Chr1 2 15 Scaffold1_9 (ACG)26 NED-CTGTCGACCTCTCCACCATT GTTTAAGGTGACGGTGAGGAGATG 7 151-214 0.64 0.62 Exon Chr4 2 99 Scaffold937 (AAGAGG)18 FAM-CTACAACATACCGCTGCGTG GTTTTCATCAACCTCCCACTTCCT 7 137-233 0.35 0.34 Exon/intron No hit 3   171 48 Scaffold1_5 (AATGGC)8 HEX-TTGGCATTGTCCTTGTCATC GTTTAAGCACTTGGCCGTATCCTA 7 218-290 0.71 0.69 Exon Chr1 3 38 Scaffold1_7 (ACGGCC)8 NED-GAAATTGGGTTACCGAGCTG GTTTGAGATCGACAGAGTGGAGGC 5 197-233 0.23 0.22 Exon Chr1 3 80 Scaffold13_2 (AAC)15 FAM-TGCTGAGACCTTGATCCTCC GTTTCAACTCGCACGCATCTACTC 7 145-229 0.31 0.30 Intergenic Chr6 4 59 Scaffold5_3 (ACACAG)10 HEX-GGGATTCCTGTGCTTGTTGT GTTTGTGCGTGAACGCAGAGATAA 6 155-251 0.53 0.51 Exon/intron Chr3 4 10 Scaffold18_2 (AAC)30 NED-CAGCTCCAGCTTCACCTTTC GTTTGGACCCGTATGTCGAGTCTG 8 159-279 0.75 0.73 Exon Chr2 4 BKR476359-KR476370  polymorphic information content  Table 5-   172 Genetic diversity and structure --------STRUCTURE analyses also supported three population clusters (K=3) based on the delta K method (Figure S5-1). All of the isolates from outside of Michigan were partitioned to two of the clusters, while isolates from Michigan were separated into all three clusters (Figure 5-4 and Figure S5-1). T  173 -   Figure 5-3     174  Figure 5-4 -  Cross-species transferability    175 ---  176 Table 5-3 Transferability of microsatellite markers developed for F. virguliforme across isolates in clade 2 of the Fusarium solani species complex   Microsatellite loci allele size range Species (n = # of isolates) Major Hosts /substrate Locus  43 Locus  12 Locus 93 Locus 83 Locus  4 Locus 15 Locus 99 Locus 48 Locus 38 Locus 80 Locus 59 Locus 10 F. virguliforme (n = 42) Soybean and Dry beana  113-224 173-225 166-193 201-219 185-217 151-214 137-233 218-290 197-233 145-229 155-251 159-279 Fusarium brasiliense (n=3) Soybean  123-161 169-171 172-184 184 177-183 151 - 139-207 190-202 145-148 149-155 159-162 Fusarium tucumaniae (n=3) Soybean  121 171 172-181 181 176 154 - 195-255 196-202 149-161 161-199 171-186 Fusarium crassistipitatum (n=1) Soybean  138 172 175 201 181 152 227 164 196 145 149 171 Fusarium phaseoli  (n=3) Dry beanb  131-152 169-171 181-184 184 151-177 151 - 163-242 196 145-148 155-174 159-168 Fusarium cuneirostrum (n=1) Dry bean  121 170 181 170 151 151 - 255 196 171 156 174 Fusarium azukicola (n = 1) Dry bean  - 170 158 177 - 167 - 133 208 133 168 - Fusarium sp.c (n=2) Bark - 171-174 - - 169 157-169 - - 181-193 - 137 - Fusarium sp.d (n=1) Bark 138 172 175 - - 152 227 164 196 145 149 171 Fusarium sp.e (n=1) Bark - 88 167 - - - - - 182 - - -     -  177 Discussion (Karaoglu et al. 2005)     178 -(Agapow & Burt 2001)  2.50×10-5 - 2.80×10-6 vs 10-9 per generation per site, respectively(Kasuga et al. 2002)--  179 - -(Koorey et al. 1993)     180 Acknowledgement We thank J. Alejandro Rojas and Janette L. Jacobs for technical and editorial support. -This work was supported by grants from Project GREEEN (number GR10-113), the Michigan Soybean Promotion Committee, the A.L. Rogers Endowed Research Scholarship, and the Carter Harrison Endowed Graduate Student Fund. Genome sequence data used in this study were generated from the support of Soybean Research and Development Council and Iowa Soybean Association and available through http://fvgbrowse.agron.iastate.edu/.    181 Data accessibility Microsatellite sequence data can be found in NCBI GenBank entries: KR476359-KR476370. Microsatellite fragment analysis allele size     182 APPENDICES  183 APPENDIX A Supplementary tables   Table S 5-1 Microsatellite markers allele sizes (bp) detected for each of the multi-locus genotype MLG* Locus 43 Locus 12 Locus 93 Locus 83 Locus 4 Locus 15 Locus 99 Locus 48 Locus 38 Locus 80 Locus 59 Locus 10 1 224 207 193 219 203 184 233 278 233 229 245 198 2 224 207 193 219 203 184 233 272 233 226 251 198 3 209 227 193 219 205 184 233 278 233 163 203 228 4 209 225 193 219 185 211 233 284 233 163 203 228 5 191 213 166 219 217 172 