Genome-scale metabolic modeling reveals SARS-CoV-2-induced metabolic changes and antiviral targets

  1. Kuoyuan Cheng
  2. Laura Martin-Sancho
  3. Lipika R. Pal
  4. Yuan Pu
  5. Laura Riva
  6. Xin Yin
  7. Sanju Sinha
  8. Nishanth Ulhas Nair
  9. Sumit K. Chanda
  10. Eytan Ruppin

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Abstract

Tremendous progress has been made to control the COVID-19 pandemic caused by the SARS-CoV-2 virus. However, effective therapeutic options are still rare. Drug repurposing and combination represent practical strategies to address this urgent unmet medical need. Viruses, including coronaviruses, are known to hijack host metabolism to facilitate viral proliferation, making targeting host metabolism a promising antiviral approach. Here, we describe an integrated analysis of 12 published in vitro and human patient gene expression datasets on SARS-CoV-2 infection using genome-scale metabolic modeling (GEM), revealing complicated host metabolism reprogramming during SARS-CoV-2 infection. We next applied the GEM-based metabolic transformation algorithm to predict anti-SARS-CoV-2 targets that counteract the virus-induced metabolic changes. We successfully validated these targets using published drug and genetic screen data and by performing an siRNA assay in Caco-2 cells. Further generating and analyzing RNA-sequencing data of remdesivir-treated Vero E6 cell samples, we predicted metabolic targets acting in combination with remdesivir, an approved anti-SARS-CoV-2 drug. Our study provides clinical data-supported candidate anti-SARS-CoV-2 targets for future evaluation, demonstrating host metabolism-targeting as a promising antiviral strategy.

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  1. ScreenIT

    SciScore for 10.1101/2021.01.27.428543: (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
    Genome-scale metabolic modeling of the remdesivir-treated Vero E6 cell samples and prediction of anti-SARS-CoV-2 metabolic targets in combination with remdesivir: As with the metabolic modeling of the other datasets on SARS-CoV-2 infection, iMAT (Shlomi et al. 2008) together with ACHR was used to compute the metabolic flux distribution for each of the experimental groups, using the median expression TPM values of each group as the input to iMAT.
    Vero E6
    suggested: None
    Software and Algorithms
    SentencesResources
    For Weingarten-Gabby et al. 2020, a nested design for DE was needed and DESeq2 failed to run properly, and limma-voom (Law et al. 2014) was used.
    DESeq2
    suggested: (DESeq, RRID:SCR_000154)
    For metabolic pathways (in Figure 1E), those under the category “Metabolism” from KEGG (Kanehisa et al. 2021) were used.
    KEGG
    suggested: (KEGG, RRID:SCR_012773)
    Lists of experimentally validated drugs reported in different studies compiled by Kuleshov et al. 2020 were downloaded from https://maayanlab.cloud/covid19/, which are then mapped to the genes they inhibit using data from DrugBank v5.1.7 (Wishart et al. 2018).
    DrugBank
    suggested: (DrugBank, RRID:SCR_002700)
    STAR (Dobin et al. 2013) was used to align the reads to reference genome of the African green monkey (Chlorocebus sabaeus, https://useast.ensembl.org/Chlorocebus_sabaeus/Info/Annotation), with the SARS-CoV-2 genome (https://www.ncbi.nlm.nih.gov/nuccore/NC_045512) added to the reference genome.
    STAR
    suggested: (STAR, RRID:SCR_015899)
    The R packages ggplot2 (Wickham 2016), ComplexHeatmap (Gu et al. 2016) and visNetwork (https://cran.r-project.org/web/packages/visNetwork/index.html) were used to create the visualizations.
    ggplot2
    suggested: (ggplot2, RRID:SCR_014601)
    ComplexHeatmap
    suggested: (ComplexHeatmap, RRID:SCR_017270)

    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: Please consider improving the rainbow (“jet”) colormap(s) used on page 27. At least one figure is not accessible to readers with colorblindness and/or is not true to the data, i.e. not perceptually uniform.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

    About SciScore

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  2. Version published to 10.1101/2021.01.27.428543 on bioRxiv