Deep mining of early antibody response in COVID-19 patients yields potent neutralisers and reveals high level of convergence

  1. Georgia Bullen
  2. Jacob D. Galson
  3. Pedro Villar
  4. Lien Moreels
  5. Line Ledsgaard
  6. Giada Mattiuzzo
  7. Gareth Hall
  8. Emma M. Bentley
  9. Edward W. Masters
  10. David Tang
  11. Sophie Millett
  12. Danielle Tongue
  13. Richard Brown
  14. Ioannis Diamantopoulos
  15. Kothai Parthiban
  16. Claire Tebbutt
  17. Rachael Leah
  18. Krishna Chaitanya
  19. Deividas Pazeraitis
  20. Sachin B. Surade
  21. Omodele Ashiru
  22. Lucia Crippa
  23. Richard Cowan
  24. Matthew W. Bowler
  25. Jamie I. Campbell
  26. Wing-Yiu Jason Lee
  27. Mark D. Carr
  28. David Matthews
  29. Paul Pfeffer
  30. Simon E. Hufton
  31. Kovilen Sawmynaden
  32. Jane Osbourn
  33. John McCafferty
  34. Aneesh Karatt-Vellatt

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Abstract

Passive immunisation using monoclonal antibodies will play a vital role in the fight against COVID-19. Until now, the majority of anti-SARS-CoV-2 antibody discovery efforts have relied on screening B cells of patients in the convalescent phase. Here, we describe deep-mining of the antibody repertoires of hospitalised COVID-19 patients using a combination of phage display technology and B cell receptor (BCR) repertoire sequencing to isolate neutralising antibodies and gain insights into the early antibody response. This comprehensive discovery approach has yielded potent neutralising antibodies with distinct mechanisms of action, including the identification of a novel non-ACE2 receptor blocking antibody that is not expected to be affected by any of the major viral variants reported. The study highlighted the presence of potent neutralising antibodies with near germline sequences within both the IgG and IgM pools at early stages of infection. Furthermore, we highlight a highly convergent antibody response with the same sequences occurring both within this study group and also within the responses described in previously published anti-SARS-CoV-2 studies.

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  1. ScreenIT

    SciScore for 10.1101/2020.12.29.424711: (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
    Recombinant antigen and control antibodies: For expression of the RBD subdomain of the SARS-CoV-1 (residues Arg306 to Phe527) and SARS-CoV-2 (residues Arg319 to Phe541) spike protein and human ACE2, genes encoding the proteins were synthesised and cloned upstream of either an Fc tag or rCD4-Avi tag, both with an additional 6 × His tag in mammalian expression vectors (37).
    Phe527
    suggested: None
    Phe541
    suggested: None
    VH and VL sequences of SARS-CoV-2 control antibodies (COV2-2196, COV2-2130, S309, B38, H4, and CR3022) were obtained from the CoV-AbDab database (25) and cloned into pINT3/pINT54 IgG expression vectors (4) as synthetic gene fragments.
    COV2-2130
    suggested: None
    H4
    suggested: None
    CR3022
    suggested: (Imported from the IEDB Cat# CR3022, RRID:AB_2848080)
    To identify unique combinations of heavy CDR3 and light CDR3, antibody frameworks and CDR regions were annotated and analysed using Geneious Biologics (Biomatters).
    light CDR3
    suggested: None
    SARS-CoV-2 RBD-ACE2 blocking assay: Black 96-well immune plates (Thermo Scientific, 10030581) were coated with mouse anti-rCD4 antibody (Bio-Rad, MCA1022R) at 4 °C overnight.
    anti-rCD4
    suggested: None
    To identify antibodies that could block the interaction between RBD and ACE2, the anti-SARS-CoV-2 antibodies were pre-incubated with 2 nM of recombinant ACE2-Fc-His-Biotin for 30 minutes before transferring to the RBD-coated plates.
    ACE2
    suggested: None
    anti-SARS-CoV-2
    suggested: None
    Protein expression and purification for crystallography studies: The Fab fragments for anti-SARS-CoV-2 antibodies ION_300 and ION_360 and SARS-CoV-2 receptor binding domain (RBD) were expressed in Expi293F™ cells (Thermo Fisher, A14527).
    SARS-CoV-2 receptor binding domain ( RBD
    suggested: None
    The antibody:RBD complexes were mixed and co-purified by size exclusion chromatography in 20 mM Tris-HCl (pH 7.5) and 50 mM NaCl.
    antibody:RBD
    suggested: None
    Both complex crystal structures were solved by molecular replacement using Phaser (46) utilising the coordinates from the SARS-CoV-2 RBD domain (PDB: 7JMP) and using homology models for ION_300 and ION_360 antibodies generated using SWISSMODEL (47) to model the heavy and light chains.
    ION_360
    suggested: None
    Detailed materials and methods for library construction, neutralisation assays and biophysical characterisation of anti-SARS-CoV-2 antibodies. Fig. S1.
    S1
    suggested: None
    Superposition of antibody and nanobody structures targeting the SARS-CoV-2 RBD. Fig. S7.
    S7
    suggested: None
    VH germline usage in convergent antibody response. Fig. S9.
    S9
    suggested: None
    Experimental Models: Cell Lines
    SentencesResources
    Neutralisation assays and combination testing: Briefly, a lentiviral pseudotyped virus was used for the infection of HEK293T/17 cells transiently expressing human ACE2 and TMPRSS2 for the pseudoviral assay.
    HEK293T/17
    suggested: ATCC Cat# CRL-11268, RRID:CVCL_1926)
    Reagents cat. no. 100980) was used to infect VERO CCL-81 cells for the authentic virus neutralisation assay.
    VERO CCL-81
    suggested: None
    For single concentration pseudovirus assay, SPR, and cross-reactivity evaluation, the antibodies expressed in 96 well plates (Expi293 cells) were purified by protein-A affinity chromatography (Generon, PC-A100), using 96 well filter plates (Whatman Polystyrene Unifilter Microplates, GE Healthcare, 11313535)
    Expi293
    suggested: RRID:CVCL_D615)
    Software and Algorithms
    SentencesResources
    Atomic models were built using Coot (48) and refined with Refmac (49).
    Coot
    suggested: (Coot, RRID:SCR_014222)

    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 10.1101/2020.12.29.424711 on bioRxiv