Higher baseline global leukocyte DNA methylation is associated with MTX non-response in early RA patients

Towards Personalized Medicine in Rheumatoid Arthritis

Rheumatoid arthritis (RA) is a chronic, incurable, multisystem, inflammatory disease characterized by synovitis and extra-articular features. Although several advanced therapies targeting inflammatory mechanisms underlying the disease are available, no advanced therapy is universally effective. Therefore, a ceiling of treatment response is currently accepted where no advanced therapy is superior to another. The current challenge for medical research is the discovery and integration of predictive markers of drug response that can be used to personalize medicine so that the patient is started on "the right drug at the right time". This review article summarizes our current understanding of predicting response to anti-rheumatic drugs in RA, obstacles impeding the development of personalized medicine approaches and future research priorities to overcome these barriers.

Role of IFN-α in Rheumatoid Arthritis

PURPOSE OF REVIEW

Type 1 interferons (IFN-I) are of increasing interest across a wide range of autoimmune rheumatic diseases. Historically, research into their role in rheumatoid arthritis (RA) has been relatively neglected, but recent work continues to highlight a potential contribution to RA pathophysiology.

RECENT FINDINGS

We emphasise the importance of disease stage when examining IFN-I in RA and provide an overview on how IFN-I may have a direct role on a variety of relevant cellular functions. We explore how clinical trajectory may be influenced by increased IFN-I signalling, and also, the limitations of scores composed of interferon response genes. Relevant environmental triggers and inheritable RA genetic risk relating to IFN-I signalling are explored with emphasis on intriguing data potentially linking IFN-I exposure, epigenetic changes, and disease relevant processes. Whilst these data cumulatively illustrate a likely role for IFN-I in RA, they also highlight the knowledge gaps, particularly in populations at risk for RA, and suggest directions for future research to both better understand IFN-I biology and inform targeted therapeutic strategies.

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.

The prognostic value of whole-genome DNA methylation in response to Leflunomide in patients with Rheumatoid Arthritis

Objective

Although Leflunomide (LEF) is effective in treating rheumatoid arthritis (RA), there are still a considerable number of patients who respond poorly to LEF treatment. Till date, few LEF efficacy-predicting biomarkers have been identified. Herein, we explored and developed a DNA methylation-based predictive model for LEF-treated RA patient prognosis.

Methods

Two hundred forty-five RA patients were prospectively enrolled from four participating study centers. A whole-genome DNA methylation profiling was conducted to identify LEF-related response signatures via comparison of 40 samples using Illumina 850k methylation arrays. Furthermore, differentially methylated positions (DMPs) were validated in the 245 RA patients using a targeted bisulfite sequencing assay. Lastly, prognostic models were developed, which included clinical characteristics and DMPs scores, for the prediction of LEF treatment response using machine learning algorithms.

Results

We recognized a seven-DMP signature consisting of cg17330251, cg19814518, cg20124410, cg21109666, cg22572476, cg23403192, and cg24432675, which was effective in predicting RA patient's LEF response status. In the five machine learning algorithms, the support vector machine (SVM) algorithm provided the best predictive model, with the largest discriminative ability, accuracy, and stability. Lastly, the AUC of the complex model(the 7-DMP scores with the lymphocyte and the diagnostic age) was higher than the simple model (the seven-DMP signature, AUC:0.74 vs 0.73 in the test set).

Conclusion

In conclusion, we constructed a prognostic model integrating a 7-DMP scores with the clinical patient profile to predict responses to LEF treatment. Our model will be able to effectively guide clinicians in determining whether a patient is LEF treatment sensitive or not.

Age, Sex, Metabolic and Pharmacologic Factors May Predict Nonresponse Status to Rheumatoid Arthritis Therapies

BACKGROUND/AIM

A real challenge for patients with rheumatoid arthritis (RA) and rheumatologists is primary nonresponse status (PNRS) or secondary nonresponse status (SNRS) to various therapies. Despite their detrimental influence on patient life quality, PNRS and SNRS have no accurate definition and no early predictive criteria for their development exist. Patients with RA under 40 years of age are rare, hence PNRS and SNRS data for this age group are scarce. This study examined the PNRS and SNRS according to sex, age, BMI, therapy type, and duration.

PATIENTS AND METHODS

Retrospectively, 115 patients with RA having PNRS and/or SNRS were stratified by age (22-39, 40-59, and 60-81). The association between body mass index (BMI), proinflammatory cytokines inhibitors, JAK inhibitors, and TNF-alpha inhibitors, sex, age, and PNRS and SNRS was examined.

