Looking for pathways related to COVID-19: confirmation of pathogenic mechanisms by SARS-CoV-2–host interactome

  1. Francesco Messina
  2. Emanuela Giombini
  3. Chiara Montaldo
  4. Ashish Arunkumar Sharma
  5. Antonio Zoccoli
  6. Rafick-Pierre Sekaly
  7. Franco Locatelli
  8. Alimuddin Zumla
  9. Markus Maeurer
  10. Maria R. Capobianchi
  11. Francesco Nicola Lauria
  12. Giuseppe Ippolito

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Abstract

In the last months, many studies have clearly described several mechanisms of SARS-CoV-2 infection at cell and tissue level, but the mechanisms of interaction between host and SARS-CoV-2, determining the grade of COVID-19 severity, are still unknown. We provide a network analysis on protein–protein interactions (PPI) between viral and host proteins to better identify host biological responses, induced by both whole proteome of SARS-CoV-2 and specific viral proteins. A host-virus interactome was inferred, applying an explorative algorithm (Random Walk with Restart, RWR) triggered by 28 proteins of SARS-CoV-2. The analysis of PPI allowed to estimate the distribution of SARS-CoV-2 proteins in the host cell. Interactome built around one single viral protein allowed to define a different response, underlining as ORF8 and ORF3a modulated cardiovascular diseases and pro-inflammatory pathways, respectively. Finally, the network-based approach highlighted a possible direct action of ORF3a and NS7b to enhancing Bradykinin Storm. This network-based representation of SARS-CoV-2 infection could be a framework for pathogenic evaluation of specific clinical outcomes. We identified possible host responses induced by specific proteins of SARS-CoV-2, underlining the important role of specific viral accessory proteins in pathogenic phenotypes of severe COVID-19 patients.

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  1. Version published to 10.1038/s41419-021-03881-8
  2. ScreenIT

    SciScore for 10.1101/2020.11.03.366666: (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
    The virus–host interactome was made by merging SARS-CoV-2 –host protein-protein interaction (PPI) data from Intact (51), with data from human PPI databases, such as BioGrid, InnateDB-All, IMEx, IntAct, MatrixDB, MBInfo, MINT, Reactome, Reactome-FIs, UniProt, VirHostNet, BioData, obtained by R packages PSICQUIC and biomaRt (52, 53).
    BioGrid
    suggested: (BioGrid Australia, RRID:SCR_006334)
    IntAct
    suggested: (IntAct, RRID:SCR_006944)
    MatrixDB
    suggested: (MatrixDB, RRID:SCR_001727)
    MBInfo
    suggested: (MBInfo, RRID:SCR_006768)
    MINT
    suggested: (MINT, RRID:SCR_001523)
    PSICQUIC
    suggested: (PSICQUIC, RRID:SCR_006389)
    Pathways of proteins involved in host response were tested by gene enrichment analysis on Kyoto Encyclopaedia of Genes and Genomes (KEGG) human pathways and WikiPathways databases (56).
    KEGG
    suggested: (KEGG, RRID:SCR_012773)
    Results for KEGG and WikiPathways were considered significant with a revised p-value < 0. 05.
    WikiPathways
    suggested: (WikiPathways, RRID:SCR_002134)
    To infer pathways involved in single viral interactome, gene enrichment analyses for each viral interactome were collected along with p-values, as reported in enrichR package output.
    enrichR
    suggested: (Enrichr, RRID:SCR_001575)

    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:
    Limitations: There are many experimental platforms for deriving such physical interactions, such as Affinity purification mass-spectrometry (AP-MS) and yeast-two-hybrid (Y2H), which enable the accurate identification of interactions with a relatively long time. The scenario reported in this study refers to few experimental data available on public databases and could be different respect to real phenotypes of COVID-19 patients. The pathways’ analysis did not consider tissue and cell type diversity. Finally, the low threshold established for the number of nodes found by RWR (200) limited the reconstruction of the entire pathways. However, this was a software-imposed threshold. Although such network-based approach showed great potential in identifying mechanisms not yet observed, experimental tests will be necessary to confirm what we have described.

    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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  3. Version published to 10.1101/2020.11.03.366666 on bioRxiv