Speaker
Description
Cellular protein interactome data are fundamental to determine mechanisms of pathogens, infection progression, and potential drug targets. Performing network analysis provides a data-driven middle ground between differential gene expression analysis, which assumes that gene expression is largely independent, and highly detailed mathematical models, which require careful measurements or estimation of parameters from training data. In the absence of detailed mathematical behaviors, network analysis has been shown to outperform differential gene expression analysis when gene interactions cannot largely be assumed to be independent. Here we reanalyze influenza A infection of patient-derived epithelial cell lines that include treatment with a sex hormone, estradiol. We show that network analysis results are largely divergent from differential analysis; network analysis enriches for cell cycle and estrogen signaling pathways whereas differential gene expression enriches for methylation, programmed cell death and meiosis pathways. Half the transcription factors we uncover were previously unstudied in viral infection and only one was previously been implicated in coronavirus replication. Finally, our network analysis significantly identifies relevant host factors in viral replication screens. Thus, we reveal the role of hormones in influenza A viral infection by identifying key signaling proteins and 9 transcription factors using our network modeling approach.
