A SARS-CoV-2 – host proximity interactome

  1. Payman Samavarchi-Tehrani
  2. Hala Abdouni
  3. James D.R. Knight
  4. Audrey Astori
  5. Reuben Samson
  6. Zhen-Yuan Lin
  7. Dae-Kyum Kim
  8. Jennifer J. Knapp
  9. Jonathan St-Germain
  10. Christopher D. Go
  11. Brett Larsen
  12. Cassandra J. Wong
  13. Patricia Cassonnet
  14. Caroline Demeret
  15. Yves Jacob
  16. Frederick P. Roth
  17. Brian Raught
  18. Anne-Claude Gingras

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Abstract

Viral replication is dependent on interactions between viral polypeptides and host proteins. Identifying virus-host protein interactions can thus uncover unique opportunities for interfering with the virus life cycle via novel drug compounds or drug repurposing. Importantly, many viral-host protein interactions take place at intracellular membranes and poorly soluble organelles, which are difficult to profile using classical biochemical purification approaches. Applying proximity-dependent biotinylation (BioID) with the fast-acting miniTurbo enzyme to 27 SARS-CoV-2 proteins in a lung adenocarcinoma cell line (A549), we detected 7810 proximity interactions (7382 of which are new for SARS-CoV-2) with 2242 host proteins (results available at covid19interactome.org). These results complement and dramatically expand upon recent affinity purification-based studies identifying stable host-virus protein complexes, and offer an unparalleled view of membrane-associated processes critical for viral production. Host cell organellar markers were also subjected to BioID in parallel, allowing us to propose modes of action for several viral proteins in the context of host proteome remodelling. In summary, our dataset identifies numerous high confidence proximity partners for SARS-CoV-2 viral proteins, and describes potential mechanisms for their effects on specific host cell functions.

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    SciScore for 10.1101/2020.09.03.282103: (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

    Antibodies
    SentencesResources
    Cells were stained for bait proteins using mouse anti-FLAG antibody (Monoclonal anti-FLAG M2 antibody, Sigma-Aldrich, Cat# F3165; used at 1:2500), Rabbit-anti-Giantin antibody (Abcam, Cat# AB24586, 1:1000) and anti-G3BP1 antibody (mouse polyclonal, BD Transduction Labs, Cat# 611126; used at 1:500), in blocking buffer.
    Rabbit-anti-Giantin
    suggested: None
    anti-G3BP1
    suggested: None
    Bait proteins were probed using mouse anti-FLAG antibody (Monoclonal anti-FLAG M2 antibody, Sigma-Aldrich, F3165; used at 1:2500) in blocking buffer, washed in TBST and detected with Sheep anti-Mouse IgG-Horseradish peroxidase (HRP; GE Healthcare Life Science, Cat#NA931; used at 1:5000).
    anti-FLAG
    suggested: (Sigma-Aldrich Cat# F3165, RRID:AB_259529)
    anti-Mouse IgG-Horseradish
    suggested: None
    Experimental Models: Cell Lines
    SentencesResources
    Lentivirus production was performed in HEK293TN cells using jetPRIME reagent as per manufacturer’s recommendations (Polyplus-transfection SA, Illkirch-Graffenstaden, France, Cat# 114 – 01) as previously18.
    HEK293TN
    suggested: RRID:CVCL_UL49)
    For the baits that posed a challenge in lentiviral production (NSP1-wt and NSP3), A549 cells were transduced with rtTA virus as described above.
    A549
    suggested: NCI-DTP Cat# A549, RRID:CVCL_0023)
    Software and Algorithms
    SentencesResources
    NIS-Elements software was used for image acquisition and processing with Denoise.
    NIS-Elements
    suggested: (NIS-Elements, RRID:SCR_014329)
    Images were processed using Volocity software V6.2 (
    Volocity
    suggested: (Volocity 3D Image Analysis Software, RRID:SCR_002668)
    SCAN) were converted to an MGF format and to an mzML format using ProteoWizard (v3.0.4468) and the AB SCIEX MS Data Converter (V1.3 beta), as implemented within ProHits69.
    ProteoWizard
    suggested: (ProteoWizard, RRID:SCR_012056)
    For human samples, the database used for searches consisted of the human and adenovirus sequences in the RefSeq protein database (version 57).
    RefSeq
    suggested: (RefSeq, RRID:SCR_003496)
    Gene Ontology (GO) enrichments were performed using g:Profiler’s Python client74 (database version e100_eg47_p14), with the g:SCS multiple testing correction method, a significance threshold of 0.01, our defined A549 custom background set and with no electronic annotations.
    Python
    suggested: (IPython, RRID:SCR_001658)
    The A549 background set included all proteins defined in the SAINT report or those found in A549 cells in ProteomicsDB75 with an MS1 intensity > 0 (Supplementary Table 5)
    ProteomicsDB75
    suggested: None
    Previously reported interactions: Known interactions for SARS-CoV-2, SARS-CoV and MERS were downloaded from BioGRID77 (version 3.5.188).
    BioGRID77
    suggested: None
    Network view: The network was generated using Cytoscape79 version 3.8.0.
    Cytoscape79
    suggested: None
    Statistical scoring was performed against six negative controls compressed to two virtual controls using Significance Analysis of INTeractome (SAINT; SAINTexpress 3.6.1 was employed) as described above (“Identification of High-Confidence Proximity Partners”).
    SAINT
    suggested: None

    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:
    Given these caveats, the detection of ~21% of the previously-reported AP-MS interactions (428) is not unexpected. Importantly, given the orthogonality of the approaches, hits detected with high confidence with both approaches are much more likely to represent bona fide interactions, and some illustrative examples are included in this manuscript. There are limitations to the current study. First, as mentioned above, any epitope tagging may influence the behavior of the tagged protein, possibly affecting its localization and/or interactions. Here, we have attempted to mitigate this variable by separately tagging the N- and C-termini. This, however, still left some unanswered questions, for instance regarding the localization and proximity interactors of ORF14 (also called ORF9c in other studies). We have analyzed proximity interactomes for viral proteins expressed individually in uninfected cells. This is useful to illuminate individual connections of each viral protein with the host proteome, but it does not reveal new connections that could be formed by the association of viral proteins with one another. For viral proteins that dimerize (e.g. NSP7-NSP8), the next step could be to analyze the proximal proteome of the dimer rather than the monomer, a task that can be facilitated by using a Protein Complementation Assay strategy for BioID (as in 58). Ultimately, though, it will be critical to profile the proximal interactome of each viral protein in the context of a SARS-CoV-2 i...

    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 10.1101/2020.09.03.282103v1 on bioRxiv