Leveraging Pathogen-Host Networks to Identify Virus-specific and Estradiol-regulated Mechanisms during Respiratory Infection

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ABSTRACT Respiratory viruses, such as influenza and SARS-CoV-2, interact with distinct molecular pathways in human cells to promote virus replication and alter immune activity. When considering patient cohort variability, morbidity and mortality are often higher for women than men for select influenza virus infections, exemplified in the 2009 H1N1 pandemic. Estradiol, a major sex hormone, has been shown to impact virus replication in a sex-specific manner. Yet much remains unknown as to the pathways different viruses engage to promote infection and alter immune activity or what pathways link estradiol activity to virus replication. This research program uses recently developed bioinformatics algorithms and NIAID-supported, published datasets in order to reveal new pathways and molecules involved in infection with influenza viruses and SAR-CoV-2 (Aim 1) and in infection in respiratory cells derived from women and treated with estradiol (Aim 2). More specifically, we will use two dynamic network perturbation algorithms, ProTINA and DeltaNeTS+, to create dynamic mathematical models of intracellular signaling in order to predict important disease modulators. Dynamic network perturbation analysis will be applied to virus-specific, virus-host interaction networks and host gene expression data induced by each virus. For Aim 2, we have identified gene expression data from influenza-infected nasal cells from female donors that are pretreated with estradiol. We will validate ProTINA’s and DeltaNeTS+ ability to identify host factors of virus replication using results from published siRNA- and CRISPR-based screens. After the validation, we will perform an in-depth characterization of the most significant proteins identified in order to generate new hypothesis on the host pathways that are involved in infection with different respiratory viruses or that interact with estradiol during infection.