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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 7

Treg gene signature algorithms differentiate type 1 or type 2 diabetes and healthy controls.

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Treg gene signature algorithms differentiate type 1 or type 2 diabetes a...
PBMC and Treg lysates from the indicated type 1 diabetes (T1D) or type 2 diabetes (T2D) cohorts were run on a nCounter SPRINT system together with age- and sex-matched healthy control (HC) samples. (A) Biomarker scores and performance when the algorithm from Figure 1B was applied to T1D and HC Treg data. (B and C) Biomarker scores and details (as described in Figure 1) of the best biomarker algorithm differentiating between Tregs from (B) T1D or (C) HCs and T2D samples. (D) Biomarker scores and performance when the algorithm from Figure 3B was applied to T1D and HC PBMC data. (E and F) Biomarker scores and details (as described in Figures 1 and 3) of the best biomarker algorithm differentiating between (E) T1D or (F) control and T2D samples in PBMCs. Horizontal lines represent means, with SD represented by error bars; dashed horizontal lines represent cutoffs for sensitivity and specificity calculations. (G and H) Summary of gene usage in each (G) Treg- or (H) PBMC-based algorithm described in A–F. Each of the 37 mRNAs measured is listed; gray and white squares indicate genes that were or were not present in the best-performing algorithm, respectively.

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