Investigating Global Protein-Protein Interactome Dynamics During Influenza A Infection

Abstract

Viruses remodel the host cell subcellular landscape to engineer an environment conducive to their own replication. For example, influenza A (IAV), a highly prevalent pathogen with important impact on human health, induces the remodeling of several membrane-bound organelles. These dramatic changes require the regulation of the abundance, localization, and protein-protein interactions (PPIs) of a diverse set of proteins across the cell. High-throughput assays are critical to capture the global protein-protein interactome dynamics that drive organelle remodeling. Thermal Proximity Coaggregation (TPCA) and Ion-Based Proteome-Integrated Solubility Alteration (I-PISA) are two such mass spectrometry-based methods. However, previous studies have shown that these methods have limitations in capturing the interactions of membrane-bound proteins, a class of proteins critical to understanding organelle remodeling. Here, we optimized and expanded these assays to address this limitation, by fine-tuning the protein denaturation conditions used and quantifying the insoluble byproducts of these assays. Overall, this work improves the quality and scope of global PPI analysis via TPCA and I-PISA, providing an experimental platform that can be deployed for studies of dynamic cell contexts. Here, TPCA and I-PISA were employed to characterize dynamic PPIs during IAV infection for the first time. These PPI findings guided follow-up studies of organelle remodeling events during IAV infection, with a focus on the interactions driving the increased size, decreased number, and decreased short-range motility observed at the peroxisomes. Two proviral ORAPs, RHOT1 and ACBD5, were found to dynamically interact with trafficking components at the microtubules, and with lipid metabolism regulatory factors at the endoplasmic reticulum respectively. These findings help characterize peroxisome function and morphology during the IAV replication cycle. Beyond this work, we hope that the rich dataset that was generated here will continue to serve as a valuable resource to study many other facets of IAV infection.