Abnormal DNA methylation pattern and expression of DNA methyltransferase 1 promote synovitis and bone destruction in rheumatoid arthritis

Abnormal DNA methylation pattern and expression of DNA methyltransferase 1 promote synovitis and bone destruction in rheumatoid arthritis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Abnormal DNA methylation pattern and expression of DNA methyltransferase 1 promote synovitis and bone destruction in rheumatoid arthritis Yazhen Su, Zewen Wu, Ruijiao Li, Hao Xing, Yang Liu, Rong Li, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8601364/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Rheumatoid arthritis (RA) is a chronic autoimmune disease characterized by synovial hyperplasia and joint destruction, in which fibroblast-like synoviocytes (FLS) play a pivotal role. However, the epigenetic mechanisms underlying FLS activation remain incompletely understood. Methods DNA methylation chip sequencing and transcriptome sequencing were performed on FLS of RA and Osteoarthritis (OA) patients, then analyzed the role of DNA methylation in RA-FLS. DNA methyltransferase 1 (DNMT1) is the major enzyme regulating DNA methylation. In in vitro and in vivo experiments, the effects of inhibiting DNMT1 on FLS function and CIA mouse synovitis were detected. Results Compared with OA-FLS, RA-FLS exhibited 353 upregulated and 144 downregulated genes, mainly enriched in extracellular matrix organization, angiogenesis, and inflammatory signaling pathways such as PI3K-AKT, MAPK, and JAK-STAT. DNA methylation analysis identified 2550 hypermethylated and 3300 hypomethylated genes, which were enriched in pathways associated with cell proliferation, migration, and apoptosis. Integration of transcriptomic and methylation data revealed 96 methylation-regulated differentially expressed genes (MeDEGs), involving Rap1, mTOR, and Hippo signaling. Among key regulators, DNA methyltransferase 1 (DNMT1) was markedly up-regulated in RA-FLS. Functional validation showed that DNMT1 knock-down significantly suppressed RA-FLS proliferation, migration, invasion, induced G2/M cell-cycle arrest and reduced pro-inflammatory cytokine secretion. In collagen-induced arthritis (CIA) mouse model, pharmacological inhibition of DNMT1 alleviated joint inflammation and bone erosion without significant systemic toxicity. Conclusions Our findings demonstrate that aberrant DNA methylation contributes to RA-FLS activation, with DNMT1 serving as a key epigenetic driver of their “tumor-like” phenotype. Targeting DNMT1 may represent a promising therapeutic strategy for RA. rheumatoid arthritis fibroblast-like synoviocytes DNA methylation DNMT1 epigenetic regulation Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1. Introduction Rheumatoid arthritis (RA) is a chronic systemic autoimmune disease characterized by persistent synovial inflammation, pannus formation, and progressive joint destruction(Di Matteo et al. 2023 ; Alivernini et al. 2022 ). Synovial hyperplasia is a pathological hallmark of RA, in which fibroblast-like synoviocytes (FLS) play a pivotal role(Chu 2020 ; Nygaard and Firestein 2020 ). RA-FLS acquire an aggressive phenotype, exhibiting excessive proliferation, invasion, and resistance to apoptosis, while producing pro-inflammatory cytokines, chemokines, and matrix-degrading enzymes that perpetuate synovial inflammation and bone erosion(Masoumi et al. 2021 ; Wu et al. 2021 ). Although biological therapies targeting immune cells and cytokines have substantially improved disease management, many patients still experience incomplete responses or relapse(Nagy et al. 2021 ). This suggests that intrinsic alterations within synovial cells may drive persistent inflammation, highlighting the need to explore new therapeutic mechanisms. Emerging evidence indicates that epigenetic modifications, including DNA methylation, histone modification, and non-coding RNA regulation, are involved in the sustained activation of RA-FLS(Huang et al. 2025 ; Karami et al. 2020 ). Among these, DNA methylation, catalyzed by DNA methyltransferases (DNMTs), plays a central role in gene silencing and genomic stability(Kielbowski et al. 2025 ). Most studies of DNA methylation in RA have focused on peripheral immune cells, which are easily influenced by external factors such as infection or medication(Deng et al. 2024 ; Cai and Yao 2025 ). In contrast, methylation profiling of synovial tissue, particularly FLS, better reflects the local inflammatory and immune microenvironment. Abnormal DNA methylation patterns have been observed in RA synovium, suggesting a role in disease progression(Zhang et al. 2022 ). However, the specific methylation alterations in RA-FLS and their functional consequences remain poorly understood. DNMT1, the maintenance DNA methyltransferase, preserves methylation patterns during DNA replication(Song et al. 2012 ; Chen and Zhang 2020 ). The studies suggest that DNMT1 dysregulation contributes to aberrant immune activation and tissue inflammation in autoimmune diseases(Ballestar et al. 2020 ). Nevertheless, research on DNMT1 in RA is limited, and its expression status in RA synovium remains controversial. Elucidating how DNMT1-mediated methylation remodeling contributes to the pathogenic phenotype of RA-FLS may provide new insights into RA pathogenesis and therapeutic intervention. In this study, we performed integrated transcriptomic and DNA methylation analyses of FLS derived from patients with RA and osteoarthritis (OA). Comprehensive bioinformatic analysis identified key methylation-regulated genes and pathways associated with FLS activation. Furthermore, we examined the functional and therapeutic effects of DNMT1 inhibition in vitro and in a collagen-induced arthritis (CIA) mouse model. Our findings demonstrate that aberrant DNA methylation is a critical epigenetic driver of RA-FLS pathogenicity and highlight DNMT1 as a promising therapeutic target in RA. 2. Materials and methods 2.1 Clinical sample collection The synovial tissues were obtained from patients with RA (n = 3) who underwent arthroscopic surgery or knee replacement surgery. RA patients met the ACR/EULAR 2010 RA diagnostic criteria. For comparison, synovial tissues were also collected from three age- and gender-matched patients with OA (n = 3), diagnosed according to the 1995 revised ACR criteria. Patients with concomitant tumours, acute or chronic infections, or other autoimmune diseases were excluded. The clinical sample collection was approved by the Ethics Committee of Shanxi Bethune Hospital (SBQKL-2021-041). Collected tissues were either processed immediately for FLS isolation or snap-frozen and stored in liquid nitrogen. 2.2 Isolation and culture of FLS Synovial tissues were minced into small fragments and digested with 0.25% trypsin (Solarbio, T1321) for 1 hour at 37°C to isolate FLS. The cells were maintained in Dulbecco’s modified Eagle’s medium (DMEM; Gibco, C11995500BT) supplemented with 10% fetal bovine serum (FBS; Gibco, 10099141C) and 1% penicillin/streptomycin (P/S; Solarbio, P1400), under a humidified atmosphere containing 5% CO₂ at 37°C. Upon reaching 90% confluence, cells were harvested by trypsinisation and passaged. FLS between passages 3 and 6 were utilised for all experiments to ensure phenotypic stability. 2.3 Cell identification The morphological features of RA-FLS were examined using an optical microscope. Cell purity was assessed via flow cytometry by staining surface markers. RA-FLS was stained with APC anti-human CD90 (Biolegend, 328113), along with anti-CD14, anti-CD34, and anti-CD45 antibodies (OriCell, HUXMX-09011). Analysis was performed on a BD flow cytometer, and data processing was conducted with FlowJo software. 2.4 Immunofluorescence RA-FLS were fixed in 4% paraformaldehyde for 20 minutes at room temperature, permeabilised with 0.1% Triton X-100 for 30 minutes, and blocked with 5% bovine serum albumin (BSA) for 1 hour. Following incubation with primary antibodies at 4°C overnight, cells were treated with fluorochrome-conjugated secondary antibodies (Boster, A1127) for 1 hour at room temperature. Nuclei were counterstained with DAPI (Boster, AR1176), and imaging was performed under a fluorescence microscope. 2.5 Transcriptome sequencing of FLS Total RNA was extracted using a reagent kit and quantified. The mRNA underwent purification and fragmentation, followed by synthesis and purification of double-stranded cDNA. Subsequent steps included end repair, dA-tailing, adapter ligation, size selection, and PCR amplification to construct the library. Library quality was verified by gel electrophoresis, and accurate quantification was achieved with the Qubit DNA detection kit prior to sequencing. Raw data quality was assessed and controlled before downstream analysis. Sequencing services were provided by Shanghai Sangon. 2.6 DNA methylation sequencing of FLS Methylation profiling was performed on passage 3 (P3) RA-FLS (n = 3) and OA-FLS (n = 3). Genomic DNA was isolated using an extraction kit, quantified with a Nanodrop2000, and quality-checked on a 1% agarose gel. Qualified DNA samples underwent bisulfite conversion with the EZ DNA Methylation kit and were then subjected to methylation array analysis using the methylation EPIC chip. All methylation sequencing was carried out by Shanghai Sangon. 2.7 Cell transfection GenePharma (Shanghai, China) supplied the small interfering RNAs (siRNAs) targeting DNMT1, along with the corresponding negative control siRNA (siRNA-NC). The sequences are listed in Table 1 . FLS were transfected with siRNAs using INTERFERin® (Polyplus, 101000028). Table 1 Sequences of small interfering RNAs (siRNAs) Gene Base sequence (5 '-3') DNMT1-130 S: GCGGCUCAAAGAUUUGGAATT AS: UUCCAAAUCUUUGAGCCGCTT DNMT1-810 S: CACCCAAACAGAAACUGAATT AS: UUCAGUUUCUGUUUGGGUGTT DNMT1-1231 S: GUCUGGCUUUGAGAGUUAUTT AS: AUAACUCUCAAAGCCAGACTT DNMT1-2961 S: CAGAGCACUACCGGAAAUATT AS: UAUUUCCGGUAGUGCUCUGTT NC S: UUCUCCGAACGUGUCACGUTT AS: ACGUGACACGUUCGGAGAATT 2.8 Quantitative real-time polymerase chain reaction (qRT–PCR) Total RNA was extracted with RNAiso Plus (Takara, 9109). cDNA was synthesised using the PrimeScript™ RT reagent Kit with gDNA Eraser (Takara, RR047A). qPCR was performed using TB Green® Premix Ex Taq™ II (Takara, RR820A). GAPDH served as the internal control, and relative mRNA expression was calculated using the 2⁻ΔΔCT method. 2.9 Western blot Proteins were extracted with RIPA buffer supplemented with protease inhibitors. Protein concentration was determined using a BCA assay kit (Beyotime, P0009). Equal amounts of protein (20–30µg) were separated by 10% SDS-PAGE and transferred to PVDF membranes. After blocking with 5% non-fat milk for 1 hour, membranes were incubated overnight at 4°C with primary antibodies against DNMT1 (Boster, 1:1000) and β-actin (Abcam, ab8226, 1:1000). Following incubation with an HRP-conjugated secondary antibody (1:5000) for 1 hour at room temperature, protein bands were visualized using an enhanced chemiluminescence (ECL) detection system and analyzed with ImageJ software. 2.10 Cell counting kit-8 (CCK-8) assay Cells from different groups (2×10⁴ per well) were plated in 96-well plates and cultured. Then, 10 µL of CCK-8 reagent (Boster, AR1199) was added to each well and incubated for 1–2 hours. Absorbance was measured at 450 nm using a microplate reader. 2.11 Cell apoptosis assay After transfection, cells were collected, washed with PBS, and resuspended in 500 µL of 1X Annexin-binding buffer. They were stained with Annexin V-APC and DAPI (Elabscience, E-CK-A258) for 15 minutes in the dark, and analysed by flow cytometry. 2.12 Cell migration Cell migration was assessed using a scratch wound healing assay. Briefly, FLS were seeded in 6-well plates (2×10⁵ cells/well) and grown to confluence. A uniform scratch was created using a sterile 200 µL pipette tip. After washing with PBS to remove detached cells, fresh medium containing 1% FBS was added. Images of the scratch were taken at 0, 12, 24, and 48 hours using an inverted microscope. The migration rate was quantified by measuring the reduction in scratch area over time using ImageJ software. 2.13 Cell invasion Invasion was examined using Matrigel-coated transwell chambers. Matrigel (Corning, 356234) was diluted 1:8 and applied to the upper chamber. After polymerisation, the cell suspension with 1% FBS was added to the upper compartment, and medium with 20% FBS was placed below. Following 24 hours of incubation, invasive cells were fixed, stained with crystal violet, and quantified. 2.14 Cell cycle assay Cells were harvested, washed with PBS, and fixed in 75% ethanol at 4°C overnight. After PBS washing, cells were resuspended in 500 µL PI/RNase A staining solution (Meilunbio, MA0334) and incubated for 30 minutes protected from light. Cell cycle distribution was analysed by flow cytometry. 2.15 ELISA Cytokine levels in supernatants were measured using ELISA kits according to the manufacturer's protocols. The following kits were used: Human IL-6 (MEIMIAN, MM-0049H1), IL-17A (MEIMIAN, MM-2117H1), RANKL (MEIMIAN, MM-1513H1), and OPG (MEIMIAN, MM-0849H1). 