Highly synergistic combinations of nanobodies that target SARS-CoV-2 and are resistant to escape

  1. Fred D Mast
  2. Peter C Fridy
  3. Natalia E Ketaren
  4. Junjie Wang
  5. Erica Y Jacobs
  6. Jean Paul Olivier
  7. Tanmoy Sanyal
  8. Kelly R Molloy
  9. Fabian Schmidt
  10. Magdalena Rutkowska
  11. Yiska Weisblum
  12. Lucille M Rich
  13. Elizabeth R Vanderwall
  14. Nicholas Dambrauskas
  15. Vladimir Vigdorovich
  16. Sarah Keegan
  17. Jacob B Jiler
  18. Milana E Stein
  19. Paul Dominic B Olinares
  20. Louis Herlands
  21. Theodora Hatziioannou
  22. D Noah Sather
  23. Jason S Debley
  24. David Fenyö
  25. Andrej Sali
  26. Paul D Bieniasz
  27. John D Aitchison
  28. Brian T Chait
  29. Michael P Rout

  • Curated by eLife

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    Evaluation Summary:

    The manuscript by Mast et al. describes an impressive collection of new nanobodies against SARS-CoV-2 spike, which probably provides the most complete coverage of the accessible epitopes of spike to date. The authors thoroughly characterize biophysical and functional properties of the nanobodies and set an example of how to best combine multiple nanobodies to target a pathogen. As the latest in a long series of SARS-CoV-2 nanobody papers, this study stands out for its completeness, although it does not provide a novel mechanism of action or biological insights.

    (This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. Reviewer #1 agreed to share their name with the authors.)

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Abstract

The emergence of SARS-CoV-2 variants threatens current vaccines and therapeutic antibodies and urgently demands powerful new therapeutics that can resist viral escape. We therefore generated a large nanobody repertoire to saturate the distinct and highly conserved available epitope space of SARS-CoV-2 spike, including the S1 receptor binding domain, N-terminal domain, and the S2 subunit, to identify new nanobody binding sites that may reflect novel mechanisms of viral neutralization. Structural mapping and functional assays show that indeed these highly stable monovalent nanobodies potently inhibit SARS-CoV-2 infection, display numerous neutralization mechanisms, are effective against emerging variants of concern, and are resistant to mutational escape. Rational combinations of these nanobodies that bind to distinct sites within and between spike subunits exhibit extraordinary synergy and suggest multiple tailored therapeutic and prophylactic strategies.

Article activity feed

  1. Version published to 10.7554/elife.73027 on eLife
  2. eLife

    Author Response:

    Reviewer #2 (Public Review):

    [...] The strength of such an impressive and comprehensive analysis of large collection of nanobodies lies in the comparison with existing nanobodies. To fully benefit from the publication of this latest collection of nanobodies, the authors should publish all the sequences and have to make the best efforts to provide comparisons with existing nanobodies described in the literature:

    -Values obtained for neutralization potency differ substantially between different techniques and labs. A good reference point for neutralization data is the use of ACE2-Fc, which is commercially available and widely used in earlier publications. It is hard to compare the described nanobodies with the control nanobodies from the literature mentioned (Wrapp et al., Xiang et al.), as they are not identified in detail. Differences between the potency described in the original description and the values determined here are not discussed.

    -Epitope mapping defines a new list of epitope groups, although similar efforts had been undertaken earlier. It would make the comprehensive list of SARS-COV-2 nanobodies even more helpful if information about representative existing nanobodies targeting the same epitopes is included throughout the paper (in particular taking advantage of determined nanobody-Spike structures). Comparison to nanobodies with structural information would permit more meaningful predictions with regards to the mechanism of action and help explain the synergistic behavior observed.

    -The manuscript substantially refers to previous antibody publications (with important contributions from the institution of the last author). Yet, important earlier publications of SARS-CoV-2 nanobody are barely mentioned/discussed. Many of these publications defined structures and epitopes, contributed to an understanding of spike activation, and determined modes of neutralization. These publications should be appropriately acknowledged.

    A comprehensive review of this topic would certainly be extremely valuable, but is beyond the scope of this work. However, we have added additional nanobody citations as requested where appropriate throughout the text.