233 272 233 187 203 279 6 191 213 166 219 205 211 233 278 233 187 215 243 7 191 213 166 219 203 208 233 290 233 187 215 243 8 191 213 166 219 203 208 233 278 233 187 209 243 9 191 213 166 219 203 208 233 272 233 187 215 243 10 191 213 166 219 203 208 233 272 233 187 209 243 11 191 213 166 219 203 208 233 218 197 187 215 243 12 191 213 166 219 203 208 209 290 227 190 215 243 13 191 213 166 219 203 208 197 278 233 187 215 243 14 191 213 166 219 203 208 185 272 233 187 215 243 15 191 213 166 219 203 208 137 278 233 187 215 243 16 191 213 166 201 217 172 185 272 233 187 203 279 17 191 213 166 201 203 178 233 272 233 187 209 243 18 191 211 166 219 203 208 233 260 233 187 203 246 19 143 215 166 219 205 211 233 278 233 187 203 249 20 143 213 166 219 205 214 233 278 233 187 203 246 21 143 213 166 219 205 211 233 284 233 187 203 246 22 143 213 166 219 205 211 233 278 233 187 203 249 23 143 213 166 219 205 211 233 278 233 187 203 246 24 143 213 166 219 205 211 233 278 233 187 203 243   184 25 143 213 166 219 205 211 233 272 233 187 203 249 26 143 213 166 219 205 211 233 272 233 187 203 246 27 143 213 166 219 205 211 209 284 209 187 203 249 28 143 213 166 219 205 211 179 224 203 187 203 246 29 143 213 166 219 203 208 233 272 233 193 203 264 30 143 211 166 219 203 211 233 284 233 187 203 246 31 113 173 178 219 205 151 173 224 203 145 155 159 *: Indicates multi-locus genotype (MLG) Table S 5-   185 APPENDIX B Supplementary figures    Figure S 5-1 (A) Delta K method to determine the optimal K (assumed ancestors) based on likelihood method (Evanno et al. 2005). 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Contrary to wide distribution of F. virguliforme in the US, SDS caused by F. virguliforme is less common in South America, and primarily distributed in Argentina. Given the wide distribution in the American continents, little is known about the center of origin or population structure of F. virguliforme. Although previous studies proposed the South American center of origin hypothesis for F. virguliforme, no direct analysis of F. virguliforme population biology to support this hypothesis. In this study, we used multilocus microsatellite typing, population genetics tools, and a collection of 539 F. virguliforme isolates from both South and North America to test the center of origin hypotheses within the US and intercontinentally. High genotypic diversity and diverse population structure composition of the Arkansas population supported the hypothesis that Arkansas is the center of origin in the US. The distribution of F. virguliforme in the US is not in isolation by distance as detected in Mantel test, suggesting a rapid and recent expansion of this pathogen in the US. The hypothesis that South America is the center of origin was supported by the coalescence based migrate analysis; however, genotypic diversity and population structure of the Argentinean populations were less diverse than the Arkansas population. The Argentinean population cannot explain the genotypic diversity and population structure composition detected in the US, and therefore the hypothesis that South America is the center of origin is not supported in this study.     193 Introduction Fusarium is a diverse and ubiquitous genus of filamentous fungi (Ascomycetes: Sordariomycetes: Hypocreales: Nectriaceae) that are widely distributed in soil and associated with plants as pathogens or endophytes (Aoki et al. 2014; Ma et al. 2013). Fusarium virguliforme causes sudden death syndrome of soybean and is one of the most devastating soybean diseases (Roy et al. 1997; Wrather & Koenning 2009). Yield losses caused by F. virguliforme ranged from 20 - 46% in SDS symptomatic fields (Brzostowski et al. 2014; Hartman et al. 1995). F. virguliforme is a soilborne fungus that completes most of its life cycle within soil or plant roots (Rupe 1989). To date, only one mating type (MAT1-1) has been found and no sexual structure was formed in the field or in the lab, thus the only known mode of reproduction for F, virguliforme is asexual reproduction through production of conidia and chlamydospores (Covert et al. 2007; Hughes et al. 2014). The lack of aboveground structure or production of aerial spores is thought to limit the mobility of this pathogen, therefore it is thought that the dispersal of F. virguliforme is associated with the movement of infested plant material or soil.  