[Traditional Chinese medicine therapy for rheumatoid arthritis: a review].

Rheumatoid arthritis(RA) is an autoimmune disease that seriously affects the physical and mental health of patients, but its pathogenesis is still unclear. At present, clinical treatment drugs include conventional synthetic disease modifing anti-rheumatic drugs(csDMARDs), nonsteroid anti-inflammtory drugs(NSAIDs), hormones, small molecule targeted drugs, biological agents, etc. These drugs can relieve the clinical symptoms of most patients with RA to a certain extent, but there are still many limitations, such as drug adverse reactions and individual differences in drug efficacy. Therefore, the research on drug treatment targets and the development of low-toxicity drugs helps further improve the precise prevention, diagnosis, and treatment of RA. There is an urgent need for efficient and low-toxic treatments to delay the clinical progress of RA. As a treasure of Chinese culture, traditional Chinese medicine(TCM) is widely used as an alternative therapy in the treatment of various diseases, and has a significant clinical efficacy. TCM therapy(including monomer traditional Chinese medicine, classical compounds, and non-drug therapies) has a significant curative effect on RA. Based on the literature research in recent years, this paper reviewed the clinical and mechanism research of TCM therapy in the treatment of RA, and provided more in-depth thinking for the wide application of TCM therapy in clinical practice.

Identification of DNA methylation-regulated differentially expressed genes in RA by integrated analysis of DNA methylation and RNA-Seq data.

OBJECTIVE: To identify novel DNA methylation-regulated differentially expressed genes (MeDEGs) in RA by integrated analysis of DNA methylation and RNA-Seq data. METHODS: The transcription and DNA methylation profiles of 9 RA and 15 OA synovial tissue were generated by RNA-Seq and Illumina 850K DNA methylation BeadChip. Gene set enrichment analysis (GSEA) and Weighted gene co-expression network analysis (WGCNA) were used to analyze methylation-regulated expressed genes by R software. The differentially expressed genes (DEGs), differentially methylated probes (DMPs), differentially methylated genes (DMGs) were analyzed by DESeq and ChAMP R package. The functional correlation of MeDEGs was analyzed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). The protein-protein interaction (PPI) network of MeDEGs was constructed by STRING and Reactome FI Cytoscape Plugin. Correlation analysis between methylation level and mRNA expression was conducted with R software. RESULTS: A total of 17,736 genes, 25,578 methylated genes and 755,852 methylation probes were detected. A total of 16,421 methylation-regulated expressed genes were obtained. The GSEA showed that these genes are associated with activation of immune response, adaptive immune response, Inflammatory response in C5 (ontology gene sets). For KEGG analysis, these genes are associated with rheumatoid arthritis, NF-kappa B signaling pathway, T cell receptor signaling pathway. The WGCNA showed that the turquoise module exhibited the strongest correlation with RA (R = 0.78, P = 1.27 × 10- 05), 660 genes were screened in the turquoise module. A total of 707 MeDEGs were obtained. GO analysis showed that MeDEGs were enriched in signal transduction, cell adhesion for BP, enriched in plasma membrane, integral component of membrane for CC, and enriched in identical protein binding, calcium ion binding for MF. The KEGG pathway analysis showed that the MeDEGs were enriched in calcium signaling pathway, T cell receptor signaling pathway, NF-kappa B signaling pathway, Rheumatoid arthritis. The PPI network containing 706 nodes and 882 edges, and the enrichment p value < 1.0 × 10- 16. With Cytoscape, based on the range of more than 10 genes, a total of 8 modules were screened out. Spearman correlation analysis showed RGS1(cg10718027), RGS1(cg02586212), RGS1(cg10861751) were significantly correlated with RA. CONCLUSIONS: RGS1 can be used as novel methylated biomarkers for RA.

[Network Meta-analysis of efficacy and safety of Chinese patent medicines in treatment of ankylosing spondylitis].

