1. Evolutionary Biology
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Binding affinity landscapes constrain the evolution of broadly neutralizing anti-influenza antibodies

  1. Angela M Phillips
  2. Katherine R Lawrence
  3. Alief Moulana
  4. Thomas Dupic
  5. Jeffrey Chang
  6. Milo S Johnson
  7. Ivana Cvijovic
  8. Thierry Mora
  9. Aleksandra M Walczak
  10. Michael M Desai  Is a corresponding author
  1. Harvard University, United States
  2. Massachusetts Institute of Technology, United States
  3. Stanford University, United States
  4. École Normale Supérieure, France
Research Article
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Cite this article as: eLife 2021;10:e71393 doi: 10.7554/eLife.71393

Abstract

Over the past two decades, several broadly neutralizing antibodies (bnAbs) that confer protection against diverse influenza strains have been isolated. Structural and biochemical characterization of these bnAbs has provided molecular insight into how they bind distinct antigens. However, our understanding of the evolutionary pathways leading to bnAbs, and thus how best to elicit them, remains limited. Here, we measure equilibrium dissociation constants of combinatorially complete mutational libraries for two naturally isolated influenza bnAbs (CR9114, 16 heavy-chain mutations; CR6261, 11 heavy-chain mutations), reconstructing all possible evolutionary intermediates back to the unmutated germline sequences. We find that these two libraries exhibit strikingly different patterns of breadth: while many variants of CR6261 display moderate affinity to diverse antigens, those of CR9114 display appreciable affinity only in specific, nested combinations. By examining the extensive pairwise and higher-order epistasis between mutations, we find key sites with strong synergistic interactions that are highly similar across antigens for CR6261 and different for CR9114. Together, these features of the binding affinity landscapes strongly favor sequential acquisition of affinity to diverse antigens for CR9114, while the acquisition of breadth to more similar antigens for CR6261 is less constrained. These results, if generalizable to other bnAbs, may explain the molecular basis for the widespread observation that sequential exposure favors greater breadth, and such mechanistic insight will be essential for predicting and eliciting broadly protective immune responses.

Data availability

Data and code used for this study are available at https://github.com/klawrence26/bnab-landscapes. CR9114 data are also available in an interactive data browser at https://yodabrowser.netlify.app/yoda_browser/.FASTQ files from high-throughput sequencing have been deposited in the NCBI BioProject database with accession number PRJNA741613, and will be publicly released upon acceptance.

The following data sets were generated

Article and author information

Author details

  1. Angela M Phillips

    Harvard University, Cambridge, United States
    Competing interests
    No competing interests declared.
  2. Katherine R Lawrence

    Physics, Massachusetts Institute of Technology, Cambridge, United States
    Competing interests
    No competing interests declared.
  3. Alief Moulana

    Harvard University, Cambridge, United States
    Competing interests
    No competing interests declared.
  4. Thomas Dupic

    Harvard University, Cambridge, United States
    Competing interests
    No competing interests declared.
  5. Jeffrey Chang

    Harvard University, Cambridge, United States
    Competing interests
    No competing interests declared.
  6. Milo S Johnson

    Harvard University, Cambridge, United States
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-0169-2494
  7. Ivana Cvijovic

    Stanford University, Stanford, United States
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-6272-2979
  8. Thierry Mora

    École Normale Supérieure, Paris, France
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-5456-9361
  9. Aleksandra M Walczak

    École Normale Supérieure, Paris, France
    Competing interests
    Aleksandra M Walczak, Senior editor, eLife.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-2686-5702
  10. Michael M Desai

    Harvard University, Cambridge, United States
    For correspondence
    mdesai@oeb.harvard.edu
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-9581-1150

Funding

Howard Hughes Medical Institute (Hanna H. Gray Postdoctoral Fellowship)

  • Angela M Phillips

International Human Frontier Science Program Organization

  • Thomas Dupic

The NSF-Simons Center for Mathematical and Statistical Analysis of Biology at Harvard (1764269)

  • Katherine R Lawrence

Hertz Foundation (Graduate Fellowship Award)

  • Katherine R Lawrence

National Science Foundation (Graduate Research Fellowship Program)

  • Katherine R Lawrence

National Science Foundation (Graduate Research Fellowship Program)

  • Milo S Johnson

National Science Foundation (Graduate Research Fellowship Program)

  • Jeffrey Chang

European Research Council (COG 724208)

  • Thierry Mora
  • Aleksandra M Walczak

National Institutes of Health (GM104239)

  • Michael M Desai

National Science Foundation (PHY-1914916)

  • Michael M Desai

Stanford University (Stanford Science Fellowship)

  • Ivana Cvijovic

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Reviewing Editor

  1. Sarel Jacob Fleishman, Weizmann Institute of Science, Israel

Publication history

  1. Preprint posted: May 25, 2021 (view preprint)
  2. Received: June 17, 2021
  3. Accepted: September 5, 2021
  4. Accepted Manuscript published: September 7, 2021 (version 1)
  5. Version of Record published: September 27, 2021 (version 2)

Copyright

© 2021, Phillips et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

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