Since the first report of SDS in 1971 in Arkansas (Hirrel 1983), SDS has been reported in most soybean producing areas in North America, with an apparent dispersive pattern (Figure 6-1) (Bernstein et al. 2007; Chilvers & Brown-Rytlewski 2010; Hartman et al. 1999; Kurle et al. 2003; Pennypacker 1999; Roy et al. 1997; Tande et al. 2014; Ziems et al. 2006). To date, although SDS is reported in most soybean producing areas, little research has been conducted to study the population biology of F. virguliforme, and thus the center of origin responsible for the dispersal of F. virguliforme is still unknown. Besides North America, SDS has also been reported in South America in the early 1990s (Nakajima et al. 1993; Ploper 1993). In North America, F. virguliforme is the only known SDS pathogen. However, SDS is caused by four   194 Fusarium species in South America, including F. brasiliense, F. crassistipitatum, F. virguliforme, and F. tucumaniae the predominant SDS pathogen in South America (Aoki et al. 2003; Aoki et al. 2005; Aoki et al. 2012; Scandiani et al. 2004). In a survey of SDS causing Fusarium species in South America, F. virguliforme was only identified in several provinces in Argentina, and not in any other soybean producing countries in South America (O'Donnell et al. 2010). To date, the US and Argentina are the only two countries confirmed to have F. virguliforme in the Americas. In recent years, SDS and F. virguliforme were reported in the areas outside of American continents, including South Africa, Malaysia, and Iran (Chehri 2015; Chehri et al. 2014; Tewoldemedhin et al. 2013). Although this pathogen has been reported in a wide range of geographical area, little is known about the dispersal method or center of origin for F. virguliforme, which raises concerns about the imminent epidemic of SDS in other soybean producing countries in Asia and Africa.   195 --- --  196  ---  197   Figure 6-1 Distribution of soybean sudden death syndrome (SDS) in the US and Canada based on the year of first report in journal articles. Since the first report of SDS in 1971 in Arkansas, SDS has been reported in the surrounding states in the following years with apparent pattern of spreading. By 2014, SDS has been confirmed in most soybean producing areas in the US and Canada, thus to continuing threat soybean production.  Materials and methods Sampling, fungal isolation, and DNA extraction ---  198 ----- ---------  199 -Wilmington, Delaware Haplotype identification --Promega Corp., Madison, WICarlsbad, CA-  200  Data analyses Genotypic diversity  - Test for population clonality -  201  Analysis of molecular variance A hierarchical analysis of molecular variance (AMOVA)  was  to test for significant variation among sampling locations. To determine the level of significance for the explained variations by each hierarchical level, 10, 000 permutations were performed to calculate the P-value.  Index of population differentiation    202 Mantel test and isolation by distance -- STRUCTURE analysis ----  203  Clustering based on individual genetic distances - Multivariate analysis - Multilocus inference of migration -  204 ----- Results Genotyping results    205 - -- Figure S 6-1   206 Clonality in populations -  207 Table 6-1 Genotypic and genetic diversity of F. virguliforme populations collected in both South and North America in this study.  