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Treg gene signatures predict and measure type 1 diabetes trajectory
Anne M. Pesenacker, … , Scott J. Tebbutt, Megan K. Levings
Anne M. Pesenacker, … , Scott J. Tebbutt, Megan K. Levings
Published February 7, 2019
Citation Information: JCI Insight. 2019;4(6):e123879. https://doi.org/10.1172/jci.insight.123879.
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Clinical Medicine Endocrinology Immunology

Treg gene signatures predict and measure type 1 diabetes trajectory

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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 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 (αIL-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.

Authors

Anne M. Pesenacker, Virginia Chen, Jana Gillies, Cate Speake, Ashish K. Marwaha, Annika Sun, Samuel Chow, Rusung Tan, Thomas Elliott, Jan P. Dutz, Scott J. Tebbutt, Megan K. Levings

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Figure 8

The Treg gene signature as a predictive biomarker of C-peptide decline.

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The Treg gene signature as a predictive biomarker of C-peptide decline.
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(A) C-peptide was quantified (2h AUC MMTT) in new-onset T1D patients in the placebo arms of the T1DAL and START clinical trials (see CONSORT flow diagrams in Supplemental Figures 1 and 2) at baseline (M0), 6 months (M6), and 24 months (M24). The absolute change in C-peptide from M0 to M24 was calculated, and subjects were divided into those with slow (n = 9), moderate (n = 8), or rapid (n = 7) decline based on terciles. (B and C). Tregs (CD4+CD25hiCD127lo) were sorted from cryopreserved PBMCs isolated from these subjects at baseline (M0), and the TGS was measured. (B) Biomarker score and details (as described in Figures 1 and 3) of the best algorithm predicting future rapid, slow, or moderate C-peptide decline. Horizontal lines represent means, with SD represented by error bars; dashed horizontal lines represent cutoffs for sensitivity and specificity calculations. (C) Expression of the indicated genes plotted by rate of C-peptide decline group, with 1-way ANOVA with Tukey’s multiple comparisons test (P ≤ 0.05, but data not significantly different).

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