Computational and functional characterization of Fusarium verticillioides transcription factors
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abstract
Fusarium verticillioides causes devastating disease of maize, including ear rots and stalk rots. More importantly the pathogen produces fumonisin mycotoxins that accumulate in maize kernels and threaten food safety and human health. The goal of this project is to systematically characterize F. verticillioides transcriptomes to computationally predict transcription factors and signaling pathways that are critical for pathogenesis-related processes, including growth, morphological differentiation, and mycotoxin biosynthesis. A number of fungal pathogens of major human crops have their genomes sequenced, but we are still far from understanding the programming of gene expression. In F. verticillioides and many other plant pathogenic fungi, little is known about the how correlated gene expressions lead to functional outcomes, namely pathogenicity and mycotoxin production. Expansive resources for functional genomics are available in F. verticillioides, including a sequenced genome and transcriptomes, along with techniques for highthroughput gene disruption, making the fungus ideal for computational analyses. In this project, we will explore network-based comparative analysis through probabilistic subnetwork inference using RNA-seq transcriptome data to identify potential F. verticillioides pathogenicity-associated subnetwork modules. Our propject goal is to identify putative hub TF genes, which are critical for regulating the subnetworks associated with pathogenicity.We will then focus on functionally characterizing these TF genes, which will be disrupted via homologous recombination. Phenotypic abnormalities in growth, pathogenesis, and metabolism will be documented for each mutant. All mutants and data generated during this project will be made publicly available to the scientific community.