2.16 Histopathologic evaluation and Immunohistochemical Staining Tissues were fixed in 4% PFA for 24 hours and embedded in paraffin. Sections were stained with H&E (Servicebio, G1005) or Safranin O-Fast Green (Solarbio, G1371). For IHC, sections were incubated with anti-DNMT1 antibody overnight, followed by secondary antibody treatment, and imaged under a microscope. 2.17 CIA model DBA1/J mice (male, 6–8 weeks old, n = 40) were obtained from GemPharmatech (China) for the CIA experiments. On day 0, mice were immunised intradermally at the base of the tail with 100 µg of bovine type II collagen (CII) (Chondrex, USA) emulsified in complete Freund's adjuvant (Chondrex, USA). A booster immunisation with CII in incomplete Freund's adjuvant (ICF) was administered on day 21. Body weight, arthritis scores, and paw thickness were recorded every three days. From day 21 onward, CIA mice received intraperitoneal injections of methotrexate (MTX, 1 mg/kg) twice weekly. The 5-azacytidine (5-Azac) intervention group was intraperitoneally injected with 1.0mg/kg once every other day for 21 days. All mice were euthanised on day 70. Ankle joints were harvested for micro-CT analysis and histopathological examination. 2.18 Statistical analysis Data are presented as mean ± SD. Statistical comparisons between two groups were performed using a two-tailed Student's t-test, while multiple group comparisons were analysed by one-way analysis of variance (ANOVA) followed by Dunnett's post-hoc test. A P-value < 0.05 was considered statistically significant. For sequencing data, raw data from DNA methylation arrays were preprocessed and normalised to beta values. Linear models were constructed using R packages such as limma and ChAMP to calculate P-values for differentially methylated sites. Sites with an adj. p-Value 0.1 were defined as differentially methylated CpG sites. For transcriptomic data, the filtering thresholds were set at q-value 2. Volcano plots and heatmaps were generated using R packages. Overlapping genes were subjected to GO analysis and KEGG pathway enrichment analysis. For Gene Set Enrichment Analysis (GSEA), significance thresholds were set at |NES| > 1, nominal P value < 0.05, and false discovery rate (FDR) < 0.25.Data are presented as mean ± SD. Statistical comparisons between two groups were performed using a two-tailed Student's t-test, while multiple group comparisons were analyzed by one-way analysis of variance (ANOVA) followed by Dunnett's post-hoc test. A P -value < 0.05 was considered statistically significant. 3. Results 3.1 Isolation and identification of RA-FLS Under arthroscopy, RA synovial tissue exhibited hyperplastic, cluster-like, and nodular growth (Fig. 1A). The isolated RA-FLS displayed a characteristic spindle-shaped, fibroblastic morphology and grew in a whirling pattern under microscopy (Fig. 1B). Immunofluorescence staining confirmed the positive expression of CD44 (Fig. 1C). Flow cytometric analysis further verified the typical FLS phenotype, the cells expressed CD90, but did not express CD14, CD34, and CD45, which met the criteria for identification of RA-FLS (Fig. 1D). 3.2 RA-FLS transcriptome sequencing We performed transcriptome sequencing on FLS isolated from three RA patients and three OA patients (Fig. 2A) . As shown in the volcano plot (Fig. 2B) and heat map (Fig. 2C), compared with OA-FLS, RA-FLS had 353 upregulated genes and 144 downregulated genes. Gene Ontology (GO) enrichment analysis indicated that differentially expressed genes (DEGs) were primarily involved in extracellular tissue structure, extracellular matrix, wound healing, cell adhesion, and angiogenesis regulation (Fig. 2D). Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis further showed significant enrichment in the the phosphatidylinositol-3-kinase-protein kinase B (PI3K-AKT) pathway, cytokine-cytokine receptor interaction, mitogen-activated protein kinase (MAPK) signaling pathway, extracellular matrix-receptor interaction, janus kinase-signal transducer and activator of transcription (JAK-STAT) signaling pathway, as well as in Th17 cell differentiation and the rheumatoid arthritis pathway (Fig. 2E). 3.3 RA-FLS DNA methylation sequencing Parallel DNA methylation sequencing was performed on the same set of FLS samples. Compared to OA-FLS, 45.64% of the differentially methylated positions (DMPs) were hypermethylated and 53.46% were hypomethylated in RA-FLS. These DMPs were distributed across gene regions: 52.17% in intergenic regions, 32.35% in promoter regions, and 3.7% in the 3'UTR (Fig. 3A, 3B). At the gene level, we identified 2550 hypermethylated and 3300 hypomethylated genes in RA-FLS (Fig. 3C, 3D). GO term analysis for these differentially methylated genes (DMGs) revealed associations with biological processes (BP) including cell growth, autophagy, and fibroblast migration; cellular components (CC) such as the actin cytoskeleton and extracellular matrix; and molecular functions (MF) like GTPase regulator activity and protein serine/threonine kinase activity (Fig. 3E). KEGG pathway enrichment analysis highlighted significant involvement in the MAPK, PI3K-AKT, Hippo, and HIF-1 signaling pathways, as well as apoptosis (Fig. 3F). 3.4 Combined analysis of DNA methylation sequencing and transcriptome sequencing A total of 20,241 genes were detected in the transcriptome sequencing data, 34,280 methylated genes and 888,966 methylated probes were detected in Illumina 850K DNA methylation array sequencing data. The intersection of the two sets of sequencing data yielded a total of 18,636 methylation-regulated expressed genes (Fig. 4A). Gene Set Enrichment Analysis (GSEA) of these genes showed significant enrichment for terms including actin filament organization, extracellular matrix collagen constituents, actin cytoskeleton, monoatomic anion transmembrane transporter activity, and type II interferon production (Fig. 4B-4H). Protein-protein interaction (PPI) network analysis, constructed using the STRING database and Cytoscape, identified four key modules: Module 1 was enriched for Rho-GTPase family members; Module 2 for cytokines and their receptors mediating intercellular communication; Module 3 for growth factor family genes; and Module 4 for transcriptional regulators (Fig. 4I). Further intersection of differentially methylated and expressed genes yielded 96 methylation-regulated differentially expressed genes (MeDEGs) (Fig. 5A). Among these, 47 genes exhibited high expression with low methylation, while 6 genes showed low expression with high methylation (Fig. 5B, 5C). Functional enrichment analysis revealed that MeDEGs were involved in extracellular matrix composition, fibroblast growth and proliferation, the extrinsic apoptotic signaling pathway, and cellular responses to growth factor and VEGF stimulation (Fig. 5D). These genes were also significantly enriched in the Rap1, MAPK, mTOR, Hippo, cytokine-cytokine receptor interaction, and PI3K-AKT signaling pathways (Fig. 5E). 3.5 Inhibition of DNMT1 can inhibit the "tumoroid-like" growth of RA-FLS Immunohistochemistry revealed significantly higher DNMT1 expression in RA synovial tissues compared to OA controls (Fig. 6A). This elevated expression was confirmed in RA-FLS at both the protein level by immunofluorescence (Fig. 6B) and the mRNA level by qRT-PCR (Fig. 6C). To investigate its function, we knocked down DNMT1 using siRNA. Transfection with four different siRNAs targeting DNMT1 effectively reduced DNMT1 mRNA levels, with siRNA-810 demonstrating the most potent knockdown efficiency and thus selected for subsequent experiments (designated as siRNA-DNMT1) (Fig. 6D). The knockdown of DNMT1 protein was verified by both western blotting and immunofluorescence (Fig. 6E, 6F). Functional assays demonstrated that DNMT1 inhibition significantly suppressed the proliferation (Fig. 6G), migration (Fig. 6J, 6K), and invasion (Fig. 6L, 6M) of RA-FLS, without significantly affecting apoptosis (Fig. 6H, 6I). Cell cycle analysis revealed a notable decrease in the proportion of cells in S phase and an accumulation in G2/M phase upon DNMT1 knockdown (Fig. 6N, 6O). Furthermore, siRNA-DNMT1 altered cytokine secretion, inhibiting IL-6 and RANKL, promoting OPG, and leaving IL-17A unchanged (Fig. 6P). 3.6 Inhibition of DNMT1 ameliorates synovial inflammation and bone destruction in CIA mice In the collagen-induced arthritis (CIA) mouse model, therapeutic intervention with methotrexate (MTX) or the DNA methyltransferase inhibitor 5-azacytidine (5-AzaC) markedly alleviated joint inflammation, cartilage and bone destruction, and synovial DNMT1 expression compared to the untreated CIA group (Fig. 7B). Consequently, the arthritis index, paw thickness, and ankle diameter were significantly reduced (Fig. 7C-7E). Histopathological examination of major organs (heart, liver, spleen, lungs, and kidneys) showed no apparent morphological abnormalities across all treatment groups, indicating no gross toxicity (Fig. 7F). Additionally, serum levels of the pro-inflammatory cytokines TNF-α, IL-1β, and IL-6 were significantly lower in the treatment groups than in the CIA control group (Fig. 7G). 4. Discussion Rheumatoid arthritis (RA) is a chronic autoimmune disease characterized by persistent synovitis and progressive joint destruction(Di Matteo et al. 2023 ; Gravallese and Firestein 2023 ). Although targeted synthetic and biological disease-modifying antirheumatic drugs (DMARDs) have significantly improved disease management, a substantial proportion of patients fail to achieve sustained remission or experience frequent disease flares, a condition often described as refractory RA(Xie et al. 2025 ; Nagy et al. 2021 ). Moreover, even when systemic inflammation appears controlled, structural joint damage may continue to progress. These clinical observations indicate that the pathophysiology of RA cannot be fully explained by immune-cell–driven mechanisms alone(Weyand and Goronzy 2021 ; Lee et al. 2023 ; Komatsu and Takayanagi 2022 ). Instead, the persistent pathogenic activation of resident synovial cells, particularly fibroblast-like synoviocytes (FLS), plays a pivotal role in disease chronicity and treatment resistance(Gao et al. 2024 ). However, current therapeutic strategies still lack effective strategies targeting the intrinsic pathological activation pathways of synovial cells. FLS plays a central role throughout the entire course of RA(Nygaard and Firestein 2020 ). In the early stage, FLS initiate innate immune responses and recruit inflammatory cells by producing abundant pro-inflammatory cytokines and chemokines, thereby amplifying both local and systemic inflammation. As the disease progresses, FLS further exacerbates synovial hyperplasia and erosion, even inducing osteoclast-mediated osteolysis, leading to cartilage and bone tissue destruction, and promoting the formation of pannus. This demonstrates that FLS are pivotal in RA's joint inflammation and the destruction of articular cartilage and bone. How to inhibit the joint inflammation and destruction caused by their tumor-like growth has become a new therapeutic focus for rheumatology researchers in the field of RA(Németh et al. 2022 ). Increasing evidence highlights the contribution of epigenetic regulation to RA pathogenesis, with DNA methylation representing one of the most important and reversible epigenetic mechanisms(Huang et al. 2024 ; Kielbowski et al. 2025 ). DNA methylation has been shown to influence gene expression, immune-cell activation, and cell differentiation(Cai and Yao 2025 ). To date, most studies have focused on peripheral blood samples, identifying both global and locus-specific abnormalities in DNA methylation among RA patients, and suggesting its involvement in immune dysregulation and potential utility in diagnosis or therapeutic response prediction(Kielbowski et al. 2025 ; Huang et al. 2025 ; Svendsen et al. 2025 ). However, peripheral immune cells are easily influenced by external stimuli such as infection or medication, and their methylation profiles may not accurately reflect the pathological microenvironment within the synovium(Cai and Yao 2025 ). By contrast, DNA methylation patterns in synovial tissue and synovial-derived cells are more likely to capture RA-specific inflammatory biology. Nevertheless, studies investigating DNA methylation in RA synovium and FLS remain limited. Studies have confirmed that DNA methylation changes in RA-FLS occur at a very early stage of the disease, a stage that has not yet been defined clinically(Karouzakis et al. 2018 ). Nakano et al. first reported 1,859 differentially methylated sites in RA-FLS compared with OA-FLS using the Illumina 450K array, implicating methylation changes in diverse cellular functions(Nakano et al. 2013 ). Subsequent small-sample studies confirmed that RA-FLS exhibit a distinct and stable methylation signature that can differentiate RA from OA and distinguish