ID Countries State/Province Years N MLG eMLG SE H G E.5 Ia  1 Argentina Buenos Aires 1999, 2002, 2012, 2013  40 31 9.39 0.71 3.35 25.81 0.90 2.97 0.31 2 Argentina Santa Fe 2004, 2012, 2013  12 10 8.50 0.58 2.21 8.00 0.86 2.59 0.26 3 Argentina Entre Rios 2012 2 2 2.00 0.00 0.69 2.00 1.00 NA NA 4 Argentina Cordoba 2012, 2013  6 4 4.00 0.00 1.33 3.60 0.94 -0.54 -0.14 5 USA Arkansas 1985, 1986, 2001, 2012, 2014  120 82 9.26 0.85 4.10 36.18 0.59 3.38 0.31 6 USA Illinois 1991, 2001, 2006  8 8 8.00 0.00 2.08 8.00 1.00 5.26 0.48 7 USA Indiana 2000, 2012  50 36 9.17 0.85 3.43 24.51 0.79 1.43 0.14 8 USA Iowa 2006 16 13 8.85 0.80 2.56 11.56 0.89 3.95 0.36 9 USA Kansas 2012 46 17 4.79 1.34 1.73 2.62 0.35 1.03 0.12 10 USA Michigan 2009, 2011, 2012, 2013, 2014  235 127 8.89 1.03 4.25 27.30 0.38 2.14 0.20 11 USA Wisconsin Unknown 2 1 1 0 0 1 NaN NA NA 12 USA Missouri 2002 1 1 1 0 0 1 NaN NA NA 13 Canada Ontario 2014 1 1 1 0 0 1 NaN NA NA        539 333                N, number of isolates; MLG, number of unique multilocus genotypes; eMLG, expected number of MLG, which was calculated using rarefaction; SE, standard error for eMLG; H, Shannon-Wiener index; GE5, MLG evenness; , corrected index of association.  *: Genetic summary statistic calculated using the clone-corrected data was indicated with an astrisk (*) a: Statistical values that cannot be calculated given the population     208 Table 6-2 Analysis of molecular variance (AMOVA) of F. virguliforme within populations, among populations, and between continents. Sampling fields and states/provinces were labeled as populations and regions, respectively.   Number of populations  Number of regions % Variation ST P-value a Dataset Among regions df Among Pops df Within pops df US 36 8 2.17 7 7.76 27 90.07 261 0.10 0.001 Argentina 15 4 4.26 3 -b - 95.74 41 0.04 0.162  Intercontinent 51 12 13.75 11 8.88 38 77.37 292 0.23 0.001  a P-value based on 1000 random sampling replicates, =0.01 b Variations cannot be calculated due to lack of enough sample size for each sampling population   209 Population structure -- Genetic structure and SDS spread in the United States --  210   Figure 6-2 F. virguliforme values among the eight F. virguliforme populations by state or provinces. The heatmap color gradients and dendrogram delineate two main clusters of F. virguliforme populations, as the US and Argentina branches. Within the US branch, two subgroups were divided based on their relative geographical locations, except for the Kansas population.  Bayesian method  STRUCTURE analysis   211 Figure S 6-2Figure S 6-2 ---   212  Figure 6-3 Population structure of F. virguliforme ancestry proportion from K=2 to K=7 clusters inferred from the STRUCTURE software. F. virguliforme isolates were grouped based on the source of origin to the hierarchical level of state or provinces. Each vertical bar represents an individual isolate that was partitioned into K segments indicating the proportion of assignment to the K clusters. For K=2, isolates from four provinces in Argentina, shown in blue, are distinct from most of the isolates collected in the United States, which are shown in red. For K=3, isolates from Argentina are mainly composed with two cluster, as shown in blue and green. In the US, Arkansas population has more similar population composition with the Argentinean populations, while the rest of the populations were primarily clustered into two clusters, shown as green and red. For K=4, which was the optimal K cluster as chosen based on deltaK method (Evano, 2005), isolates from Argentina still remained with two clusters composition, whereas the isolates from the US populations are mainly composed with three clusters, shown as green, red and purple. Admixed isolates are less common at K=4 clusters. With the increase of K clusters from K=5 to K=7, more admixed individuals started to appear in the US populations, but not in the Argentinean populations.     