Systematic discovery and functional interrogation of SARS-CoV-2 viral RNA-host protein interactions during infection

  1. Ryan A. Flynn
  2. Julia A. Belk
  3. Yanyan Qi
  4. Yuki Yasumoto
  5. Cameron O. Schmitz
  6. Maxwell R. Mumbach
  7. Aditi Limaye
  8. Jin Wei
  9. Mia Madel Alfajaro
  10. Kevin R. Parker
  11. Howard Y. Chang
  12. Tamas L. Horvath
  13. Jan E. Carette
  14. Carolyn Bertozzi
  15. Craig B. Wilen
  16. Ansuman T. Satpathy

Reviewed by

Read the full article See related articles

Listed in

Log in to save this article

Abstract

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the cause of a pandemic with growing global mortality. There is an urgent need to understand the molecular pathways required for host infection and anti-viral immunity. Using comprehensive identification of RNA-binding proteins by mass spectrometry (ChIRP-MS), we identified 309 host proteins that bind the SARS-CoV-2 RNA during active infection. Integration of this data with viral ChIRP-MS data from three other positive-sense RNA viruses defined pan-viral and SARS-CoV-2-specific host interactions. Functional interrogation of these factors with a genome-wide CRISPR screen revealed that the vast majority of viral RNA-binding proteins protect the host from virus-induced cell death, and we identified known and novel anti-viral proteins that regulate SARS-CoV-2 pathogenicity. Finally, our RNA-centric approach demonstrated a physical connection between SARS-CoV-2 RNA and host mitochondria, which we validated with functional and electron microscopy data, providing new insights into a more general virus-specific protein logic for mitochondrial interactions. Altogether, these data provide a comprehensive catalogue of SARS-CoV-2 RNA-host protein interactions, which may inform future studies to understand the mechanisms of viral pathogenesis, as well as nominate host pathways that could be targeted for therapeutic benefit.

Highlights

  • · ChIRP-MS of SARS-CoV-2 RNA identifies a comprehensive viral RNA-host protein interaction network during infection across two species

  • · Comparison to RNA-protein interaction networks with Zika virus, dengue virus, and rhinovirus identify SARS-CoV-2-specific and pan-viral RNA protein complexes and highlights distinct intracellular trafficking pathways

  • · Intersection of ChIRP-MS and genome-wide CRISPR screens identify novel SARS-CoV-2-binding proteins with pro- and anti-viral function

  • · Viral RNA-RNA and RNA-protein interactions reveal specific SARS-CoV-2-mediated mitochondrial dysfunction during infection

    • Article activity feed

    1. ScreenIT

      SciScore for 10.1101/2020.10.06.327445: (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
      Cell lines, SARS-CoV-2 infection, and cell processing: Vero-E6 and Huh7.5 cells were seeded at 1×106 cells per T150 flask and were grown in Dulbecco’s Modified Eagle Medium (DMEM) supplemented with 10% heat-inactivated fetal bovine serum (FBS), and 1% Penicillin/Streptomycin.
      Huh7.5
      suggested: RRID:CVCL_7927)
      Software and Algorithms
      SentencesResources
      Peptides were separated over a 50 cm EasySpray reversed phase LC column (75 µm inner diameter packed with 2 μm, 100 Å, PepMap C18 particles, Thermo Fisher Scientific).
      PepMap
      suggested: (BioWorks, RRID:SCR_014594)
      This concatenated file was used to search the ChIRP-MS data with MaxQuant with the following parameters: semi-specific cleavage specificity at the C-terminal site of R and K allowing for 2 missed cleavages.
      MaxQuant
      suggested: (MaxQuant, RRID:SCR_014485)
      Host genomes (for homo sapiens and chlorocebus sabaeus) were obtained from Ensembl along with annotation (gtf) files for use with feature counts.
      Ensembl
      suggested: (Ensembl, RRID:SCR_002344)
      Hisat2 was used to index all genomes and align reads6.
      Hisat2
      suggested: (HISAT2, RRID:SCR_015530)
      Remaining reads were then aligned to the host genome and reads overlapping genomic features (genes) were quantified using the featureCounts command line utility7.
      featureCounts
      suggested: (featureCounts, RRID:SCR_012919)
      Aggregated counts matrices were loaded into DESeq2 for normalization and differential gene expression analysis8.
      DESeq2
      suggested: (DESeq, RRID:SCR_000154)
      For GO term analysis, expanded interactomes of each virus were annotated with the DAVID Bioinformatics Resource (Huang et al., 2009a, 2009b).
      DAVID
      suggested: (DAVID, RRID:SCR_001881)
      ImageJ software was used to measure mitochondrial area.
      ImageJ
      suggested: (ImageJ, RRID:SCR_003070)

      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: 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 10.1101/2020.10.06.327445v1 on bioRxiv