synovial samples from different joints(Ai et al. 2016 ). These observations suggest that persistent epigenetic remodeling may drive and maintain the aggressive phenotype of RA-FLS(Whitaker et al. 2013 ). However, most previous studies analyzed only single-omics datasets(Lin and Luo 2017 ), limiting the ability to establish functional links between methylation changes and corresponding gene expression alterations. An integrative study by Zhang et al. combining DNA methylation and transcriptomic sequencing identified methylation-regulated biomarkers such as RGS1 in RA synovium, yet similar multi-omics analysis specifically in RA-FLS has been lacking(Zhang et al. 2022 ). In this study, RA-FLS and OA-FLS were sequenced by Illumina 850k DNA methylation chip. In order to better interpret the regulation of DNA methylation on gene expression, transcriptome sequencing was performed simultaneously on the same sample. We identified 18636 genes that may be regulated by DNA methylation through the intersection of the two sets of data, and revealed that these genes were enriched in key biological functions such as cell proliferation, extracellular matrix remodeling, inflammatory response, suggesting that methylation remodeling is widely involved in the pathological activation of FLS. Further protein interaction network construction identified several key modules, among which the gene in module 1 is mainly a member of Rho GTPase family, which is a necessary molecular switch in cells(Chong et al. 2023 ). It is considered to be a core player in regulating various biological processes, and is closely related to cell migration, invasion, extracellular matrix adhesion, cell cycle and cytoskeleton reorganization. It was confirmed that Rho GTPase signal abnormality is related to immune dysregulation in the study of RA(Zeng et al. 2022 ). The genes in module 2 are mainly cytokines and their receptors related to cell-cell interactions. TNFSF10, ISG20, VCAM1, CD226, and ICAM5 are closely related to the effects of cytokines, including TNF-α, interferon, integrin, and mediate the interactions between immune cells. Module 3 is mainly growth factor family genes, including fibroblast growth factor (FGF) 1, FGF9, FGF18, platelet-derived growth factor B (PDGFB) and PDGF receptor alpha (PDGFRA). Growth factors (GF) are also considered as a kind of cytokines, which are naturally produced by cells and can stimulate cell growth, differentiation and tissue repair(Goldfarb 2005 ). FGFs are intracellular signaling proteins that are associated with cell proliferation and differentiation, and can regulate wound healing, angiogenesis, the corresponding receptors belong to the receptor tyrosine kinase family, which participate in biological processes such as tumor cell migration and proliferation(Meng et al. 2024 ; Xie et al. 2020 ). In addition, FGF9 is associated with osteoclast differentiation(Santos-Ocampo et al. 1996 ). The genes of module 4 are mainly transcriptional regulators. In order to further clarify the role of genes regulated by DNA methylation in RA-FLS, the differentially methylated sites and differentially expressed genes between RA and OA were intersected, and a total of 96 DNA methylation-regulated differentially expressed genes (MeDEGs) were obtained. These genes were enriched in the functions of extracellular matrix components, fibroblast growth and proliferation, as well as the stimulation and regulation of growth factors and vascular endothelial growth factor by cells, and were enriched in Rap1 pathway, MAPK pathway, mTOR signaling pathway, Hippo pathway, PI3K-Akt pathway and other signaling pathways, which were closely related to the functional regulation of cells, further proving that abnormal DNA methylation methylation is an important mechanism to promote RA-FLS to acquire an aggressive phenotype(Ding et al. 2023 ). Among the enzymes regulating DNA methylation, DNMT1 is primarily responsible for maintaining methylation patterns during DNA replication. The existing research on DNMT1 in RA is limited, and its expression level in RA is not uniform. Nakano K et al detected that there was no significant difference in protein and mRNA levels between FLS of RA and OA patients(Nakano et al. 2013 ). However, Li XF and other researchers found that the expression of DNMT1 protein in FLS of RA patients and the AIA animal model was increased(Li et al. 2021 ). Similarly, Miao CG and Liu YR found that the expression of DNMT1 protein and mRNA levels in synovial tissue and FLS of RA animal models were increased(Miao et al. 2014 ; Liu et al. 2019 ). In order to further explore the expression of DNMT1 in RA patients, our study confirmed that DNMT1 was significantly upregulated in synovial tissue and RA-FLS. Functional experiments showed that siRNA-mediated DNMT1 knockdown suppressed FLS proliferation, migration, and invasion, induced G2/M cell-cycle arrest, and markedly reduced IL-6 and RANKL production while increasing OPG levels, collectively attenuating the tumor-like behavior of FLS. In addition, DNMT inhibitor can also significantly reduce joint inflammation and bone erosion in CIA animal models, suggesting that DNMT1-mediated DNA methylation remodeling is not only involved in the acquisition of pathogenicity in RA-FLS, but also has intervention on disease progression. Although this study systematically revealed the abnormal methylation pattern of RA-FLS and demonstrated the key role of DNMT1 mediated DNA methylation in the pathological activation of RA-FLS, there are still several limitations. First, RA synovial samples were obtained mainly from patients with long-standing or treatment-refractory disease, which may introduce selection bias and limit generalizability. Larger cohorts including early-stage and treatment-naïve patients are needed to construct a more comprehensive multi-omics atlas of FLS. Second, although we identified the key role of DNMT1 in RA-FLS, the specific regulatory network and epigenetic target molecules downstream of DNMT1 still need to be further analyzed. There may be differences in the methylation regulation mode of DNMT1 in different gene regions. How it affects specific signaling pathways and the core nodes that affect FLS phenotype need to be mechanistically verified by higher resolution technology. In addition, 5-azacytidine used in this study is a non-selective DNMT inhibitor, which may have off-target effects, and other unpredictable adverse reactions may occur when used systemically. Therefore, more selective and safe DNMT1 specific inhibitors need to be developed in the future, and their therapeutic potential and safety should be verified in larger animal models and even clinical samples. Finally, although our study focused on FLS, the interaction between epigenetically altered FLS and immune cells also deserves a detailed study. In summary, this study provides integrated multi-omics and functional evidence that aberrant DNA methylation is a defining pathological feature of RA-FLS. We identify DNMT1 as a central regulator of FLS pathogenic activation and joint destruction, and demonstrate that DNMT1 inhibition exerts therapeutic benefits both in vitro and in vivo. This not only deepens the understanding of the epigenetic pathological mechanism of RA, but also provides a theoretical basis for the development of new therapeutic strategies targeting the intrinsic pathogenicity of synovial cells. In the future, targeting DNMT1 and its downstream methylation network is expected to become a new direction for RA treatment, but its safety and effectiveness still need to be further validated through large-scale patient samples and preclinical studies. 5. Conclusion This study suggests that DNMT1-mediated DNA methylation reprogramming is a key driving factor for FLS activation and joint destruction in RA. Declarations Ethics approval and consent to participate The clinical sample collection was approved by the Ethics Committee of Shanxi Bethune Hospital (SBQKL-2021-041). Consent for publication No individual person’s data are given. Competing interests The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Funding This work was supported by the basic research project of Shanxi Science and Technology Department (202303021222313); Shanxi Province Clinical Research Center for Dermatologic and Immunologic Diseases (LYZX-202305); Research and Innovation Team Project for Scientific Breakthroughs at Shanxi Bethune Hospital (2024AOXIANG02) ; 2024 Annual "Promising Candidates" Cultivation Project for National Natural Science Foundation at Shanxi Bethune Hospital (2024GZRZ04). CRediT authorship contribution statement Yazhen Su: Methodology, Resources, Visualization, Writing-original draft and editing. Zewen Wu: Methodology, Visualization Ruijiao Li: Methodology, Visualization Hao Xing: Methodology Yang Liu: Methodology, Data curation Rong Li: Methodology Jingxuan Li: Resources Xinling Liu: Data curation, Visualization Liyun Zhang: Supervision, Validation. Acknowledgements Data Availability All data that support the findings of this study are available within the article upon reasonable request. References Ai R, Hammaker D, Boyle DL et al (2016) Joint-specific DNA methylation and transcriptome signatures in rheumatoid arthritis identify distinct pathogenic processes. Nat Commun 7:11849. http://doi.org/10.1038/ncomms11849 Alivernini S, Firestein GS, Mcinnes IB (2022) The pathogenesis of rheumatoid arthritis. Immunity 55:2255-2270. http://doi.org/10.1016/j.immuni.2022.11.009 Ballestar E, Sawalha AH, Lu Q (2020) Clinical value of DNA methylation markers in autoimmune rheumatic diseases. Nat Rev Rheumatol 16:514-524. http://doi.org/10.1038/s41584-020-0470-9 Cai X, Yao Y (2025) Epigenetic modifications of immune cells in rheumatoid arthritis. Ann Med 57:2533432. http://doi.org/10.1080/07853890.2025.2533432 Cai X, Yao Y (2025) Epigenetic modifications of immune cells in rheumatoid arthritis. Ann Med 57:2533432. http://doi.org/10.1080/07853890.2025.2533432 Chen Z, Zhang Y (2020) Role of Mammalian DNA Methyltransferases in Development. Annu Rev Biochem 89:135-158. http://doi.org/10.1146/annurev-biochem-103019-102815 Chong CF, Hasnizan N, Ahmad MA (2023) Navigating the landscape of Rho GTPase signalling system in autoimmunity: A bibliometric analysis spanning over three decades (1990 to 2023). Cell Signal 111:110855. http://doi.org/10.1016/j.cellsig.2023.110855 Chu CQ (2020) Fibroblasts in Rheumatoid Arthritis. N Engl J Med 383:1679-1681. http://doi.org/10.1056/NEJMcibr2024718 Deng T, Wang Z, Geng Q et al (2024) Methylation of T and B Lymphocytes in Autoimmune Rheumatic Diseases. Clin Rev Allergy Immunol 66:401-422. http://doi.org/10.1007/s12016-024-09003-4 Di Matteo A, Bathon JM, Emery P (2023) Rheumatoid arthritis. Lancet 402:2019-2033. http://doi.org/10.1016/S0140-6736(23)01525-8 Ding Q, Hu W, Wang R et al (2023) Signaling pathways in rheumatoid arthritis: implications for targeted therapy. Signal Transduct Target Ther 8:68. http://doi.org/10.1038/s41392-023-01331-9 Gao Y, Zhang Y, Liu X (2024) Rheumatoid arthritis: pathogenesis and therapeutic advances. Medcomm (2020) 5:e509. http://doi.org/10.1002/mco2.509 Goldfarb M (2005) Fibroblast growth factor homologous factors: evolution, structure, and function. Cytokine Growth Factor Rev 16:215-220. http://doi.org/10.1016/j.cytogfr.2005.02.002 Gravallese EM, Firestein GS (2023) Rheumatoid Arthritis - Common Origins, Divergent Mechanisms. The New England Journal of Medicine 388:529-542. http://doi.org/10.1056/NEJMra2103726 Huang C, Liang Y, Li Y et al (2025) The epigenetic landscape of rheumatoid arthritis: Pathogenesis and drug therapeutic potentials. Acta Pharm Sin B 15:5601-5631. http://doi.org/10.1016/j.apsb.2025.08.022 Huang X, Huang L, Gao X et al (2024) Global research trends in DNA methylation in rheumatoid arthritis: A bibliometric analysis and visual analysis. Medicine (Baltimore) 103:e36218. http://doi.org/10.1097/MD.0000000000036218 Karami J, Aslani S, Tahmasebi MN et al (2020) Epigenetics in rheumatoid arthritis; fibroblast-like synoviocytes as an emerging paradigm in the pathogenesis of the disease. Immunol Cell Biol 98:171-186. http://doi.org/10.1111/imcb.12311 Karouzakis E, Raza K, Kolling C et al (2018) Analysis of early changes in DNA methylation in synovial fibroblasts of RA patients before diagnosis. Sci Rep 8:7370. http://doi.org/10.1038/s41598-018-24240-2 Kielbowski K, Bakinowska E, Goracy-Rosik A et al (2025) DNA and RNA Methylation in Rheumatoid Arthritis-A Narrative Review. Epigenomes 9 http://doi.org/10.3390/epigenomes9010002 Komatsu N, Takayanagi H (2022) Mechanisms of joint destruction in rheumatoid arthritis - immune cell-fibroblast-bone interactions. Nature Reviews. Rheumatology 18:415-429. http://doi.org/10.1038/s41584-022-00793-5 Lee ES, Ko H, Kim CH et al (2023) Disease-microenvironment modulation by bare- or engineered-exosome for rheumatoid arthritis treatment. Biomater Res 27:81. http://doi.org/10.1186/s40824-023-00418-2 Li X, Wu S, Yan Q et al (2021) PTEN Methylation