Characterising proteolysis during SARS-CoV-2 infection identifies viral cleavage sites and cellular targets with therapeutic potential

  1. Bjoern Meyer
  2. Jeanne Chiaravalli
  3. Stacy Gellenoncourt
  4. Philip Brownridge
  5. Dominic P. Bryne
  6. Leonard A. Daly
  7. Arturas Grauslys
  8. Marius Walter
  9. Fabrice Agou
  10. Lisa A. Chakrabarti
  11. Charles S. Craik
  12. Claire E. Eyers
  13. Patrick A. Eyers
  14. Yann Gambin
  15. Andrew R. Jones
  16. Emma Sierecki
  17. Eric Verdin
  18. Marco Vignuzzi
  19. Edward Emmott

Reviewed by

Read the full article

Listed in

Log in to save this article

Abstract

SARS-CoV-2 is the causative agent behind the COVID-19 pandemic, responsible for over 170 million infections, and over 3.7 million deaths worldwide. Efforts to test, treat and vaccinate against this pathogen all benefit from an improved understanding of the basic biology of SARS-CoV-2. Both viral and cellular proteases play a crucial role in SARS-CoV-2 replication. Here, we study proteolytic cleavage of viral and cellular proteins in two cell line models of SARS-CoV-2 replication using mass spectrometry to identify protein neo-N-termini generated through protease activity. We identify previously unknown cleavage sites in multiple viral proteins, including major antigens S and N: the main targets for vaccine and antibody testing efforts. We discover significant increases in cellular cleavage events consistent with cleavage by SARS-CoV-2 main protease, and identify 14 potential high-confidence substrates of the main and papain-like proteases. We show that siRNA depletion of these cellular proteins inhibits SARS-CoV-2 replication, and that drugs targeting two of these proteins: the tyrosine kinase SRC and Ser/Thr kinase MYLK, show a dose-dependent reduction in SARS-CoV-2 titres. Overall, our study provides a powerful resource to understand proteolysis in the context of viral infection, and to inform the development of targeted strategies to inhibit SARS-CoV-2 and treat COVID-19.

Article activity feed

  1. Version published to 10.1038/s41467-021-25796-w
  2. Version published to 10.1101/2020.09.16.297945v3 on bioRxiv
  3. Version published to 10.1101/2020.09.16.297945v2 on bioRxiv
  4. ScreenIT

    SciScore for 10.1101/2020.09.16.297945: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board Statementnot detected.
    RandomizationLabel allocation was randomized using the Mat-lab Randperm function.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.
    Cell Line AuthenticationContamination: Cell culture Virus: Vero E6 (Vero 76, clone E6, Vero E6, ATCC® CRL-1586TM) authenticated by ATCC and tested negative for mycoplasma contamination prior to commencement were maintained in Dulbecco’s modified Eagle’s medium (DMEM; Thermo Fisher Scientific) containing 10% (v/v

    Table 2: Resources

    Experimental Models: Cell Lines
    SentencesResources
    A549-Ace2 cells were cultured in DMEM supplemented with 10% FBS, penicillin/streptavidin and 10 µg/ml blasticidin (Sigma) and maintained at 37°C with 5% CO2.
    A549-Ace2
    suggested: None
    Viral stocks were prepared by propagation in Vero E6 cells in DMEM supplemented with 2% FBS.
    Vero E6
    suggested: RRID:CVCL_XD71)
    Software and Algorithms
    SentencesResources
    Label allocation was randomized using the Mat-lab Randperm function.
    Mat-lab
    suggested: None

    Results from OddPub: Thank you for sharing your code and data.

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    Limitations: In this study, we used two cell line models to characterise the effects of SARS-CoV-2 infection on protease activity and the generation of viral and cellular cleavage products. Notably, we tested the efficiency of several inhibitors against SARS-CoV-2 infection only in the context of the A549-Ace2 cell line model. These results present preliminary data that must be further validated in other models, in vivo, and through clinical trials before use in patients for the treatment of COVID-19 disease.

    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.

  5. Version published to 10.1101/2020.09.16.297945v1 on bioRxiv