Rapid discovery of antiviral targets through dimensionality reduction of genome-scale metabolic models

The COVID-19 pandemic underscored the urgent need for rapid and broadly applicable strategies to identify antiviral targets against emerging pathogens. Conventional approaches, which rely on detailed viral characterization and large-scale drug screening, remain too slow to address this challenge. Here, we introduce a transcriptome-based computational framework that integrates genome-scale metabolic models with dimensionality reduction to uncover host metabolic vulnerabilities that support viral replication. Applying this approach to bulk and single-cell RNA-seq data from HCoV-OC43-infected cells and organoids identified oxidative phosphorylation as a key vulnerability, and pharmacological inhibition of complex I effectively curtailed viral replication. Extending the framework to SARS-CoV-2 and MERS-CoV revealed pyrimidine catabolism as a conserved antiviral pathway, with inhibition of its rate-limiting enzyme DPYD suppressing replication in organoid models. Re-analysis of patient metabolome data further confirmed elevated DPYD activity during COVID-19, underscoring its clinical relevance. Together, these findings establish a generalizable and rapid strategy for host-directed antiviral discovery, providing a foundation for precision therapeutics and pandemic preparedness.