Pathogen-sugar interactions revealed by universal saturation transfer analysis

  1. Charles J. Buchanan
  2. Ben Gaunt
  3. Peter J. Harrison
  4. Yun Yang
  5. Jiwei Liu
  6. Aziz Khan
  7. Andrew M. Giltrap
  8. Audrey Le Bas
  9. Philip N. Ward
  10. Kapil Gupta
  11. Maud Dumoux
  12. Tiong Kit Tan
  13. Lisa Schimaski
  14. Sergio Daga
  15. Nicola Picchiotti
  16. Margherita Baldassarri
  17. Elisa Benetti
  18. Chiara Fallerini
  19. Francesca Fava
  20. Annarita Giliberti
  21. Panagiotis I. Koukos
  22. Matthew J. Davy
  23. Abirami Lakshminarayanan
  24. Xiaochao Xue
  25. Georgios Papadakis
  26. Lachlan P. Deimel
  27. Virgínia Casablancas-Antràs
  28. Timothy D. W. Claridge
  29. Alexandre M. J. J. Bonvin
  30. Quentin J. Sattentau
  31. Simone Furini
  32. Marco Gori
  33. Jiandong Huo
  34. Raymond J. Owens
  35. Christiane Schaffitzel
  36. Imre Berger
  37. Alessandra Renieri
  38. GEN-COVID Multicenter Study
  39. James H. Naismith
  40. Andrew J. Baldwin
  41. Benjamin G. Davis

Reviewed by

Read the full article

Listed in

Log in to save this article

Abstract

Many pathogens exploit host cell-surface glycans. However, precise analyses of glycan ligands binding with heavily modified pathogen proteins can be confounded by overlapping sugar signals and/or compounded with known experimental constraints. Universal saturation transfer analysis (uSTA) builds on existing nuclear magnetic resonance spectroscopy to provide an automated workflow for quantitating protein-ligand interactions. uSTA reveals that early-pandemic, B-origin-lineage severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike trimer binds sialoside sugars in an “end-on” manner. uSTA-guided modeling and a high-resolution cryo–electron microscopy structure implicate the spike N-terminal domain (NTD) and confirm end-on binding. This finding rationalizes the effect of NTD mutations that abolish sugar binding in SARS-CoV-2 variants of concern. Together with genetic variance analyses in early pandemic patient cohorts, this binding implicates a sialylated polylactosamine motif found on tetraantennary N-linked glycoproteins deep in the human lung as potentially relevant to virulence and/or zoonosis.

Article activity feed

  1. Version published to 10.1126/science.abm3125
  2. Version published to 10.1101/2021.04.14.439284v2 on bioRxiv
  3. ScreenIT

    SciScore for 10.1101/2021.04.14.439284: (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

    Antibodies
    SentencesResources
    Pseudovirus was detected via CR3022, anti-Human IgG-HRP (Jackson ImmunoResearch Europe) and 1-Step Ultra TMB ELISA substrate (Thermo Fisher). 1×104 ACE-2-expressing MDCK cells in PBS were seeded in a flat-bottom 96-well plate and treated with 0.3mU Arthrobacter ureafaciens neuraminidase (Merck) or PBS (mock) for 30 minutes (37°C, 5% CO2).
    anti-Human IgG-HRP
    suggested: None
    Experimental Models: Cell Lines
    SentencesResources
    Expi293 cells (Thermofisher Scientific) were used to express the Spike-Bap protein.
    Expi293
    suggested: RRID:CVCL_D615)
    Pseudoviral Cell-entry Asssay: A spike-expressing lentivirus in HEK 293T cells was generated using a two-plasmid system.
    HEK 293T
    suggested: NCBI_Iran Cat# C498, RRID:CVCL_0063)
    Pseudovirus was detected via CR3022, anti-Human IgG-HRP (Jackson ImmunoResearch Europe) and 1-Step Ultra TMB ELISA substrate (Thermo Fisher). 1×104 ACE-2-expressing MDCK cells in PBS were seeded in a flat-bottom 96-well plate and treated with 0.3mU Arthrobacter ureafaciens neuraminidase (Merck) or PBS (mock) for 30 minutes (37°C, 5% CO2).
    MDCK
    suggested: None
    Software and Algorithms
    SentencesResources
    The values we obtain performing this analysis on BSA/Trp closely match those measured by ITC, and the values we measure for ligand 2 and SPIKE are in good agreement with those measured by SPR as described in the text.
    SPIKE
    suggested: (SPIKE, RRID:SCR_010466)
    The uSTA analysis pipeline then provides a user with a report that shows the results of the various stages of analysis, and uses pymol to render the surfaces.
    pymol
    suggested: (PyMOL, RRID:SCR_000305)
    Genetic analysis of clinical samples: Variant calling: Reads were mapped to the hg19 reference genome by the Burrow-Wheeler aligner BWA.
    BWA
    suggested: (BWA, RRID:SCR_010910)
    Variants were annotated by ANNOVAR.
    ANNOVAR
    suggested: (ANNOVAR, RRID:SCR_012821)
    All data pre-processing and the RFE procedure was coded in Python; the LR model was used, as included, in the scikit-learn module with the liblinear coordinate descent optimization algorithm.
    Python
    suggested: (IPython, RRID:SCR_001658)

    Results from OddPub: Thank you for sharing your data.

    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: We found the following clinical trial numbers in your paper:

    IdentifierStatusTitle
    NCT04549831RecruitingGenetic Bases of COVID-19 Clinical Variability

    Results from Barzooka: We found bar graphs of continuous data. We recommend replacing bar graphs with more informative graphics, as many different datasets can lead to the same bar graph. The actual data may suggest different conclusions from the summary statistics. For more information, please see Weissgerber et al (2015).

    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.

  4. Version published to 10.1101/2021.04.14.439284v1 on bioRxiv