  3. eLife

    Evaluation Summary:

    The manuscript by Mast et al. describes an impressive collection of new nanobodies against SARS-CoV-2 spike, which probably provides the most complete coverage of the accessible epitopes of spike to date. The authors thoroughly characterize biophysical and functional properties of the nanobodies and set an example of how to best combine multiple nanobodies to target a pathogen. As the latest in a long series of SARS-CoV-2 nanobody papers, this study stands out for its completeness, although it does not provide a novel mechanism of action or biological insights.

    (This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. Reviewer #1 agreed to share their name with the authors.)

  4. eLife

    Reviewer #1 (Public Review):

    The manuscript by Mast et. al. describes the generation and characterisation of a collection of nanobodies against the SARS-CoV-2 Spike protein. The data presented here comprise a large body of work with some detailed characterisation and functional analysis. The generation of nanobodies against non-RBD epitopes, and particularly against the S2 subunit is novel, and has potential to be of interest in a therapeutic setting.

    There is considerable effort taken to demonstrate that the collection includes nanobodies that are functionally competent with the SARS-CoV-2 variants of concern (VoC) Alpha, Beta and to some extent Gamma. The authors also test the ability of multiple nanobodies to bind simultaneously to spike and act synergistically in viral neutralisation as a strategy to avoid loss of potency with variants. Nanobody combinations are further demonstrated to reduce the ability of escape variants to arise in selection assays, though their efficacy is not actually tested against existing VoCs.

    A stated aim of the paper is to show novel mechanisms of neutralisation, and although there is much speculation about the neutralisation mechanisms of the nanobodies presented, and given their predicted binding sites several may indeed have interesting mechanisms of neutralisation, there is very little experimental work undertaken to demonstrate that the suggested mechanisms are actually occurring.

    Much of the discussion of results here revolves around demonstrating the differences between nanobody and antibody engagement of spike that could give nanobodies an advantage as therapeutics in their monomeric, unmodified format. However, due to rapid clearance in vivo, this is not a format currently used and the authors do not demonstrate that their nanobodies are able to prevent or treat in vivo infection in the manuscript.

    Overall this work adds a well-characterised collection of nanobodies to the body of antibody-like molecules targeting SARS-CoV-2 and introduces several neutralising agents outside of the highly targeted RBD domain.

  5. eLife

    Reviewer #2 (Public Review):

    The manuscript 'Highly synergistic combinations of nanobodies that target 2 SARS-CoV-2 and are resistant to escape' by Mast et al. describes an impressive collection of new nanobodies binding SARS-CoV-2 spike. The authors employ a comprehensive method to identify novel nanobodies from immunized camelids, which was initially described by the same lab and relies on proteomic analysis of the serum and correlation with the sequence repertoire found in purified B cells.

    Strengths:
    The extended number of new nanobodies as well as their meticulous characterization on a biophysical and functional level (i.e. neutralization data) provide a rich resource for the community. The nanobodies fall into 10 groups of nanobodies with differential binding sites/modes and include the first examples of neutralizing nanobodies binding S1 independent of the RBD, as well as the S2 subunit of spike. As such, the study likely provides one of the most complete coverages of the possible epitope space of SARS-CoV-2 spike described to date. Characterizing a representative collection of nanobodies, the authors provide detailed affinities (including kinetics), neutralization data (including data on the emerging variants alpha, beta, and gamma), escape variant data, epitope mapping, and the systematic analysis of synergy between different nanobodies and their epitopes.
    Most conclusions are backed up by solid data and the authors provide a fairly complete functional characterizations despite the large nanobody collection (mostly on the basis of methods that can be conducted in high and medium throughput). A particular strength is the identification of escape variants for most representative nanobodies as well as the systematic analysis of synergy, which is typically backed up with much less complete data in other publications.

    The new nanobodies add some previously missing epitopes and therefore make an important contribution, although the list of described SARS-CoV-2 nanobodies is ever growing and no completely new type of neutralizing nanobodies or novel mechanism of action was identified.

