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Treg gene signatures predict and measure type 1 diabetes trajectory

Pesenacker, AM; Chen, V; Gillies, J; Speake, C; Marwaha, AK; Sun, AC; Chow, S; ... Levings, MK; + view all (2019) Treg gene signatures predict and measure type 1 diabetes trajectory. JCI Insight 10.1172/jci.insight.123879. (In press). Green open access

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Abstract

BACKGROUND: Multiple therapeutic strategies to restore immune regulation and slow type 1 diabetes (T1D) progression are in development and testing. A major challenge has been defining biomarkers to prospectively identify subjects likely to benefit from immunotherapy and/or measure intervention effects. We previously found that compared to healthy controls, Tregs from children with new-onset T1D have an altered Treg gene signature (TGS), suggesting this could be an immunoregulatory biomarker. METHODS: nanoString was used to assess the TGS in sorted Tregs (CD4+CD25hiCD127lo) or Peripheral Blood Mononuclear Cells (PBMC) from individuals with T1D or type 2 diabetes, healthy controls, or T1D recipients of immunotherapy. Biomarker discovery pipelines were developed and applied to various sample group comparisons. RESULTS: Compared to controls, the TGS in isolated Tregs or PBMCs is altered in adult new-onset and cross-sectional T1D cohorts, with sensitivity and specificity of biomarkers increased by including T1D-associated single nucleotide polymorphisms in algorithms. The TGS was distinct in T1D versus type 2 diabetes, indicating disease-specific alterations. TGS measurement at the time of T1D onset revealed an algorithm that accurately predicted future rapid versus slow C-peptide decline, as determined by longitudinal analysis of placebo arms of START and T1DAL trials. The same algorithm stratified participants in a phase I/II clinical trial of ustekinumab (αIL-12/23p40) for future rapid versus slow C-peptide decline. CONCLUSION: These data suggest that biomarkers based on measuring Treg gene signatures could be a new approach to stratify patients and monitor autoimmune activity in T1D.

Type: Article
Title: Treg gene signatures predict and measure type 1 diabetes trajectory
Location: United States
Open access status: An open access version is available from UCL Discovery
DOI: 10.1172/jci.insight.123879
Publisher version: http://doi.org/10.1172/jci.insight.123879
Language: English
Additional information: This version is the version of record. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Bioinformatics, Diabetes, Endocrinology, Immunology, Tolerance
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences > Div of Infection and Immunity
URI: https://discovery.ucl.ac.uk/id/eprint/10067985
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