Network Inference from Multi-omic Data Uncovers Dynamic Transcriptional Regulation Modules in Pathogenic Fungus Fusarium graminearum

Abstract

The filamentous fungus Fusarium graminearum causes devastating crop disease and produces harmful mycotoxins worldwide. Understanding the complex F. graminearum transcriptional regulatory networks (TRNs) is vital for effective disease management. Reconstructing F. graminearum dynamic TRNs, an NP-hard problem, remains unsolved using commonly adopted reductionist or co-expression based approaches. Multi-omic data such as fungal genomic, transcriptomic data and phenomic data are vital to but so far have been largely isolated and untapped for unraveling phenotype-specific TRNs. Here for the first time, we harnessed these resources to infer global TRNs for F. graminearum using a Bayesian network based algorithm, "module networks". The inferred TRNs contain 49 regulatory modules that show condition-specific gene regulation. Through a robust validation based on prior biological knowledge including functional annotations and TF binding site enrichment, our network prediction displayed high accuracy and concordance with existing knowledge, highlighted by its accurate capture of the well-known trichothecene gene cluster. In addition, we developed a new computational method to calculate the associations between modules and phenotypes, and discovered subnetworks responsible for fungal virulence, sexual reproduction and mycotoxin production. Finally, we found a clear compartmentalization of TRN modules in core and lineage-specific genomic regions in F. graminearum, reflecting the evolution of the TRNs in fungal speciation. This system-level reconstruction of filamentous fungal TRNs provides novel insights into the intricate networks of gene regulation that underlie key processes in F. graminearum pathobiology and offers promise for the development of improved disease control strategies.