Promotes Inflammation and Activation of Fibroblast-Like Synoviocytes in Rheumatoid Arthritis. Front Pharmacol 12:700373. http://doi.org/10.3389/fphar.2021.700373 Lin Y, Luo Z (2017) Aberrant methylation patterns affect the molecular pathogenesis of rheumatoid arthritis. Int Immunopharmacol 46:141-145. http://doi.org/10.1016/j.intimp.2017.02.008 Liu YR, Yang L, Xu QQ et al (2019) Long noncoding RNA MEG3 regulates rheumatoid arthritis by targeting NLRC5. J Cell Physiol 234:14270-14284. http://doi.org/10.1002/jcp.28126 Masoumi M, Bashiri H, Khorramdelazad H et al (2021) Destructive Roles of Fibroblast-like Synoviocytes in Chronic Inflammation and Joint Damage in Rheumatoid Arthritis. Inflammation 44:466-479. http://doi.org/10.1007/s10753-020-01371-1 Meng X, Chen Z, Li T et al (2024) Role and Therapeutic Potential for Targeting Fibroblast Growth Factor 10/FGFR1 in Relapsed Rheumatoid Arthritis. Arthritis Rheumatol 76:32-47. http://doi.org/10.1002/art.42674 Miao CG, Yang YY, He X et al (2014) MicroRNA-152 modulates the canonical Wnt pathway activation by targeting DNA methyltransferase 1 in arthritic rat model. Biochimie 106:149-156. http://doi.org/10.1016/j.biochi.2014.08.016 Nagy G, Roodenrijs N, Welsing PM et al (2021) EULAR definition of difficult-to-treat rheumatoid arthritis. Ann Rheum Dis 80:31-35. http://doi.org/10.1136/annrheumdis-2020-217344 Nakano K, Boyle DL, Firestein GS (2013) Regulation of DNA methylation in rheumatoid arthritis synoviocytes. J Immunol 190:1297-1303. http://doi.org/10.4049/jimmunol.1202572 Nakano K, Whitaker JW, Boyle DL et al (2013) DNA methylome signature in rheumatoid arthritis. Ann Rheum Dis 72:110-117. http://doi.org/10.1136/annrheumdis-2012-201526 Németh T, Nagy G, Pap T (2022) Synovial fibroblasts as potential drug targets in rheumatoid arthritis, where do we stand and where shall we go? Ann Rheum Dis 81:1055-1064. http://doi.org/10.1136/annrheumdis-2021-222021 Nygaard G, Firestein GS (2020) Restoring synovial homeostasis in rheumatoid arthritis by targeting fibroblast-like synoviocytes. Nat Rev Rheumatol 16:316-333. http://doi.org/10.1038/s41584-020-0413-5 Santos-Ocampo S, Colvin JS, Chellaiah A et al (1996) Expression and biological activity of mouse fibroblast growth factor-9. J Biol Chem 271:1726-1731. http://doi.org/10.1074/jbc.271.3.1726 Song J, Teplova M, Ishibe-Murakami S et al (2012) Structure-based mechanistic insights into DNMT1-mediated maintenance DNA methylation. Science (New York, N.Y.) 335:709-712. http://doi.org/10.1126/science.1214453 Svendsen AJ, Mengel-From J, Junker P et al (2025) Differential DNA methylation patterns in whole blood from ACPA-positive patients with DMARD-naïve rheumatoid arthritis at clinical disease onset. Front Immunol 16:1488161. http://doi.org/10.3389/fimmu.2025.1488161 Weyand CM, Goronzy JJ (2021) The immunology of rheumatoid arthritis. Nat Immunol 22:10-18. http://doi.org/10.1038/s41590-020-00816-x Whitaker JW, Shoemaker R, Boyle DL et al (2013) An imprinted rheumatoid arthritis methylome signature reflects pathogenic phenotype. Genome Med 5:40. http://doi.org/10.1186/gm444 Wu Z, Ma D, Yang H et al (2021) Fibroblast-like synoviocytes in rheumatoid arthritis: Surface markers and phenotypes. Int Immunopharmacol 93:107392. http://doi.org/10.1016/j.intimp.2021.107392 Xie W, Chen T, Xiao S et al (2025) Difficult-to-treat rheumatoid arthritis (part 1): systematic review and meta-analysis on global prevalence. Ann Rheum Dis http://doi.org/10.1016/j.ard.2025.10.007 Xie Y, Su N, Yang J et al (2020) FGF/FGFR signaling in health and disease. Signal Transduct Target Ther 5:181. http://doi.org/10.1038/s41392-020-00222-7 Zeng R, Zhuo Z, Luo Y et al (2022) Rho GTPase signaling in rheumatic diseases. Iscience 25:103620. http://doi.org/10.1016/j.isci.2021.103620 Zhang R, Chang C, Jin Y et al (2022) Identification of DNA methylation-regulated differentially expressed genes in RA by integrated analysis of DNA methylation and RNA-Seq data. J Transl Med 20:481. http://doi.org/10.1186/s12967-022-03664-5 Additional Declarations No competing interests reported. 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1","display":"","copyAsset":false,"role":"figure","size":430572,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIsolation and identification of RA-FLS.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Representative arthroscopic image showing the hyperplastic, cluster-like, and nodular appearance of RA synovial tissue. (B) Morphology of isolated fibroblast-like synoviocytes (FLS) exhibiting a characteristic spindle-shaped, fibroblastic morphology growing in a whirling pattern (scale bar, 200μm). (C) Immunofluorescence staining confirming the positive expression of CD44 (green) in RA-FLS. Nuclei are counterstained with DAPI (blue) (scale bar, 100μm). (D) Flow cytometric analysis of RA-FLS surface markers, demonstrating positive expression for CD90 and negative expression for CD14, CD34, and CD45.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8601364/v1/4f0b2813667646eaa524217e.png"},{"id":100950197,"identity":"70dc920f-1a64-4991-bd1b-744bd4f2e335","added_by":"auto","created_at":"2026-01-23 07:07:12","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":311868,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRA-FLS transcriptome sequencing.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Transcriptome sequencing and DNA methylation microarray sequencing were performed on FLS of 3 RA and 3 OA patients respectively, and the data were analyzed to focus on the expression genes and differentially expressed genes regulated by DNA methylation, so as to analyze the role of DNA methylation in RA-FLS. (B) Volcano plot of differentially expressed genes (DEGs) in RA-FLS versus OA-FLS. Red dots represent significantly upregulated genes (n=353), and blue dots represent downregulated genes (n=144). (C) Heatmap depicting the expression profiles of DEGs. (D) Gene Ontology (GO) enrichment analysis of DEGs. (E) Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of DEGs.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8601364/v1/3806ec26ce041e49bc2e16ea.png"},{"id":100865762,"identity":"c12d0315-7220-4e4a-821d-d446c9f25b0a","added_by":"auto","created_at":"2026-01-22 08:27:29","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":251719,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRA-FLS DNA methylation sequencing.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A, B) Genomic distribution of differentially methylated probes (DMPs) in RA-FLS compared to OA-FLS. (C) Volcano plot of DMPs. Red dots represent hypermethylated probes and blue dots represent hypomethylated probes in RA-FLS. (D) Heatmap of differentially methylated genes (DMGs). (E) Gene Ontology (GO) enrichment analysis of DMGs across biological process (BP), cellular component (CC), and molecular function (MF) categories. (F) KEGG pathway enrichment analysis of DMGs.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8601364/v1/121267d1b655e02cb3e8fbd5.png"},{"id":100866001,"identity":"91b318ef-e522-4c22-94c7-1d5f01e1bf60","added_by":"auto","created_at":"2026-01-22 08:28:03","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":338281,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCombined analysis of DNA methylation sequencing and transcriptome sequencing.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Venn diagram of methylation-regulated expressed genes. (B-H) Gene set enrichment analysis (GSEA) of methylation-regulated expressed genes. (I) Protein-protein interaction network. Digits from 1 to 4 represent different modules constructed by Reactome functional interactions Cytoscape Plugin. Each edge between any two proteins (dots) indicates an interaction.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-8601364/v1/d548e7015bc759f3cf5ec794.png"},{"id":100865876,"identity":"d53b6840-7b8f-4b51-8bf2-59d2980ec78c","added_by":"auto","created_at":"2026-01-22 08:27:46","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":317619,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCombined analysis of DNA methylation sequencing and transcriptome sequencing.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) The Venn diagram of DNA methylation-regulated differentially expressed genes (MeDEGs). (B) Overlap between highly expressed genes and hypomethylated genes in RA-FLS. (C) Overlap between lowly expressed genes and hypermethylated genes in RA-FLS. (D) GO analysis of MeDEGs. (E) KEGG analysis of MeDEGs.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-8601364/v1/62621e420144af3d2a33af1c.png"},{"id":100866002,"identity":"0f4f1245-220a-4b9b-bcb2-d7d91b5d5d96","added_by":"auto","created_at":"2026-01-22 08:28:03","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1085856,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eInhibition of DNMT1 can inhibit the \"tumoroid-like\" growth of RA-FLS.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Immunohistochemical staining of DNMT1 in synovial tissues from OA and RA patients. (B) Immunofluorescence staining of DNMT1 (green) in RA-FLS. Nuclei are counterstained with DAPI (blue). (C) RT-qPCR analysis of DNMT1 mRNA expression in OA-FLS and RA-FLS. (D) The expression of DNMT1 mRNA was reduced after transfection with siRNA, the inhibition effect of siRNA-810 group was the most obvious. (E, F) Western-blot and immunofluorescence showed DNMT1 expression was reduced in RA-FLS after transfection with siRNA; (G) Inhibition of DNMT1 inhibited cell proliferation of RA-FLS. (H, I) Inhibition of DNMT1 had no effect on apoptosis of RA-FLS. (J, K) Inhibition of DNMT1 inhibited RA-FLS cell migration at 12h, 24h, and 48h. (L, M) Through the transwell test, the invasion of siRNA-DNMT1 were inhibited at 24h. (N, O) Cell cycle analysis was measured by flow cytometry after transfection with siRNA-DNMT1 for 48h, the cells in S phase was significant decrease and the cells in G2 was increased. (P) The levels of IL-6 and RANKL were decreased in siRNA-DNMT1, the level of OPG was elevated, there was no significant difference in the level of IL-17A. \u003csup\u003e*\u003c/sup\u003e\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05, \u003csup\u003e**\u003c/sup\u003e\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01, \u003csup\u003e***\u003c/sup\u003e\u003cem\u003eP \u003c/em\u003e\u0026lt; 0.001 vs the siRNA-NC group.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-8601364/v1/bf6e5746f5fe8b5365e7a255.png"},{"id":100865972,"identity":"a2b53027-ed1e-49c3-9bf2-86fd9e172b00","added_by":"auto","created_at":"2026-01-22 08:28:01","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":692514,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eInhibition of DNMT1 ameliorates synovial inflammation and bone destruction in CIA mice.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) DBA1/J mice were immunized on day 0 and day 21, MTX was intraperitoneal injected with 1.0mg/kg twice a week from day 21 to day 70, the 5-azacytidine intervention group (5-Azac) was intraperitoneally injected with 1.0mg/kg once every other day for 21 days (D28-D49). All mice were sacrificed, and specimens were harvested on D70. (B) Micro view showed the paws of CIA mice were swelling, MicroCT demonstrated the bone injury, HE staining and Safranine O/fast green staining showed the joint pathological injury. The treatment of MTX and 5-Azac could reduce the joint inflammation, cartilage destruction, and bone destruction. Immunohistochemical staining results showed that DNMT1 expression was significantly elevated in the CIA model and decreased after treatment. (C-E) Dynamic changes in body weight (C), clinical arthritis score (D), and paw thickness (E) from day 21 to 70. MTX and 5-Azac treatments significantly ameliorated disease severity. (F) The heart, liver, spleen, lungs, and kidneys in the different intervention groups were visually normal, histopathological examination showed that all organ sections maintained typical physiological structures and cellular morphology. (G) Serum concentrations of pro-inflammatory cytokines TNF-α, IL-1β, and IL-6 at the endpoint. Data are presented as mean ± SD. \u003csup\u003e#\u003c/sup\u003e\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05, \u003csup\u003e###\u003c/sup\u003e\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001 versus the naive DBA1/J group; \u003csup\u003e***\u003c/sup\u003e\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001 versus the CIA model group.