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JCI Insight. 2019 Mar 21;4(6). pii: 123879. doi: 10.1172/jci.insight.123879. eCollection 2019 Mar 21.

Treg gene signatures predict and measure type 1 diabetes trajectory.

Author information

1
Department of Surgery, University of British Columbia (UBC), and BC Children's Hospital Research Institute (BCCHRI), Vancouver, British Columbia, Canada.
2
Department of Medicine and Centre for Heart Lung Innovation, UBC, and Prevention of Organ Failure (PROOF) Centre of Excellence, St. Paul's Hospital, Vancouver, British Columbia, Canada.
3
Diabetes Clinical Research Program, Benaroya Research Institute, Seattle, Washington, USA.
4
Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada.
5
Department of Dermatology, UBC, and BCCHRI, Vancouver, British Columbia, Canada.
6
Department of Pathology, Sidra Medicine, Weill Cornell Medicine, Doha, Qatar.
7
Department of Medicine, UBC, and BCDiabetes, Vancouver, British Columbia, Canada.

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 with healthy controls, Tregs from children with new-onset T1D have an altered Treg gene signature (TGS), suggesting that this could be an immunoregulatory biomarker.

METHODS:

nanoString was used to assess the TGS in sorted Tregs (CD4+CD25hiCD127lo) or peripheral blood mononuclear cells (PBMCs) 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 with controls, the TGS in isolated Tregs or PBMCs was altered in adult new-onset and cross-sectional T1D cohorts, with sensitivity or specificity of biomarkers increased by including T1D-associated SNPs 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 (╬▒IL-12/23p40) for future rapid versus slow C-peptide decline.

CONCLUSION:

These data suggest that biomarkers based on measuring TGSs could be a new approach to stratify patients and monitor autoimmune activity in T1D.

FUNDING:

JDRF (1-PNF-2015-113-Q-R, 2-PAR-2015-123-Q-R, 3-SRA-2016-209-Q-R, 3-PDF-2014-217-A-N), the JDRF Canadian Clinical Trials Network, the National Institute of Allergy and Infectious Diseases of the National Institutes of Health (UM1AI109565 and FY15ITN168), and BCCHRI.

KEYWORDS:

Bioinformatics; Diabetes; Endocrinology; Immunology; Tolerance

PMID:
30730852
DOI:
10.1172/jci.insight.123879
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