Contrasting Effects of SARS-CoV-2 Vaccination vs. Infection on Antibody and TCR Repertoires

  1. Jasper Braun
  2. Elliot D. Hill
  3. Elisa Contreras
  4. Michie Yasuda
  5. Alexandra Morgan
  6. Sarah Ditelberg
  7. Ethan Winter
  8. Cody Callahan
  9. Gabrielle Mazzoni
  10. Andrea Kirmaier
  11. Ghee Rye Lee
  12. Hamid Mirebrahim
  13. Hosseinali Asgharian
  14. Dilduz Telman
  15. Ai-Ris Y. Collier
  16. Dan H. Barouch
  17. Stefan Riedel
  18. Sanjucta Dutta
  19. Florian Rubelt
  20. Ramy Arnaout

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Abstract

Antibodies and helper T cells play important roles in SARS-CoV-2 infection and vaccination. We sequenced B- and T-cell receptor repertoires (BCR/TCR) from the blood of 251 infectees, vaccinees, and controls to investigate whether features of these repertoires could predict subjects’ SARS-CoV-2 neutralizing antibody titer (NAbs), as measured by enzyme-linked immunosorbent assay (ELISA). We sequenced recombined immunoglobulin heavy-chain (IGH), TCRβ (TRB), and TCRδ (TRD) genes in parallel from all subjects, including select B- and T-cell subsets in most cases, with a focus on their hypervariable CDR3 regions, and correlated this AIRRseq data with demographics and clinical findings from subjects’ electronic health records. We found that age affected NAb levels in vaccinees but not infectees. Intriguingly, we found that vaccination and infection have an effect on non-productively recombined IGHs, suggesting an effect that precedes clonal selection. We found that repertoires’ binding capacity to known SARS-CoV-2-specific CD4+ TRBs performs as well as the best hand-tuned approximate or “fuzzy” matching at predicting a protective level of NAbs, while also being more robust to repertoire sample size and not requiring hand-tuning. The overall conclusion from this large, unbiased, clinically well annotated dataset is that B- and T-cell adaptive responses to SARS-CoV-2 infection and vaccination are surprising, subtle, and diffuse. We discuss methodological and statistical challenges faced in attempting to define and quantify such strong-but-diffuse repertoire signatures and present tools and strategies for addressing these challenges.

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  1. Review Commons

    Note: This response was posted by the corresponding author to Review Commons. The content has not been altered except for formatting.

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    Reply to the reviewers

    We thank the reviewers for their careful and thoughtful read of our work.

    Reviewer 1 helpfully suggested that the audience might not know what fuzzy matching is. The manuscript contains the following explanation of fuzzy matching:

    "That subjects had almost no exact matches to SARS-CoV-2-specific IGH sequences did not exclude the possibility that they have sequences that are functionally similar to these reference sequences. The same possibility exists for TRBs. A standard method for finding similar sequences is using the Levenshtein (edit) distance. Sequences with a distance of less than or equal to a tolerance t are considered similar (for example, sequences that differ by no more than t=1 amino acid). This is known as “fuzzy matching” with tolerance t. (Note that exact matches are just fuzzy matches with tolerance 0.)"

    We now also add the word "approximate" in conjunction with earlier uses of the word "fuzzy."

    Reviewer 2 asked whether we "focused on potential contributions [to CDR3 length variations] based on germline gene usage, rather than directly observed contributions from the V and J segments within the CDR3 regions;" the answer is, the latter. Reviewer 2 also pointed out that it would be valuable to have HLA typing for a more comprehensive analysis. We wholeheartedly agree and have added a sentence to this effect in the discussion.

    Reviewer 3 had several specific comments. The first was regarding the overall implication of the study. There are several:

    • binding capacity is as predictive as, and more robust than, prior approaches. As we write: "We found that repertoires’ binding capacity to known SARS-CoV-2-specific CD4+ TRBs performs as well as the best hand-tuned approximate or “fuzzy” matching at predicting a protective level of NAbs, while also being more robust to repertoire sample size and not requiring hand-tuning."