    Weaknesses/Room for improvement:

    A substantial number of SARS-CoV-2 nanobodies has meanwhile been described and it is therefore challenging to extract genuine novelty about the biology of SARS-CoV-2 spike and the fusion it catalyzes, as well as the neutralization mechanism of nanobodies or antibodies. The field (and translational efforts) are aiming at neutralizing nanobodies targeting well-conserved regions of S2 that potently interfere with the fusion mechanism per se and therefore allow broad neutralization activity with little room for genuine escape variants. Although the authors describe the first neutralizing nanobodies targeting S2 and the NTD of S1, these expectations are not met as neutralization by the identified nanobodies can also be avoided by single point mutations. This underlines that efforts to identify broadly neutralizing nanobodies that do not allow mutational escape are not trivial (and may be difficult to achieve with a typically RBD-dominated immune response).
    The strength of such an impressive and comprehensive analysis of large collection of nanobodies lies in the comparison with existing nanobodies. To fully benefit from the publication of this latest collection of nanobodies, the authors should publish all the sequences and have to make the best efforts to provide comparisons with existing nanobodies described in the literature:

    - Values obtained for neutralization potency differ substantially between different techniques and labs. A good reference point for neutralization data is the use of ACE2-Fc, which is commercially available and widely used in earlier publications. It is hard to compare the described nanobodies with the control nanobodies from the literature mentioned (Wrapp et al., Xiang et al.), as they are not identified in detail. Differences between the potency described in the original description and the values determined here are not discussed.

    - Epitope mapping defines a new list of epitope groups, although similar efforts had been undertaken earlier. It would make the comprehensive list of SARS-COV-2 nanobodies even more helpful if information about representative existing nanobodies targeting the same epitopes is included throughout the paper (in particular taking advantage of determined nanobody-Spike structures). Comparison to nanobodies with structural information would permit more meaningful predictions with regards to the mechanism of action and help explain the synergistic behavior observed.

    - The manuscript substantially refers to previous antibody publications (with important contributions from the institution of the last author). Yet, important earlier publications of SARS-CoV-2 nanobody are barely mentioned/discussed. Many of these publications defined structures and epitopes, contributed to an understanding of spike activation, and determined modes of neutralization. These publications should be appropriately acknowledged.

  6. eLife

    Reviewer #3 (Public Review):

    By combining in vivo antibody affinity maturation and proteomics, the authors report the discovery of hundreds of high-affinity nanobodies that target the whole SARS-CoV-2 spike. The authors produced, bioengineered, and comprehensively characterized a repertoire of the spike nanobodies and their combinations by complementary biophysical, structural (epitope mapping), and functional assays. While other papers have been published on the related topic, this work can be distinguished by 1) identification and characterization of non-RBD nanobodies (including novel neutralizers that target the highly conserved S2 epitope) 2) extensive bioengineering to substantially improve potency and resistance to escaping variants, 3) demonstration of synergistic activities using nanobody cocktails. Overall, the science is thorough, solid, and of the highest caliber.