To evaluate the efficacy and safety of Chinese patent medicines in the treatment of ankylosing spondylitis(AS) by frequency network Meta-analysis. Randomized controlled trials(RCTs)of Chinese patent medicines for AS were retrieved from CNKI, Wanfang, VIP, CBM, PubMed, EMbase and Cochrane Library databases from the time of database establishment to January 2021. The quality of the included RCTs was evaluated according to the Cochrane bias risk standard, and the data was analyzed by RevMan 5.3 and Stata/MP 15.1. A total of 12 kinds of Chinese patent medicines in 55 RCTs were included. According to Meta-analysis, in term of the effectiveness, the top three optimal medication regimens were Biqi Capsules, Yishen Juanbi Pills and Yaobitong Capsules combined with western medicine. The top three interventions to reduce the erythrocyte sedimentation rate(ESR)were Yishen Juanbi Pills, Xianling Gubao Capsules and Fufang Xuanju Capsules combined with western medicine. The top three interventions to reduce the C-reactive protein(CRP)were Biqi Capsules, Xianling Gubao Capsules and Fufang Xuanju Capsules combined with western medicine. In terms of the safety, top three optimal medication regimens were Total Glucosides of Paeony Capsules, Yishen Juanbi Pills, and Wangbi Tablets combined with western medicine. This network Meta-analysis suggests that Chinese patent medicines combined with conventional western medicine can effectively improve the joint pain symptoms of AS patients and reduce the acute inflammatory indicators, with high safety. However, the literature included in this study is generally of low methodological quality, and the conclusion needs to be verified by high-quality research.

Role of SIRT2 in regulating the dexamethasone-activated autophagy pathway in skeletal muscle atrophy.

The proteolytic autophagy system is involved in a major regulatory pathway in dexamethasone (Dex)-induced muscle atrophy. Sirtuin 2 (SIRT2) is known to modulate autophagy signaling, exerting effects in skeletal muscle atrophy. We examined the effects of SIRT2 on autophagy in Dex-induced myoatrophy. Tostudy this, mice were randomly distributed among the normal, Dex, and sirtinol groups. C2C12 cells were differentiated into myotubes and transduced with lentivirus carrying Sirt2-green fluorescent protein (GFP) or Sirt2 short hairpin RNA (Sirt2-shRNA)-GFP. To evaluate the mass and function of skeletal muscles, we measured myofiber cross-sectional area, myotube size, gastrocnemius (GA) muscle wet mass:body mass ratio (%), and time to exhaustion. The expression levels of SIRT2, myosin heavy chain, microtubule-associated protein 1 light chain 3 (LC3), and Beclin-1 were measured using Western blotting and quantitative reverse transcription - polymerase chain reaction. Inhibition of SIRT2 markedly attenuated GA muscle mass and endurance capacity. The same phenotype was observed in Sirt2-shRNA-treated myotubes, as evidenced by their decreased size. Conversely, overexpression of SIRT2 alleviated Dex-induced myoatrophy in vitro. Moreover, SIRT2 negatively regulated the expression of LC3b and Beclin-1 in skeletal muscles. These findings suggest that SIRT2 activation protects myotubes against Dex-induced atrophy through inhibition of the autophagy system; this phenomenon may serve as a target for treating glucocorticoid-induced myopathy.

Advancing precision rheumatology: applications of machine learning for rheumatoid arthritis management.

Rheumatoid arthritis (RA) is an autoimmune disease causing progressive joint damage. Early diagnosis and treatment is critical, but remains challenging due to RA complexity and heterogeneity. Machine learning (ML) techniques may enhance RA management by identifying patterns within multidimensional biomedical data to improve classification, diagnosis, and treatment predictions. In this review, we summarize the applications of ML for RA management. Emerging studies or applications have developed diagnostic and predictive models for RA that utilize a variety of data modalities, including electronic health records, imaging, and multi-omics data. High-performance supervised learning models have demonstrated an Area Under the Curve (AUC) exceeding 0.85, which is used for identifying RA patients and predicting treatment responses. Unsupervised learning has revealed potential RA subtypes. Ongoing research is integrating multimodal data with deep learning to further improve performance. However, key challenges remain regarding model overfitting, generalizability, validation in clinical settings, and interpretability. Small sample sizes and lack of diverse population testing risks overestimating model performance. Prospective studies evaluating real-world clinical utility are lacking. Enhancing model interpretability is critical for clinician acceptance. In summary, while ML shows promise for transforming RA management through earlier diagnosis and optimized treatment, larger scale multisite data, prospective clinical validation of interpretable models, and testing across diverse populations is still needed. As these gaps are addressed, ML may pave the way towards precision medicine in RA.

