UC San Diego

Rheumatoid arthritis (RA) is an autoimmune disease that begins as systemic dysregulation of the immune system and progresses to an inflammatory attack on the synovial joints. This body of research represents the application of translational bioinformatics methods to understand both the transcriptional topology of the rheumatoid synovium and the epigenetic trajectory of lymphocytes in RA. In chapter 1, we demonstrate the inability of the current, purportedly optimal, single-cell disaggregation protocol to accurately assay transcription in the synovium. Using RNAseq to evaluate transcription, we apply multiple disaggregation protocols to RA and OA synovial tissue and demonstrate that alternative protocols characterize transcription with greater fidelity, with implications for single-cell methods applied to adhesive tissue. In chapter 2, we present a novel application of Laser Capture Microdissection followed by RNAseq (LCM-RNAseq) to accurately describe the transcriptional topology of the rheumatoid synovium. This method facilitates progress towards spatial investigation of transcription in the synovium but avoids the need for an adhesive-tissue-optimized, single-cell disaggregation protocol. By applying LCM-RNAseq to 7 RA samples and 7 controls (synovial tissue derived from osteoarthritis patients), we demonstrate that the lining, sublining, and vessel compartments of the rheumatic synovium each contribute unique aspects of the transcriptional signature that generates the inflammatory phenotype characteristic of symptomatic RA. In chapters 3 and 4, we report the cross-sectional and longitudinal results, respectively, of the Targeting Immune Responses for Prevention of Rheumatoid Arthritis (TIP-RA) consortium’s study of the epigenetic changes in RA progression. Here we report a genome-wide analysis of methylation among ACPA- Controls, ACPA+ At-Risk, and Early RA. In this study, participants were followed for up to 5 years, with blood samples taken annually and at RA diagnosis. Peripheral blood mononuclear cells (PBMCs) were separated into CD19+ B cells, memory CD4+ T cells, and naïve CD4+ T cells using antibodies and magnetic beads. Genome-wide methylation within each cell lineage was assayed using the Illumina MethylationEPIC v1.0 beadchip. In chapter 3, initial cross-sectional analysis at baseline reveals that the ACPA+ “At-Risk” methylome exhibits non-specific methylome dysregulation while Early RA epigenetic changes occur in a more coherent manner. In chapter 4, with the additional knowledge of which ACPA+ “At-Risk” participants would develop clinical RA and those that would not (“Pre-RA” and “Non-converters”, respectively), we perform an additional cross-sectional comparison. We find DMLs that distinguish the Pre-RA methylome from ACPA+ Non-converters, which closely resembles ACPA- Controls. Longitudinal analysis shows that ACPA- Control and ACPA+ Non-converter methylomes are relatively constant. In contrast, the Pre-RA methylome remodels along a dynamic “RA methylome trajectory” characterized by epigenetic changes in active regulatory elements.