Vulnerabilities in coronavirus glycan shields despite extensive glycosylation

  1. Yasunori Watanabe
  2. Zachary T. Berndsen
  3. Jayna Raghwani
  4. Gemma E. Seabright
  5. Joel D. Allen
  6. Oliver G. Pybus
  7. Jason S. McLellan
  8. Ian A. Wilson
  9. Thomas A. Bowden
  10. Andrew B. Ward
  11. Max Crispin

Reviewed by

Read the full article

Listed in

Log in to save this article

Abstract

Severe acute respiratory syndrome (SARS) and Middle East respiratory syndrome (MERS) coronaviruses (CoVs) are zoonotic pathogens with high fatality rates and pandemic potential. Vaccine development focuses on the principal target of the neutralizing humoral immune response, the spike (S) glycoprotein. Coronavirus S proteins are extensively glycosylated, encoding around 66–87 N-linked glycosylation sites per trimeric spike. Here, we reveal a specific area of high glycan density on MERS S that results in the formation of oligomannose-type glycan clusters, which were absent on SARS and HKU1 CoVs. We provide a comparison of the global glycan density of coronavirus spikes with other viral proteins including HIV-1 envelope, Lassa virus glycoprotein complex, and influenza hemagglutinin, where glycosylation plays a known role in shielding immunogenic epitopes. Overall, our data reveal how organisation of glycosylation across class I viral fusion proteins influence not only individual glycan compositions but also the immunological pressure across the protein surface.

Article activity feed

  1. ScreenIT

    SciScore for 10.1101/2020.02.20.957472: (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

    Experimental Models: Cell Lines
    SentencesResources
    H3N2 Victoria 2011 hemagglutinin was also expressed in the HEK 293F cells.
    HEK 293F
    suggested: RRID:CVCL_6642)
    Software and Algorithms
    SentencesResources
    Data acquisition and processing were carried out using MassLynx v4.11 and Driftscope version 2.8 software (Waters)
    MassLynx
    suggested: (MassLynx , RRID:SCR_014271)
    Empirical distributions of time-scaled phylogenies were obtained by combining (after the removal of burnin) the posterior tree distributions from the separate runs, which were subsequently used to estimate dN/dS ratios using the renaissance counting approach 79,80 implemented in BEAST v 1.8.4.
    BEAST
    suggested: (BEAST, RRID:SCR_010228)
    Single-particle data processing was performed using CryoSparc v.
    CryoSparc
    suggested: (cryoSPARC, RRID:SCR_016501)
    283 and Relion v.384. 3D variability analyses were performed in SPARX85,86.
    Relion
    suggested: (RELION, RRID:SCR_016274)

    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:
    • No conflict of interest statement was detected. If there are no conflicts, we encourage authors to explicit state so.
    • 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 10.1038/s41467-020-16567-0
  3. Version published to 10.1101/2020.02.20.957472v1 on bioRxiv