Impact of Comorbidities on SARS-CoV-2 Viral Entry-Related Genes

  1. Joshua D. Breidenbach
  2. Prabhatchandra Dube
  3. Subhanwita Ghosh
  4. Nikolai N. Modyanov
  5. Deepak Malhotra
  6. Lance D. Dworkin
  7. Steven T. Haller
  8. David J. Kennedy

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Abstract

Viral entry mechanisms for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are an important aspect of virulence. Proposed mechanisms involve host cell membrane-bound angiotensin-converting enzyme 2 (ACE2) and type II transmembrane serine proteases (TTSPs), such as transmembrane serine protease isoform 2 (TMPRSS2). The distribution of expression of these genes across cell types representing multiple organ systems in healthy individuals has been recently demonstrated. However, comorbidities such as diabetes and cardiovascular disease are highly prevalent in patients with Coronavirus Disease 2019 (COVID-19) and associated with worse outcomes. Whether these conditions contribute directly to SARS-CoV-2 virulence remain unclear. Here we show that the expression levels of ACE2, TMPRSS2 and other viral entry-related genes are modulated in target organs of select disease states. In tissues such as heart, which normally express ACE2 but minimal TMPRSS2 , we found that TMPRSS2 as well as other TTSPs are elevated in individuals with comorbidities vs healthy individuals. Additionally, we found increased expression of viral entry-related genes in the settings of hypertension, cancer or smoking across target organ systems. Our results demonstrate that common comorbidities may contribute directly to SARS-CoV-2 virulence and suggest new therapeutic targets to improve outcomes in vulnerable patient populations.

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  1. ScreenIT

    SciScore for 10.1101/2020.05.26.117440: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board Statementnot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Differential gene expression was curated from genetic data deposited in the National Center for Biotechnology Information (NCBI), U.S. National Library of Medicine, Gene Expression Omnibus (GEO) DataSets and the European Molecular Biology Laboratory (EMBL), European Bioinformatics Institute (EBI)
    Gene Expression Omnibus
    suggested: (Gene Expression Omnibus (GEO, RRID:SCR_005012)
    EMBL
    suggested: (ChEMBL, RRID:SCR_014042)
    For comorbidity disease condition data, expression levels are described in log2FC in relation to each unaffected control group specific to each DataSeries and heatmaps were generated in GraphPad Prism (GraphPad Software Inc.)
    GraphPad Prism
    suggested: (GraphPad Prism, RRID:SCR_002798)
    GraphPad
    suggested: (GraphPad Prism, RRID:SCR_002798)

    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.
    • 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 10.1101/2020.05.26.117440v2 on bioRxiv
  3. Version published to 10.1101/2020.05.26.117440v1 on bioRxiv