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).
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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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