\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-8601364/v1/11d23a0203dbe573d0f42766.png"},{"id":100952862,"identity":"7d68c482-d2e6-4128-8ba6-730e90dc8d2f","added_by":"auto","created_at":"2026-01-23 07:18:33","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4393740,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8601364/v1/ccb9921b-ce94-4188-97ae-8157a7a1b803.pdf"},{"id":100865711,"identity":"086d6993-ccad-45be-8449-201a4a3cf7c6","added_by":"auto","created_at":"2026-01-22 08:27:26","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":25997,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable.docx","url":"https://assets-eu.researchsquare.com/files/rs-8601364/v1/408fd1188ad9c05768e3f3f2.docx"},{"id":100865957,"identity":"ad4df9b8-3ae1-48e0-9c0b-635bf6276b6f","added_by":"auto","created_at":"2026-01-22 08:27:57","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":3880659,"visible":true,"origin":"","legend":"","description":"","filename":"supplementaryfileDNMT1WB.docx","url":"https://assets-eu.researchsquare.com/files/rs-8601364/v1/2dd3fd1e753707dc9f1be088.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Abnormal DNA methylation pattern and expression of DNA methyltransferase 1 promote synovitis and bone destruction in rheumatoid arthritis","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eRheumatoid arthritis (RA) is a chronic systemic autoimmune disease characterized by persistent synovial inflammation, pannus formation, and progressive joint destruction(Di Matteo et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Alivernini et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Synovial hyperplasia is a pathological hallmark of RA, in which fibroblast-like synoviocytes (FLS) play a pivotal role(Chu \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Nygaard and Firestein \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). RA-FLS acquire an aggressive phenotype, exhibiting excessive proliferation, invasion, and resistance to apoptosis, while producing pro-inflammatory cytokines, chemokines, and matrix-degrading enzymes that perpetuate synovial inflammation and bone erosion(Masoumi et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Wu et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Although biological therapies targeting immune cells and cytokines have substantially improved disease management, many patients still experience incomplete responses or relapse(Nagy et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This suggests that intrinsic alterations within synovial cells may drive persistent inflammation, highlighting the need to explore new therapeutic mechanisms.\u003c/p\u003e \u003cp\u003eEmerging evidence indicates that epigenetic modifications, including DNA methylation, histone modification, and non-coding RNA regulation, are involved in the sustained activation of RA-FLS(Huang et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Karami et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Among these, DNA methylation, catalyzed by DNA methyltransferases (DNMTs), plays a central role in gene silencing and genomic stability(Kielbowski et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Most studies of DNA methylation in RA have focused on peripheral immune cells, which are easily influenced by external factors such as infection or medication(Deng et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Cai and Yao \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). In contrast, methylation profiling of synovial tissue, particularly FLS, better reflects the local inflammatory and immune microenvironment. Abnormal DNA methylation patterns have been observed in RA synovium, suggesting a role in disease progression(Zhang et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). However, the specific methylation alterations in RA-FLS and their functional consequences remain poorly understood.\u003c/p\u003e \u003cp\u003eDNMT1, the maintenance DNA methyltransferase, preserves methylation patterns during DNA replication(Song et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Chen and Zhang \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The studies suggest that DNMT1 dysregulation contributes to aberrant immune activation and tissue inflammation in autoimmune diseases(Ballestar et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Nevertheless, research on DNMT1 in RA is limited, and its expression status in RA synovium remains controversial. Elucidating how DNMT1-mediated methylation remodeling contributes to the pathogenic phenotype of RA-FLS may provide new insights into RA pathogenesis and therapeutic intervention.\u003c/p\u003e \u003cp\u003eIn this study, we performed integrated transcriptomic and DNA methylation analyses of FLS derived from patients with RA and osteoarthritis (OA). Comprehensive bioinformatic analysis identified key methylation-regulated genes and pathways associated with FLS activation. Furthermore, we examined the functional and therapeutic effects of DNMT1 inhibition in vitro and in a collagen-induced arthritis (CIA) mouse model. Our findings demonstrate that aberrant DNA methylation is a critical epigenetic driver of RA-FLS pathogenicity and highlight DNMT1 as a promising therapeutic target in RA.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Clinical sample collection\u003c/h2\u003e \u003cp\u003eThe synovial tissues were obtained from patients with RA (n\u0026thinsp;=\u0026thinsp;3) who underwent arthroscopic surgery or knee replacement surgery. RA patients met the ACR/EULAR 2010 RA diagnostic criteria. For comparison, synovial tissues were also collected from three age- and gender-matched patients with OA (n\u0026thinsp;=\u0026thinsp;3), diagnosed according to the 1995 revised ACR criteria. Patients with concomitant tumours, acute or chronic infections, or other autoimmune diseases were excluded. The clinical sample collection was approved by the Ethics Committee of Shanxi Bethune Hospital (SBQKL-2021-041). Collected tissues were either processed immediately for FLS isolation or snap-frozen and stored in liquid nitrogen.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Isolation and culture of FLS\u003c/h2\u003e \u003cp\u003eSynovial tissues were minced into small fragments and digested with 0.25% trypsin (Solarbio, T1321) for 1 hour at 37\u0026deg;C to isolate FLS. The cells were maintained in Dulbecco\u0026rsquo;s modified Eagle\u0026rsquo;s medium (DMEM; Gibco, C11995500BT) supplemented with 10% fetal bovine serum (FBS; Gibco, 10099141C) and 1% penicillin/streptomycin (P/S; Solarbio, P1400), under a humidified atmosphere containing 5% CO₂ at 37\u0026deg;C. Upon reaching 90% confluence, cells were harvested by trypsinisation and passaged. FLS between passages 3 and 6 were utilised for all experiments to ensure phenotypic stability.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Cell identification\u003c/h2\u003e \u003cp\u003eThe morphological features of RA-FLS were examined using an optical microscope. Cell purity was assessed via flow cytometry by staining surface markers. RA-FLS was stained with APC anti-human CD90 (Biolegend, 328113), along with anti-CD14, anti-CD34, and anti-CD45 antibodies (OriCell, HUXMX-09011). Analysis was performed on a BD flow cytometer, and data processing was conducted with FlowJo software.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Immunofluorescence\u003c/h2\u003e \u003cp\u003eRA-FLS were fixed in 4% paraformaldehyde for 20 minutes at room temperature, permeabilised with 0.1% Triton X-100 for 30 minutes, and blocked with 5% bovine serum albumin (BSA) for 1 hour. Following incubation with primary antibodies at 4\u0026deg;C overnight, cells were treated with fluorochrome-conjugated secondary antibodies (Boster, A1127) for 1 hour at room temperature. Nuclei were counterstained with DAPI (Boster, AR1176), and imaging was performed under a fluorescence microscope.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Transcriptome sequencing of FLS\u003c/h2\u003e \u003cp\u003eTotal RNA was extracted using a reagent kit and quantified. The mRNA underwent purification and fragmentation, followed by synthesis and purification of double-stranded cDNA. Subsequent steps included end repair, dA-tailing, adapter ligation, size selection, and PCR amplification to construct the library. Library quality was verified by gel electrophoresis, and accurate quantification was achieved with the Qubit DNA detection kit prior to sequencing. Raw data quality was assessed and controlled before downstream analysis. Sequencing services were provided by Shanghai Sangon.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 DNA methylation sequencing of FLS\u003c/h2\u003e \u003cp\u003eMethylation profiling was performed on passage 3 (P3) RA-FLS (n\u0026thinsp;=\u0026thinsp;3) and OA-FLS (n\u0026thinsp;=\u0026thinsp;3). Genomic DNA was isolated using an extraction kit, quantified with a Nanodrop2000, and quality-checked on a 1% agarose gel. Qualified DNA samples underwent bisulfite conversion with the EZ DNA Methylation kit and were then subjected to methylation array analysis using the methylation EPIC chip. All methylation sequencing was carried out by Shanghai Sangon.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Cell transfection\u003c/h2\u003e \u003cp\u003eGenePharma (Shanghai, China) supplied the small interfering RNAs (siRNAs) targeting DNMT1, along with the corresponding negative control siRNA (siRNA-NC). The sequences are listed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. FLS were transfected with siRNAs using INTERFERin\u0026reg; (Polyplus, 101000028).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSequences of small interfering RNAs (siRNAs)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGene\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBase sequence (5 '-3')\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eDNMT1-130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eS: GCGGCUCAAAGAUUUGGAATT\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAS: UUCCAAAUCUUUGAGCCGCTT\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eDNMT1-810\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eS: CACCCAAACAGAAACUGAATT\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAS: UUCAGUUUCUGUUUGGGUGTT\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eDNMT1-1231\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eS: GUCUGGCUUUGAGAGUUAUTT\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAS: AUAACUCUCAAAGCCAGACTT\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eDNMT1-2961\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eS: CAGAGCACUACCGGAAAUATT\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAS: UAUUUCCGGUAGUGCUCUGTT\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eNC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eS: UUCUCCGAACGUGUCACGUTT\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAS: ACGUGACACGUUCGGAGAATT\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.8 Quantitative real-time polymerase chain reaction (qRT\u0026ndash;PCR)\u003c/h2\u003e \u003cp\u003eTotal RNA was extracted with RNAiso Plus (Takara, 9109). cDNA was synthesised using the PrimeScript\u0026trade; RT reagent Kit with gDNA Eraser (Takara, RR047A). qPCR was performed using TB Green\u0026reg; Premix Ex Taq\u0026trade; II (Takara, RR820A). GAPDH served as the internal control, and relative mRNA expression was calculated using the 2⁻ΔΔCT method.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.9 Western blot\u003c/h2\u003e \u003cp\u003eProteins were extracted with RIPA buffer supplemented with protease inhibitors. Protein concentration was determined using a BCA assay kit (Beyotime, P0009). Equal amounts of protein (20\u0026ndash;30\u0026micro;g) were separated by 10% SDS-PAGE and transferred to PVDF membranes. After blocking with 5% non-fat milk for 1 hour, membranes were incubated overnight at 4\u0026deg;C with primary antibodies against DNMT1 (Boster, 1:1000) and β-actin (Abcam, ab8226, 1:1000). Following incubation with an HRP-conjugated secondary antibody (1:5000) for 1 hour at room temperature, protein bands were visualized using an enhanced chemiluminescence (ECL) detection system and analyzed with ImageJ software.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e2.10 Cell counting kit-8 (CCK-8) assay\u003c/h2\u003e \u003cp\u003eCells from different groups (2\u0026times;10⁴ per well) were plated in 96-well plates and cultured. Then, 10 \u0026micro;L of CCK-8 reagent (Boster, AR1199) was added to each well and incubated for 1\u0026ndash;2 hours. Absorbance was measured at 450 nm using a microplate reader.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e2.11 Cell apoptosis assay\u003c/h2\u003e \u003cp\u003eAfter transfection, cells were collected, washed with PBS, and resuspended in 500 \u0026micro;L of 1X Annexin-binding buffer. They were stained with Annexin V-APC and DAPI (Elabscience, E-CK-A258) for 15 minutes in the dark, and analysed by flow cytometry.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e2.12 Cell migration\u003c/h2\u003e \u003cp\u003eCell migration was assessed using a scratch wound healing assay. Briefly, FLS were seeded in 6-well plates (2\u0026times;10⁵ cells/well) and grown to confluence. A uniform scratch was created using a sterile 200 \u0026micro;L pipette tip. After washing with PBS to remove detached cells, fresh medium containing 1% FBS was added. Images of the scratch were taken at 0, 12, 24, and 48 hours using an inverted microscope. The migration rate was quantified by measuring the reduction in scratch area over time using ImageJ software.