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Background <p>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.</p> Methods <p>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).</p> Results <p>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.</p> Conclusions <p>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.</p>

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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

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月2日

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  • Mohadeseh Naseri,
  • Alicia Hiemisch,
  • André Dietz,
  • Marcus Oswald,
  • Rainer Koenig

摘要

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.