Implications of central carbon metabolism in SARS-CoV-2 replication and disease severity
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Abstract
Viruses hijack host metabolic pathways for their replicative advantage. Several observational trans-omics analyses associated carbon and amino acid metabolism in coronavirus disease 2019 (COVID-19) severity in patients but lacked mechanistic insights. In this study, using patient- derived multi-omics data and in vitro infection assays, we aimed to understand i) role of key metabolic pathways in severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) reproduction and ii) its association with disease severity. Our data suggests that monocytes are key to the altered immune response during COVID-19. COVID-19 infection was associated with increased plasma glutamate levels, while glucose and mannose levels were determinants of the disease severity. Monocytes showed altered expression pattern of carbohydrate and amino acid transporters, GLUT1 and xCT respectively in severe COVID-19. Furthermore, lung epithelial cells (Calu-3) showed a strong acute metabolic adaptation following infection in vitro by modulating central carbon metabolism. We found that glycolysis and glutaminolysis are essential for virus replication and blocking these metabolic pathways caused significant reduction in virus production. Taken together, our study highlights that the virus utilizes and re-wires pathways governing central carbon metabolism leading to metabolic toxicity. Thus, the host metabolic perturbation could be an attractive strategy to limit the viral replication and disease severity.
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SciScore for 10.1101/2021.02.24.432759: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement not detected. Randomization not detected. Blinding not detected. Power Analysis not detected. Sex as a biological variable not detected. Table 2: Resources
Software and Algorithms Sentences Resources Data and Code Availability: The scaled normalised metabolomics data can be obtained from the dx.doi.org/ 10.6084/m9.figshare.13336862 Proteomics data can be obtained from the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD022847. PRIDEsuggested: (Pride-asap, RRID:SCR_012052)Results from OddPub: Thank you for sharing your code.
Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this …SciScore for 10.1101/2021.02.24.432759: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement not detected. Randomization not detected. Blinding not detected. Power Analysis not detected. Sex as a biological variable not detected. Table 2: Resources
Software and Algorithms Sentences Resources Data and Code Availability: The scaled normalised metabolomics data can be obtained from the dx.doi.org/ 10.6084/m9.figshare.13336862 Proteomics data can be obtained from the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD022847. PRIDEsuggested: (Pride-asap, RRID:SCR_012052)Results from OddPub: Thank you for sharing your code.
Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- No funding statement was detected.
- No protocol registration statement was detected.
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