A BioID-Derived Proximity Interactome for SARS-CoV-2 Proteins

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Abstract

The novel coronavirus SARS-CoV-2 is responsible for the ongoing COVID-19 pandemic and has caused a major health and economic burden worldwide. Understanding how SARS-CoV-2 viral proteins behave in host cells can reveal underlying mechanisms of pathogenesis and assist in development of antiviral therapies. Here, the cellular impact of expressing SARS-CoV-2 viral proteins was studied by global proteomic analysis, and proximity biotinylation (BioID) was used to map the SARS-CoV-2 virus–host interactome in human lung cancer-derived cells. Functional enrichment analyses revealed previously reported and unreported cellular pathways that are associated with SARS-CoV-2 proteins. We have established a website to host the proteomic data to allow for public access and continued analysis of host–viral protein associations and whole-cell proteomes of cells expressing the viral–BioID fusion proteins. Furthermore, we identified 66 high-confidence interactions by comparing this study with previous reports, providing a strong foundation for future follow-up studies. Finally, we cross-referenced candidate interactors with the CLUE drug library to identify potential therapeutics for drug-repurposing efforts. Collectively, these studies provide a valuable resource to uncover novel SARS-CoV-2 biology and inform development of antivirals.

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  1. SciScore for 10.1101/2021.09.17.460814: (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
    For labeling fusion proteins, chicken anti-BioID2 (1:5000; BID2-CP-100; BioFront Technologies) or mouse anti-hemagglutinin primary antibody was used (HA; 1:1000; 12CA5; Covance).
    anti-BioID2
    suggested: None
    anti-hemagglutinin
    suggested: None
    HA
    suggested: None
    The primary antibody was detected using Alexa Fluor 568–conjugated goat anti-chicken (1:1000; A11041, Invitrogen) or Alexa Fluor 568–conjugated goat anti-mouse (1:1000; A11004; Thermo Fisher Scientific).
    anti-mouse
    suggested: (Thermo Fisher Scientific Cat# A-11004, RRID:AB_2534072)
    The primary antibodies were detected using horseradish peroxidase (HRP)–conjugated anti-chicken (1:40,000; A9046; Sigma-Aldrich) or anti-rabbit (1:40,000; G21234; Thermo Fisher Scientific).
    anti-chicken
    suggested: (Sigma-Aldrich Cat# A9046, RRID:AB_258432)
    anti-rabbit
    suggested: (Thermo Fisher Scientific Cat# G-21234, RRID:AB_2536530)
    Experimental Models: Cell Lines
    SentencesResources
    HEK293 Phoenix cells (National Gene Vector Biorepository, Indianapolis, IN) were transfected with each construct using Lipofectamine 3000 (Thermo Fisher Scientific) per manufacturer’s recommendation.
    HEK293
    suggested: None
    The culture media was filtered through a 0.45-μm filter and added to A549 cells along with Polybrene (4 μg/ml; Santa Cruz Biotechnology, Dallas, TX).
    A549
    suggested: NCI-DTP Cat# A549, RRID:CVCL_0023)
    Recombinant DNA
    SentencesResources
    Amplified PCR products were fused to biotin ligases via In-Fusion Recombination into myc-BioID2 pBabe (Addgene #80900; XhoI/PmeI), BioID2-HA pBabe (Addgene #120308; BamHI/EcoRI), or TurboID-3xHA pBabe (BamHI/EcoRI) [25].
    myc-BioID2 pBabe
    suggested: None
    mycBioID2 (Addgene #80900) was used as a control for BioID2 cell lines.
    mycBioID2
    suggested: None
    Human albumin signal sequence-3xHA-TurboID-KDEL pBabe control construct was made by two-step In-Fusion Recombination.
    pBabe
    suggested: RRID:Addgene_21836)
    Software and Algorithms
    SentencesResources
    HEK293 Phoenix cells (National Gene Vector Biorepository, Indianapolis, IN) were transfected with each construct using Lipofectamine 3000 (Thermo Fisher Scientific) per manufacturer’s recommendation.
    National Gene Vector Biorepository
    suggested: (National Gene Vector Biorepository, RRID:SCR_004760)
    Following digestion, samples were acidified with formic acid (FA) and subsequently desalted using AssayMap C18 cartridges mounted on an Agilent AssayMap BRAVO liquid handling system.
    Agilent AssayMap
    suggested: None
    Digested peptides were then desalted in the Bravo platform using AssayMap C18 cartridges, and dried down in a SpeedVac concentrator.
    AssayMap
    suggested: None
    Data Analysis: All raw files were processed with MaxQuant (version 1.5.5.1) using the integrated Andromeda Search engine against a target/decoy version of the curated human Uniprot proteome without isoforms (downloaded in January of 2020) and the GPM cRAP sequences (commonly known protein contaminants).
    MaxQuant
    suggested: (MaxQuant, RRID:SCR_014485)
    First, peptide intensities were log2-transformed and loess-normalized (limma package) across replicates of each bait or control batch to account for systematic errors.
    limma
    suggested: (LIMMA, RRID:SCR_010943)
    Testing for differential abundance was performed using MSstats bioconductor package based on a linear mixed-effects model.
    MSstats
    suggested: (MSstats, RRID:SCR_014353)
    bioconductor
    suggested: (Bioconductor, RRID:SCR_006442)
    We selected a network derived from the STRING database as our starting network: the subset of the STRING interactions with a combined confidence score greater than 0.7 (available in the Network Data Exchange (NDEx) at https://www.ndexbio.org/viewer/networks/275bd84e-3d18-11e8-a935-0ac135e8bacf [66, 67].
    STRING
    suggested: (STRING, RRID:SCR_005223)
    Finally, the hierarchy network (https://doi.org/10.18119/N9531R) was styled, communities were subjected to enrichment analysis in GO biological processes using the g:Profiler package in CDAPS, p values were calculated based on the hypergeometric distribution, and a layout was applied.
    g:Profiler
    suggested: (G:Profiler, RRID:SCR_006809)
    The resultant high-confidence interactors were visualized using Cytoscape (v3.8.0) and tested for enrichment in GO biological process terms using the hypergeometric distribution [74].
    Cytoscape
    suggested: (Cytoscape, RRID:SCR_003032)
    The application uses several applications, including clusterProfiler (v3.18.1) for functional analysis with the enricher function using the Broad Institute molecular signature databases (v7.4) including canonical pathways (Reactome, KEGG, WikiPathways), Immune collection, chemical and genetic perturbation signatures, regulatory transcription factor targets (TFT), oncogenic signatures, and Gene Ontology (Human Phenotype, Cellular Component, Biological Process and Molecular Function).
    clusterProfiler
    suggested: (clusterProfiler, RRID:SCR_016884)
    KEGG
    suggested: (KEGG, RRID:SCR_012773)

