Novel Insights Into Rheumatoid Arthritis Through Characterization of Concordant Changes in DNA Methylation and Gene Expression in Synovial Biopsies of Patients With Differing Numbers of Swollen Joints

Current cutting-edge omics techniques on musculoskeletal tissues and diseases

Musculoskeletal disorders, including osteoarthritis, rheumatoid arthritis, osteoporosis, bone fracture, intervertebral disc degeneration, tendinopathy, and myopathy, are prevalent conditions that profoundly impact quality of life and place substantial economic burdens on healthcare systems. Traditional bulk transcriptomics, genomics, proteomics, and metabolomics have played a pivotal role in uncovering disease-associated alterations at the population level. However, these approaches are inherently limited in their ability to resolve cellular heterogeneity or to capture the spatial organization of cells within tissues, thus hindering a comprehensive understanding of the complex cellular and molecular mechanisms underlying these diseases. To address these limitations, advanced single-cell and spatial omics techniques have emerged in recent years, offering unparalleled resolution for investigating cellular diversity, tissue microenvironments, and biomolecular interactions within musculoskeletal tissues. These cutting-edge techniques enable the detailed mapping of the molecular landscapes in diseased tissues, providing transformative insights into pathophysiological processes at both the single-cell and spatial levels. This review presents a comprehensive overview of the latest omics technologies as applied to musculoskeletal research, with a particular focus on their potential to revolutionize our understanding of disease mechanisms. Additionally, we explore the power of multi-omics integration in identifying novel therapeutic targets and highlight key challenges that must be overcome to successfully translate these advancements into clinical applications.

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 naive 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 naive 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.

Building a modular and multi-cellular virtual twin of the synovial joint in Rheumatoid Arthritis

Rheumatoid arthritis is a complex disease marked by joint pain, stiffness, swelling, and chronic synovitis, arising from the dysregulated interaction between synoviocytes and immune cells. Its unclear etiology makes finding a cure challenging. The concept of digital twins, used in engineering, can be applied to healthcare to improve diagnosis and treatment for complex diseases like rheumatoid arthritis. In this work, we pave the path towards a digital twin of the arthritic joint by building a large, modular biochemical reaction map of intra- and intercellular interactions. This network, featuring over 1000 biomolecules, is then converted to one of the largest executable Boolean models for biological systems to date. Validated through existing knowledge and gene expression data, our model is used to explore current treatments and identify new therapeutic targets for rheumatoid arthritis.

Novel DNA methylome biomarkers associated with adalimumab response in rheumatoid arthritis patients

Background and aims

Rheumatoid arthritis (RA) patients are currently treated with biological agents mostly aimed at cytokine blockade, such as tumor necrosis factor-alpha (TNFα). Currently, there are no biomarkers to predict therapy response to these agents. Here, we aimed to predict response to adalimumab (ADA) treatment in RA patients using DNA methylation in peripheral blood (PBL).

Methods

DNA methylation profiling on whole peripheral blood from 92 RA patients before the start of ADA treatment was determined using Illumina HumanMethylationEPIC BeadChip array. After 6 months, treatment response was assessed according to the European Alliance of Associations for Rheumatology (EULAR) criteria for disease activity. Patients were classified as responders (Disease Activity Score in 28 Joints (DAS28) < 3.2 or decrease of 1.2 points) or as non-responders (DAS28 > 5.1 or decrease of less than 0.6 points). Machine learning models were built through stability-selected gradient boosting to predict response prior to ADA treatment with predictor DNA methylation markers.

Results

Of the 94 RA patients, we classified 49 and 43 patients as responders and non-responders, respectively. We were capable of differentiating responders from non-responders with a high performance (area under the curve (AUC) 0.76) using a panel of 27 CpGs. These classifier CpGs are annotated to genes involved in immunological and pathophysiological pathways related to RA such as T-cell signaling, B-cell pathology, and angiogenesis.

Conclusion

Our findings indicate that the DNA methylome of PBL provides discriminative capabilities in discerning responders and non-responders to ADA treatment and may therefore serve as a tool for therapy prediction.

Identification of Plasma Biomarkers from Rheumatoid Arthritis Patients Using an Optimized Sequential Window Acquisition of All THeoretical Mass Spectra (SWATH) Proteomics Workflow

Rheumatoid arthritis (RA) is a systemic autoimmune and inflammatory disease. Plasma biomarkers are critical for understanding disease mechanisms, treatment effects, and diagnosis. Mass spectrometry-based proteomics is a powerful tool for unbiased biomarker discovery. However, plasma proteomics is significantly hampered by signal interference from high-abundance proteins, low overall protein coverage, and high levels of missing data from data-dependent acquisition (DDA). To achieve quantitative proteomics analysis for plasma samples with a balance of throughput, performance, and cost, we developed a workflow incorporating plate-based high abundance protein depletion and sample preparation, comprehensive peptide spectral library building, and data-independent acquisition (DIA) SWATH mass spectrometry-based methodology. In this study, we analyzed plasma samples from both RA patients and healthy donors. The results showed that the new workflow performance exceeded that of the current state-of-the-art depletion-based plasma proteomic platforms in terms of both data quality and proteome coverage. Proteins from biological processes related to the activation of systemic inflammation, suppression of platelet function, and loss of muscle mass were enriched and differentially expressed in RA. Some plasma proteins, particularly acute-phase reactant proteins, showed great power to distinguish between RA patients and healthy donors. Moreover, protein isoforms in the plasma were also analyzed, providing even deeper proteome coverage. This workflow can serve as a basis for further application in discovering plasma biomarkers of other diseases.

