A pan-viral map of host dependency factors from multi-omics integration and machine learning across influenza A, SARS-CoV-2, Zika, and dengue viruses

BACKGROUND: Host dependency factors (HDF) are essential for viral replication and are promising targets for broad-spectrum antivirals. However, most work has focused on individual viruses or individual data types, limiting our understanding of shared host mechanisms across viruses. METHODS: We developed a pan-viral framework that integrates multi-omics data-including genome-wide perturbation screens, single-cell transcriptomes and viral interactomes-and combines graph-based learning with classical machine-learning models to prioritize HDF for four RNA viruses (SARS-CoV-2, influenza A virus, dengue virus and Zika virus). RESULTS: Across viruses, the framework achieved high discrimination, with area under the receiver operating characteristic curve (ROC-AUC) greater than 0.90 on benchmark datasets, and identified a conserved signature of 118 genes shared by all four viruses and 427 genes shared by at least three. These genes converge on recurrent host programmes such as clathrin-mediated entry and endomembrane trafficking, nuclear transport, RNA processing and stress granules, and proteostasis and ubiquitin-proteasome signalling. The pan-viral signature generalizes beyond the training set, as genes shared by three or more viruses are strongly enriched among top-ranked Ebola virus candidates. We further provide a prioritized shortlist and an experimental validation roadmap to guide follow-up perturbation studies. CONCLUSIONS: Our integrative multi-omics and machine-learning approach outlines a prediction-based, data-driven map of pan-viral host liabilities and highlights tractable opportunities for host-directed therapy against diverse RNA viruses.