Distorted TCR repertoires define multisystem inflammatory syndrome in children

  1. Amna Malik
  2. Eszter N. Tóth
  3. Michelle S. Teng
  4. Jacob Hurst
  5. Eleanor Watt
  6. Lauren Wise
  7. Natalie Kent
  8. Jack Bartram
  9. Louis Grandjean
  10. Margarita Dominguez-Villar
  11. Stuart Adams
  12. Nichola Cooper

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Abstract

While the majority of children infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) display mild or no symptoms, rare individuals develop severe disease presenting with multisystem inflammatory syndrome (MIS-C). The reason for variable clinical manifestations is not understood. Here, we carried out TCR sequencing and conducted comparative analyses of TCR repertoires between children with MIS-C (n = 12) and mild (n = 8) COVID-19. We compared these repertoires with unexposed individuals (samples collected pre-COVID-19 pandemic: n = 8) and with the Adaptive Biotechnologies MIRA dataset, which includes over 135,000 high-confidence SARS-CoV-2-specific TCRs. We show that the repertoires of children with MIS-C are characterised by the expansion of TRBV11-2 chains with high junctional and CDR3 diversity. Moreover, the CDR3 sequences of TRBV11-2 clones shift away from SARS-CoV-2 specific T cell clones, resulting in distorted TCR repertoires. In conclusion, our study reports that CDR3-independent expansion of TRBV11-2+ cells, lacking SARS-CoV-2 specificity, defines MIS-C in children.

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  1. Version published to 10.1371/journal.pone.0274289
  2. ScreenIT

    SciScore for 10.1101/2021.04.12.21255098: (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

    Software and Algorithms
    SentencesResources
    The antigens include Sars-Cov2 epitopes presented by a diverse set of MHC class I as well as MHC class II alleles, thus capturing response by CD8+ and CD4+ T-cells Data analysis and visualization: All visualization and standard statistical analysis were conducted using R version 4.0.3 and Python 3.9.1.
    Python
    suggested: (IPython, RRID:SCR_001658)
    Plots were generated using the ggplot2 R package 32
    ggplot2
    suggested: (ggplot2, RRID:SCR_014601)
    Illustrations were prepared with the BioRender package.
    BioRender
    suggested: (Biorender, RRID:SCR_018361)
    Differential gene expression analysis of TRBV genes was conducted using the EdgeR package 33.
    EdgeR
    suggested: (edgeR, RRID:SCR_012802)

    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 found bar graphs of continuous data. We recommend replacing bar graphs with more informative graphics, as many different datasets can lead to the same bar graph. The actual data may suggest different conclusions from the summary statistics. For more information, please see Weissgerber et al (2015).

    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.

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  3. Version published to 10.1101/2021.04.12.21255098v1 on medRxiv