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e2.13 Cell invasion\u003c/h2\u003e \u003cp\u003eInvasion was examined using Matrigel-coated transwell chambers. Matrigel (Corning, 356234) was diluted 1:8 and applied to the upper chamber. After polymerisation, the cell suspension with 1% FBS was added to the upper compartment, and medium with 20% FBS was placed below. Following 24 hours of incubation, invasive cells were fixed, stained with crystal violet, and quantified.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e2.14 Cell cycle assay\u003c/h2\u003e \u003cp\u003eCells were harvested, washed with PBS, and fixed in 75% ethanol at 4\u0026deg;C overnight. After PBS washing, cells were resuspended in 500 \u0026micro;L PI/RNase A staining solution (Meilunbio, MA0334) and incubated for 30 minutes protected from light. Cell cycle distribution was analysed by flow cytometry.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e2.15 ELISA\u003c/h2\u003e \u003cp\u003eCytokine levels in supernatants were measured using ELISA kits according to the manufacturer's protocols. The following kits were used: Human IL-6 (MEIMIAN, MM-0049H1), IL-17A (MEIMIAN, MM-2117H1), RANKL (MEIMIAN, MM-1513H1), and OPG (MEIMIAN, MM-0849H1).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e2.16 Histopathologic evaluation and Immunohistochemical Staining\u003c/h2\u003e \u003cp\u003eTissues were fixed in 4% PFA for 24 hours and embedded in paraffin. Sections were stained with H\u0026amp;E (Servicebio, G1005) or Safranin O-Fast Green (Solarbio, G1371). For IHC, sections were incubated with anti-DNMT1 antibody overnight, followed by secondary antibody treatment, and imaged under a microscope.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e2.17 CIA model\u003c/h2\u003e \u003cp\u003eDBA1/J mice (male, 6\u0026ndash;8 weeks old, n\u0026thinsp;=\u0026thinsp;40) were obtained from GemPharmatech (China) for the CIA experiments. On day 0, mice were immunised intradermally at the base of the tail with 100 \u0026micro;g of bovine type II collagen (CII) (Chondrex, USA) emulsified in complete Freund's adjuvant (Chondrex, USA). A booster immunisation with CII in incomplete Freund's adjuvant (ICF) was administered on day 21. Body weight, arthritis scores, and paw thickness were recorded every three days. From day 21 onward, CIA mice received intraperitoneal injections of methotrexate (MTX, 1 mg/kg) twice weekly. The 5-azacytidine (5-Azac) intervention group was intraperitoneally injected with 1.0mg/kg once every other day for 21 days. All mice were euthanised on day 70. Ankle joints were harvested for micro-CT analysis and histopathological examination.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e2.18 Statistical analysis\u003c/h2\u003e \u003cp\u003eData are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD. Statistical comparisons between two groups were performed using a two-tailed Student's t-test, while multiple group comparisons were analysed by one-way analysis of variance (ANOVA) followed by Dunnett's post-hoc test. A P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003cp\u003eFor sequencing data, raw data from DNA methylation arrays were preprocessed and normalised to beta values. Linear models were constructed using R packages such as limma and ChAMP to calculate P-values for differentially methylated sites. Sites with an adj. p-Value\u0026thinsp;\u0026lt;\u0026thinsp;0.01 and |Delta Beta| \u0026gt; 0.1 were defined as differentially methylated CpG sites. For transcriptomic data, the filtering thresholds were set at q-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and |FoldChange| \u0026gt; 2. Volcano plots and heatmaps were generated using R packages. Overlapping genes were subjected to GO analysis and KEGG pathway enrichment analysis. For Gene Set Enrichment Analysis (GSEA), significance thresholds were set at |NES| \u0026gt; 1, nominal P value\u0026thinsp;\u0026lt;\u0026thinsp;0.05, and false discovery rate (FDR)\u0026thinsp;\u0026lt;\u0026thinsp;0.25.Data are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD. Statistical comparisons between two groups were performed using a two-tailed Student's t-test, while multiple group comparisons were analyzed by one-way analysis of variance (ANOVA) followed by Dunnett's post-hoc test. A \u003cem\u003eP\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cp\u003e\u003cstrong\u003e3.1 Isolation and identification of RA-FLS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUnder arthroscopy, RA synovial tissue exhibited hyperplastic, cluster-like, and nodular growth (Fig. 1A). The isolated RA-FLS displayed a characteristic spindle-shaped, fibroblastic morphology and grew in a whirling pattern under microscopy (Fig. 1B). Immunofluorescence staining confirmed the positive expression of CD44 (Fig. 1C). Flow cytometric analysis further verified the typical FLS phenotype, the cells expressed CD90, but did not express CD14, CD34, and CD45, which met the criteria for identification of RA-FLS (Fig. 1D).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2 RA-FLS transcriptome sequencing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe performed transcriptome sequencing on FLS isolated from three RA patients and three OA patients (Fig. 2A) . As shown in the volcano plot (Fig. 2B) and heat map (Fig. 2C), compared with OA-FLS, RA-FLS had 353 upregulated genes and 144 downregulated genes. Gene Ontology (GO) enrichment analysis indicated that differentially expressed genes (DEGs) were primarily involved in extracellular tissue structure, extracellular matrix, wound healing, cell adhesion, and angiogenesis regulation (Fig. 2D). Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis further showed significant enrichment in the the phosphatidylinositol-3-kinase-protein kinase B (PI3K-AKT) pathway, cytokine-cytokine receptor interaction, mitogen-activated protein kinase (MAPK) signaling pathway, extracellular matrix-receptor interaction, janus kinase-signal transducer and activator of transcription (JAK-STAT) signaling pathway, as well as in Th17 cell differentiation and the rheumatoid arthritis pathway (Fig. 2E).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3 RA-FLS DNA methylation sequencing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eParallel DNA methylation sequencing was performed on the same set of FLS samples. Compared to OA-FLS, 45.64% of the differentially methylated positions (DMPs) were hypermethylated and 53.46% were hypomethylated in RA-FLS. These DMPs were distributed across gene regions: 52.17% in intergenic regions, 32.35% in promoter regions, and 3.7% in the 3\u0026apos;UTR (Fig. 3A, 3B). At the gene level, we identified 2550 hypermethylated and 3300 hypomethylated genes in RA-FLS (Fig. 3C, 3D). GO term analysis for these differentially methylated genes (DMGs) revealed associations with biological processes (BP) including cell growth, autophagy, and fibroblast migration; cellular components (CC) such as the actin cytoskeleton and extracellular matrix; and molecular functions (MF) like GTPase regulator activity and protein serine/threonine kinase activity (Fig. 3E). KEGG pathway enrichment analysis highlighted significant involvement in the MAPK, PI3K-AKT, Hippo, and HIF-1 signaling pathways, as well as apoptosis (Fig. 3F).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.4 Combined analysis of DNA methylation sequencing and transcriptome sequencing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 20,241 genes were detected in the transcriptome sequencing data, 34,280 methylated genes and 888,966 methylated probes were detected in Illumina 850K DNA methylation array sequencing data. The intersection of the two sets of sequencing data yielded a total of 18,636 methylation-regulated expressed genes (Fig. 4A). Gene Set Enrichment Analysis (GSEA) of these genes showed significant enrichment for terms including actin filament organization, extracellular matrix collagen constituents, actin cytoskeleton, monoatomic anion transmembrane transporter activity, and type II interferon production (Fig. 4B-4H). Protein-protein interaction (PPI) network analysis, constructed using the STRING database and Cytoscape, identified four key modules: Module 1 was enriched for Rho-GTPase family members; Module 2 for cytokines and their receptors mediating intercellular communication; Module 3 for growth factor family genes; and Module 4 for transcriptional regulators (Fig. 4I).\u003c/p\u003e\n\u003cp\u003eFurther intersection of differentially methylated and expressed genes yielded 96 methylation-regulated differentially expressed genes (MeDEGs) (Fig. 5A). Among these, 47 genes exhibited high expression with low methylation, while 6 genes showed low expression with high methylation (Fig. 5B, 5C). Functional enrichment analysis revealed that MeDEGs were involved in extracellular matrix composition, fibroblast growth and proliferation, the extrinsic apoptotic signaling pathway, and cellular responses to growth factor and VEGF stimulation (Fig. 5D). These genes were also significantly enriched in the Rap1, MAPK, mTOR, Hippo, cytokine-cytokine receptor interaction, and PI3K-AKT signaling pathways (Fig. 5E).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.5 Inhibition of DNMT1 can inhibit the \u0026quot;tumoroid-like\u0026quot; growth of RA-FLS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eImmunohistochemistry revealed significantly higher DNMT1 expression in RA synovial tissues compared to OA controls (Fig. 6A). This elevated expression was confirmed in RA-FLS at both the protein level by immunofluorescence (Fig. 6B) and the mRNA level by qRT-PCR (Fig. 6C). To investigate its function, we knocked down DNMT1 using siRNA. Transfection with four different siRNAs targeting DNMT1 effectively reduced DNMT1 mRNA levels, with siRNA-810 demonstrating the most potent knockdown efficiency and thus selected for subsequent experiments (designated as siRNA-DNMT1) (Fig. 6D). The knockdown of DNMT1 protein was verified by both western blotting and immunofluorescence (Fig. 6E, 6F).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFunctional assays demonstrated that DNMT1 inhibition significantly suppressed the proliferation (Fig. 6G), migration (Fig. 6J, 6K), and invasion (Fig. 6L, 6M) of RA-FLS, without significantly affecting apoptosis (Fig. 6H, 6I). Cell cycle analysis revealed a notable decrease in the proportion of cells in S phase and an accumulation in G2/M phase upon DNMT1 knockdown (Fig. 6N, 6O). Furthermore, siRNA-DNMT1 altered cytokine secretion, inhibiting IL-6 and RANKL, promoting OPG, and leaving IL-17A unchanged (Fig. 6P).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.6 Inhibition of DNMT1 ameliorates synovial inflammation and bone destruction in CIA mice\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn the collagen-induced arthritis (CIA) mouse model, therapeutic intervention with methotrexate (MTX) or the DNA methyltransferase inhibitor 5-azacytidine (5-AzaC) markedly alleviated joint inflammation, cartilage and bone destruction, and synovial DNMT1 expression compared to the untreated CIA group (Fig. 7B). Consequently, the arthritis index, paw thickness, and ankle diameter were significantly reduced (Fig. 7C-7E). Histopathological examination of major organs (heart, liver, spleen, lungs, and kidneys) showed no apparent morphological abnormalities across all treatment groups, indicating no gross toxicity (Fig. 7F). Additionally, serum levels of the pro-inflammatory cytokines TNF-\u0026alpha;, IL-1\u0026beta;, and IL-6 were significantly lower in the treatment groups than in the CIA control group (Fig. 7G).\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eRheumatoid arthritis (RA) is a chronic autoimmune disease characterized by persistent synovitis and progressive joint destruction(Di Matteo et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Gravallese and Firestein \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Although targeted synthetic and biological disease-modifying antirheumatic drugs (DMARDs) have significantly improved disease management, a substantial proportion of patients fail to achieve sustained remission or experience frequent disease flares, a condition often described as refractory RA(Xie et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Nagy et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Moreover, even when systemic inflammation appears controlled, structural joint damage may continue to progress. These clinical observations indicate that the pathophysiology of RA cannot be fully explained by immune-cell\u0026ndash;driven mechanisms alone(Weyand and Goronzy \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Lee et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Komatsu and Takayanagi \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Instead, the persistent pathogenic activation of resident synovial cells, particularly fibroblast-like synoviocytes (FLS), plays a pivotal role in disease chronicity and treatment resistance(Gao et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). However, current therapeutic strategies still lack effective strategies targeting the intrinsic pathological activation pathways of synovial cells.