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

Abstract 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, are 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 SARS-CoV-2 patient metabolome data further confirmed elevated DPYD activity, 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. Significance Statement Host-directed antiviral therapies offer several advantages in antiviral research, but identifying key host factors poses a significant challenge. By integrating genome-scale metabolic models with single-gene knockout simulation and dimensionality reduction, we developed a computational framework based on single and bulk RNA-seq data that can systematically pinpoint host pathways whose downregulation is predicted to rewire virus-induced metabolic alterations. Applying this approach to multiple human coronaviruses reveals unique metabolic vulnerabilities, and we experimentally demonstrate that inhibiting these host metabolic pathways reduces viral replication. This framework provides a generalizable antiviral strategy to discern effective targets and can be further extended to investigate virus–host metabolic interactions. Competing Interest Statement The authors have declared no competing interest. Footnotes Competing Interest Statement: The authors declare that they have no competing interests. Updated the Discussion session; Reorganized the Results subheadings.