RESULTS

All three proinflammatory cytokine inhibitors (rituximab, tocilizumab, and abatacept) were associated with PNRS and SNRS in women with a high BMI aged 40-59 years. Abatacept-related PNRS and SNRS was significant in women with normal BMI aged 60-81 years. Adalimumab, infliximab, and golimumab affected SNRS differently in women with normal BMI aged 22-39 years and women with high BMI aged 60-81 years. Etanercept and infliximab were associated with SNRS status in men with high-BMI aged 40-59 years.

CONCLUSION

PNRS and SNRS development in patients with RA is significantly influenced by age, sex, and BMI, but most importantly is closely and differentially related to therapy type and duration.

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.

Identification of Cell-Specific Differential DNA Methylation Associated With Methotrexate Treatment Response in Rheumatoid Arthritis

OBJECTIVE

We undertook this study to estimate changes in cell-specific DNA methylation (DNAm) associated with methotrexate (MTX) response using whole blood samples collected from rheumatoid arthritis (RA) patients before and after initiation of MTX treatment.

METHODS

Patients included in this study were from the Rheumatoid Arthritis Medication Study (n = 66) and the University of California San Francisco Rheumatoid Arthritis study (n = 11). All patients met the American College of Rheumatology RA classification criteria. Blood samples were collected at baseline and following treatment. Disease Activity Scores in 28 joints using the C-reactive protein level were collected at baseline and after 3-6 months of treatment with MTX. Methylation profiles were generated using the Illumina Infinium HumanMethylation450 and MethylationEPIC v1.0 BeadChip arrays using DNA from whole blood. MTX response was defined using the EULAR response criteria (responders showed good/moderate response; nonresponders showed no response). Differentially methylated positions were identified using the Limma software package and Tensor Composition Analysis, which is a method for identifying cell-specific differential DNAm at the CpG level from tissue-level ("bulk") data. Differentially methylated regions were identified using Comb-p software.

RESULTS

We found evidence of differential global methylation between treatment response groups. Further, we found patterns of cell-specific differential global methylation associated with MTX response. After correction for multiple testing, 1 differentially methylated position was associated with differential DNAm between responders and nonresponders at baseline in CD4+ T cells, CD8+ T cells, and natural killer cells. Thirty-nine cell-specific differentially methylated regions associated with MTX treatment response were identified. There were no significant findings in analyses of whole blood samples.

CONCLUSION

We identified cell-specific changes in DNAm that were associated with MTX treatment response in RA patients. Future studies of DNAm and MTX treatment response should include measurements of DNAm from sorted cells.

Seropositivity-Dependent Association between LINE-1 Methylation and Response to Methotrexate Therapy in Early Rheumatoid Arthritis Patients

Background: Methotrexate (MTX) is considered the first choice among disease-modifying anti-rheumatic drugs (DMARDs) for rheumatoid arthritis (RA) treatment. However, response to it varies as approximately 40% of the patients do not respond and would lose the most effective period of treatment time. Therefore, having a predictive biomarker before starting MTX treatment is of utmost importance. Methylation of long interspersed nucleotide element-1 (LINE-1) is generally considered a surrogate marker for global genomic methylation, which has been reported to associate with disease activity after MTX therapy. Methods: We performed a prospective study on 273 naïve early RA (ERA) patients who were treated with MTX, followed up to 12 months, and classified according to their therapy response. The baseline LINE-1 methylation levels in peripheral blood mononuclear cells (PBMC) of cases were assessed by bisulfite pyrosequencing. Results: Baseline LINE-1 methylation level per se turned out not to predict the response to the therapy, nor did age, sex, body mass index, or smoking status. However, if cases were stratified according to positivity to rheumatoid factor (RF) and anti-citrullinated protein antibody (ACPA) or seronegativity, we observed an opposite association between baseline LINE-1 methylation levels and optimal response to MTX therapy among responders. The best response to MTX therapy was associated with hypermethylated LINE-1 among double-positive ERA cases (p-value: 0.002) and with hypomethylated LINE-1 in seronegative ERA patients (p-value: 0.01). Conclusion: The LINE-1 methylation level in PBMCs of naïve ERA cases associates with the degree of response to MTX therapy in an opposite way depending on the presence of RF and ACPA antibodies. Our results suggest LINE-1 methylation level as a new epigenetic biomarker for predicting the degree of response to MTX in both double-positive and seronegative ERA patients.