\u003c/p\u003e \u003cp\u003eFLS plays a central role throughout the entire course of RA(Nygaard and Firestein \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In the early stage, FLS initiate innate immune responses and recruit inflammatory cells by producing abundant pro-inflammatory cytokines and chemokines, thereby amplifying both local and systemic inflammation. As the disease progresses, FLS further exacerbates synovial hyperplasia and erosion, even inducing osteoclast-mediated osteolysis, leading to cartilage and bone tissue destruction, and promoting the formation of pannus. This demonstrates that FLS are pivotal in RA's joint inflammation and the destruction of articular cartilage and bone. How to inhibit the joint inflammation and destruction caused by their tumor-like growth has become a new therapeutic focus for rheumatology researchers in the field of RA(N\u0026eacute;meth et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIncreasing evidence highlights the contribution of epigenetic regulation to RA pathogenesis, with DNA methylation representing one of the most important and reversible epigenetic mechanisms(Huang et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Kielbowski et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). DNA methylation has been shown to influence gene expression, immune-cell activation, and cell differentiation(Cai and Yao \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). To date, most studies have focused on peripheral blood samples, identifying both global and locus-specific abnormalities in DNA methylation among RA patients, and suggesting its involvement in immune dysregulation and potential utility in diagnosis or therapeutic response prediction(Kielbowski et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Huang et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Svendsen et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). However, peripheral immune cells are easily influenced by external stimuli such as infection or medication, and their methylation profiles may not accurately reflect the pathological microenvironment within the synovium(Cai and Yao \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBy contrast, DNA methylation patterns in synovial tissue and synovial-derived cells are more likely to capture RA-specific inflammatory biology. Nevertheless, studies investigating DNA methylation in RA synovium and FLS remain limited. Studies have confirmed that DNA methylation changes in RA-FLS occur at a very early stage of the disease, a stage that has not yet been defined clinically(Karouzakis et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Nakano et al. first reported 1,859 differentially methylated sites in RA-FLS compared with OA-FLS using the Illumina 450K array, implicating methylation changes in diverse cellular functions(Nakano et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Subsequent small-sample studies confirmed that RA-FLS exhibit a distinct and stable methylation signature that can differentiate RA from OA and distinguish synovial samples from different joints(Ai et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). These observations suggest that persistent epigenetic remodeling may drive and maintain the aggressive phenotype of RA-FLS(Whitaker et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). However, most previous studies analyzed only single-omics datasets(Lin and Luo \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), limiting the ability to establish functional links between methylation changes and corresponding gene expression alterations. An integrative study by Zhang et al. combining DNA methylation and transcriptomic sequencing identified methylation-regulated biomarkers such as RGS1 in RA synovium, yet similar multi-omics analysis specifically in RA-FLS has been lacking(Zhang et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn this study, RA-FLS and OA-FLS were sequenced by Illumina 850k DNA methylation chip. In order to better interpret the regulation of DNA methylation on gene expression, transcriptome sequencing was performed simultaneously on the same sample. We identified 18636 genes that may be regulated by DNA methylation through the intersection of the two sets of data, and revealed that these genes were enriched in key biological functions such as cell proliferation, extracellular matrix remodeling, inflammatory response, suggesting that methylation remodeling is widely involved in the pathological activation of FLS. Further protein interaction network construction identified several key modules, among which the gene in module 1 is mainly a member of Rho GTPase family, which is a necessary molecular switch in cells(Chong et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). It is considered to be a core player in regulating various biological processes, and is closely related to cell migration, invasion, extracellular matrix adhesion, cell cycle and cytoskeleton reorganization. It was confirmed that Rho GTPase signal abnormality is related to immune dysregulation in the study of RA(Zeng et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The genes in module 2 are mainly cytokines and their receptors related to cell-cell interactions. TNFSF10, ISG20, VCAM1, CD226, and ICAM5 are closely related to the effects of cytokines, including TNF-α, interferon, integrin, and mediate the interactions between immune cells. Module 3 is mainly growth factor family genes, including fibroblast growth factor (FGF) 1, FGF9, FGF18, platelet-derived growth factor B (PDGFB) and PDGF receptor alpha (PDGFRA). Growth factors (GF) are also considered as a kind of cytokines, which are naturally produced by cells and can stimulate cell growth, differentiation and tissue repair(Goldfarb \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). FGFs are intracellular signaling proteins that are associated with cell proliferation and differentiation, and can regulate wound healing, angiogenesis, the corresponding receptors belong to the receptor tyrosine kinase family, which participate in biological processes such as tumor cell migration and proliferation(Meng et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Xie et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In addition, FGF9 is associated with osteoclast differentiation(Santos-Ocampo et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e1996\u003c/span\u003e). The genes of module 4 are mainly transcriptional regulators.\u003c/p\u003e \u003cp\u003eIn order to further clarify the role of genes regulated by DNA methylation in RA-FLS, the differentially methylated sites and differentially expressed genes between RA and OA were intersected, and a total of 96 DNA methylation-regulated differentially expressed genes (MeDEGs) were obtained. These genes were enriched in the functions of extracellular matrix components, fibroblast growth and proliferation, as well as the stimulation and regulation of growth factors and vascular endothelial growth factor by cells, and were enriched in Rap1 pathway, MAPK pathway, mTOR signaling pathway, Hippo pathway, PI3K-Akt pathway and other signaling pathways, which were closely related to the functional regulation of cells, further proving that abnormal DNA methylation methylation is an important mechanism to promote RA-FLS to acquire an aggressive phenotype(Ding et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAmong the enzymes regulating DNA methylation, DNMT1 is primarily responsible for maintaining methylation patterns during DNA replication. The existing research on DNMT1 in RA is limited, and its expression level in RA is not uniform. Nakano K et al detected that there was no significant difference in protein and mRNA levels between FLS of RA and OA patients(Nakano et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). However, Li XF and other researchers found that the expression of DNMT1 protein in FLS of RA patients and the AIA animal model was increased(Li et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Similarly, Miao CG and Liu YR found that the expression of DNMT1 protein and mRNA levels in synovial tissue and FLS of RA animal models were increased(Miao et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Liu et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). In order to further explore the expression of DNMT1 in RA patients, our study confirmed that DNMT1 was significantly upregulated in synovial tissue and RA-FLS. Functional experiments showed that siRNA-mediated DNMT1 knockdown suppressed FLS proliferation, migration, and invasion, induced G2/M cell-cycle arrest, and markedly reduced IL-6 and RANKL production while increasing OPG levels, collectively attenuating the tumor-like behavior of FLS. In addition, DNMT inhibitor can also significantly reduce joint inflammation and bone erosion in CIA animal models, suggesting that DNMT1-mediated DNA methylation remodeling is not only involved in the acquisition of pathogenicity in RA-FLS, but also has intervention on disease progression.\u003c/p\u003e \u003cp\u003eAlthough this study systematically revealed the abnormal methylation pattern of RA-FLS and demonstrated the key role of DNMT1 mediated DNA methylation in the pathological activation of RA-FLS, there are still several limitations. First, RA synovial samples were obtained mainly from patients with long-standing or treatment-refractory disease, which may introduce selection bias and limit generalizability. Larger cohorts including early-stage and treatment-na\u0026iuml;ve patients are needed to construct a more comprehensive multi-omics atlas of FLS. Second, although we identified the key role of DNMT1 in RA-FLS, the specific regulatory network and epigenetic target molecules downstream of DNMT1 still need to be further analyzed. There may be differences in the methylation regulation mode of DNMT1 in different gene regions. How it affects specific signaling pathways and the core nodes that affect FLS phenotype need to be mechanistically verified by higher resolution technology. In addition, 5-azacytidine used in this study is a non-selective DNMT inhibitor, which may have off-target effects, and other unpredictable adverse reactions may occur when used systemically. Therefore, more selective and safe DNMT1 specific inhibitors need to be developed in the future, and their therapeutic potential and safety should be verified in larger animal models and even clinical samples. Finally, although our study focused on FLS, the interaction between epigenetically altered FLS and immune cells also deserves a detailed study.\u003c/p\u003e \u003cp\u003eIn summary, this study provides integrated multi-omics and functional evidence that aberrant DNA methylation is a defining pathological feature of RA-FLS. We identify DNMT1 as a central regulator of FLS pathogenic activation and joint destruction, and demonstrate that DNMT1 inhibition exerts therapeutic benefits both in vitro and in vivo. This not only deepens the understanding of the epigenetic pathological mechanism of RA, but also provides a theoretical basis for the development of new therapeutic strategies targeting the intrinsic pathogenicity of synovial cells. In the future, targeting DNMT1 and its downstream methylation network is expected to become a new direction for RA treatment, but its safety and effectiveness still need to be further validated through large-scale patient samples and preclinical studies.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThis study suggests that DNMT1-mediated DNA methylation reprogramming is a key driving factor for FLS activation and joint destruction in RA.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe clinical sample collection was approved by the Ethics Committee of Shanxi Bethune Hospital (SBQKL-2021-041).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo individual person’s data are given.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the basic research project of Shanxi Science and Technology Department (202303021222313); Shanxi Province Clinical Research Center for Dermatologic and Immunologic Diseases (LYZX-202305); Research and Innovation Team Project for Scientific Breakthroughs at Shanxi Bethune Hospital (2024AOXIANG02) ; 2024 Annual \"Promising Candidates\" Cultivation Project for National Natural Science Foundation at Shanxi Bethune Hospital (2024GZRZ04).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCRediT authorship contribution statement\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYazhen Su: Methodology, Resources, Visualization, Writing-original draft and editing.