Host innate immune response profiling reveals hidden viral infections across diverse animal species

Virus discovery using RNA-seq data from wildlife and livestock offers a powerful strategy for identifying unknown pathogens with pandemic potential. However, conventional approaches rely on homology-based searches that have limited sensitivity for highly divergent viruses, are computationally intensive at scale, and cannot distinguish true infections from contamination. Viral infection induces interferon-stimulated genes (ISGs), key components of the frontline antiviral defense, and their expression serves as a robust indicator of viral infection. Here, we developed a host-response-based virus discovery framework that rapidly quantifies ISG expression and predicts viral infection status. Applying this framework to [~]210,000 RNA-seq data sets from diverse mammalian and avian species, we identified hidden viral infections across diverse hosts, including those caused by highly divergent viruses missed by a conventional approach. Our framework complements existing virus discovery strategies by adding host innate immune response context and enabling computationally efficient prescreening for scalable viral surveillance. HighlightsO_LIHost response-based virus discovery in wildlife and livestock RNA-seq data C_LIO_LIQuantify interferon-stimulated gene (ISG) expression and predict viral infection C_LIO_LIAnalysis of [~]210,000 RNA-seq data sets reveals hidden viral infections C_LIO_LIDetects highly divergent viruses and scalable viral surveillance through rapid prescreening C_LI