-Omic Approaches and Treatment Response in Rheumatoid Arthritis

Rheumatoid arthritis (RA) is an inflammatory disorder characterized by an aberrant activation of innate and adaptive immune cells. There are different drugs used for the management of RA, including disease-modifying antirheumatic drugs (DMARDs). However, a significant percentage of RA patients do not initially respond to DMARDs. This interindividual variation in drug response is caused by a combination of environmental, genetic and epigenetic factors. In this sense, recent -omic studies have evidenced different molecular signatures involved in this lack of response. The aim of this review is to provide an updated overview of the potential role of -omic approaches, specifically genomics, epigenomics, transcriptomics, and proteomics, to identify molecular biomarkers to predict the clinical efficacy of therapies currently used in this disorder. Despite the great effort carried out in recent years, to date, there are still no validated biomarkers of response to the drugs currently used in RA. -Omic studies have evidenced significant differences in the molecular profiles associated with treatment response for the different drugs used in RA as well as for different cell types. Therefore, global and cell type-specific -omic studies analyzing response to the complete therapeutical arsenal used in RA, including less studied therapies, such as sarilumab and JAK inhibitors, are greatly needed.

Global analysis of cytosine and adenine DNA modifications across the tree of life

Interpreting the function and metabolism of enzymatic DNA modifications requires both position-specific and global quantities. Sequencing-based techniques that deliver the former have become broadly accessible, but analytical methods for the global quantification of DNA modifications have thus far been applied mostly to individual problems. We established a mass spectrometric method for the sensitive and accurate quantification of multiple enzymatic DNA modifications. Then, we isolated DNA from 124 archean, bacterial, fungal, plant, and mammalian species, and several tissues and created a resource of global DNA modification quantities. Our dataset provides insights into the general nature of enzymatic DNA modifications, reveals unique biological cases, and provides complementary quantitative information to normalize and assess the accuracy of sequencing-based detection of DNA modifications. We report that only three of the studied DNA modifications, methylcytosine (5mdC), methyladenine (N6mdA) and hydroxymethylcytosine (5hmdC), were detected above a picomolar detection limit across species, and dominated in higher eukaryotes (5mdC), in bacteria (N6mdA), or the vertebrate central nervous systems (5hmdC). All three modifications were detected simultaneously in only one of the tested species, Raphanus sativus. In contrast, these modifications were either absent or detected only at trace quantities, across all yeasts and insect genomes studied. Further, we reveal interesting biological cases. For instance, in Allium cepa, Helianthus annuus, or Andropogon gerardi, more than 35% of cytosines were methylated. Additionally, next to the mammlian CNS, 5hmdC was also detected in plants like Lepidium sativum and was found on 8% of cytosines in the Garra barreimiae brain samples. Thus, identifying unexpected levels of DNA modifications in several wild species, our resource underscores the need to address biological diversity for studying DNA modifications.

What can we learn from DNA methylation studies in lupus?

During the past twenty years, a wide range of studies have established the existence of epigenetic alterations, particularly DNA methylation changes, in lupus. Epigenetic changes might have different contributions in children-onset versus adult-onset lupus. DNA methylation alterations have been identified and characterized in relation to disease activity and damage, different lupus subtypes and responses to drugs. However, to date there has been no practical application of these findings in the clinical milieu. In this article, we provide a review of key studies showing the relationship between DNA methylation and the many clinical aspects related to lupus. We also propose several options, in relation to the range of methodological developments and experimental design, that could optimize these findings and make them amenable for use in clinical practice.

Vitamin D Metabolic Pathway Genes Polymorphisms and Their Methylation Levels in Association With Rheumatoid Arthritis

