Wouters, C. | Search bioRxiv

bioRxiv2018

Machine learning identifies the immunological signature of Juvenile Idiopathic Arthritis

Juvenile idiopathic arthritis (JIA) is the most common childhood rheumatic disease, with a strongly debated pathophysiological origin. Both adaptive and innate immune processes have been proposed as primary drivers, which may account for the observed clinical heterogeneity, but few high-depth studies have been performed. Here we profiled the adaptive immune system of 85 JIA patients and 43 age-matched controls, identifying immunological changes unique to JIA and others common across a broad spectrum of childhood inflammatory diseases. The JIA immune signature was shared between clinically distinct subsets, but was accentuated in the systemic JIA patients and those patients with active disease. Despite the extensive overlap in the immunological spectrum exhibited by healthy children and JIA patients, machine learning analysis of the dataset proved capable of diagnosis of JIA patients with ~90% accuracy. These results pave the way for large-scale longitudinal studies of JIA, where machine learning could be used to predict immune signatures that correspond to treatment response group.

immunology↗

bioRxiv2017

An Amino Acid Motif In HLA-DRB1 Distinguishes Patients With Uveitis In Juvenile Idiopathic Arthritis

ObjectivesUveitis is a visually-debilitating disorder that affects up to 30% of children with the most common forms of juvenile idiopathic arthritis (JIA). The disease mechanisms predisposing only a subgroup of children to uveitis are unknown. To identify genetic susceptibility loci for uveitis in JIA, we conducted a genome-wide association study totalling 522 JIA cases.\n\nMethodsTwo cohorts of JIA patients with ophthalmological follow-up were separately genotyped and then imputed using a genome-wide imputation reference panel, and an HLA-specific reference panel used for imputing amino acids and HLA types in the major histocompatibility complex (MHC). After imputation, we performed genome-wide and MHC-specific analyses. We used a reverse immunology approach to model antigen presentation at 13 common HLA-DRB1 allotypes.\n\nResultsWe identified the amino acid serine at position 11 (serine-11) in HLA-DRB1 as associated to increased risk of uveitis (OR = 2.60, p = 5.43 x 10-10). We found the serine-11 signal to be specific to females (pfemales = 7.61 x 10-10, pmales = 0.18). Serine-11 resides in the YST-motif in the peptide binding groove of the HLA-DRB1 protein; all three amino acids are in perfect linkage disequilibrium and show identical association to disease. Quantitative prediction of binding affinity revealed that discernable peptide-binding preferences distinguish HLA-DRB1 allotypes with the YST-motif.\n\nConclusionOur findings highlight a genetically distinct, sexually-dimorphic feature of JIA-uveitis compared to JIA without uveitis in HLA-DRB1. The association indicates the potential involvement for antigen presentation by HLA-DRB1 in the development of uveitis in JIA.

genetics↗