Global BioID-based SARS-CoV-2 proteins proximal interactome unveils novel ties between viral polypeptides and host factors involved in multiple COVID19-associated mechanisms

This article has been Reviewed by the following groups

Read the full article

Abstract

The worldwide SARS-CoV-2 outbreak poses a serious challenge to human societies and economies. SARS-CoV-2 proteins orchestrate complex pathogenic mechanisms that underlie COVID-19 disease. Thus, understanding how viral polypeptides rewire host protein networks enables better-founded therapeutic research. In complement to existing proteomic studies, in this study we define the first proximal interaction network of SARS-CoV-2 proteins, at the whole proteome level in human cells. Applying a proximity-dependent biotinylation (BioID)-based approach greatly expanded the current knowledge by detecting interactions within poorly soluble compartments, transient, and/or of weak affinity in living cells. Our BioID study was complemented by a stringent filtering and uncovered 2,128 unique cellular targets (1,717 not previously associated with SARS-CoV-1 or 2 proteins) connected to the N- and C-ter BioID-tagged 28 SARS-CoV-2 proteins by a total of 5,415 (5,236 new) proximal interactions. In order to facilitate data exploitation, an innovative interactive 3D web interface was developed to allow customized analysis and exploration of the landscape of interactions (accessible at http://www.sars-cov-2-interactome.org/ ). Interestingly, 342 membrane proteins including interferon and interleukin pathways factors, were associated with specific viral proteins. We uncovered ORF7a and ORF7b protein proximal partners that could be related to anosmia and ageusia symptoms. Moreover, comparing proximal interactomes in basal and infection-mimicking conditions (poly(I:C) treatment) allowed us to detect novel links with major antiviral response pathway components, such as ORF9b with MAVS and ISG20; N with PKR and TARB2; NSP2 with RIG-I and STAT1; NSP16 with PARP9-DTX3L. Altogether, our study provides an unprecedented comprehensive resource for understanding how SARS-CoV-2 proteins orchestrate host proteome remodeling and innate immune response evasion, which can inform development of targeted therapeutic strategies.

Article activity feed

  1. SciScore for 10.1101/2020.08.28.272955: (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

    Antibodies
    SentencesResources
    Transfected cells were then incubated overnight at 4°C with monoclonal anti-FLAG M2 mouse antibody (dilution 1:2,000, Sigma-Aldrich).
    anti-FLAG
    suggested: None
    After 3 washes for 10 min with PBS 0.01% Triton X-100, cells were incubated for 1 h at 37 °C with the Alexa Fluor 647-conjugated secondary antibody donkey anti mouse IgG (H+L) (1:2,000
    anti mouse IgG
    suggested: None
    Experimental Models: Cell Lines
    SentencesResources
    T-REx™ HEK293 cells were grown in Dulbecco’s Modified Eagle’s Medium (DMEM, Gibco) supplemented with 10% fetal bovine serum (FBS, Sigma-Aldrich), GlutaMAX™ and Penicillin-Streptomycin (1x).
    HEK293
    suggested: None
    Software and Algorithms
    SentencesResources
    BioID data analysis: The proteins were identified by comparing all MS/MS data with the Homo sapiens proteome database (Uniprot, release March 2020, Canonical+Isoforms, comprising 42,360 entries + viral bait protein sequences added manually), using the MaxQuant software version 1.5.8.3.
    MaxQuant
    suggested: (MaxQuant, RRID:SCR_014485)
    The statistical analysis was done by Perseus software (version 1.6.2.3).
    Perseus
    suggested: (Perseus, RRID:SCR_015753)
    The tabs ‘Enrichment basal condition’ and ‘Enrichment poly(I:C)’ have been generated entering the lists of high confidence proximal interactors of each viral bait protein in the ToppCluster online tool4 (
    ToppCluster
    suggested: ( ToppCluster , RRID:SCR_001503)
    All interactors individual annotations are shown in Supplemental Table 2, which was generated using the Metascape annotation tool5 (https://metascape.org/).
    Metascape
    suggested: (Metascape, RRID:SCR_016620)
    Processing of the images was performed using Zeiss Zen 2 software and assembled using Adobe Illustrator.
    Adobe Illustrator
    suggested: (Adobe Illustrator, RRID:SCR_010279)
    Our code is written in Python 3.87 and makes use of several modules, primarily: NetworkX8 for graph operations, NumPy9 for array manipulation and numerical computations, pandas10 for data handling and Plotly11 for visualization.
    Python
    suggested: (IPython, RRID:SCR_001658)

    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:
    We thus do think that this apparent limitation could be also seen as an asset of our study; (ii) In our opinion, the main limitation sits in the expression of a single protein at a time. Knowing that several viral proteins require viral cofactors, infected context and/or the presence viral RNA to function properly (e.g. NSP10-NSP14 or NSP10-NSP16), the present analysis almost certainly misses cooperative viral interactions. Similar studies performed in infected cells will thus bring highly valuable additional information on putative SARS-CoV-2 pathogenesis mechanisms. As an attempt to mimic a physiopathological context, we artificially induced an anti-viral response by transfecting poly(I:C) and repeating the proximal interactome analysis. These experiments already revealed novel interactions of the utmost importance; and (iii), the proximal interactions are not necessarily physical and should therefore be considered as a discovery step systematically requiring orthogonal or functional validation. However, the proximal interactomics multiple analysis generated by us and others have been at the basis of fundamental mechanism discoveries, supporting the validity of the approach for identifying new biology (see177 for review). This first proximal interaction mapping of SARS-CoV-2 proteins provides a plethora of novel research tracks to better understand this virus pathogenesis. Although for a few proteins our approach did not lead to satisfying results (NSP3, NSP5, NSP8, ORF8 an...

    Results from TrialIdentifier: No clinical trial numbers were referenced.


    Results from Barzooka: We found bar graphs of continuous data. We recommend replacing bar graphs with more informative graphics, as many different datasets can lead to the same bar graph. The actual data may suggest different conclusions from the summary statistics. For more information, please see Weissgerber et al (2015).


    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

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.