Identification of clinical characteristics biomarkers for rheumatoid arthritis through targeted DNA methylation sequencing.

OBJECTIVES: Rheumatoid arthritis (RA) is a complex disease with a challenging diagnosis, especially in seronegative patients. The aim of this study is to investigate whether the methylation sites associated with the overall immune response in RA can assist in clinical diagnosis, using targeted methylation sequencing technology on peripheral venous blood samples. METHODS: The study enrolled 241 RA patients, 30 osteoarthritis patients (OA), and 30 healthy volunteers control (HC). Fifty significant cytosine guanine (CG) sites between undifferentiated arthritis and RA were selected and analyzed using targeted DNA methylation sequencing. Logistic regression models were used to establish diagnostic models for different clinical features of RA, and six machine learning methods (logit model, random forest, support vector machine, adaboost, naive bayes, and learning vector quantization) were used to construct clinical diagnostic models for different subtypes of RA. Least absolute shrinkage and selection operator regression and detrended correspondence analysis were utilized to screen for important CGs. Spearman correlation was used to calculate the correlation coefficient. RESULTS: The study identified 16 important CG sites, including tumor necrosis factort receptor associated factor 5 (TRAF5) (chr1:211500151), mothers against decapentaplegic homolog 3 (SMAD3) (chr15:67357339), tumor endothelial marker 1 (CD248) (chr11:66083766), lysosomal trafficking regulator (LYST) (chr1:235998714), PR domain zinc finger protein 16 (PRDM16) (chr1:3307069), A-kinase anchoring protein 10 (AKAP10) (chr17:19850460), G protein subunit gamma 7 (GNG7) (chr19:2546620), yes1 associated transcriptional regulator (YAP1) (chr11:101980632), PRDM16 (chr1:3163969), histone deacetylase complex subunit sin3a (SIN3A) (chr15:75747445), prenylated rab acceptor protein 2 (ARL6IP5) (chr3:69134502), mitogen-activated protein kinase kinase kinase 4 (MAP3K4) (chr6:161412392), wnt family member 7A (WNT7A) (chr3:13895991), inhibin subunit beta B (INHBB) (chr2:121107018), deoxyribonucleic acid replication helicase/nuclease 2 (DNA2) (chr10:70231628) and chromosome 14 open reading frame 180 (C14orf180) (chr14:105055171). Seven CG sites showed abnormal changes between the three groups (P < 0.05), and 16 CG sites were significantly correlated with common clinical indicators (P < 0.05). Diagnostic models constructed using different CG sites had an area under the receiver operating characteristic curve (AUC) range of 0.64-0.78 for high-level clinical indicators of high clinical value, with specificity ranging from 0.42 to 0.77 and sensitivity ranging from 0.57 to 0.88. The AUC range for low-level clinical indicators of high clinical value was 0.63-0.72, with specificity ranging from 0.48 to 0.74 and sensitivity ranging from 0.72 to 0.88. Diagnostic models constructed using different CG sites showed good overall diagnostic accuracy for the four subtypes of RA, with an accuracy range of 0.61-0.96, a balanced accuracy range of 0.46-0.94, and an AUC range of 0.46-0.94. CONCLUSIONS: This study identified potential clinical diagnostic biomarkers for RA and provided novel insights into the diagnosis and subtyping of RA. The use of targeted deoxyribonucleic acid (DNA) methylation sequencing and machine learning methods for establishing diagnostic models for different clinical features and subtypes of RA is innovative and can improve the accuracy and efficiency of RA diagnosis.

Macrophage polarization in rheumatoid arthritis: signaling pathways, metabolic reprogramming, and crosstalk with synovial fibroblasts.

