What can we learn from DNA methylation studies in lupus?

Obesity as an aggravating factor of systemic lupus erythematosus disease: What we already know and what we must explore. A rapid scoping review

Systemic lupus erythematosus (SLE) is a chronic autoimmune disease that can affect various organs and systems. Symptoms of SLE can vary widely from person to person and over time, including fatigue, joint pain, skin rashes, fever, and inflammation of multiple organs. The association between SLE and excess body weight has been the subject of study, with evidence suggesting that overweight and obesity can worsen the disease´s clinical presentation. Obesity is linked to a state of low-grade chronic inflammation, which can exacerbate the inflammation present in SLE. Additionally, obesity may negatively impact treatment response, disease progression, and patient prognosis. Patients with SLE and obesity may face additional challenges in managing the disease, such as increased symptom severity, higher risk of cardiovascular and renal complications, and a reduced response to conventional treatments. Obesity can also influence the quality of life of patients with SLE, making a holistic approach that considers the individual's nutritional status essential. Therefore, understanding the relationship between obesity and SLE is crucial for optimizing treatment, improving clinical outcomes, and enhancing patients' quality of life. Further research is needed to elucidate the underlying pathophysiological mechanisms, develop more precise and personalized management strategies, and identify biomarkers that can predict disease prognosis and treatment response.

Epigenetic trajectory predicts development of clinical rheumatoid arthritis in ACPA+ individuals: Targeting Immune Responses for Prevention of Rheumatoid Arthritis (TIP-RA)

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.

Disease activity drives divergent epigenetic and transcriptomic reprogramming of monocyte subpopulations in systemic lupus erythematosus

OBJECTIVES

Systemic lupus erythematosus (SLE) is characterised by systemic inflammation involving various immune cell types. Monocytes, pivotal in promoting and regulating inflammation in SLE, differentiate from classic monocytes into intermediate and non-classic monocytes, assuming diverse roles and changing their proportions in inflammation. In this study, we investigated the epigenetic and transcriptomic profiles of these and novel monocyte subsets in SLE in relation to activity and progression.

METHODS

We obtained the DNA methylomes and transcriptomes of classic, intermediate, non-classic monocytes in patients with SLE (at first and follow-up visits) and healthy donors. We integrated these data with single-cell transcriptomics of SLE and healthy donors and interrogated their relationships with activity and progression.

RESULTS

In addition to shared DNA methylation and transcriptomic alterations associated with a strong interferon signature, we identified monocyte subset-specific alterations, especially in DNA methylation, which reflect an impact of SLE on monocyte differentiation. SLE classic monocytes exhibited a proinflammatory profile and were primed for macrophage differentiation. SLE non-classic monocytes displayed a T cell differentiation-related phenotype, with Th17-regulating features. Changes in monocyte proportions, DNA methylation and expression occurred in relation to disease activity and involved the STAT pathway. Integration of bulk with single-cell RNA sequencing datasets revealed disease activity-dependent expansion of SLE-specific monocyte subsets, further supported the interferon signature for classic monocytes, and associated intermediate and non-classic populations with exacerbated complement activation.

CONCLUSIONS

Disease activity in SLE drives a subversion of the epigenome and transcriptome programme in monocyte differentiation, impacting the function of different subsets and allowing to generate predictive methods for activity and progression.

DNA Methylation Signatures of Response to Conventional Synthetic and Biologic Disease-Modifying Antirheumatic Drugs (DMARDs) in Rheumatoid Arthritis

Rheumatoid arthritis (RA) is a complex condition that displays heterogeneity in disease severity and response to standard treatments between patients. Failure rates for conventional, target synthetic, and biologic disease-modifying rheumatic drugs (DMARDs) are significant. Although there are models for predicting patient response, they have limited accuracy, require replication/validation, or for samples to be obtained through a synovial biopsy. Thus, currently, there are no prediction methods approved for routine clinical use. Previous research has shown that genetics and environmental factors alone cannot explain the differences in response between patients. Recent studies have demonstrated that deoxyribonucleic acid (DNA) methylation plays an important role in the pathogenesis and disease progression of RA. Importantly, specific DNA methylation profiles associated with response to conventional, target synthetic, and biologic DMARDs have been found in the blood of RA patients and could potentially function as predictive biomarkers. This review will summarize and evaluate the evidence for DNA methylation signatures in treatment response mainly in blood but also learn from the progress made in the diseased tissue in cancer in comparison to RA and autoimmune diseases. We will discuss the benefits and challenges of using DNA methylation signatures as predictive markers and the potential for future progress in this area.