Genomic Responses of Mouse Synovial Fibroblasts During Tumor Necrosis Factor-Driven Arthritogenesis Greatly Mimic Those in Human Rheumatoid Arthritis.
Ntougkos E., Chouvardas P., Roumelioti F., Ospelt C., Frank-Bertoncelj M., Filer A., Buckley CD., Gay S., Nikolaou C., Kollias G.
OBJECTIVE: Aberrant activation of synovial fibroblasts is a key determinant in the pathogenesis of rheumatoid arthritis (RA). The aims of this study were to produce a map of gene expression and epigenetic changes occurring in this cell type during disease progression in the human tumor necrosis factor (TNF)-transgenic model of arthritis and to identify commonalities with human synovial fibroblasts. METHODS: We used deep sequencing to probe the transcriptome, the methylome, and the chromatin landscape of cultured mouse arthritogenic synovial fibroblasts at 3 stages of disease, as well as synovial fibroblasts stimulated with human TNF. We performed bioinformatics analyses at the gene, pathway, and network levels, compared mouse and human data, and validated selected genes in both species. RESULTS: We found that synovial fibroblast arthritogenicity was reflected in distinct dynamic patterns of transcriptional dysregulation, which was especially enriched in pathways of the innate immune response and mesenchymal differentiation. A functionally representative subset of these changes was associated with methylation, mostly in gene bodies. The arthritogenic state involved highly active promoters, which were marked by histone H3K4 trimethylation. There was significant overlap between the mouse and human data at the level of dysregulated genes and to an even greater extent at the level of pathways. CONCLUSION: This study is the first systematic examination of the pathogenic changes that occur in mouse synovial fibroblasts during progressive TNF-driven arthritogenesis. Significant correlations with the respective human RA synovial fibroblast data further validate the human TNF-transgenic mouse as a reliable model of the human disease. The resource of data generated in this work may serve as a framework for the discovery of novel pathogenic mechanisms and disease biomarkers.