Rheumatoid arthritis (RA) is a chronic autoimmune disease characterized by persistent synovial inflammation and progressive joint destruction. Macrophages are key effector cells that play a central role in RA pathogenesis through their ability to polarize into distinct functional phenotypes. An imbalance favoring pro-inflammatory M1 macrophages over anti-inflammatory M2 macrophages disrupts immune homeostasis and exacerbates joint inflammation. Multiple signaling pathways, including Notch, JAK/STAT, NF-κb, and MAPK, regulate macrophage polarization towards the M1 phenotype in RA. Metabolic reprogramming also contributes to this process, with M1 macrophages prioritizing glycolysis while M2 macrophages utilize oxidative phosphorylation. Redressing this imbalance by modulating macrophage polarization and metabolic state represents a promising therapeutic strategy. Furthermore, complex bidirectional interactions exist between synovial macrophages and fibroblast-like synoviocytes (FLS), forming a self-perpetuating inflammatory loop. Macrophage-derived factors promote aggressive phenotypes in FLS, while FLS-secreted mediators contribute to aberrant macrophage activation. Elucidating the signaling networks governing macrophage polarization, metabolic adaptations, and crosstalk with FLS is crucial to developing targeted therapies that can restore immune homeostasis and mitigate joint pathology in RA.

Targeted siRNA Therapy for Psoriasis: Translating Preclinical Potential into Clinical Treatments.

Psoriasis is a chronic inflammatory skin disease characterized by the excessive proliferation of keratinocytes and heightened immune activation. Targeting pathogenic genes through small interfering RNA (siRNA) therapy represents a promising strategy for the treatment of psoriasis. This mini-review provides a comprehensive summary of siRNA research targeting the pathogenesis of psoriasis, covering aspects such as keratinocyte function, inflammatory cell roles, preclinical animal studies, and siRNA delivery mechanisms. It details recent advancements in RNA interference that modulate key factors including keratinocyte proliferation (Fibroblast Growth Factor Receptor 2, FGFR2), apoptosis (Interferon Alpha Inducible Protein 6, G1P3), differentiation (Grainyhead Like Transcription Factor 2, GRHL2), and angiogenesis (Vascular Endothelial Growth Factor, VEGF); immune cell infiltration and inflammation (Tumor Necrosis Factor-Alpha, TNF-α; Interleukin-17, IL-17); and signaling pathways (JAK-STAT, Nuclear Factor Kappa B, NF-κB) that govern immunopathology. Despite significant advances in siRNA-targeted treatments for psoriasis, several challenges persist. Continued scientific developments promise the creation of more effective and safer siRNA medications, potentially enhancing the quality of life for psoriasis patients and revolutionizing treatments for other diseases. This article focuses on the most recent research advancements in targeting the pathogenesis of psoriasis with siRNA and explores its future therapeutic prospects.

Circulating Level of Blood Iron and Copper Associated with Inflammation and Disease Activity of Rheumatoid Arthritis.

This study aims to compare the concentrations of circulating levels of iron, zinc, and copper in blood samples of rheumatoid arthritis (RA) patients which determine the correlations with inflammation and disease activity. A total of 102 RA patients and 66 healthy controls were enrolled. Circulation of iron, zinc, and copper levels in whole blood were assessed. Hemoglobin, C-reactive protein (CRP), erythrocyte sedimentation rate (ESR), anticyclic citrullinated peptide antibody (anti-CCP) levels were collected. A meta-analysis was performed to validate our findings. Single and multiple variate generalized linear regression were applied to identify the correlation between trace elements and clinical characteristics. Blood copper level was significantly higher in RA patients (P < 0.001), while iron and zinc levels were decreased (P < 0.001 and P = 0.02, respectively). Meta-analysis confirmed our findings for zinc (SMD = - 1.17, P < 0.001) and copper (SMD = 1.24, P < 0.001). Copper level was positively correlated with DAS28-CRP (r = 0.35, P < 0.01), CRP (r = 0.45, P < 0.01) and ESR (r = 0.58, P < 0.01). Iron level was negatively correlated with DAS28-CRP (r = - 0.37, P < 0.01), CRP (r = - 0.46, P < 0.01) and ESR (r = - 0.55, P < 0.01). Circulating blood copper was significantly higher and positively correlated with DAS28-CRP and inflammatory markers, while circulating blood iron was decreased and negatively correlated with DAS28-CRP and inflammatory markers in RA patients.

