Kim, Mansuck (2016-05). Computational Identification of Functional Modules and Hub Genes Involved in Pathogenicity-Associated or Defense Response on Fusarium Verticillioides-Maize Interactions. Doctoral Dissertation.
Fusarium verticillioides is one of the key pathogens for stalk rot and ear rot on maize. While several genes associated with F. verticillioides pathogenicity and mycotoxin biosynthesis have been characterized, our knowledge of the cellular and genetic networks for these events is still very limited. Also, underlying molecular and cellular mechanisms associated with the maize defense response against the F. verticillioides pathogenicity are complex. Therefore, in order to better understand maize defense as well as F. verticillioides pathogenicity, an approach systematically investigating the host-pathogen interactions is needed. In this PhD study, a systematic network-based comparative analysis approach using large-scale F. verticillioides-maize RNA-seq data was applied to identify F. verticillioides pathogenicity-associated subnetwork modules and also key pathogenicity genes as well as maize subnetwork modules involved in the defense response. For each study, we constructed corresponding co-expression networks through partial correlation based on the given comparable conditions. For the first work, predicting F. verticillioides pathogenicity-associated subnetwork modules, we established a pipeline identifying the functional modules by a branch-out technique with probabilistic subnetwork activity inference. For identifying maize defense modules, we first collected candidate maize genes by comparing expression pattern of maize genes and that of the selected four F. verticillioides pathogenicity genes through cointegration, correlation, and expression level change. Then, we inferred potential subnetwork modules among the candidate genes by adopting the previously established pipeline. For identifying specific key F. verticillioides pathogenicity genes based on the predicted subnetwork modules, we analytically investigated on each gene in its predicted subnetwork module. In this investigation, we considered its influence on others, association to pathogenicity, and distinctive differentiation between the two conditions. Through our systematic investigation of the F. verticillioides-maize RNA-seq data, we identified pathogenicity-associated or defensive subnetwork modules, where the member genes were harmoniously coordinated and significantly differentially activated between the two different conditions. Also, we identified specific F. verticillioides pathogenicity genes playing a key role in the predicted pathogenicity-associated subnetwork modules.