213 --- - Relationships among genotypes -  214   Figure 6-4 minimum spanning networks of F. virguliforme multilocus genotypes (MLG) based distance. Each node in the network represents a unique MLG. Line thickness indicates the genetic distance between MLGs, with thicker line represents closer distance, vice versa. MLG from different populations were labeled with different colors.  -  215 -  Figure 6-5 Scatter plot of the discriminant analysis of principal components of F. virguliforme microsatellite multilocus genotypic data. Each point represents one individual, and individual points were colored based on their source of origin as states or provinces.    216 Spatial correlations - isolation by distance ----  Figure 6-6 and geographic distance (km) with linear regression line fitting, and 95% confidence interval was plot in grey. No significant correlation between genetic distance and geographical distances (P=0.065).    217 Migrate analysis --------- Figure S 6-3---  218 -   Figure 6-7 Migrate analysis with three pooled populations: 1) Argentina, 2) Arkansas, and 3) Indiana and Michigan. Arrows connecting between locations showed directional migration model as supported in the Migrate-n analysis. The line thickness represents the number of migrants per generation as descried in the legends.  219 Table 6-3 Migration models selection using marginal log-likelihood calculated in the coalescence method Migrate-n. Model Migration Model a Model description Number of parameters Marginal log-likelihood b Rank 10 N-US <-> S-US <-  Arg  Argentina migrate to the US, unidirectional from Arkansas to Michigan 6 -28356.55 1 12 N-US <-  S-US <-  Arg  Argentina migrate to the US, bidirection migration within the US 5 -35355.2 2 9 N-US <-> S-US  -> Arg  US migrate to Argentina 6 -69960.51 3 7 N-US <-> S-US <-> Arg  Stepping stone I 7 -83037.33 4 8 S-US <-> N-US <-> Arg  Stepping stone II 7 -92220.71 5 2 Full migration N-US migrate to S-US, but isolate with Argentina 9 -145591.91 6 11 N-US  -> S-US <-  Arg  US migrate to the Argentina, unidirection migration from Michigan to Arkansas 5 -738770.71 7 4 N-US <-  S-US  |  Arg  Migration within US, but isolate with Argentina 5 -1299050.74 8 3 N-US <-> S-US  |  Arg  N-US migrate to S-US, but isolate with Argentina 5 -1406090.29 9 5 N-US  -> S-US  |  Arg  S-US migrate to N-US, but isolate with Argentina 4 -1510248.35 10 6 N-US  |  S-US  |  Arg  Full isolation 3 -1764858.37 11 1 Panmixia Panmixia 1 -4890524.81 12  a migration scenario models. N-US represents the F. virguliforme populations located in Indiana and Michigan; S-US represents the F. virguliforme populations located in Arkansas; Arg represents the F. virguliforme populations located in Argentina. Dash arrow symbol (->) represents migration event and direction from one population to the other. b Bezier log marginal likelihood was calculated for migration model selection. The higher likelihood, the better the model is.   220 Discussion --- Arkansas is center of origin in the US -(Hartl et al. 1997). The Arkansas population demonstrated the highest genotypic diversity among populations in the US, particularly Arkansas has the highest adjusted genotypic   221 richness (eMLG, Table 6-1). In the population STRUCTURE analysis, four clusters were used to delineate the population structure in the US populations, and Arkansas was demonstrated to be the most diverse with assignment to all four clusters with roughly equal ratios.  - ---  222 - South America is the center of origin  in South America (O'Donnell et al. 2010; Scandiani et al. 2004)(2009) labeled as a Brazilian sourced isolate was proved to be erronepersonal communication).  ---  223 - --   224   Means of pathogen dispersal  -(Excoffier et al. 2009)---  225 -     226 Acknowledgement -     227 Data accessibility -    228 APPENDICES  229 APPENDIX A Supplementary tables   Table S 6-1 Shared genotypes between historical F. virguliforme isolates from Arkansas and current isolates. Of 13 historical F. virguliforme isolates, 13 unique MLGs were identified. In current populations, three MLGs were found to be identical with the historical isolates recovered from year 1985. MLG 110 was the most predominant shared genotype across a wide range of current geographic distributions.  Historical isolates Current populations (from 2012-2014) ID Year of collection Sources Field locations MLG a Arkansas Kansas Illinois Indiana Iowa Michigan Total JR-4 1985 Arkansas Pine Tree 4 - - - - - - - JR-8 1985 Arkansas Pine Tree 13 - - - - - - - JR-16 1985 Arkansas Pine Tree 37 - - - - - - - JR-18 1985 Arkansas Pine Tree 51 1 1 - - - - 2 JR-7 1985 Arkansas Pine Tree 52 - - - - - 1 1 JR-12 1985 Arkansas Pine Tree 53 - - - - - - - JR-1b 1985 Arkansas St. Charles 110 12 - 1 6 1 16 36 JR-9 1985 Arkansas Pine Tree 118 - - - - - - - JR-14 1985 Arkansas Pine Tree 122 - - - - - - - JR-11 1985 Arkansas Pine Tree 147 - - - - - - - JR-10 1985 Arkansas Pine Tree 155 - - - - - - - JR-13 1985 Arkansas Pine Tree 233 - - - - - - - JR-197 1986 Arkansas Pine Tree 269 - - - - - - - a MLG, Multilocus genotypes.     230 Table S 6-2 Simplified migration models selection using marginal log-likelihood calculated in the coalescence method Migrate-n.       Migration Model a Model description Number of parameters Marginal log-likelihood b Rank N-US  <-    S-US S-US migrate to N-US 3 -45357.21 1 N-US   ->   S-US N-US migrate to S-US 3 -47125.66 2 Panmix Panmixia 1 -72551.19 3 N-US  <->  S-US Full Migration 4 -164591.91 4   231 APPENDIX B Supplementary figures    Figure S 6-1 Shared multilocus genotypes (MLG) among populations by state/provinces within countries.     232  Figure S 6-2 In STRUCTURE analysis, determination of optimal K for clustering individuals for each assigned populations. (A) log likelihood values of delta K against a range of K values. (B) The mean likelihood values calculated under varying K values, from K=2 to K=13.     233  Figure S 6-3 is the mutation scaled population size and M is mutation scale migration rates (migrants per generation).      234  Figure S 6-4 Geographical distribution of F. virguliforme sample locations in Midwest - United States, Ontario - Canada, and Pampas area in Argentina.  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Plant Disease 90, 109-109.   241 CONCLUSION AND FUTURE DIRECTIONS    242 Summary of dissertation  Soybean sudden death syndrome (SDS) is one of the biggest concerns for American soybean growers. Fusarium virguliforme is the only known SDS causal pathogen in the US, however there are multiple closely causal species identified in South America. The surviving propagules of F. virguliforme, macroconidia and chlamydospores, can be viable in soil for an extended period of time. In addition, F. virguliforme can colonize several plant species that are commonly present or rotated in soybean fields, including corn. Therefore, it is very difficult to reduce F. virguliforme inoculum, once a field is infested. The primary SDS disease management strategy is the use of soybean cultivars that are partially resistant to SDS foliar symptoms. Chapter 1 is a review of the literature with respect to Fusarium virguliforme.  In chapter 2, a quantitate real-time (qPCR) assay is described for the detection and quantification of F. virguliforme from plant and soil samples. The assay was designed to target the rDNA IGS region, which has been demonstrated to differentiate F. virguliforme from closely related Fusarium species in a phylogenetic study. The target of this qPCR assay contains multiple copies in the F. virguliforme genome, which significantly increases assay detection sensitivity, when compared to a single copy target. The assay was validated on two real-time PCR thermal cycler platforms, demonstrating similar detection sensitivity and specificity. The qPCR assay has been adopted by multiple labs and applied in routine