Treg fitness signatures as a biomarker for disease activity in Juvenile Idiopathic Arthritis

Abstract

Juvenile Idiopathic Arthritis (JIA) is an autoimmune condition characterised by flares of joint inflammation. However, no reliable biomarker exists to predict the erratic disease course. Normally, regulatory T cells (Tregs) maintain tolerance, with altered Tregs associated with autoimmunity. Treg signatures have shown promise in monitoring other conditions, therefore a Treg gene/protein signature could offer novel biomarker potential for predicting disease activity in JIA.

Machine learning on our nanoString Treg 48-gene signature on peripheral blood (PB) Tregs generated a model to distinguish active JIA (active joint count, AJC≥1) Tregs from healthy controls (HC, AUC = 0.9875 on test data). Biomarker scores from this model successfully differentiated inactive (AJC = 0) from active JIA PB Tregs. Moreover, scores correlated with clinical activity scores (cJADAS), and discriminated subclinical disease (AJC = 0, cJADAS≥0.5) from remission (cJADAS<0.5).

To investigate altered protein expression as a surrogate measure for Treg fitness in JIA, we utilised spectral flow cytometry and unbiased clustering analysis. Three Treg clusters were of interest in active JIA PB, including TIGIT_highCD226_highCD25_low Teff-like Tregs, CD39-TNFR2-Helios_high, and a 4-1BB_lowTIGIT_lowID2_intermediate Treg cluster predominated in inactive JIA PB (AJC = 0). The ratio of these Treg clusters correlated to cJADAS, and higher ratios could potentially predict inactive individuals that flared by 9-month follow-up.

Thus, we demonstrate altered Treg signatures and subsets as an important factor, and useful biomarker, for disease progression versus remission in JIA, revealing genes and proteins contributing to Treg fitness. Ultimately, PB Treg fitness measures could serve as routine biomarkers to guide disease and treatment management to sustain remission in JIA.