Host metabolic reprogramming in response to SARS-Cov-2 infection

  1. S T R Moolamalla
  2. Ruchi Chauhan
  3. U Deva Priyakumar
  4. P K Vinod

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Abstract

Understanding the pathogenesis of SARS-CoV-2 is important for developing effective treatment strategies. Viruses hijack the host metabolism to redirect the resources for their replication and survival. How SARS-CoV-2 influences the host metabolism is still unclear. In this study, we analyzed transcriptomic data obtained from different human respiratory cell lines and patient samples (Swab, PBMC, lung biopsy, BALF) to understand the metabolic alterations in response to SARS-CoV-2 infection. For this purpose, the expression pattern of metabolic genes in the human genome-scale metabolic network model Recon3D was explored. We identified metabolic genes and pathways and reporter metabolites under each SARS-CoV-2-infected condition and compared them to identify common and unique changes in the metabolism. Our analysis revealed host-dependent dysregulation of glycolysis, mitochondrial metabolism, amino acid metabolism, glutathione metabolism, polyamine synthesis, and lipid metabolism. We observed different metabolic changes that are pro- and antiviral in nature. We generated hypotheses on how antiviral metabolism can be targeted/enhanced for reducing viral titers. These warrant further exploration with more samples and in vitro studies to test predictions.

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    SciScore for 10.1101/2020.08.02.232645: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    NIH rigor criteria are not applicable to paper type.

    Table 2: Resources

    Experimental Models: Cell Lines
    SentencesResources
    A549 and A549-ACE2 cell lines were infected with a SARS-CoV-2 viral load, multiplicity of infection (MOI) equal to 0.2 and 2, whereas Calu3 and NHBE were infected with a viral load, MOI=2.
    A549
    suggested: None
    A549-ACE2
    suggested: None
    Software and Algorithms
    SentencesResources
    We performed differential gene expression analysis using the DESeq2 (v1.26.1) in R (v 3.6.1) for each dataset to obtain differentially expressed genes (DEGs) of SAR-CoV-2 (Love et al., 2014).
    DESeq2
    suggested: (DESeq, RRID:SCR_000154)
    The KEGG pathways associated with DEGs were obtained using EnrichR (adjusted p-value < 0.05) (Kuleshov et al., 2016).
    KEGG
    suggested: (KEGG, RRID:SCR_012773)
    EnrichR
    suggested: (Enrichr, RRID:SCR_001575)
    Networks were visualized in Cytoscape version 3.3.
    Cytoscape
    suggested: (Cytoscape, RRID:SCR_003032)

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

    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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  2. Version published to 10.1101/2020.08.02.232645v1 on bioRxiv