Case report: Whole exome sequencing and genome-wide methylation profiling of Czech dysplasia in a Chinese pedigree.

Background: Czech dysplasia is a rare skeletal disorder with symptomatology including platyspondyly, brachydactyly of the third and fourth toes, and early-onset progressive pseudorheumatoid arthritis. The disorder segregates in an autosomal dominant fashion. A specific missense mutation (R275C, c.823C > T) in exon 13 of the COL2A1 gene has been identified in German and Japanese families. Case summary: We present the case of a Chinese woman diagnosed with Czech dysplasia (proband) who carried a variant in the COL2A1 gene. Whole-exome sequencing (WES) identified the COL2A1 missense mutation (R275C, c.823C > T) in close relatives of the proband who also exhibited the same disorder. Conclusion: This study is a thorough clinical and physiological description of Czech dysplasia in a Chinese patient.

Circulating DNA methylation level of CXCR5 correlates with inflammation in patients with rheumatoid arthritis.

OBJECTIVES: To assess the differences in circulating DNA methylation levels of CXCR5 between rheumatoid arthritis (RA) and osteoarthritis (OA) and healthy controls (HC), and the correlation of methylation changes with clinical characteristics of RA patients. METHODS: Peripheral blood samples were collected from 239 RA patients, 30 patients with OA, and 29 HC. Target region methylation sequencing to the promoter region of CXCR5 was achieved using MethylTarget. The methylation level of cg04537602 and methylation haplotype were compared among the three groups, and the correlation between methylation levels and clinical characteristics of RA patients was performed by Spearman's rank correlation analysis. RESULTS: The methylation level of cg04537602 was significantly higher in the peripheral blood of RA patients compared with OA patients (p = 1.3 × 10-3 ) and in the HC group (p = 5.5 × 10- 4 ). The sensitivity was enhanced when CXCR5 methylation level combined with rheumatoid factor and anti-cyclic citrullinated peptide with area under curve (AUC) of 0.982 (95% confidence interval 0.970-0.995). The methylation level of cg04537602 in RA was positively correlated with C-reactive protein (CRP) (r = .16, p = .01), and in RA patients aged 60 years and above, cg04537602 methylation levels were positively correlated with CRP (r = .31, p = 4.7 × 10- 4 ), tender joint count (r = .21, p = .02), visual analog scales score (r = .21, p = .02), Disease Activity Score in 28 joints (DAS28) using the CRP level DAS28-CRP (r = .27, p = 2.1 × 10- 3 ), and DAS28-ESR (r = .22, p = .01). We also observed significant differences of DNA methylation haplotypes in RA patients compared with OA patients and HC, which was consistent with single-loci-based CpG methylation measurement. CONCLUSION: The methylation level of CXCR5 was significantly higher in RA patients than in OA and HC, and correlated with the level of inflammation in RA patients, our study establishes a link between CXCR5 DNA methylation and clinical features that may help in the diagnosis and disease management of RA patients.

Identification of immunological characterization and Anoikis-related molecular clusters in rheumatoid arthritis.

Objective: To investigate the potential association between Anoikis-related genes, which are responsible for preventing abnormal cellular proliferation, and rheumatoid arthritis (RA). Methods: Datasets GSE89408, GSE198520, and GSE97165 were obtained from the GEO with 282 RA patients and 28 healthy controls. We performed differential analysis of all genes and HLA genes. We performed a protein-protein interaction network analysis and identified hub genes based on STRING and cytoscape. Consistent clustering was performed with subgrouping of the disease. SsGSEA were used to calculate immune cell infiltration. Spearman's correlation analysis was employed to identify correlations. Enrichment scores of the GO and KEGG were calculated with the ssGSEA algorithm. The WGCNA and the DGIdb database were used to mine hub genes' interactions with drugs. Results: There were 26 differentially expressed Anoikis-related genes (FDR = 0.05, log2FC = 1) and HLA genes exhibited differential expression (P < 0.05) between the disease and control groups. Protein-protein interaction was observed among differentially expressed genes, and the correlation between PIM2 and RAC2 was found to be the highest; There were significant differences in the degree of immune cell infiltration between most of the immune cell types in the disease group and normal controls (P < 0.05). Anoikis-related genes were highly correlated with HLA genes. Based on the expression of Anoikis-related genes, RA patients were divided into two disease subtypes (cluster1 and cluster2). There were 59 differentially expressed Anoikis-related genes found, which exhibited significant differences in functional enrichment, immune cell infiltration degree, and HLA gene expression (P < 0.05). Cluster2 had significantly higher levels in all aspects than cluster1 did. The co-expression network analysis showed that cluster1 had 51 hub differentially expressed genes and cluster2 had 72 hub differentially expressed genes. Among them, three hub genes of cluster1 were interconnected with 187 drugs, and five hub genes of cluster2 were interconnected with 57 drugs. Conclusion: Our study identified a link between Anoikis-related genes and RA, and two distinct subtypes of RA were determined based on Anoikis-related gene expression. Notably, cluster2 may represent a more severe state of RA.

