Predictive Systems Biology Modeling: Unraveling Host Metabolic Disruptions and Potential Drug Targets in Acute Viral Infections

Abstract

BackgroundHost response is critical to the onset, progression, and outcome of viral infections. Since viruses hijack the host cellular metabolism for their replications, we hypothesized that restoring host cell metabolism can efficiently reduce viral production. ResultsHere, we present a viral-host Metabolic Modeling (vhMM) method to systematically evaluate the disturbances in host metabolism in viral infection and computationally identify targets for modulation by integrating genome-wide precision metabolic modeling and cheminformatics. We applied vhMM to SARS-CoV-2 infections and identified consistent changes in host metabolism and gene and endogenous metabolite targets between the original SARS-COV-2 and different variants (Alpha, Delta, and Omicron). Among six compounds predicted for repurposing, methotrexate, cinnamaldehyde, and deferiprone were tested in vitro and effective in inhibiting viral production with IC50 less than 4uM. Further, an analysis of real-world patient data showed that cinnamon usage significantly reduced the SARS-CoV-2 infection rate with an odds ratio of 0.65 [95%CI: 0.55[~]0.75]. ConclusionsThese results demonstrated that vhMM is an efficient method for predicting targets and drugs for viral infections.