    Results from OddPub: Thank you for sharing your data.


    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    The use of human A549 lung-cancer derived cells for these studies is both a strength and a limitation. These cells do retain some fundamental traits of alveolar type-II pulmonary epithelial cells; however, A549 cells are not clearly representative of normal human pulmonary epithelial cells. Our global profiles of human lung cells overexpressing individual SARS-CoV-2 viral proteins produced a large dataset of significantly upregulated or downregulated cellular proteins, enabling the ability to identify specific viral proteins influencing specific changes in cell biology. This data supports previous reports of ITGB3 overexpression in SARS-CoV-2 infected cells and tissues, and further identifies the specific viral proteins that could be influencing the overexpression. If ITGB3 is indeed working as an alternate receptor for SARS-CoV-2 viral uptake, it may be that targeting ITGB3 or the specific viral proteins that upregulate ITGB3 levels could have therapeutic benefit to slow cell-to-cell spread of the virus. Additionally, our findings that several of the SARS-CoV-2 proteins can reduce cellular levels of MUC5AC/B, possibly via increased secretion [62, 63], gives insight into one of the mechanisms by which the virus causes devastation of the respiratory system in the most severe COVID-19 cases. While previous interactome studies have reported PPI candidates even when identified in up to 6 viral protein interactomes [16, 17], we highlighted here only unique protein candidates for e...

    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.

    Results from scite Reference Check: We found no unreliable references.


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