    • the importance of looking for unexpected patterns, for example in non-productive joins as was done here, and for global small-scale perturbations that together result in unavoidable signals. As we write, "B- and T-cell adaptive responses to SARS-CoV-2 infection and vaccination are surprising, subtle, and diffuse," and "One open question is to what extent infection affects antibody and TCR repertoires as a whole vs. enriching specific clones within it. One can refer to these ends of the continuum of possible effects as “diffuse” vs. “precise.”"

    • caution against over-interpreting correlations with specific gene segments and. As we write: "With these caveats in mind, to our knowledge previous studies have identified 20 IGH V genes to be enriched in sequences produced during various immune responses to SARS-CoV-2.8–16 Given that human genomes encode 54 IGH V genes,17 collectively these studies implicate 37% of V genes in the response to this single viral exposure, indicating that the SARS-CoV-2 response is either quite broad within individuals, quite heterogeneous among individuals, or both."

    Each of these challenges prevailing approaches, understanding, and conclusions about patterns and signatures in repertoire sequence. We should hope this would be of some benefit.

    Reviewer 3 also asked what type of vaccines the participants received. We have now clarified that they all received an mRNA vaccine: 80% receiving Pfizer Comirnaty and the rest receiving Moderna Spikevax.

    We looked at anti-spike neutralizing antibodies because this is where the evidence for neutralization is strongest. It would have been great to have diagnostics for every protein as well as Fc function, but these were not available and therefore not possible to study.

    Reviewer 3 noted that we mention that it is impossible to know a priori what study size would be adequate to identify public sequences comprehensively in COVID-19 and asks if 251 individuals are enough. Assuming this is in reference to the size of our study, we would like to point out that this study does not claim to identify public sequences comprehensively. The rationale is more is better. The statistics tell the reader the extent to which to reject the null hypotheses put forth.

    Regarding comorbidities: we probably could perform an analysis on their impact in a future study. We thank the reviewer for this idea.

    Regarding timing: samples were collected from the vaccinee cohort 4 to 84 days (mean = 44.3 days, standard deviation = 15.3 days) after administration of the initial vaccine dose. Supplementary Figure S13 shows sampling times vs. NAb titers.

    We feel the length of the introduction is required to contextualize the implications and benefits of this study.

    We were unable to find the typos referred to but did run the manuscript through spelling and grammar checks again. We thank the Reviewer for the thoughtful attentiveness.

  2. Review Commons

    Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

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    Referee #3

    Evidence, reproducibility and clarity

    This study by Braun et al. looked at B and T cell receptor repertoires in SARS-CoV-2 infected and vaccinated individuals in comparison to healthy controls to evaluate the impact on neutralizing antibody titers. The results are clearly presented. As expected, vaccination and infection have differential effects on the repertoires. The major finding is that vaccinated and infected individuals with more SARS-CoV-2-specific TRBs have higher neutralizing antibody titers.

    Major comments:

    • It is not clear what the overall implication of this study is? What are the benefits?

    • The authors did not specify what type of vaccines the participants received. If different vaccines were used, a comparison is necessary.

    • The authors only looked at anti-spike neutralizing antibodies while other SARS-CoV-2 proteins could have a different impact. It is also known that Fc-functions participate to protection and should be studied.

    • Line 55 the authors mentioned it's difficult to know what study size is enough to represent the population. What is the rationale for including 251 individuals? Is this enough to represent the population?

    • With so many comorbidities in the different cohorts, could the authors perform an analysis of the impact of such comorbidities?

    • The timing of the SARS-CoV-2 infection or vaccination of the individuals is missing. Were all samples collected at the same time? As we know neutralizing antibodies are waning over time, it is important to include this data.

    Minor comments:

    • Introduction is quite long and needs to be summarized better.

