Go to The Journal of Clinical Investigation
  • About
  • Editors
  • Consulting Editors
  • For authors
  • Publication ethics
  • Transfers
  • Advertising
  • Job board
  • Contact
  • Current issue
  • Past issues
  • By specialty
    • COVID-19
    • Cardiology
    • Immunology
    • Metabolism
    • Nephrology
    • Oncology
    • Pulmonology
    • All ...
  • Videos
  • Collections
    • Resource and Technical Advances
    • Clinical Medicine
    • Reviews
    • Editorials
    • Perspectives
    • Top read articles
  • JCI This Month
    • Current issue
    • Past issues

  • Current issue
  • Past issues
  • Specialties
  • In-Press Preview
  • Editorials
  • Viewpoint
  • Top read articles
  • About
  • Editors
  • Consulting Editors
  • For authors
  • Publication ethics
  • Transfers
  • Advertising
  • Job board
  • Contact
Temporal development of T cell receptor repertoires during childhood in health and disease
Angela M. Mitchell, … , Maki Nakayama, Aaron W. Michels
Angela M. Mitchell, … , Maki Nakayama, Aaron W. Michels
Published August 23, 2022
Citation Information: JCI Insight. 2022;7(18):e161885. https://doi.org/10.1172/jci.insight.161885.
View: Text | PDF
Research Article Immunology

Temporal development of T cell receptor repertoires during childhood in health and disease

  • Text
  • PDF
Abstract

T cell receptor (TCR) sequences are exceptionally diverse and can now be comprehensively measured with next-generation sequencing technologies. However, a thorough investigation of longitudinal TCR repertoires throughout childhood in health and during development of a common childhood disease, type 1 diabetes (T1D), has not been undertaken. Here, we deep sequenced the TCR-β chain repertoires from longitudinal peripheral blood DNA samples at 4 time points beginning early in life (median age of 1.4 years) from children who progressed to T1D (n = 29) and age/sex-matched islet autoantibody-negative controls (n = 25). From 53 million TCR-β sequences, we show that the repertoire is extraordinarily diverse early in life and narrows with age independently of disease. We demonstrate the ability to identify specific TCR sequences, including those known to recognize influenza A and, separately, those specific for insulin and its precursor, preproinsulin. Insulin-reactive TCR-β sequences were more common and frequent in number as the disease progressed in those who developed T1D compared with genetically at risk nondiabetic children, and this was not the case for influenza-reactive sequences. As an independent validation, we sequenced and analyzed TCR-β repertoires from a cohort of new-onset T1D patients (n = 143), identifying the same preproinsulin-reactive TCRs. These results demonstrate an enrichment of preproinsulin-reactive TCR sequences during the progression to T1D, highlighting the importance of using disease-relevant TCR sequences as powerful biomarkers in autoimmune disorders.

Authors

Angela M. Mitchell, Erin E. Baschal, Kristen A. McDaniel, Kimber M. Simmons, Laura Pyle, Kathleen Waugh, Andrea K. Steck, Liping Yu, Peter A. Gottlieb, Marian J. Rewers, Maki Nakayama, Aaron W. Michels

×

Figure 2

TCR-β V gene usage remains consistent throughout childhood.

Options: View larger image (or click on image) Download as PowerPoint
TCR-β V gene usage remains consistent throughout childhood.
(A) Heatmaps...
(A) Heatmaps of TCR-β V gene usage at the 4 time points in controls (top) and cases (bottom), with darker green indicating a higher frequency of a given gene. (B) Plots of principal component analyses depicting Vβ gene usage by individual at the 4 time points for controls (blue, n = 29), cases (dark red n = 18), and a subset of cases (light red, n = 11). Ellipses denote the same subset of cases throughout the 4 time points. Below each PCA plot is a heatmap quantifying the principal components (Vβ genes) contributing to the variance of each plot, with darker green indicating a higher proportion of the variance. (C) Box-and-whisker plots displaying Vβ gene usage accounting for the highest proportion of variance by cases (dark red, n = 18) and the subset of cases (light red, n = 11). The black center line denotes the median value (50th percentile), while the black box contains the 25th to 75th percentiles of the data set. The black whiskers mark the 10th and 90th percentiles. (D) Pie graphs showing the percentage of cases (dark red, top, n = 18) and the subset of cases (light red, bottom, n = 11) who developed each of the 4 islet autoantibodies tested: glutamic acid decarboxylase autoantibodies (GADA), tyrosine phosphatase–related islet antigen-2 autoantibodies (IA-2A), insulin autoantibodies (IAA), and zinc transporter 8 autoantibodies (ZnT8). Percentages of individuals in each group who were negative for each autoantibody are indicated in white. Time points in cases: 1, early in life; 2, before islet autoantibody positivity; 3, after islet autoantibody positivity; and 4, visit prior to clinical T1D diagnosis. Controls were age matched to cases at each time point. P values were calculated using Mann-Whitney U tests for Vβ gene usage in cases compared with the subset of cases at each time point. *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001.

Copyright © 2023 American Society for Clinical Investigation
ISSN 2379-3708

Sign up for email alerts