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

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 9

The Treg gene signature as a predictive biomarker of C-peptide decline in T1D subjects treated with ustekinumab.

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The Treg gene signature as a predictive biomarker of C-peptide decline i...
Adult new-onset T1D patients were treated with ustekinumab, as outlined in the CONSORT flow diagram in Supplemental Figure 3. (A) C-peptide was quantified (2h AUC MMTT) at baseline (M0), 1 month (M1), and 12 months (M12), and absolute change in C-peptide from M0 to M12 was calculated. Subjects were divided into those with slow (n = 11) or rapid (n = 5) decline based on the absolute decline at M12, with slow subjects defined as those who lost less than 0.3 pmol C-peptide/year. (B–E) The TGS was measured in sorted CD25hiCD127lo Tregs or PBMCs from M0 and M9 samples. (B) Test details when the algorithm from Figure 8B was applied to M0 Treg data. (C) Treg-based algorithm and biomarker scores for slow versus rapid C-peptide decline using relative TGS data (M9/M0 prior to log2 transformation). (D) Treg- and (E) PBMC-based algorithm and biomarker scores using M9 TGS expression data. Horizontal lines in C–E represent means, with SD represented by error bars; dashed horizontal lines represent cutoffs for sensitivity and specificity calculations. (F) Summary of gene usage in C-peptide decline algorithms described in B–E.