Longitudinal analysis of blood DNA methylation identifies mechanisms of response to tumor necrosis factor inhibitor therapy in rheumatoid arthritis

Background

Rheumatoid arthritis (RA) is a chronic, immune-mediated inflammatory disease of the joints that has been associated with variation in the peripheral blood methylome. In this study, we aim to identify epigenetic variation that is associated with the response to tumor necrosis factor inhibitor (TNFi) therapy.

Methods

Peripheral blood genome-wide DNA methylation profiles were analyzed in a discovery cohort of 62 RA patients at baseline and at week 12 of TNFi therapy. DNA methylation of individual CpG sites and enrichment of biological pathways were evaluated for their association with drug response. Using a novel cell deconvolution approach, altered DNA methylation associated with TNFi response was also tested in the six main immune cell types in blood. Validation of the results was performed in an independent longitudinal cohort of 60 RA patients.

Findings

Treatment with TNFi was associated with significant longitudinal peripheral blood methylation changes in biological pathways related to RA (FDR<0.05). 139 biological functions were modified by therapy, with methylation levels changing systematically towards a signature similar to that of healthy controls. Differences in the methylation profile of T cell activation and differentiation, GTPase-mediated signaling, and actin filament organization pathways were associated with the clinical response to therapy. Cell type deconvolution analysis identified CpG sites in CD4+T, NK, neutrophils and monocytes that were significantly associated with the response to TNFi.

Interpretation

Our results show that treatment with TNFi restores homeostatic blood methylation in RA. The clinical response to TNFi is associated to methylation variation in specific biological pathways, and it involves cells from both the innate and adaptive immune systems.

Funding

The Instituto de Salud Carlos III.

New Druggable Targets for Rheumatoid Arthritis Based on Insights From Synovial Biology

Rheumatoid arthritis (RA) is a multifactorial autoimmune disease characterized by chronic inflammation and destruction of multiple small joints which may lead to systemic complications. Altered immunity via pathogenic autoantibodies pre-date clinical symptom development by several years. Incompletely understood range of mechanisms trigger joint-homing, leading to clinically evident articular disease. Advances in therapeutic approaches and understanding pathogenesis have improved prognosis and likely remission. However, partial/non-response to conventional and biologic therapies witnessed in a subset of patients highlights the need for new therapeutics. It is now evident that joint disease chronicity stems from recalcitrant inflammatory synovial environment, majorly maintained by epigenetically and metabolically reprogrammed synoviocytes. Therefore, interference with effector functions of activated cell types seems a rational strategy to reinstate synovial homeostasis and complement existing anti-inflammatory interventions to mitigate chronic RA. Presenting this newer aspect of fibroblast-like synoviocytes and myeloid cells underlying the altered synovial biology in RA and its potential for identification of new druggable targets is attempted in this review. Major leads from i) molecular insights of pathogenic cell types from hypothesis free OMICS approaches; ii) hierarchy of their dysregulated signaling pathways; and iii) knowledge of druggability of molecular nodes in these pathways are highlighted. Development of such synovial biology-directed therapeutics hold promise for an enriched drug repertoire for RA.

A broad look into the future of rheumatoid arthritis

Despite all improvements in rheumatoid arthritis, we are still not able to prevent or cure the disease. Diagnostic delays due to lack of access to a specialist and costly therapies are still a major obstacle for many patients. Even in first-world countries, the treat-to-target principle and the goal of disease remission are often missed. Thus, rheumatoid arthritis (RA) is still the reason for disability and reduced quality of life for many patients. So, is it time to move the goalpost even further? Where are we heading next? And will we finally be able to cure the disease? These questions are addressed in our review article.

A Final Frontier in Environment-Genome Interactions? Integrated, Multi-Omic Approaches to Predictions of Non-Communicable Disease Risk

Epidemiological and associative research from humans and animals identifies correlations between the environment and health impacts. The environment-health inter-relationship is effected through an individual's underlying genetic variation and mediated by mechanisms that include the changes to gene regulation that are associated with the diversity of phenotypes we exhibit. However, the causal relationships have yet to be established, in part because the associations are reduced to individual interactions and the combinatorial effects are rarely studied. This problem is exacerbated by the fact that our genomes are highly dynamic; they integrate information across multiple levels (from linear sequence, to structural organisation, to temporal variation) each of which is open to and responds to environmental influence. To unravel the complexities of the genomic basis of human disease, and in particular non-communicable diseases that are also influenced by the environment (e.g., obesity, type II diabetes, cancer, multiple sclerosis, some neurodegenerative diseases, inflammatory bowel disease, rheumatoid arthritis) it is imperative that we fully integrate multiple layers of genomic data. Here we review current progress in integrated genomic data analysis, and discuss cases where data integration would lead to significant advances in our ability to predict how the environment may impact on our health. We also outline limitations which should form the basis of future research questions. In so doing, this review will lay the foundations for future research into the impact of the environment on our health.