The Role of Host Cell Glycans on Virus Infectivity: The SARS‐CoV‐2 Case

  1. Silvia Acosta‐Gutiérrez
  2. Joseph Buckley
  3. Giuseppe Battaglia

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Abstract

Glycans are ubiquitously expressed sugars, coating the cell and protein surfaces. They are found on many proteins as either short and branched chains or long chains sticking out from special membrane proteins, known as proteoglycans. This sugar cushion, the glycocalyx, modulates specific interactions and protects the cell. Here it is shown that both the expression of proteoglycans and the glycans expressed on the surface of both the host and virus proteins have a critical role in modulating viral attachment to the cell. A mathematical model using SARS‐Cov‐2 as an archetypical virus to study the glycan role during infection is proposed. It is shown that this occurs via a tug‐of‐war of forces. On one side, the multivalent molecular recognition that viral proteins have toward specific host glycans and receptors. On the other side, the glycan steric repulsion that a virus must overcome to approach such specific receptors. By balancing both interactions, viral tropism can be predicted. In other words, the authors can map out the cells susceptible to virus infection in terms of receptors and proteoglycans compositions.

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  1. Version published to 10.1002/advs.202201853
  2. ScreenIT

    SciScore for 10.1101/2021.05.08.443212: (What is this?)

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

    Table 1: Rigor

    Ethicsnot detected.
    Sex as a biological variablenot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Cell Line Authenticationnot detected.

    Table 2: Resources

    Experimental Models: Cell Lines
    SentencesResources
    Two different systems (different glycan types) were generated by adding glycans using the Glycam web server Glycoprotein Builder tool[47], the insect-like system (DManpa1-3[DManpa1-6]DManpb1-4DGlcpNAcb1-4 DGlcpNAcb1-OH) and high mannose (DManpa1-6[DManpa1-3]DManpa1-6[DManpa1-3] DManpb1-4DGlcpNAcb1-4DGlcpNAcb1-OH).
    DManpa1-6
    suggested: None
    Software and Algorithms
    SentencesResources
    All simulations were performed with Gromacs v2019.2 [50] with a 2fs integration step.
    Gromacs
    suggested: (GROMACS, RRID:SCR_014565)

    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.
    • No funding statement was detected.
    • No protocol registration statement was detected.

    Results from scite Reference Check: We found no unreliable references.

    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.

  3. Version published to 10.1101/2021.05.08.443212v1 on bioRxiv