A comprehensive review of Tripterygium wilfordii hook. f. in the treatment of rheumatic and autoimmune diseases: Bioactive compounds, mechanisms of action, and future directions.

Rheumatic and autoimmune diseases are a group of immune system-related disorders wherein the immune system mistakenly attacks and damages the body's tissues and organs. This excessive immune response leads to inflammation, tissue damage, and functional impairment. Therapeutic approaches typically involve medications that regulate immune responses, reduce inflammation, alleviate symptoms, and target specific damaged organs. Tripterygium wilfordii Hook. f., a traditional Chinese medicinal plant, has been widely studied in recent years for its application in the treatment of autoimmune diseases, including rheumatoid arthritis, systemic lupus erythematosus, and multiple sclerosis. Numerous studies have shown that preparations of Tripterygium wilfordii have anti-inflammatory, immunomodulatory, and immunosuppressive effects, which effectively improve the symptoms and quality of life of patients with autoimmune diseases, whereas the active metabolites of T. wilfordii have been demonstrated to inhibit immune cell activation, regulate the production of inflammatory factors, and modulate the immune system. However, although these effects contribute to reductions in inflammatory responses and the suppression of autoimmune reactions, as well as minimize tissue and organ damage, the underlying mechanisms of action require further investigation. Moreover, despite the efficacy of T. wilfordii in the treatment of autoimmune diseases, its toxicity and side effects, including its potential hepatotoxicity and nephrotoxicity, warrant a thorough assessment. Furthermore, to maximize the therapeutic benefits of this plant in the treatment of autoimmune diseases and enable more patients to utilize these benefits, efforts should be made to strengthen the regulation and standardized use of T. wilfordii.

Role of signaling lymphocytic activation molecule family of receptors in the pathogenesis of rheumatoid arthritis: insights and application.

Rheumatoid arthritis (RA) is an autoimmune disease characterized by chronic inflammation and joint damage. The signaling lymphocytic activation molecule (SLAMF) family of receptors are expressed on various hematopoietic and non-hematopoietic cells and can regulate both immune cell activation and cytokine production. Altered expression of certain SLAMF receptors contributes to aberrant immune responses in RA. In RA, SLAMF1 is upregulated on T cells and may promote inflammation by participating in immune cell-mediated responses. SLAMF2 and SLAMF4 are involved in regulating monocyte tumor necrosis factor production and promoting inflammation. SLAMF7 activates multiple inflammatory pathways in macrophages to drive inflammatory gene expression. SLAMF8 inhibition can reduce inflammation in RA by blocking ERK/MMPs signaling. Of note, there are differences in SLAMF receptor (SFR) expression between normal and arthritic joint tissues, suggesting a role as potential diagnostic biomarkers. This review summarizes recent advances on the roles of SLAMF receptors 1, 2, 4, 7, and 8 in RA pathogenesis. However, further research is needed to elucidate the mechanisms of SLAMF regulation of immune cells in RA. Understanding interactions between SLAMF receptors and immune cells will help identify selective strategies for targeting SLAMF signaling without compromising normal immunity. Overall, the SLAMF gene family holds promise as a target for precision medicine in RA, but additional investigation of the underlying immunological mechanisms is needed. Targeting SLAMF receptors presents opportunities for new diagnostic and therapeutic approaches to dampen damaging immune-mediated inflammation in RA.