\u003c/p\u003e\n\u003cp\u003eZewen Wu: Methodology, Visualization\u003c/p\u003e\n\u003cp\u003eRuijiao Li: Methodology, Visualization\u003c/p\u003e\n\u003cp\u003eHao Xing: Methodology\u003c/p\u003e\n\u003cp\u003eYang Liu: Methodology, Data curation\u003c/p\u003e\n\u003cp\u003eRong Li: Methodology\u003c/p\u003e\n\u003cp\u003eJingxuan Li: Resources\u003c/p\u003e\n\u003cp\u003eXinling Liu: Data curation, Visualization\u003c/p\u003e\n\u003cp\u003eLiyun Zhang: Supervision, Validation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data that support the findings of this study are available within the article upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAi R, Hammaker D, Boyle DL et al (2016) Joint-specific DNA methylation and transcriptome signatures in rheumatoid arthritis identify distinct pathogenic processes. Nat Commun 7:11849. http://doi.org/10.1038/ncomms11849\u003c/li\u003e\n \u003cli\u003eAlivernini S, Firestein GS, Mcinnes IB (2022) The pathogenesis of rheumatoid arthritis. Immunity 55:2255-2270. http://doi.org/10.1016/j.immuni.2022.11.009\u003c/li\u003e\n \u003cli\u003eBallestar E, Sawalha AH, Lu Q (2020) Clinical value of DNA methylation markers in autoimmune rheumatic diseases. Nat Rev Rheumatol 16:514-524. http://doi.org/10.1038/s41584-020-0470-9\u003c/li\u003e\n \u003cli\u003eCai X, Yao Y (2025) Epigenetic modifications of immune cells in rheumatoid arthritis. Ann Med 57:2533432. http://doi.org/10.1080/07853890.2025.2533432\u003c/li\u003e\n \u003cli\u003eCai X, Yao Y (2025) Epigenetic modifications of immune cells in rheumatoid arthritis. Ann Med 57:2533432. http://doi.org/10.1080/07853890.2025.2533432\u003c/li\u003e\n \u003cli\u003eChen Z, Zhang Y (2020) Role of Mammalian DNA Methyltransferases in Development. Annu Rev Biochem 89:135-158. http://doi.org/10.1146/annurev-biochem-103019-102815\u003c/li\u003e\n \u003cli\u003eChong CF, Hasnizan N, Ahmad MA (2023) Navigating the landscape of Rho GTPase signalling system in autoimmunity: A bibliometric analysis spanning over three decades (1990 to 2023). Cell Signal 111:110855. http://doi.org/10.1016/j.cellsig.2023.110855\u003c/li\u003e\n \u003cli\u003eChu CQ (2020) Fibroblasts in Rheumatoid Arthritis. N Engl J Med 383:1679-1681. http://doi.org/10.1056/NEJMcibr2024718\u003c/li\u003e\n \u003cli\u003eDeng T, Wang Z, Geng Q et al (2024) Methylation of T and B Lymphocytes in Autoimmune Rheumatic Diseases. Clin Rev Allergy Immunol 66:401-422. http://doi.org/10.1007/s12016-024-09003-4\u003c/li\u003e\n \u003cli\u003eDi Matteo A, Bathon JM, Emery P (2023) Rheumatoid arthritis. Lancet 402:2019-2033. http://doi.org/10.1016/S0140-6736(23)01525-8\u003c/li\u003e\n \u003cli\u003eDing Q, Hu W, Wang R et al (2023) Signaling pathways in rheumatoid arthritis: implications for targeted therapy. Signal Transduct Target Ther 8:68. http://doi.org/10.1038/s41392-023-01331-9\u003c/li\u003e\n \u003cli\u003eGao Y, Zhang Y, Liu X (2024) Rheumatoid arthritis: pathogenesis and therapeutic advances. Medcomm (2020) 5:e509. http://doi.org/10.1002/mco2.509\u003c/li\u003e\n \u003cli\u003eGoldfarb M (2005) Fibroblast growth factor homologous factors: evolution, structure, and function. Cytokine Growth Factor Rev 16:215-220. http://doi.org/10.1016/j.cytogfr.2005.02.002\u003c/li\u003e\n \u003cli\u003eGravallese EM, Firestein GS (2023) Rheumatoid Arthritis - Common Origins, Divergent Mechanisms. The New England Journal of Medicine 388:529-542. http://doi.org/10.1056/NEJMra2103726\u003c/li\u003e\n \u003cli\u003eHuang C, Liang Y, Li Y et al (2025) The epigenetic landscape of rheumatoid arthritis: Pathogenesis and drug therapeutic potentials. Acta Pharm Sin B 15:5601-5631. http://doi.org/10.1016/j.apsb.2025.08.022\u003c/li\u003e\n \u003cli\u003eHuang X, Huang L, Gao X et al (2024) Global research trends in DNA methylation in rheumatoid arthritis: A bibliometric analysis and visual analysis. Medicine (Baltimore) 103:e36218. http://doi.org/10.1097/MD.0000000000036218\u003c/li\u003e\n \u003cli\u003eKarami J, Aslani S, Tahmasebi MN et al (2020) Epigenetics in rheumatoid arthritis; fibroblast-like synoviocytes as an emerging paradigm in the pathogenesis of the disease. Immunol Cell Biol 98:171-186. http://doi.org/10.1111/imcb.12311\u003c/li\u003e\n \u003cli\u003eKarouzakis E, Raza K, Kolling C et al (2018) Analysis of early changes in DNA methylation in synovial fibroblasts of RA patients before diagnosis. Sci Rep 8:7370. http://doi.org/10.1038/s41598-018-24240-2\u003c/li\u003e\n \u003cli\u003eKielbowski K, Bakinowska E, Goracy-Rosik A et al (2025) DNA and RNA Methylation in Rheumatoid Arthritis-A Narrative Review. Epigenomes 9 http://doi.org/10.3390/epigenomes9010002\u003c/li\u003e\n \u003cli\u003eKomatsu N, Takayanagi H (2022) Mechanisms of joint destruction in rheumatoid arthritis - immune cell-fibroblast-bone interactions. Nature Reviews. Rheumatology 18:415-429. http://doi.org/10.1038/s41584-022-00793-5\u003c/li\u003e\n \u003cli\u003eLee ES, Ko H, Kim CH et al (2023) Disease-microenvironment modulation by bare- or engineered-exosome for rheumatoid arthritis treatment. Biomater Res 27:81. http://doi.org/10.1186/s40824-023-00418-2\u003c/li\u003e\n \u003cli\u003eLi X, Wu S, Yan Q et al (2021) PTEN Methylation Promotes Inflammation and Activation of Fibroblast-Like Synoviocytes in Rheumatoid Arthritis. Front Pharmacol 12:700373. http://doi.org/10.3389/fphar.2021.700373\u003c/li\u003e\n \u003cli\u003eLin Y, Luo Z (2017) Aberrant methylation patterns affect the molecular pathogenesis of rheumatoid arthritis. Int Immunopharmacol 46:141-145. http://doi.org/10.1016/j.intimp.2017.02.008\u003c/li\u003e\n \u003cli\u003eLiu YR, Yang L, Xu QQ et al (2019) Long noncoding RNA MEG3 regulates rheumatoid arthritis by targeting NLRC5. J Cell Physiol 234:14270-14284. http://doi.org/10.1002/jcp.28126\u003c/li\u003e\n \u003cli\u003eMasoumi M, Bashiri H, Khorramdelazad H et al (2021) Destructive Roles of Fibroblast-like Synoviocytes in Chronic Inflammation and Joint Damage in Rheumatoid Arthritis. Inflammation 44:466-479. http://doi.org/10.1007/s10753-020-01371-1\u003c/li\u003e\n \u003cli\u003eMeng X, Chen Z, Li T et al (2024) Role and Therapeutic Potential for Targeting Fibroblast Growth Factor 10/FGFR1 in Relapsed Rheumatoid Arthritis. Arthritis Rheumatol 76:32-47. http://doi.org/10.1002/art.42674\u003c/li\u003e\n \u003cli\u003eMiao CG, Yang YY, He X et al (2014) MicroRNA-152 modulates the canonical Wnt pathway activation by targeting DNA methyltransferase 1 in arthritic rat model. Biochimie 106:149-156. http://doi.org/10.1016/j.biochi.2014.08.016\u003c/li\u003e\n \u003cli\u003eNagy G, Roodenrijs N, Welsing PM et al (2021) EULAR definition of difficult-to-treat rheumatoid arthritis. Ann Rheum Dis 80:31-35. http://doi.org/10.1136/annrheumdis-2020-217344\u003c/li\u003e\n \u003cli\u003eNakano K, Boyle DL, Firestein GS (2013) Regulation of DNA methylation in rheumatoid arthritis synoviocytes. J Immunol 190:1297-1303. http://doi.org/10.4049/jimmunol.1202572\u003c/li\u003e\n \u003cli\u003eNakano K, Whitaker JW, Boyle DL et al (2013) DNA methylome signature in rheumatoid arthritis. Ann Rheum Dis 72:110-117. http://doi.org/10.1136/annrheumdis-2012-201526\u003c/li\u003e\n \u003cli\u003eN\u0026eacute;meth T, Nagy G, Pap T (2022) Synovial fibroblasts as potential drug targets in rheumatoid arthritis, where do we stand and where shall we go? Ann Rheum Dis 81:1055-1064. http://doi.org/10.1136/annrheumdis-2021-222021\u003c/li\u003e\n \u003cli\u003eNygaard G, Firestein GS (2020) Restoring synovial homeostasis in rheumatoid arthritis by targeting fibroblast-like synoviocytes. Nat Rev Rheumatol 16:316-333. http://doi.org/10.1038/s41584-020-0413-5\u003c/li\u003e\n \u003cli\u003eSantos-Ocampo S, Colvin JS, Chellaiah A et al (1996) Expression and biological activity of mouse fibroblast growth factor-9. J Biol Chem 271:1726-1731. http://doi.org/10.1074/jbc.271.3.1726\u003c/li\u003e\n \u003cli\u003eSong J, Teplova M, Ishibe-Murakami S et al (2012) Structure-based mechanistic insights into DNMT1-mediated maintenance DNA methylation. Science (New York, N.Y.) 335:709-712. http://doi.org/10.1126/science.1214453\u003c/li\u003e\n \u003cli\u003eSvendsen AJ, Mengel-From J, Junker P et al (2025) Differential DNA methylation patterns in whole blood from ACPA-positive patients with DMARD-na\u0026iuml;ve rheumatoid arthritis at clinical disease onset. Front Immunol 16:1488161. http://doi.org/10.3389/fimmu.2025.1488161\u003c/li\u003e\n \u003cli\u003eWeyand CM, Goronzy JJ (2021) The immunology of rheumatoid arthritis. Nat Immunol 22:10-18. http://doi.org/10.1038/s41590-020-00816-x\u003c/li\u003e\n \u003cli\u003eWhitaker JW, Shoemaker R, Boyle DL et al (2013) An imprinted rheumatoid arthritis methylome signature reflects pathogenic phenotype. Genome Med 5:40. http://doi.org/10.1186/gm444\u003c/li\u003e\n \u003cli\u003eWu Z, Ma D, Yang H et al (2021) Fibroblast-like synoviocytes in rheumatoid arthritis: Surface markers and phenotypes. Int Immunopharmacol 93:107392. http://doi.org/10.1016/j.intimp.2021.107392\u003c/li\u003e\n \u003cli\u003eXie W, Chen T, Xiao S et al (2025) Difficult-to-treat rheumatoid arthritis (part 1): systematic review and meta-analysis on global prevalence. Ann Rheum Dis http://doi.org/10.1016/j.ard.2025.10.007\u003c/li\u003e\n \u003cli\u003eXie Y, Su N, Yang J et al (2020) FGF/FGFR signaling in health and disease. Signal Transduct Target Ther 5:181. http://doi.org/10.1038/s41392-020-00222-7\u003c/li\u003e\n \u003cli\u003eZeng R, Zhuo Z, Luo Y et al (2022) Rho GTPase signaling in rheumatic diseases. Iscience 25:103620. http://doi.org/10.1016/j.isci.2021.103620\u003c/li\u003e\n \u003cli\u003eZhang R, Chang C, Jin Y et al (2022) Identification of DNA methylation-regulated differentially expressed genes in RA by integrated analysis of DNA methylation and RNA-Seq data. J Transl Med 20:481. http://doi.org/10.1186/s12967-022-03664-5\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"rheumatoid arthritis, fibroblast-like synoviocytes, DNA methylation, DNMT1, epigenetic regulation","lastPublishedDoi":"10.21203/rs.3.rs-8601364/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8601364/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eRheumatoid arthritis (RA) is a chronic autoimmune disease characterized by synovial hyperplasia and joint destruction, in which fibroblast-like synoviocytes (FLS) play a pivotal role. However, the epigenetic mechanisms underlying FLS activation remain incompletely understood.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eDNA methylation chip sequencing and transcriptome sequencing were performed on FLS of RA and Osteoarthritis (OA) patients, then analyzed the role of DNA methylation in RA-FLS. DNA methyltransferase 1 (DNMT1) is the major enzyme regulating DNA methylation. In in vitro and in vivo experiments, the effects of inhibiting DNMT1 on FLS function and CIA mouse synovitis were detected.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eCompared with OA-FLS, RA-FLS exhibited 353 upregulated and 144 downregulated genes, mainly enriched in extracellular matrix organization, angiogenesis, and inflammatory signaling pathways such as PI3K-AKT, MAPK, and JAK-STAT. DNA methylation analysis identified 2550 hypermethylated and 3300 hypomethylated genes, which were enriched in pathways associated with cell proliferation, migration, and apoptosis. Integration of transcriptomic and methylation data revealed 96 methylation-regulated differentially expressed genes (MeDEGs), involving Rap1, mTOR, and Hippo signaling. Among key regulators, DNA methyltransferase 1 (DNMT1) was markedly up-regulated in RA-FLS. Functional validation showed that DNMT1 knock-down significantly suppressed RA-FLS proliferation, migration, invasion, induced G2/M cell-cycle arrest and reduced pro-inflammatory cytokine secretion. In collagen-induced arthritis (CIA) mouse model, pharmacological inhibition of DNMT1 alleviated joint inflammation and bone erosion without significant systemic toxicity.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eOur findings demonstrate that aberrant DNA methylation contributes to RA-FLS activation, with DNMT1 serving as a key epigenetic driver of their \u0026ldquo;tumor-like\u0026rdquo; phenotype. Targeting DNMT1 may represent a promising therapeutic strategy for RA.\u003c/p\u003e","manuscriptTitle":"Abnormal DNA methylation pattern and expression of DNA methyltransferase 1 promote synovitis and bone destruction in rheumatoid arthritis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-22 08:25:52","doi":"10.21203/rs.3.rs-8601364/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b56b8218-c772-408c-b741-8471a00a146e","owner":[],"postedDate":"January 22nd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-01-23T05:10:22+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-22 08:25:52","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8601364","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8601364","identity":"rs-8601364","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}