Prior to extension, Transcriptomes of fibroblast-like Synoviocytes from extended and Polyarticular juvenile idiopathic arthritis are indistinguishable

Abstract Background Our intent was to identify differences between the transcriptome of fibroblast-like synoviocytes (FLS) in oligoarticular juvenile idiopathic arthritis (JIA) before extension when compared to persistent subtype of JIA, when the two are clinically indistinguishable. Additionally, we sought to determine if differences between the transcriptomes of FLS from extended-to-be and polyarticular course JIA could be detected. Our hypothesis was that intrinsic differences in the transcriptome of the FLS from extended-to-be JIA would distinguish them from persistent oligoarticular JIA, before the course is clinically apparent. Methods Global gene expression was defined in cultured FLS from 6 controls, 12 JIA with persistent course, 7 JIA prior to extension (extended-to-be), 4 JIA with extended course and 6 polyarticular onset, using Affymetrix Human GeneChips 133plus2.0. Results Bioconductor Linear Models for Microarray Analysis revealed 22 probesets with differential expression between persistent and extended-to-be FLS at 15% FDR, however only 2 probesets distinguished extended-to-be from extended and none distinguished extended-to-be and polyarticular at 15% FDR. Differences in extended and polyarticular gene expression profiles were not detected. Confirmation of select genes was done on the RNA level by RT-qPCR and on the protein level in synovial fluid by ELISA. Conclusions The transcriptome of FLS from extended-to-be juvenile idiopathic arthritis is distinct from persistent course before a clinical distinction can be made. Additionally, the transcriptome of extended-to-be and polyarticular course, including those who have already extended, are indistinguishable. These gene expression data suggest that FLS already reflect a polyarticular behavior early in disease course, suggesting that extended-to-be may be “latent polyarticular” at onset. These differences can be used to develop early biomarkers of disease course, allowing for better-informed treatment decisions.