Identification of a type 1 diabetes-associated T cell receptor repertoire signature from the human peripheral blood

  • Puneet Rawat
  • , Melanie R. Shapiro
  • , Leeana D. Peters
  • , Michael Widrich
  • , Koshlan Mayer-Blackwell
  • , Keshav Motwani
  • , Milena Pavlović
  • , Ghadi al Hajj
  • , Amanda L. Posgai
  • , Chakravarthi Kanduri
  • , Giulio Isacchini
  • , Maria Chernigovskaya
  • , Lonneke Scheffer
  • , Kartik Motwani
  • , Leandro Octavio Balzano-Nogueira
  • , Camryn M. Pettenger-Willey
  • , Sebastiaan Valkiers
  • , Laura Jacobsen
  • , Michael J. Haller
  • , Desmond A. Schatz
  • Clive H. Wasserfall, Ryan O. Emerson, Andew J. Fiore-Gartland, Mark A. Atkinson, Günter Klambauer, Geir K. Sandve, Viktor Greiff, Todd M. Brusko*
*Corresponding author for this work

Research output: Working paper and reportsPreprint

Abstract

Type 1 Diabetes (T1D) is a T-cell mediated disease with a strong immunogenetic HLA dependence. HLA allelic influence on the T cell receptor (TCR) repertoire shapes thymic selection and controls activation of diabetogenic clones, yet remains largely unresolved in T1D. We sequenced the circulating TCRβ chain repertoire from 2250 HLA-typed individuals across three cross-sectional cohorts, including T1D patients, and healthy related and unrelated controls. We found that HLA risk alleles show higher restriction of TCR repertoires in T1D individuals. Machine learning analysis yielded AUROC of 0.77 on test cohorts for T1D classification. T1D-specific TCR features predominantly localized to the subsequence motifs, indicating absence of T1D-associated public clones. These TCR motifs were also observed in independent TCR cohorts residing in pancreas-draining lymph nodes of T1D individuals. Collectively, our data demonstrate T1D-related TCR motif enrichment based on genetic risk, offering a potential metric for autoreactivity and basis for TCR-based diagnostics and therapeutics.
Original languageEnglish
DOIs
Publication statusPublished - 12 Dec 2024

Fields of science

  • 102004 Bioinformatics
  • 305905 Medical informatics
  • 102 Computer Sciences
  • 102019 Machine learning
  • 102018 Artificial neural networks

JKU Focus areas

  • Digital Transformation

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