Abnormal vitamin D metabolism is involved in the pathogenesis of rheumatoid arthritis (RA). In this study, we evaluated the association of single nucleotide polymorphisms (SNPs) and methylation levels in vitamin D metabolic pathway genes with RA susceptibility. Ten SNPs in vitamin D metabolic pathway genes (CYP2R1, CYP24A1, VDR, CYP27B1) were genotyped in 477 RA patients and 496 controls by improved multiple ligase detection reaction (iMLDR). The methylation levels of the promoter regions of these genes were detected in 122 RA patients and 123 controls using Illumina Hiseq platform. We found that the CYP2R1 rs1993116 GA genotype, CYP27B1 rs4646536 GA genotype, rs4646536 A allele frequencies were significantly increased in RA patients when compared to controls. The decreased risk of rs1993116, rs4646536 was found under the dominant mode in RA patients. However, no significant association was found between CYP2R1 rs7936142, rs12794714, CYP24A1 rs2762934, rs6068816, rs2296239, rs2296241, VDR rs11574129, rs3847987 polymorphism, and RA susceptibility. The VDR, CYP27B1 methylation levels in RA patients were significantly lower than those in controls, while CYP2R1, CYP24A1 methylation levels were not associated with RA. There were no statistical associations between CYP2R1, CYP24A1, VDR, CYP27B1 methylation levels and their respective genotype in RA patients. In addition, plasma 25OHD level in RA patients was significantly lower than that in healthy controls. In summary, our results showed that CYP2R1, CYP27B1 genetic variations were associated with the genetic background of RA, while altered VDR, CYP27B1 methylation levels were related to the risk of RA.

A review of liquid biopsy as a tool to assess epigenetic, cfDNA and miRNA variability as methotrexate response predictors in patients with rheumatoid arthritis

Rheumatoid arthritis (RA) is a common autoimmune inflammatory disease affecting 0.5-1% of adults worldwide. Achieving long term remission or low disease activity is possible through early diagnosis, rapid initiation of disease modifying anti-rheumatic drugs (DMARDs) and implementation of a treat to target approach. Initial DMARD therapy usually involves methotrexate (MTX), either alone or in combination with other agents, however 40% of RA patients do not respond adequately, putting them at risk of disease progression and unnecessary exposure to MTX related adverse effects. Early predictors of MTX response would therefore enable a more personalized treatment strategy, ensuring timely access to MTX for those likely to respond and importantly, early initiation of alternative treatment for those in which MTX is unlikely to be efficacious. Predicting response to treatment will most likely require consideration of the clinical characteristics of the patient and interrogation of a number of factors including genetic, epigenetic, cell free DNA (cfDNA) and microRNA (miRNA), all of which can be investigated through blood derived liquid biopsies. This review will summarize the existing literature examining the use of epigenetic factors, cfDNA and miRNA as response predictors among RA patients treated with MTX.

Cell-Free DNA in Rheumatoid Arthritis

Endogenous DNA derived from the nuclei or mitochondria is released into the bloodstream following cell damage or death. Extracellular DNA, called cell-free DNA (cfDNA), is associated with various pathological conditions. Recently, multiple aspects of cfDNA have been assessed, including cfDNA levels, integrity, methylation, and mutations. Rheumatoid arthritis (RA) is the most common form of autoimmune arthritis, and treatment of RA has highly varied outcomes. cfDNA in patients with RA is elevated in peripheral blood and synovial fluid and is associated with disease activity. Profiling of cfDNA in patients with RA may then be utilized in various aspects of clinical practice, such as the prediction of prognosis and treatment responses; monitoring disease state; and as a diagnostic marker. In this review, we discuss cfDNA in patients with RA, particularly the sources of cfDNA and the correlation of cfDNA with RA pathogenesis. We also highlight the potential of analyzing cfDNA profiles to guide individualized treatment approaches for RA.

Responses of patients with juvenile idiopathic arthritis to methotrexate: a genomic outlook

INTRODUCTION

Juvenile idiopathic arthritis (JIA) is a chronic disease characterized by persistent joint inflammation. JIA is the most common pediatric chronic rheumatic disease and no curative therapy is currently available. Methotrexate (MTX) is an important treatment for JIA even though a high inter-individual variability in response is observed in patients. Among the factors of this variability, genetics and epigenetics might play an important role.

AREAS COVERED

This review summarizes the results of pharmacogenetic and pharmacoepigenetic studies regarding MTX response in JIA. Studies considering epigenetic factors in JIA patients are still very limited, therefore this review includes also studies performed in adult patients with rheumatoid arthritis. Moreover, the relevance of biomarkers measured in blood or urine of JIA patients in relation to MTX treatment is discussed.

EXPERT OPINION

Nowadays, even though many pharmacogenomics studies have been published, a specific genetic marker predictor of MTX efficacy or adverse events has not yet been identified. Encouraging results are available and great expectations rely on the study of epigenetics. Future studies are needed in order to identify genetic and epigenetic biomarkers that can be implemented in the clinical practice.