Inflammatory Response to Regulated Cell Death in Gout and Its Functional Implications.

Gout, a chronic inflammatory arthritis disease, is characterized by hyperuricemia and caused by interactions between genetic, epigenetic, and metabolic factors. Acute gout symptoms are triggered by the inflammatory response to monosodium urate crystals, which is mediated by the innate immune system and immune cells (e.g., macrophages and neutrophils), the NACHT, LRR, and PYD domains-containing protein 3 (NLRP3) inflammasome activation, and pro-inflammatory cytokine (e.g., IL-1ß) release. Recent studies have indicated that the multiple programmed cell death pathways involved in the inflammatory response include pyroptosis, NETosis, necroptosis, and apoptosis, which initiate inflammatory reactions. In this review, we explore the correlation and interactions among these factors and their roles in the pathogenesis of gout to provide future research directions and possibilities for identifying potential novel therapeutic targets and enhancing our understanding of gout pathogenesis.

RNA-seq and Network Analysis Reveal Unique Chemokine Activity Signatures in the Synovial Tissue of Patients With Rheumatoid Arthritis.

Purpose: This study aimed to provide a comprehensive understanding of the genome-wide expression patterns in the synovial tissue samples of patients with rheumatoid arthritis (RA) to investigate the potential mechanisms regulating RA occurrence and development. Methods: Transcription profiles of the synovial tissue samples from nine patients with RA and 15 patients with osteoarthritis (OA) (control) from the East Asian population were generated using RNA sequencing (RNA-seq). Gene set enrichment analysis (GSEA) was used to analyze all the detected genes and the differentially expressed genes (DEGs) were identified using DESeq. To further analyze the DEGs, the Gene Ontology (GO) functional enrichment and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were performed. The protein-protein interaction (PPI) network of the DEGs was constructed using the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) and the hub genes were identified by topology clustering with the Molecular Complex Detection (MCODE)-Cytoscape. The most important hub genes were validated using quantitative real-time PCR (qRT-PCR). Results: Of the 17,736 genes detected, 851 genes were identified as the DEGs (474 upregulated and 377 downregulated genes) using the false discovery rate (FDR) approach. GSEA revealed that the significantly enriched gene sets that positively correlated with RA were CD40 signaling overactivation, Th1 cytotoxic module, overactivation of the immune response, adaptive immune response, effective vs. memory CD8+ T cells (upregulated), and naïve vs. effective CD8+ T cells (downregulated). Biological process enrichment analysis showed that the DEGs were significantly enriched for signal transduction (P = 3.01 × 10-6), immune response (P = 1.65 × 10-24), and inflammatory response (P = 5.76 × 10-10). Molecule function enrichment analysis revealed that the DEGs were enriched in calcium ion binding (P = 1.26 × 10-5), receptor binding (P = 1.26 × 10-5), and cytokine activity (P = 2.01 × 10-3). Cellular component enrichment analysis revealed that the DEGs were significantly enriched in the plasma membrane (P = 1.91 × 10-31), an integral component of the membrane (P = 7.39 × 10-13), and extracellular region (P = 7.63 × 10-11). The KEGG pathway analysis showed that the DEGs were enriched in the cytokine-cytokine receptor interaction (P = 3.05 × 10-17), chemokine signaling (P = 3.50 × 10-7), T-cell receptor signaling (P = 5.17 × 10-4), and RA (P = 5.17 × 10-4) pathways. We confirmed that RA was correlated with the upregulation of the PPI network hub genes, such as CXCL13, CXCL6, CCR5, CXCR5, CCR2, CXCL3, and CXCL10, and the downregulation of the PPI network hub gene such as SSTR1. Conclusion: This study identified and validated the DEGs in the synovial tissue samples of patients with RA, which highlighted the activity of a subset of chemokine genes, thereby providing novel insights into the molecular mechanisms of RA pathogenesis and identifying potential diagnostic and therapeutic targets for RA.

