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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. Availability: https://github.com/draeger-lab/pymCADRE/.

SUBMITTER: Andreas Dräger

PROVIDER: MODEL2202240001 | BioModels | 2024-04-18

REPOSITORIES: BioModels

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Project description:COVID-19 is a pandemic that shares only certain clinical characteristics with other acute viral infections. Here, we studied the whole-blood transcriptomic host response to SARS-CoV-2 and compared it with other viral infections to understand similarities and differences in host response. We profiled peripheral blood from 24 healthy controls and 62 prospectively enrolled patients with community-acquired lower respiratory tract infection by SARS-Cov-2 within the first 24 hours of hospital admission using RNA-seq. We also collected 23 independent studies that profiled 1,855 blood samples from patients with one of six viruses (influenza, RSV, HRV, Ebola, Dengue, and SARS). We identified differentially expressed genes that change in patients with COVID-19 or other viral infections. We show changes in gene expression in peripheral blood from patients with COVID-19 are highly correlated with changes in response to other viral infections (r=0.74, p<0.001). However, two genes, ACO1 and ATL3, show significantly different changes in expression. Pathway analysis of differentially expressed genes in patients with COVID-19 or other viral infections versus healthy controls also identified similar pathways including neutrophil activation, innate immune response, immune response to viral infection, and cytokine production for over-expressed genes, as well as lymphocyte differentiation and T cell activation for under-expressed genes. When comparing transcriptome profiles of patients with COVID-19 directly with those with other viral infections, we found 114 and 302 genes were over- and under-expressed, respectively during COVID-19. Pathways analysis did not identify any significant pathways in these genes. Statistical deconvolution using immunoStates found that M1 macrophages, plasmacytoid dendritic cells, CD14+ monocytes, CD4+ T cells, and total B cells showed changes consistently in the same direction across all viral infections including COVID-19. Those that increased in COVID-19 but decreased in non-COVID were CD56bright NK cells, M2 macrophages, and total NK cells. The concordant and discordant responses mapped out via such a comparison provides a window to explore the underlying biology of why COVID-19 is so different from other viral infections encountered so far. Together, results from pathway and immunoStates analyses help dissect major shifts in cellularity and activation of signaling pathways as part of host response to SARS-CoV-2

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

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