Lung Disease Network Reveals the Impact of Comorbidity on SARS-CoV-2 infection

  1. Asim Bikas Das

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Abstract

Higher mortality of COVID19 patients with comorbidity is the formidable challenge faced by the health care system. In response to the present crisis, understanding the molecular basis of comorbidity is essential to accelerate the development of potential drugs. To address this, we have measured the genetic association between COVID19 and various lung disorders and observed a remarkable resemblance. 141 lung disorders directly or indirectly linked to COVID19 result in a high-density disease-disease association network that shows a small-world property. The clustering of many lung diseases with COVID19 demonstrates a greater complexity and severity of SARS-CoV-2 infection. Furthermore, our results show that the functional protein-protein interaction modules involved RNA and protein metabolism, substantially hijacked by SARS-CoV-2, are connected to several lung disorders. Therefore we recommend targeting the components of these modules to inhibit the viral growth and improve the clinical conditions in comorbidity.

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    SciScore for 10.1101/2020.05.13.092577: (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
    Construction of lung-specific PPI network of SARS-CoV-2 targets: Human lung tissue-specific interactome data was retrieved from the TissueNet v.
    TissueNet
    suggested: (TissueNet, RRID:SCR_004489)
    PPIs from four major PPI databases, BioGrid,
    BioGrid
    suggested: (BioGrid Australia, RRID:SCR_006334)
    IntAct, MINT and DIP, were obtained and consolidated.
    IntAct
    suggested: (IntAct, RRID:SCR_006944)
    MINT
    suggested: (MINT, RRID:SCR_001523)
    Process and pathway enrichment analysis and gene ontology (GO) Semantic similarity: Pathway and process enrichment analysis were performed using the Metascape [25].
    Metascape
    suggested: (Metascape, RRID:SCR_016620)
    GO Biological Processes,
    GO Biological
    suggested: None
    KEGG Pathway, and Reactome were used as ontology sources.
    KEGG
    suggested: (KEGG, RRID:SCR_012773)
    GO semantic similarity between genes was measured by Wang et al.[26] method using GOSemSim package in R.
    GOSemSim
    suggested: None
    Tools for data analysis and plotting: R packages tidyverse and stringr were used for data analysis, and plotting of graphs was done by ggplot2.
    ggplot2
    suggested: (ggplot2, RRID:SCR_014601)
    Networks were visualized using Gephi.
    Gephi
    suggested: (Gephi, RRID:SCR_004293)

    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.

    About SciScore

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  2. Version published to 10.1101/2020.05.13.092577 on bioRxiv