Gene expression network analysis provides potential targets against SARS-CoV-2

  1. Ana I. Hernández Cordero
  2. Xuan Li
  3. Chen Xi Yang
  4. Stephen Milne
  5. Yohan Bossé
  6. Philippe Joubert
  7. Wim Timens
  8. Maarten van den Berge
  9. David Nickle
  10. Ke Hao
  11. Don D. Sin

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Abstract

Cell entry of SARS-CoV-2, the novel coronavirus causing COVID-19, is facilitated by host cell angiotensin-converting enzyme 2 (ACE2) and transmembrane serine protease 2 (TMPRSS2). We aimed to identify and characterize genes that are co-expressed with ACE2 and TMPRSS2 , and to further explore their biological functions and potential as druggable targets. Using the gene expression profiles of 1,038 lung tissue samples, we performed a weighted gene correlation network analysis (WGCNA) to identify modules of co-expressed genes. We explored the biology of co-expressed genes using bioinformatics databases, and identified known drug-gene interactions. ACE2 was in a module of 681 co-expressed genes; 10 genes with moderate-high correlation with ACE2 (r > 0.3, FDR < 0.05) had known interactions with existing drug compounds. TMPRSS2 was in a module of 1,086 co-expressed genes; 31 of these genes were enriched in the gene ontology biologic process ‘receptor-mediated endocytosis’, and 52 TMPRSS2- correlated genes had known interactions with drug compounds. Dozens of genes are co-expressed with ACE2 and TMPRSS2 , many of which have plausible links to COVID-19 pathophysiology. Many of the co-expressed genes are potentially targetable with existing drugs, which may accelerate the development of COVID-19 therapeutics.

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  1. ScreenIT

    SciScore for 10.1101/2020.07.06.182634: (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

    Software and Algorithms
    SentencesResources
    Gene expression network analysis: Using the WGCNA37 R package, we explored gene networks correlated to ACE2 and TMPRSS2 in order to identify potential interactions in the Lung eQTL Consortium cohort
    WGCNA37
    suggested: None
    Enrichment analysis and correlations of ACE2 and TMPRSS2 modules: Enrichment analysis of KEGG pathways and GO biological processes was performed using the genes in the modules containing ACE2 (ACE2 module) and TMPRSS2 (TMPRSS2 module).
    KEGG
    suggested: (KEGG, RRID:SCR_012773)
    Drug-gene interactions and biological information of ACE2 and TMPRSS2 correlated genes: We cross-referenced the ACE2 and TMPRSS2 correlated genes with the Mouse Genome Informatics (MGI), the Online Mendelian Inheritance in Man (OMIM), and the ClinVar databases in order to identify biologically relevant genes.
    ClinVar
    suggested: (ClinVar, RRID:SCR_006169)
    We first combined the gene expression from the three centres using ComBat from the R package sva to correct for any batch effect39 Then, the differential expression was assessed for each gene-risk factor pair by a robust linear regression using the package MASS40 in R, in which the dependent variable was the gene expression and the explanatory variable was the risk factor.
    ComBat
    suggested: (ComBat, RRID:SCR_010974)

    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: We detected the following sentences addressing limitations in the study:
    However, there were limitations to this study. First, we used an in-silico approach to identify ACE2 and TMPRSS2 correlated genes, but we did not confirm these association in vivo nor determine how these correlated genes physically interacted with ACE2 and TMPRSS2. Second, we identified the most promising drugs based on drug-gene interactions from bioinformatic databases, but we are yet to test their effects on gene and/or protein expression in in vitro experiments. Third, the lungs of our study cohort were not exposed to SARS-CoV-2, therefore it is possible that the gene expression of these key identified genes in lung tissue could be changed upon SARS-CoV-2 infection. Lastly, the cohort used for gene expression was of European ancestry and the results may not be generalizable to other ethnic groups, which is of critical importance in a global pandemic. In summary, ACE2 and TMPRSS2 gene networks contained genes that could contribute to the pathophysiology of COVID-19. These findings show that computational in silico approaches can lead to the rapid identification of potential drugs, which could be repurposed as treatments against COVID-19. Given the exponential spread of COVID-19 across the globe and the unprecedented rise in deaths, such rapidity is necessary in our ongoing fight against the pandemic.

    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.

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  2. Version published to 10.1038/s41598-020-78818-w
  3. Version published to 10.1101/2020.07.06.182634v1 on bioRxiv