A SARS-CoV-2 BioID-based virus-host membrane protein interactome and virus peptide compendium: new proteomics resources for COVID-19 research

  1. Jonathan R. St-Germain
  2. Audrey Astori
  3. Payman Samavarchi-Tehrani
  4. Hala Abdouni
  5. Vinitha Macwan
  6. Dae-Kyum Kim
  7. Jennifer J. Knapp
  8. Frederick P. Roth
  9. Anne-Claude Gingras
  10. Brian Raught

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Abstract

Key steps of viral replication take place at host cell membranes, but the detection of membrane-associated protein-protein interactions using standard affinity-based approaches (e.g. immunoprecipitation coupled with mass spectrometry, IP-MS) is challenging. To learn more about SARS-CoV-2 - host protein interactions that take place at membranes, we utilized a complementary technique, proximity-dependent biotin labeling (BioID). This approach uncovered a virus-host topology network comprising 3566 proximity interactions amongst 1010 host proteins, highlighting extensive virus protein crosstalk with: (i) host protein folding and modification machinery; (ii) membrane-bound vesicles and organelles, and; (iii) lipid trafficking pathways and ER-organelle membrane contact sites. The design and implementation of sensitive mass spectrometric approaches for the analysis of complex biological samples is also important for both clinical and basic research proteomics focused on the study of COVID-19. To this end, we conducted a mass spectrometry-based characterization of the SARS-CoV-2 virion and infected cell lysates, identifying 189 unique high-confidence virus tryptic peptides derived from 17 different virus proteins, to create a high quality resource for use in targeted proteomics approaches. Together, these datasets comprise a valuable resource for MS-based SARS-CoV-2 research, and identify novel virus-host protein interactions that could be targeted in COVID-19 therapeutics.

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  1. ScreenIT

    SciScore for 10.1101/2020.08.28.269175: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    Institutional Review Board Statementnot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.
    Cell Line Authenticationnot detected.

    Table 2: Resources

    Experimental Models: Cell Lines
    SentencesResources
    Cloning, Cell culture, Transfection: Individual SARS-CoV-2 ORF sequences15 were cloned into a Gateway compatible BioID vector, and transfected into human Flp-In T-REx 293 cells (Invitrogen, Carlsbad,
    Flp-In T-REx 293
    suggested: RRID:CVCL_U427)
    Sample Preparation for LC-MS Analysis of SARS-CoV-2 Proteome: Virus-containing media (TCID50 =1.4⨯107) from Vero E6 infection was centrifuged (2 hours, 20,000 x g, 4°C) to pellet viral particles.
    Vero E6
    suggested: RRID:CVCL_XD71)
    Software and Algorithms
    SentencesResources
    Data were searched against human RefSeq version 45 (36113 entries) supplemented with the SARS-CoV-2 proteome sequence (2019-nCoC HKU-SZ-005b1).
    RefSeq
    suggested: (RefSeq, RRID:SCR_003496)
    : Gene ontology enrichment calculated using PANTHER (http://www.pantherdb.org/) Homo sapiens GO Slim dataset.
    PANTHER
    suggested: (PANTHER, RRID:SCR_004869)

    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.

    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.

  2. Version published to 10.1101/2020.08.28.269175v1 on bioRxiv