Epigenome wide association study of response to methotrexate in early rheumatoid arthritis patients

Aim

To identify differentially methylated positions (DMPs) and regions (DMRs) that predict response to Methotrexate (MTX) in early rheumatoid arthritis (RA) patients.

Materials and methods

DNA from baseline peripheral blood mononuclear cells was extracted from 72 RA patients. DNA methylation, quantified using the Infinium MethylationEPIC, was assessed in relation to response to MTX (combination) therapy over the first 3 months.

Results

Baseline DMPs associated with response were identified; including hits previously described in RA. Additionally, 1309 DMR regions were observed. However, none of these findings were genome-wide significant. Likewise, no specific pathways were related to response, nor could we replicate associations with previously identified DMPs.

Conclusion

No baseline genome-wide significant differences were identified as biomarker for MTX (combination) therapy response; hence meta-analyses are required.

Complex Machine-Learning Algorithms and Multivariable Logistic Regression on Par in the Prediction of Insufficient Clinical Response to Methotrexate in Rheumatoid Arthritis

The goals of this study were to examine whether machine-learning algorithms outperform multivariable logistic regression in the prediction of insufficient response to methotrexate (MTX); secondly, to examine which features are essential for correct prediction; and finally, to investigate whether the best performing model specifically identifies insufficient responders to MTX (combination) therapy. The prediction of insufficient response (3-month Disease Activity Score 28-Erythrocyte-sedimentation rate (DAS28-ESR) > 3.2) was assessed using logistic regression, least absolute shrinkage and selection operator (LASSO), random forest, and extreme gradient boosting (XGBoost). The baseline features of 355 rheumatoid arthritis (RA) patients from the “treatment in the Rotterdam Early Arthritis CoHort” (tREACH) and the U-Act-Early trial were combined for analyses. The model performances were compared using area under the curve (AUC) of receiver operating characteristic (ROC) curves, 95% confidence intervals (95% CI), and sensitivity and specificity. Finally, the best performing model following feature selection was tested on 101 RA patients starting tocilizumab (TCZ)-monotherapy. Logistic regression (AUC = 0.77 95% CI: 0.68–0.86) performed as well as LASSO (AUC = 0.76, 95% CI: 0.67–0.85), random forest (AUC = 0.71, 95% CI: 0.61 = 0.81), and XGBoost (AUC = 0.70, 95% CI: 0.61–0.81), yet logistic regression reached the highest sensitivity (81%). The most important features were baseline DAS28 (components). For all algorithms, models with six features performed similarly to those with 16. When applied to the TCZ-monotherapy group, logistic regression’s sensitivity significantly dropped from 83% to 69% (p = 0.03). In the current dataset, logistic regression performed equally well compared to machine-learning algorithms in the prediction of insufficient response to MTX. Models could be reduced to six features, which are more conducive for clinical implementation. Interestingly, the prediction model was specific to MTX (combination) therapy response.

Prediction of Targets of Curculigoside A in Osteoporosis and Rheumatoid Arthritis Using Network Pharmacology and Experimental Verification

Purpose

Network pharmacology is considered to be the next-generation drug development model that uses bioinformatics to predict and identify multiple drug targets and interactions in diseases. Here, network pharmacology was used to investigate the mechanism by which Curculigoside A (CA) acts in rheumatoid arthritis (RA) and osteoporosis.

Methods

First, TCMSP and SwissADME were applied to predict the druggability of CA. Then, potential targets were identified from overlapping data in SwissTarget and TargetNet, and targets were analyzed using Genemania and DAVID6.8 to obtain information about the GO and KEGG pathways. Ultimately, the drug-target-pathway network was identified after using Cytoscape 3.0 for visualization. Besides, qPCR was used to validate the predicted five major genes targets (EGFR, MAP2K1, MMP2, FGFR1, and MCL1).

Results

The results of TCMSP and SwissADME demonstrated that CA exhibits good druggability; 26 potential protein targets were classified by SwissTarget and TargetNet. The results of Genemania and DAVID6.8 indicated that CA probably caused anti-osteoporosis and anti-RA effects by regulating some biological pathways, especially nitrogen metabolism, estrogen signaling pathway, Rap1 signaling pathway, and PI3K/Akt signaling pathway. Besides, the result of Cytoscape 3.0 showed that the 26 targets participate in osteoporosis and RA-related pathways, metabolism, and other physiological processes. In vitro induced inflammation cell model experiments, the qPCR results showed that CA pretreatment significantly decreased the expression of EGFR, MAP2K1, MMP2, FGFR1, and MCL1 genes.

