New workflow predicts drug targets against SARS-CoV-2 via metabolic changes in infected cells

Abstract

COVID-19 is one of the deadliest respiratory diseases, and its emergence caught the pharmaceutical industry off guard. While vaccines have been rapidly developed, treatment options for infected people remain scarce, and COVID-19 poses a substantial global threat. This study presents a novel workflow to predict robust druggable targets against emerging RNA viruses using metabolic networks and information of the viral structure and its genome sequence. For this purpose, we implemented pymCADRE and PREDICATE to create tissue-specific metabolic models, construct viral biomass functions and predict host-based antiviral targets from more than one genome. We observed that pymCADRE reduces the computational time of flux variability analysis for internal optimizations. We applied these tools to create a new metabolic network of primary bronchial epithelial cells infected with SARS-CoV-2 and identified enzymatic reactions with inhibitory effects. The most promising reported targets were from the purine metabolism, while targeting the pyrimidine and carbohydrate metabolisms seemed to be promising approaches to enhance viral inhibition. Finally, we computationally tested the robustness of our targets in all known variants of concern, verifying our targets’ inhibitory effects. Since laboratory tests are time-consuming and involve complex readouts to track processes, our workflow focuses on metabolic fluxes within infected cells and is applicable for rapid hypothesis-driven identification of potentially exploitable antivirals concerning various viruses and host cell types.

Uncovering the Effect of SARS-CoV-2 on Liver Metabolism via Genome-Scale Metabolic Modeling for Reprogramming and Therapeutic Strategies

“…The inhibition of adenylate kinase reaction (MAR04002), which reversibly catalyzes interconversion of ADP to AMP and ATP, was also identified to be critical for viral production via integrated computational analysis of SARS-CoV-2 and liver metabolism (Figure a). The guanylate kinase converting ATP and GMP to ADP and GDP was essentially required for viral growth and identified as a potential target for antiviral therapies against SARS-CoV-2 in the human macrophage model, , the human lung cell metabolic network, , and the human bronchial epithelial cell model . In the present study, knockout of guanylate kinase reaction (MAR04020) completely inhibited both virus and hepatocyte biomass, and hence the corresponding reaction was not regarded as a potential therapeutical target in SARS-CoV-2-infected liver metabolism.…”

“…11 When considering the viral infection, an additional virus biomass reaction is required to analyze host−virus interactions. Virus biomass reaction was obtained from Leonidou et al (2023) 40 and added to both Human1 and essential tasks for the extraction of virus infected hepatocyte GEMs by using the tINIT algorithm. Gene expression data were given as TPM (transcripts per million reads) normalized with three replicates for each uninfected and infected hepatocyte.…”

“…essential metabolic tasks, SARS-CoV-2 biomass reaction, representing the component of the virus (obtained fromLeonidou et al, 2023), was used as an essential task in the tINIT algorithm. The combined SARS-CoV-2infected liver-specific GEM (iHepatocytesCovid2406) resulted in 8350 metabolic reactions, 2406 genes, and 5843 metabolites to elucidate liver-virus interactions (Supplementary data 1).…”

Fuzzy Optimization for Identifying Antiviral Targets for Treating SARS-CoV-2 Infection in the Heart

“…Constraint-based models have been used to discover antiviral targets for the treatment of lungs infected with SARS-CoV-2 [24][25][26][27][28][29][30][31][32][33]. The viral biomass reaction (VBR) of SARS-CoV-2 is incorporated into a constraint-based metabolic model of lung cells to analyze the metabolic mechanism of host-virus cells.…”

“…The Alpha variant of SARS-CoV-2 has been incorporated into the iAB-AMØ-1410 human alveolar macrophage model [24][25][26][27][28][29] for analyzing the metabolic behaviors of host cells infected with this variant. Moreover, VBRs have been integrated into generic human genome-scale metabolic networks Recon2.2 [34] and Recon3D [35] to identify inhibitors for treating SARS-CoV-2, respectively [29][30][31][32]. Whole-body metabolic modeling was conducted in a previous study to investigate host-virus (HV) co-metabolism during SARS-CoV-2 infection in the lungs [33].…”