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  • Title: Gene expression in juvenile arthritis and spondyloarthropathy: pro-angiogenic ELR+ chemokine genes relate to course of arthritis.
    Author: Barnes MG, Aronow BJ, Luyrink LK, Moroldo MB, Pavlidis P, Passo MH, Grom AA, Hirsch R, Giannini EH, Colbert RA, Glass DN, Thompson SD.
    Journal: Rheumatology (Oxford); 2004 Aug; 43(8):973-9. PubMed ID: 15150433.
    Abstract:
    OBJECTIVE: To evaluate the ability of microarray-based methods to identify genes with disease-specific expression patterns in peripheral blood mononuclear cells (PBMC) and synovial fluid mononuclear cells (SFMC) of juvenile arthritis patients and healthy controls. METHODS: Microarray data (Affymetrix U95Av2) from 26 PBMC and 20 SFMC samples collected from patients with active disease (classified by course according to ACR criteria) were analysed for expression patterns that correlated with disease characteristics. For comparison, PBMC gene expression profiles were obtained from 15 healthy controls. Real-time PCR was used for confirmation of gene expression differences. RESULTS: Statistical analysis of gene expression patterns in PBMC identified 378 probe sets corresponding to 342 unique genes with differing expression levels between polyarticular course patients and controls (t test, P<0.0001). The genes represented by these probe sets were enriched for functions related to regulation of immune cell functions, receptor signalling as well as protein metabolism and degradation. Included in these probe sets were a group of CXCL chemokines with functions related to angiogenesis. Further analysis showed that, whereas angiogenic CXCL (ELR+) gene expression was elevated in polyarticular PBMC, expression of angiostatic CXCL (ELR-) chemokines was lower in polyarticular SFMC compared with corresponding pauciarticular samples (t test, P<0.05). CONCLUSIONS: This pilot study demonstrates that juvenile arthritis patients exhibit complex patterns of gene expression in PBMC and SFMC. The presence of disease-correlated biologically relevant gene expression patterns suggests that the power of this approach will allow better understanding of disease mechanisms, identify distinct clinical phenotypes in disease subtypes, and suggest new therapeutic approaches.
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