Biomarkers to Predict DMARDs Efficacy and Adverse Effect in Rheumatoid Arthritis.

Rheumatoid arthritis (RA), one of the most common immune system diseases, mainly affects middle-aged and elderly individuals and has a serious impact on the quality of life of patients. Pain and disability caused by RA are significant symptoms negatively affecting patients, and they are especially seen when inappropriate treatment is administered. Effective therapeutic strategies have evolved over the past few decades, with many new disease-modifying antirheumatic drugs (DMARDs) being used in the clinic. Owing to the breakthrough in the treatment of RA, the symptoms of patients who could not be treated effectively in the past few years have been relieved. However, some patients complain about symptoms that have not been reported, implying that there are still some limitations in the RA treatment and evaluation system. In recent years, biomarkers, an effective means of diagnosing and evaluating the condition of patients with RA, have gradually been used in clinical practice to evaluate the therapeutic effect of RA, which is constantly being improved for accurate application of treatment in patients with RA. In this article, we summarize a series of biomarkers that may be helpful in evaluating the therapeutic effect and improving the efficiency of clinical treatment for RA. These efforts may also encourage researchers to devote more time and resources to the study and application of biomarkers, resulting in a new evaluation system that will reduce the inappropriate use of DMARDs, as well as patients' physical pain and financial burden.

G-Protein-Coupled Receptors in Rheumatoid Arthritis: Recent Insights into Mechanisms and Functional Roles.

Rheumatoid arthritis (RA) is a chronic inflammatory disease that leads to joint damage and even disability. Although there are various clinical therapies for RA, some patients still have poor or no response. Thus, the development of new drug targets remains a high priority. In this review, we discuss the role of G-protein-coupled receptors (GPCRs), including chemokine receptors, melanocortin receptors, lipid metabolism-related receptors, adenosine receptors, and other inflammation-related receptors, on mechanisms of RA, such as inflammation, lipid metabolism, angiogenesis, and bone destruction. Additionally, we summarize the latest clinical trials on GPCR targeting to provide a theoretical basis and guidance for the development of innovative GPCR-based clinical drugs for RA.

DNA Methylation of T Lymphocytes as a Therapeutic Target: Implications for Rheumatoid Arthritis Etiology.

Rheumatoid arthritis (RA) is an autoimmune disease that can cause joint damage and disability. Epigenetic variation, especially DNA methylation, has been shown to be involved in almost all the stages of the pathology of RA, from autoantibody production to various self-effector T cells and the defects of protective T cells that can lead to chronic inflammation and erosion of bones and joints. Given the critical role of T cells in the pathology of RA, the regulatory functions of DNA methylation in T cell biology remain unclear. In this review, we elaborate on the relationship between RA pathogenesis and DNA methylation in the context of different T cell populations. We summarize the relevant methylation events in T cell development, differentiation, and T cell-related genes in disease prediction and drug efficacy. Understanding the epigenetic regulation of T cells has the potential to profoundly translate preclinical results into clinical practice and provide a framework for the development of novel, individualized RA therapeutics.

Metabolomics in rheumatoid arthritis: Advances and review.

Rheumatoid arthritis (RA) is an autoimmune disease accompanied by metabolic alterations. The metabolic profiles of patients with RA can be determined using targeted and non-targeted metabolomics technology. Metabolic changes in glucose, lipid, and amino acid levels are involved in glycolysis, the tricarboxylic acid cycle, the pentose phosphate pathway, the arachidonic acid metabolic pathway, and amino acid metabolism. These alterations in metabolic pathways and metabolites can fulfill bio-energetic requirements, promote cell proliferation, drive inflammatory mediator secretion, mediate leukocyte infiltration, induce joint destruction and muscle atrophy, and regulate cell proliferation, which may reflect the etiologies of RA. Differential metabolites can be used as biomarkers for the diagnosis, prognosis, and risk prediction, improving the specificity and accuracy of diagnostics and prognosis prediction. Additionally, metabolic changes associated with therapeutic responses can improve the understanding of drug mechanism. Metabolic homeostasis and regulation are new therapeutic strategies for RA. In this review, we provide a comprehensive overview of advances in metabolomics for RA.