Pandemic, Epidemic, Endemic: B Cell Repertoire Analysis Reveals Unique Anti-Viral Responses to SARS-CoV-2, Ebola and Respiratory Syncytial Virus

  1. Alexander Stewart
  2. Emma Sinclair
  3. Joseph Chi-Fung Ng
  4. Joselli Silva O’Hare
  5. Audrey Page
  6. Ilaria Serangeli
  7. Christian Margreitter
  8. Federica Orsenigo
  9. Katherine Longman
  10. Cecile Frampas
  11. Catia Costa
  12. Holly-May Lewis
  13. Nora Kasar
  14. Bryan Wu
  15. David Kipling
  16. Peter JM Openshaw
  17. Christopher Chiu
  18. J Kenneth Baillie
  19. Janet T. Scott
  20. Malcolm G. Semple
  21. Melanie J. Bailey
  22. Franca Fraternali
  23. Deborah K. Dunn-Walters

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Abstract

Immunoglobulin gene heterogeneity reflects the diversity and focus of the humoral immune response towards different infections, enabling inference of B cell development processes. Detailed compositional and lineage analysis of long read IGH repertoire sequencing, combining examples of pandemic, epidemic and endemic viral infections with control and vaccination samples, demonstrates general responses including increased use of IGHV4-39 in both Zaire Ebolavirus (EBOV) and COVID-19 patient cohorts. We also show unique characteristics absent in Respiratory Syncytial Virus or yellow fever vaccine samples: EBOV survivors show unprecedented high levels of class switching events while COVID-19 repertoires from acute disease appear underdeveloped. Despite the high levels of clonal expansion in COVID-19 IgG1 repertoires there is a striking lack of evidence of germinal centre mutation and selection. Given the differences in COVID-19 morbidity and mortality with age, it is also pertinent that we find significant differences in repertoire characteristics between young and old patients. Our data supports the hypothesis that a primary viral challenge can result in a strong but immature humoral response where failures in selection of the repertoire risk off-target effects.

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  1. Version published to 10.3389/fimmu.2022.807104
  2. ScreenIT

    SciScore for 10.1101/2021.08.19.456951: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    EthicsIACUC: Ebola RNA blood samples were collected from convalescent patients with viral RNA negative PCR tests in the 2014-2016 West African outbreak, three patients were Caucasian treated in the UK, and the remaining were convalescent plasma donor participants from a trial in Sierra Leone25 (consented under the Sierra Leone Ethics and Scientific Review Committee ISRCTN13990511 and PACTR201602001355272 and authorised by Pharmacy Board of Sierra Leone, #PBSL/CTAN/MOHS-CST001).
    Sex as a biological variablenot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Cell Line AuthenticationAuthentication: SARS-CoV-2 protein targets) were obtained from either supplementary data files in the cited publications or by visual inspection (for PDB structures).

    Table 2: Resources

    Antibodies
    SentencesResources
    Healthy samples taken after SARS-CoV-2 emergence were all confirmed negative for anti-SARS-CoV-2 antibodies by SureScreen lateral flow test and by ELISA 24.
    anti-SARS-CoV-2
    suggested: None
    Software and Algorithms
    SentencesResources
    Analysis of BCR clone lineage trees: Lineage trees were reconstructed using the maximum parsimony method implemented in the dnapars executable in the phylip package32.
    phylip
    suggested: (PHYLIP, RRID:SCR_006244)
    This was performed using a Python implementation of the Edmond’s algorithm to construct a minimum spanning arborescence tree with the given germline V gene sequence as root.
    Python
    suggested: (IPython, RRID:SCR_001658)
    Gene usage analysis was performed in GraphPad Prism 8.4.3 using a two-way ANOVA with a Dunnett’s post hoc test.
    GraphPad Prism
    suggested: (GraphPad Prism, RRID:SCR_002798)
    Data visualisation was performed using the R ggplot2 package and the following specialised R packages: visNetwork (for visualising convergent CDRH3 network clusters) and ggseqlogo (for visualising CDRH3 sequence logos).
    ggplot2
    suggested: (ggplot2, RRID:SCR_014601)
    PDB structures were visualised using PyMOL (version 2.3.0).
    PyMOL
    suggested: (PyMOL, RRID:SCR_000305)

    Results from OddPub: Thank you for sharing your code and data.

    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: We found the following clinical trial numbers in your paper:

    IdentifierStatusTitle
    ISRCTN13990511NANA

    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.

    Results from scite Reference Check: We found no unreliable references.

    About SciScore

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  3. Version published to 10.1101/2021.08.19.456951v1 on bioRxiv