    • Include a table with all the abbreviations

    • Missing data in the individuals demographics table

    • Typo line 65

    • Typo in the methods section

    Significance

    This study highlights the effects of SARS-CoV-2 vaccination versus infection on antibody and T cell repertoires. Increasing evidence shows the differential effects of vaccination compared to infection. This substantial study provides new data to the field. However, for a virus for which many vaccines and treatments have already been developed and proven effective, the implications and benefits to the field should be more clearly explained.

  3. Review Commons

    Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

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    Referee #2

    Evidence, reproducibility and clarity

    SUMMARY

    The paper focuses on identifying signatures in specific antibody and T-cell receptor repertoires related to SARS-CoV-2 infection and vaccination within a cohort of 250 patients. This study addresses the limitations of prior research, which often relied on small sample sizes, possibly leading to an underestimation of the variability in adaptive immune responses to SARS-CoV-2 infection and vaccination. Researchers analyzed functional features by sequencing IGH, TRD, and TRB, aiming to identify signatures that correlate antibody and T-cell responses with SARS-CoV-2 exposure.

    MAJOR COMMENTS

    • The study's major limitations, as acknowledged by the authors, primarily relate to the timing of sample collections during the pandemic and current methodological constraints, such as the challenge of reliably predicting receptor-antigen binding using a unified approach. Despite these challenges, the methodologies and statistical approaches employed were carefully designed to minimize potential biases. The tools and findings from this study could prove valuable for future research, particularly in this rapidly evolving field.

    • One intriguing finding was the observed pattern of IGH CDR3 lengths among vaccinated individuals. When investigating the contributions of germline genes to CDR3 regions as a potential explanation for length variations, did I understand correctly that authors focused on potential contributions based on germline gene usage, rather than directly observed contributions from the V and J segments within the CDR3 regions? This discovery highlights a potential impact of vaccination on V-D-J recombination machinery.

    • OPTIONAL For future studies, it may be valuable to have HLA typing for a more comprehensive analysis.

    Significance

    Overall, this manuscript uncovers previously undescribed patterns of immune responses to SARS-CoV-2 infection and vaccination. It is supported by a statistically robust methodological approach to effectively interpret the complex features resulting from exposure to a specific immunogen.

    The manuscript could be of broad interest for immunologists, clinicians and bioinformaticians.

    My area of expertise lies in the molecular biology of T cells, with a focus on applying multi-omics approaches (e.g., transcriptomics, epigenomics) to elucidate the molecular mechanisms governing T cell function and their role in the immune responses.

  4. Review Commons

    Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

    Learn more at Review Commons

    Referee #1

    Evidence, reproducibility and clarity

    In this study, the authors sequenced B- and T-cell receptor repertoires (recombined immunoglobulin heavy-chain, TCRβ, and TCRδ genes) from the blood of infected, vaccinated, and control subjects (tested for negative). They focused on their hypervariable CDR3 regions and correlated this AIRRseq data with demographics and clinical findings from subject data. They investigated whether features of these repertoires could predict subjects' SARS-CoV-2 neutralizing antibody titer. They discovered that age affected NAb levels in vaccinated subjects but not infectees. Furthermore, they found that vaccination, but not infection, substantially affects non-productively recombined IGHs, and that repertoires' binding capacity to known SARS-CoV-2-specific CD4+ TCRβ performs as well as the best matching at predicting a protective level of NAbs. The overall conclusion from this dataset is that B- and T-cell adaptive responses to SARS-CoV-2 infection and vaccination are subtle and diffuse.

    The data support the claims and the conclusions and do not require additional analyses.

    The study is robust and large, with over 250 subjects, and involved sequencing IGH and TCRdelta as well as TCRbeta, to a depth of over 100000 cells/subject.

    Significance

    The study is very specific and sectorial, and I do not think it is easily accessible to a broad audience of immunologists; despite this, however, the authors have managed to explain quite understandably the results achieved, the challenges faced, and the conclusions obtained. If the aim is to inform the scientific community that deals with immunology, I suggest not assuming that the audience knows what fuzzy is. So, I would recommend explaining the statistical tools used in a few words.

  5. Version published to 10.1101/2023.09.08.556703 on bioRxiv