Developing/Validating a Weather-Based Decision Support System for the Management of White Mold (sclerotium rolfsii) in Peanut

Further understanding of a pathogen's population diversity is important for disease management through improved breeding programs and a better understanding of population responses to varying environments (e.g. temperature and :fungicides). The goals of this project were to clarify the genetic diversity of S. rolfsii in the Southeastern U.S., and assess this diversity with isolates from around the world. A total of 26 S. rolfsii isolates were previously collected from Florida, Georgia, Alabama, and South Carolina with 18 new isolates being collected from Florida, Georgia, Alabama, Mississippi and Arkansas in this study. Type sequence data had also been previously collected from Spain, Italy, Portugai Brazil and Chile. DNA was extracted from these isolates and amplified by PCR using ITS, RPB1, RPB2, and MS204 loci The resuhing sequences were aligned and analyz.ed using Geneious and BioEdit. Phylogenies were constructed with MEGA7 and a TCS (Templeton, Crandall and Sing, 1992) population network created in R Using the 4 loci S. rolfsii isolates where separated into 2 groups with no differentiation related to host or geography. The network analysis using RPB2 indicated that isolates from different Florida counties had different haplotypes, and that the other isolates from Georgia and South Carolina, Europe and South America grouped in different haplotypes as well. This data shows that variation does exists in the S. rolfsii populations, which means pathogen populations should be considered when assessing disease management strategies ( e.g. cultivar resistance).
A majority of the new isolates were collected during the 2018 growing season for this grant due to low levels of white mold in 2017. Novel genes (EFla and ??-tublin) were also examined in this study, however, data collected from these genes was not clean. Currently, tests are being done to re-run these genes and add the 18 new isolates to the data set.