Computational drug repurposing against SARS-CoV-2 reveals plasma membrane cholesterol depletion as key factor of antiviral drug activity

  1. Szilvia Barsi
  2. Henrietta Papp
  3. Alberto Valdeolivas
  4. Dániel J. Tóth
  5. Anett Kuczmog
  6. Mónika Madai
  7. László Hunyady
  8. Péter Várnai
  9. Julio Saez-Rodriguez
  10. Ferenc Jakab
  11. Bence Szalai

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Abstract

Comparing SARS-CoV-2 infection-induced gene expression signatures to drug treatment-induced gene expression signatures is a promising bioinformatic tool to repurpose existing drugs against SARS-CoV-2. The general hypothesis of signature-based drug repurposing is that drugs with inverse similarity to a disease signature can reverse disease phenotype and thus be effective against it. However, in the case of viral infection diseases, like SARS-CoV-2, infected cells also activate adaptive, antiviral pathways, so that the relationship between effective drug and disease signature can be more ambiguous. To address this question, we analysed gene expression data from in vitro SARS-CoV-2 infected cell lines, and gene expression signatures of drugs showing anti-SARS-CoV-2 activity. Our extensive functional genomic analysis showed that both infection and treatment with in vitro effective drugs leads to activation of antiviral pathways like NFkB and JAK-STAT. Based on the similarity—and not inverse similarity—between drug and infection-induced gene expression signatures, we were able to predict the in vitro antiviral activity of drugs. We also identified SREBF1/2, key regulators of lipid metabolising enzymes, as the most activated transcription factors by several in vitro effective antiviral drugs. Using a fluorescently labeled cholesterol sensor, we showed that these drugs decrease the cholesterol levels of plasma-membrane. Supplementing drug-treated cells with cholesterol reversed the in vitro antiviral effect, suggesting the depleting plasma-membrane cholesterol plays a key role in virus inhibitory mechanism. Our results can help to more effectively repurpose approved drugs against SARS-CoV-2, and also highlights key mechanisms behind their antiviral effect.

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  1. Version published to 10.1371/journal.pcbi.1010021
  2. ScreenIT

    SciScore for 10.1101/2021.09.10.459786: (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
    For fluorescent imaging, HEK293A cells (ATCC, USA) were maintained in Dulbecco’s Modified Eagle Medium (DMEM - Lonza, Switzerland) complemented with 10% fetal bovine serum (Biosera, France) and Penicillin/Streptomycin (100 U/ml and 100 μg/ml, respectively - Lonza, Switzerland).
    HEK293A
    suggested: None
    Vero-E6 cells were seeded in a 96-well plate on the day before the experiments.
    Vero-E6
    suggested: None
    Recombinant DNA
    SentencesResources
    The D4H domain-coding sequence from this plasmid was subcloned into the pEYFP-C1 plasmid containing mVenus in place of EYFP, using BglII and BamHI restriction enzymes.
    pEYFP-C1
    suggested: None
    Cytosolic Cerulean was expressed from a pEYFP-N1 plasmid where EYFP had been replaced with Cerulean.
    pEYFP-N1
    suggested: RRID:Addgene_37202)
    Software and Algorithms
    SentencesResources
    Virus infection-induced gene expression signatures: Microarray gene expression profiles of different virus-infected cell lines were downloaded from Gene Expression Omnibus (GEO) with accession numbers GSE28166 (H5N1), GSE37571 (Influenza), GSE33267 (SARS-CoV-1), GSE56677 and GSE45042 (MERS-CoV).
    Gene Expression Omnibus
    suggested: (Gene Expression Omnibus (GEO, RRID:SCR_005012)
    Preprocessing and differential expression (DE) analysis was performed by using R package limma (Ritchie et al. 2015).
    limma
    suggested: (LIMMA, RRID:SCR_010943)
    Differential expression (DE) analysis was performed using R library DESeq2 (Love, Huber, and Anders 2014).
    DESeq2
    suggested: (DESeq, RRID:SCR_000154)
    We matched LINCS-L1000 drugs with ChEMBL effective drug dataset (http://chembl.blogspot.com/2020/05/chembl27-sars-cov-2-release.html) using drug names and simplified molecular-input line-entry system (SMILES).
    ChEMBL
    suggested: (ChEMBL, RRID:SCR_014042)
    In addition, we used PROGENy pathway activity scores to weight the prior knowledge network and assist CARNIVAL in the discovery of optimal networks connecting the upstream perturbation (RIG-I like receptors) to the downstream targets (TFs).
    PROGENy
    suggested: (PROGENY, RRID:SCR_006647)
    We performed ROC analysis using scikit-learn Python library to evaluate similarity-based and machine learning-based predictions.
    Python
    suggested: (IPython, RRID:SCR_001658)

    Results from OddPub: Thank you for sharing your code and data.

    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.
    • 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.

    Results from scite Reference Check: We found no unreliable references.

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  3. Version published to 10.1101/2021.09.10.459786v1 on bioRxiv