COVID-19: Viral-host interactome analyzed by network based-approach model to study pathogenesis of SARS-CoV-2 infection

  1. Francesco Messina
  2. Emanuela Giombini
  3. Chiara Agrati
  4. Francesco Vairo
  5. Tommaso Ascoli Bartoli
  6. Samir Al Moghazi
  7. Mauro Piacentini
  8. Franco Locatelli
  9. Gary Kobinger
  10. Markus Maeurer
  11. Alimuddin Zumla
  12. Maria R. Capobianchi
  13. Francesco Nicola Lauria
  14. Giuseppe Ippolito
  15. COVID 19 INMI Network Medicine for IDs Study Group

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Abstract

Background

Epidemiological, virological and pathogenetic characteristics of SARS-CoV-2 infection are under evaluation. A better understanding of the pathophysiology associated with COVID-19 is crucial to improve treatment modalities and to develop effective prevention strategies. Transcriptomic and proteomic data on the host response against SARS-CoV-2 still have anecdotic character; currently available data from other coronavirus infections are therefore a key source of information.

Methods

We investigated selected molecular aspects of three human coronavirus (HCoV) infections, namely SARS-CoV, MERS-CoV and HCoV-229E, through a network based-approach. A functional analysis of HCoV-host interactome was carried out in order to provide a theoretic host-pathogen interaction model for HCoV infections and in order to translate the results in prediction for SARS-CoV-2 pathogenesis.

The 3D model of S-glycoprotein of SARS-CoV-2 was compared to the structure of the corresponding SARS-CoV, HCoV-229E and MERS-CoV S-glycoprotein. SARS-CoV, MERS-CoV, HCoV-229E and the host interactome were inferred through published protein-protein interactions (PPI) as well as gene co-expression, triggered by HCoV S-glycoprotein in host cells.

Results

Although the amino acid sequences of the S-glycoprotein were found to be different between the various HCoV, the structures showed high similarity, but the best 3D structural overlap shared by SARS-CoV and SARS-CoV-2, consistent with the shared ACE2 predicted receptor. The host interactome, linked to the S-glycoprotein of SARS-CoV and MERS-CoV, mainly highlighted innate immunity pathway components, such as Toll Like receptors, cytokines and chemokines.

Conclusions

In this paper, we developed a network-based model with the aim to define molecular aspects of pathogenic phenotypes in HCoV infections. The resulting pattern may facilitate the process of structure-guided pharmaceutical and diagnostic research with the prospect to identify potential new biological targets.

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  1. ScreenIT

    SciScore for 10.1101/2020.05.07.082487: (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
    259 sequences of CoVs, infecting different animal hosts (Table S1 in Additional file 1), were downloaded by GSAID and NCBI database in order to evaluate the variability in the S gene.
    NCBI
    suggested: (NCBI, RRID:SCR_006472)
    SARS-CoV, HCoV-229E and MERS-CoV and other CoV full genome sequence groups were aligned with MAFFT [12], synonymous and non-synonymous mutations, and amino acid similarity were calculated using the SSE program with a sliding windows of 250 nucleotides and a pass of 25 nu [13].
    MAFFT
    suggested: (MAFFT, RRID:SCR_011811)
    Virus-Host interactomes (SARS-CoV, MERS-CoV, HCoV-229E) were inferred through published PPI data, using two publicly accessible databases (STRING Viruses and VirHostNet), as well as published scientific reports with a focus on virus-host interactions [18–20].
    STRING
    suggested: (STRING, RRID:SCR_005223)
    As a next step, the virus-host PPI list, extracted in this first step, was merged with additional PPI databases, i.e. BioGrid, InnateDB-All, IMEx, IntAct, MatrixDB, MBInfo, MINT, Reactome, Reactome-FIs, UniProt, VirHostNet, BioData, CCSB Interactome Database, using R packages PSICQUIC and biomaRt [21, 22].
    IntAct
    suggested: (IntAct, RRID:SCR_006944)
    MatrixDB
    suggested: (MatrixDB, RRID:SCR_001727)
    Functional Enrichment Analysis: To evaluate functional pathways of proteins involved in host response, gene enrichment analysis was performed, using Kyoto Encyclopedia of Genes and Genomes (KEGG) human pathways and Gene Ontology databases.
    KEGG
    suggested: (KEGG, RRID:SCR_012773)
    Gene Ontology
    suggested: (Gene Ontology, RRID:SCR_002811)
    Network representation from the gene enrichment analysis was performed using ShinyGO v0.61[25].
    ShinyGO
    suggested: (ShinyGO, RRID:SCR_019213)
    Human PPI databases (BioGrid, InnateDB-All, IMEx, IntAct, MatrixDB, MBInfo, MINT, Reactome, Reactome-FIs, UniProt, VirHostNet, BioData, CCSB Interactome Database), using R packages PSICQUIC (https://bioconductor.org/packages/release/bioc/html/PSICQUIC.html) and biomaRt (https://bioconductor.org/packages/release/bioc/html/biomaRt.html) [21, 22].
    Human PPI
    suggested: None
    BioGrid
    suggested: (BioGrid Australia, RRID:SCR_006334)
    MBInfo
    suggested: (MBInfo, RRID:SCR_006768)
    MINT
    suggested: (MINT, RRID:SCR_001523)
    PSICQUIC
    suggested: (PSICQUIC, RRID:SCR_006389)
    biomaRt
    suggested: (biomaRt, RRID:SCR_019214)
    The gene expression data set was built from the Protein Atlas database (https://www.proteinatlas.org/) [23].
    https://www.proteinatlas.org/
    suggested: (HPA, RRID:SCR_006710)

    Results from OddPub: Thank you for sharing your data.

    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.

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  2. Version published to 10.1101/2020.05.07.082487 on bioRxiv