Conclusion

These results suggested that network pharmacology may provide possible mechanism of how CA exerts therapeutic effects in osteoporosis and RA.

Clinical value of DNA methylation markers in autoimmune rheumatic diseases

Methylation of cytosine residues in DNA, the best studied epigenetic modification, is associated with gene transcription and nuclear organization, and ultimately the function of a cell. DNA methylation can be influenced by various factors, including changes in neighbouring genomic sites such as those induced by transcription factor binding. The DNA methylation profiles in relevant cell types are altered in most human diseases compared with the healthy state. Given the physical stability of DNA and methylated DNA compared with other epigenetic modifications, DNA methylation is an ideal marker for clinical purposes. However, few DNA methylation-based markers have made it into clinical practice, with the notable exception of some markers used in the field of oncology. Autoimmune rheumatic diseases are genetically complex entities that can vary widely in terms of prognosis, subtypes, progression and treatment responses. Increasing reports showing strong links between DNA methylation profiles and different clinical outcomes and other clinical aspects in autoimmune rheumatic diseases reinforce the usefulness of DNA methylation profiles as novel clinical markers. In this Review, we provide an updated discussion on DNA methylation alterations in autoimmune rheumatic diseases and the advantages and disadvantages of using these markers in clinical practice.

Epigenetic Regulation Mediated by Methylation in the Pathogenesis and Precision Medicine of Rheumatoid Arthritis

Rheumatoid arthritis (RA) is a complex disease triggered by the interaction between genetics and the environment, especially through the shared epitope (SE) and cell surface calreticulin (CSC) theory. However, the available evidence shows that genetic diversity and environmental exposure cannot explain all the clinical characteristics and heterogeneity of RA. In contrast, recent studies demonstrate that epigenetics play important roles in the pathogenesis of RA, especially DNA methylation and histone modification. DNA methylation and histone methylation are involved in innate and adaptive immune cell differentiation and migration, proliferation, apoptosis, and mesenchymal characteristics of fibroblast-like synoviocytes (FLS). Epigenetic-mediated regulation of immune-related genes and inflammation pathways explains the dynamic expression network of RA. In this review, we summarize the comprehensive evidence to show that methylation of DNA and histones is significantly involved in the pathogenesis of RA and could be applied as a promising biomarker in the disease progression and drug-response prediction. We also explain the advantages and challenges of the current epigenetics research in RA. In summary, epigenetic modules provide a possible interface through which genetic and environmental risk factors connect to contribute to the susceptibility and pathogenesis of RA. Additionally, epigenetic regulators provide promising drug targets to develop novel therapeutic drugs for RA. Finally, DNA methylation and histone modifications could be important features for providing a better RA subtype identification to accelerate personalized treatment and precision medicine.

Global DNA (hydroxy)methylation is stable over time under several storage conditions and temperatures

Background: Epigenetic markers are often quantified and related to disease in stored samples. While, effects of storage on stability of these markers have not been thoroughly examined. In this longitudinal study, we investigated the influence of storage time, material, temperature, and freeze-thaw cycles on stability of global DNA (hydroxy)methylation. Methods: EDTA blood was collected from 90 individuals. Blood (n = 30, group 1) and extracted DNA (n = 30, group 2) were stored at 4°C, −20°C and −80°C for 0, 1 (endpoint blood 4°C), 6, 12 or 18 months. Additionally, freeze-thaw cycles of blood and DNA samples (n = 30, group 3) were performed over three days. Global DNA methylation and hydroxymethylation (mean ± SD) were quantified using liquid chromatography–electrospray ionization–tandem mass spectrometry (LC-ESI-MS/MS) with between-run precision of 2.8% (methylation) and 6.3% (hydroxymethylation). Effects on stability were assessed using linear mixed models. Results: global DNA methylation was stable over 18 months in blood at −20°C and −80°C and DNA at 4°C and −80°C. However, at 18 months DNA methylation from DNA stored at −20°C relatively decreased −6.1% compared to baseline. Global DNA hydroxymethylation was more stable in DNA samples compared to blood, independent of temperature (p = 0.0131). Stability of global DNA methylation and hydroxymethylation was not affected up to three freeze – thaw cycles. Conclusion: Global DNA methylation and hydroxymethylation stored as blood and DNA can be used for epigenetic studies. The relevance of  small differences occuring during storage depend on the expected effect size and research question.