Objective: The presence of autoantibodies to citrullinated protein antigens (ACPAs) in the absence of clinically-apparent inflammatory arthritis (IA) identifies individuals "at-risk" for developing future clinical rheumatoid arthritis (RA). However, it is unclear why some ACPA+ individuals convert to clinical RA while others do not. We explored the possibility in the Targeting Immune Responses for Prevention of Rheumatoid Arthritis (TIP-RA) study that epigenetic remodeling is part of the trajectory from an at-risk state to clinical disease and identifies novel biomarkers associated with conversion to clinical RA.
Methods: ACPA-Controls, ACPA+ At-Risk, and Early RA individuals were followed for up to 5 years, including obtaining blood samples 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. ACPA+ At-Risk participants who did or did not develop RA were designated "Pre-RA" or "Non-converters", respectively.Differentially methylated loci (DML) were selected using the Limma software package. Using the Caret package, we constructed machine learning models in test and validation cohorts and identified the most predictive loci of clinical RA conversion.
Results: Cross-sectional differential methylation analysis at baseline revealed DMLs that distinguish the Pre-RA methylome from ACPA+ Non-converters, the latter which closely resembled ACPA-Controls. Genes overlapping these DMLs correspond to aberrant NOTCH signaling and DNA repair pathways in B cells. Longitudinal analysis showed that ACPA-Control and ACPA+ Non-converter methylomes are relatively constant. In contrast, the Pre-RA methylome remodeled along a dynamic "RA methylome trajectory" characterized by epigenetic changes in active regulatory elements. Clinical conversion to RA, defined based on diagnosis, marked an epigenetic inflection point for cell cycle pathways in B cells and adaptive immunity pathways in naïve T cells. Machine learning revealed individual loci associated with RA conversion. This model significantly outperformed autoantibodies plus acute phase reactants as predictors of RA conversion.
Conclusion: DNA methylation is a dynamic process in ACPA+ individuals at-risk for developing RA that eventually transition to clinical disease. In contrast, non-converters and controls have stable methylomes. The accumulation of epigenetic marks over time prior to conversion to clinical RA conforms to pathways that are associated with immunity and can be used to identify potential pathogenic pathways for therapeutic targeting and/or use as prognostic biomarkers.