Lung disease network reveals impact of comorbidity on SARS-CoV-2 infection and opportunities of drug repurposing

  1. Asim Bikas Das

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Abstract

Background

Higher mortality of COVID-19 patients with lung disease is a formidable challenge for the health care system. Genetic association between COVID-19 and various lung disorders must be understood to comprehend the molecular basis of comorbidity and accelerate drug development.

Methods

Lungs tissue-specific neighborhood network of human targets of SARS-CoV-2 was constructed. This network was integrated with lung diseases to build a disease–gene and disease-disease association network. Network-based toolset was used to identify the overlapping disease modules and drug targets. The functional protein modules were identified using community detection algorithms and biological processes, and pathway enrichment analysis.

Results

In total, 141 lung diseases were linked to a neighborhood network of SARS-CoV-2 targets, and 59 lung diseases were found to be topologically overlapped with the COVID-19 module. Topological overlap with various lung disorders allows repurposing of drugs used for these disorders to hit the closely associated COVID-19 module. Further analysis showed that functional protein–protein interaction modules in the lungs, substantially hijacked by SARS-CoV-2, are connected to several lung disorders. FDA-approved targets in the hijacked protein modules were identified and that can be hit by exiting drugs to rescue these modules from virus possession.

Conclusion

Lung diseases are clustered with COVID-19 in the same network vicinity, indicating the potential threat for patients with respiratory diseases after SARS-CoV-2 infection. Pathobiological similarities between lung diseases and COVID-19 and clinical evidence suggest that shared molecular features are the probable reason for comorbidity. Network-based drug repurposing approaches can be applied to improve the clinical conditions of COVID-19 patients.

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  1. Version published to 10.1186/s12920-021-01079-7
  2. ScreenIT

    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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  3. Version published to 10.1101/2020.05.13.092577v1 on bioRxiv