SDS disease diagnostics and research projects. In the future, the qPCR assay may be used to make association between F. virguliforme propagule density in soil prior to planting, so that soybean growers can make timely SDS disease management decisions. In chapter 3, the qPCR assay was utilized to study the temporal dynamics of F. virguliforme colonization of soybean roots. Previously the association between SDS foliar symptom severity   243 and F. virguliforme root colonization was not clear. To address this issue, soybean roots from four soybean cultivars were sampled at nine time points throughout the season to quantify F. virguliforme quantity in soybean root. Despite significant differences in SDS foliar symptoms among cultivars, the quantities of F. virguliforme in roots were not significantly different. Indicating that SDS foliar symptom severity is not only driven by F. virguliforme quantities in roots, and suggests the planting partially resistant varieties that demonstrate no foliar symptoms may not be managing F. virguliforme inoculum levels in the field. In addition, quantification of F. virguliforme in soybean roots provided a quantitative phenotype to screen soybean lines for resistance to F. virguliforme root colonization.  In chapter 4, the in-vitro sensitivity of F. virguliforme isolates to the SDHI fungicide fluopyram was investigated. Fluopyram has demonstrated in reducing SDS foliar symptoms in field trials, but the in vitro efficacy to inhibit mycelia growth had not been evaluated. In this study, 185 F. virguliforme isolates collected from multiple locations in the US were tested against the fungicide fluopyram in a poison plate assay. Most F. virguliforme isolates (>95%) appear to be sensitive to fluopyram, but eight isolates were determined to be less sensitive to fluopyram in the poison plate assay.  In chapter 5, a set of microsatellite markers were developed to detect genetic diversity within F. virguliforme. Previous studies using standard loci for phylogeny studies could not resolve genetic diversity within F. virguliforme, and they proposed F. virguliforme in the US belong to one clonal lineage. In this study, a set of robust microsatellite markers were developed to measure genetic diversity within F. virguliforme. The markers were demonstrated to be polymorphic and informative through validation with a set of F. virguliforme isolates collected from Michigan.    244 After the first report of SDS in Arkansas in 1971, SDS has subsequently been confirmed in surrounding states with an apparent spread from Arkansas. It had also been proposed that the center of origin for F. virguliforme was South America, given that F. virguliforme and multiple closely related species causing SDS had been found in the continent. However, the hypothesis of F. virguliforme origin in the US and inter-continental had not been tested. In chapter 6, F. virguliforme isolates were collected from multiple states in the US and from Argentina in South American to test the center of F. virguliforme center of origin hypothesis. A total of 539 F. virguliforme isolates were recovered and genotyped. High genotypic diversity and diverse population structure was observed in Arkansas, which supported the hypothesis that Arkansas is the center of origin in the US. The distribution of F. virguliforme in the US was not in isolation by distance as detected in a Mantel test, suggesting a rapid and recent expansion of F. virguliforme in the US. The hypothesis of South America as the center was supported by the coalescence based migrate analysis; however, the highest genotypic diversity and population structure diversity was detected in Arkansas. Therefore, the South America as the center of origin hypothesis is not clear. Additional sampling effort in South America particularly outside of Argentina is needed to further test South America as the center of origin hypothesis.