Poster: Molecular & Cellular Plant-Microbe Interactions: Proteomics/Metabolomics/Genomics
776-P
Systematic computational network-based analysis to predict subnetwork modules associated with pathogenicity and fumonisins in Fusarium verticillioides
M. KIM (1), B. Yoon (1), W. Shim (1) (1) Texas A&M Univ., U.S.A.
Fusarium verticillioides is a notorious pathogen of maize causing ear rot and fumonisin contamination. In order to develop innovative preharvest control strategies, such as new biocontrol agents and resistant hybrids, to minimize the entry of fumonisins into our food supply, there is a need to identify new molecular targets for disrupting F. verticillioides pathogenicity. In this study, we performed a systematic computational network-based comparative analysis of two distinct F. verticillioides – maize kernel RNA-seq datasets (one on moderately resistant inbred and the other on susceptible hybrid). For the systematic analysis of the pathogenicity mechanism, we first inferred F. verticillioides co-expression networks. Subsequently, we identified functional subnetwork modules on the co-expression networks consisting of interacting genes that display harmoniously coordinated behavior in the respective datasets. A computationally efficient branch-out technique applied with an adopted probabilistic pathway activity inference method was used to identify functional subnetwork modules likely involved in F. verticillioides pathogenicity. Here we exhibit key potential subnetwork modules, where the modules contain several enriched GO terms as well as potential pathogenicity genes from other pathogenic fungi. Putative hub genes in each subnetwork will be functionally characterized to test their candidacy as new targets for ear rot and fumonisin control strategies.