1. Immunology and Inflammation
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Different B cell subpopulations show distinct patterns in their IgH repertoire metrics

  1. Marie Ghraichy
  2. Valentin von Niederhäusern
  3. Aleksandr Kovaltsuk
  4. Jacob D Galson
  5. Charlotte M Deane
  6. Johannes Trück  Is a corresponding author
  1. University Children's Hospital, University of Zurich (UZH), Switzerland
  2. University of Oxford, United Kingdom
  3. Alchemab Ltd, United Kingdom
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Cite this article as: eLife 2021;10:e73111 doi: 10.7554/eLife.73111

Abstract

Several human B-cell subpopulations are recognized in the peripheral blood, which play distinct roles in the humoral immune response. These cells undergo developmental and maturational changes involving VDJ recombination, somatic hypermutation and class switch recombination, altogether shaping their immunoglobulin heavy chain (IgH) repertoire. Here, we sequenced the IgH repertoire of naïve, marginal zone, switched and plasma cells from 10 healthy adults along with matched unsorted and in silico separated CD19+ bulk B cells. Using advanced bioinformatic analysis and machine learning, we show that sorted B cell subpopulations are characterised by distinct repertoire characteristics on both the individual sequence and the repertoire level. Sorted subpopulations shared similar repertoire characteristics with their corresponding in silico separated subsets. Furthermore, certain IgH repertoire characteristics correlated with the position of the constant region on the IgH locus. Overall, this study provides unprecedented insight over mechanisms of B cell repertoire control in peripherally circulating B cell subpopulations.

Data availability

Raw data used in this study are available at the NCBI Sequencing Read Archive (www.ncbi.nlm.nih.gov/sra) under BioProject number PRJNA748239 including metadata meeting MiAIRR standards (32). The processed dataset is available in Zenodo (https://doi.org/10.5281/zenodo.3585046) along with the protocol describing the exact processing steps with the software tools and version numbers.

The following data sets were generated

Article and author information

Author details

  1. Marie Ghraichy

    University Children's Hospital, University of Zurich (UZH), Zurich, Switzerland
    Competing interests
    No competing interests declared.
  2. Valentin von Niederhäusern

    University Children's Hospital, University of Zurich (UZH), Zurich, Switzerland
    Competing interests
    No competing interests declared.
  3. Aleksandr Kovaltsuk

    University of Oxford, Oxford, United Kingdom
    Competing interests
    No competing interests declared.
  4. Jacob D Galson

    NA, Alchemab Ltd, London, United Kingdom
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-4916-800X
  5. Charlotte M Deane

    University of Oxford, Oxford, United Kingdom
    Competing interests
    Charlotte M Deane, Reviewing editor, eLife.
  6. Johannes Trück

    University Children's Hospital, University of Zurich (UZH), Zurich, Switzerland
    For correspondence
    Johannes.Trueck@kispi.uzh.ch
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-0418-7381

Funding

Swiss National Science Foundation (PZ00P3_161147)

  • Johannes Trück

Swiss National Science Foundation (PZ00P3_183777)

  • Johannes Trück

Gottfried und Julia Bangerter-Rhyner-Stiftung

  • Johannes Trück

Olga Mayenfisch Stiftung

  • Johannes Trück

Palatin-Stiftung

  • Johannes Trück

Biotechnology and Biological Sciences Research Council (BB/M011224/1)

  • Aleksandr Kovaltsuk

UCB Pharma Ltd

  • Aleksandr Kovaltsuk

Royal Commission for the Exhibition of 1851 Industrial Fellowship

  • Aleksandr Kovaltsuk

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

Ethics

Human subjects: Buffy coat samples were obtained from 10 anonymous healthy adults, hence no approval from the local ethics committee was necessary.

Reviewing Editor

  1. Tomohiro Kurosaki, Osaka University, Japan

Publication history

  1. Received: August 17, 2021
  2. Preprint posted: September 5, 2021 (view preprint)
  3. Accepted: October 17, 2021
  4. Accepted Manuscript published: October 18, 2021 (version 1)
  5. Version of Record published: November 1, 2021 (version 2)

Copyright

© 2021, Ghraichy 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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Further reading

    1. Immunology and Inflammation
    2. Microbiology and Infectious Disease
    George Elias et al.
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    Antigen recognition through the T cell receptor (TCR) αβ heterodimer is one of the primary determinants of the adaptive immune response. Vaccines activate naïve T cells with high specificity to expand and differentiate into memory T cells. However, antigen-specific memory CD4 T cells exist in unexposed antigen-naïve hosts. In this study, we use high-throughput sequencing of memory CD4 TCRβ repertoire and machine learning to show that individuals with preexisting vaccine-reactive memory CD4 T cell clonotypes elicited earlier and higher antibody titers and mounted a more robust CD4 T cell response to hepatitis B vaccine. In addition, integration of TCRβ sequence patterns into a hepatitis B epitope-specific annotation model can predict which individuals will have an early and more vigorous vaccine-elicited immunity. Thus, the presence of preexisting memory T cell clonotypes has a significant impact on immunity and can be used to predict immune responses to vaccination.

    1. Immunology and Inflammation
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    Meghan E Garrett et al.
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    Background: Control of the COVID-19 pandemic will rely on SARS-CoV-2 vaccine-elicited antibodies to protect against emerging and future variants; an understanding of the unique features of the humoral responses to infection and vaccination, including different vaccine platforms, is needed to achieve this goal.

    Methods: The epitopes and pathways of escape for Spike-specific antibodies in individuals with diverse infection and vaccination history were profiled using Phage-DMS. Principal component analysis was performed to identify regions of antibody binding along the Spike protein that differentiate the samples from one another. Within these epitope regions we determined potential sites of escape by comparing antibody binding of peptides containing wildtype residues versus peptides containing a mutant residue.

    Results: Individuals with mild infection had antibodies that bound to epitopes in the S2 subunit within the fusion peptide and heptad-repeat regions, whereas vaccinated individuals had antibodies that additionally bound to epitopes in the N- and C-terminal domains of the S1 subunit, a pattern that was also observed in individuals with severe disease due to infection. Epitope binding appeared to change over time after vaccination, but other covariates such as mRNA vaccine dose, mRNA vaccine type, and age did not affect antibody binding to these epitopes. Vaccination induced a relatively uniform escape profile across individuals for some epitopes, whereas there was much more variation in escape pathways in mildly infected individuals. In the case of antibodies targeting the fusion peptide region, which was a common response to both infection and vaccination, the escape profile after infection was not altered by subsequent vaccination.

    Conclusions: The finding that SARS-CoV-2 mRNA vaccination resulted in binding to additional epitopes beyond what was seen after infection suggests protection could vary depending on the route of exposure to Spike antigen. The relatively conserved escape pathways to vaccine-induced antibodies relative to infection-induced antibodies suggests that if escape variants emerge, they may be readily selected for across vaccinated individuals. Given that the majority of people will be first exposed to Spike via vaccination and not infection, this work has implications for predicting the selection of immune escape variants at a population level.

    Funding: This work was supported by NIH grants AI138709 (PI Overbaugh) and AI146028 (PI Matsen). Julie Overbaugh received support as the Endowed Chair for Graduate Education (FHCRC). The research of Frederick Matsen was supported in part by a Faculty Scholar grant from the Howard Hughes Medical Institute and the Simons Foundation. Scientific Computing Infrastructure at Fred Hutch was funded by ORIP grant S10OD028685.