  7. ScreenIT

    SciScore for 10.1101/2021.04.08.438911: (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
    After permeabilization, the cells were incubated with a blocking buffer (1% (w/v) bovine serum albumin (Calbiochem) and 0.5% (w/v) Triton X-100 in DBPS) for 60 min, and then stained with primary anti-Spike CR3022 (Absolute Antibody) monoclonal antibodies (1:1000), and secondary anti-human IgG antibodies (1:2000) conjugated to Alexa fluor 488 (Invitrogen)
    anti-Spike CR3022
    suggested: (Abcam Cat# ab273074, RRID:AB_2847846)
    anti-human IgG
    suggested: None
    Experimental Models: Cell Lines
    SentencesResources
    Immunization and Isolation of VHH Antibody Fractions: Two llamas, Marley (9 year old male) and Rocky (5 year old male) were immunized with recombinant SARS-CoV2 Spike S1 and SARS-CoV2 Spike S2 expressed in HEK293 cells as Fc fusion proteins.
    HEK293
    suggested: CLS Cat# 300192/p777_HEK293, RRID:CVCL_0045)
    293T/17 and 293T-hACE2 (Crawford et al., 2020) cells (Life Technologies Cat# R70007, RRID:CVCL_6911) were cultured in DMEM (Gibco) supplemented with 10% FBS, penicillin/streptomycin, 10 mM HEPES, and with 0.1 mM MEM non-essential amino acids (Thermo Fisher)
    293T/17 and 293T-hACE2 ( Crawford et
    detected: (ATCC Cat# PTA-5077, RRID:CVCL_6911)
    SARS-CoV-2 Pseudovirus Neutralization Assay: All periplasmic purified nanobodies were treated with Triton X-114 to remove any residual endotoxins so as to not have endotoxins contribute to the effective neutralization. 293-hACE2 cells were plated at 2500-4000 cells per well on 384 solid white TC treated plates. 3-fold serially diluted nanobodies (10 dilutions in total) were incubated with 40,000-60,000 RLU equivalents of pseudotyped SARS-CoV-2-Luc for 1 h at 37 °C.
    293-hACE2
    suggested: None
    The nanobody-pseudovirus mixtures were then added in quadruplicate to 293T-hACE2 cells along with 2 µg/ml polybrene (Sigma).
    293T-hACE2
    suggested: None
    The mixture was then added to a confluent monolayer of Vero E6 cells or 293-ACE2 (Crawford et al., 2020) plated at ~1.5 × 105 cells per well and seeded in 48-well plates.
    Vero E6
    suggested: RRID:CVCL_XD71)
    The nanobody/recombinant virus mixture was then added to 293T/ACE2.cl22 cells.
    293T/ACE2.cl22
    suggested: None
    Then, the virus-nanobody mixtures were incubated with 5 × 105 293T/ACE2.22 cells in 6-well plates.
    293T/ACE2.22
    suggested: None
    Software and Algorithms
    SentencesResources
    Nanobody Screening: To validate nanobody candidates, pelB-fused nanobodies were expressed in 50 ml cultures of Arctic Express (DE3) cells, and the periplasmic fractions were isolated by osmotic shock as previously described (Fridy et al., 2014).
    Arctic Express
    suggested: None
    Input and elution samples were separated by SDS-PAGE and Coomassie-stained bands were quantified using ImageJ software.
    ImageJ
    suggested: (ImageJ, RRID:SCR_003070)
    Data were processed and analyzed using the ProteOn Manager software.
    ProteOn Manager
    suggested: None
    Octet response values were used to compute a Pearson’s Correlation Coefficient for pairwise combinations of nanobodies using Pandas (McKinney, 2010) in Python 3.7.6 (https://www.python.org/).
    https://www.python.org/
    suggested: (CVXOPT - Python Software for Convex Optimization, RRID:SCR_002918)
    Structural Analysis: Integrative structural modeling proceeded through the standard four-stage protocol (Kim et al., 2018; Rout and Sali, 2019; Russel et al., 2012; Saltzberg et al., 2021), which was scripted using the Python Modeling Interface package, a library for modeling macromolecular complexes based on the Integrative Modeling Platform software (Russel et al., 2012), version develop-31a0ad09b4 (https://integrativemodeling.org).
    Python
    suggested: (IPython, RRID:SCR_001658)
    https://integrativemodeling.org
    suggested: (Integrative Modeling Platform, RRID:SCR_002982)
    Comparative models of the nanobodies S1-1, S1-23 and S1-RBD-15 were built from the crystal structure of the human Vsig4 targeting nanobody Nb119 (PDB ID: 5IML (Wen et al., 2017)) as template using MODELLER (Sali and Blundell, 1993), and their CDR3 regions were refined using MODELLER’s loop modeling algorithm (Fiser et al., 2000).
    MODELLER
    suggested: (MODELLER, RRID:SCR_008395)
    The half-maximal inhibitory concentrations (IC50) for the nanobodies were determined using four-parameter nonlinear regression (least squares regression method without weighting) (GraphPad Prism)
    GraphPad
    suggested: (GraphPad Prism, RRID:SCR_002798)
    The mass accuracy in pLink was set to 10 p.p.m. for MS and 20☐p.p.m. for MS/MS.
    pLink
    suggested: (PLINK, RRID:SCR_001757)

    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.

  8. Version published to 10.1101/2021.04.08.438911v1 on bioRxiv