Biomarkers For Rheumatoid Arthritis Research Articles

LncRNA SNHG3 discriminates rheumatoid arthritis from healthy individuals and regulates inflammatory response and oxidative stress via modulating miR-128-3p.

This study evaluated the expression and significance of SNHG3 in rheumatoid arthritis (RA), aiming to explore a biomarker and regulator for RA. The expression of SNHG3 in serum and synovial tissue was compared between RA patients and healthy individuals using polymerase chain reaction (PCR). The RA animal models were induced by the Porcine Type II collagen in Wistar rats and validated by the foot volume and arthritis index score. The human fibroblast-like synoviocytes were treated with lipopolysaccharide (LPS) to mimic the injury during RA onset, and the cell growth was assessed by cell counting kit-8 (CCK8) assay. SNHG3 was significantly downregulated in the serum and synovial tissue of RA patients compared with healthy individuals. Downregulated SNHG3 could discriminate RA patients from healthy individuals with high sensitivity (0.875) and specificity (0.844). Porcine Type II collagen induced increasing foot volume and arthritis index scores of rats, and SNHG3 was downregulated in RA rats. In LPS-induced human fibroblast-like synoviocytes, SNHG3 negatively regulated miR-128-3p, and the alleviated effect of SNHG3 overexpression on cellular inflammation and oxidative stress was reversed by miR-128-3p upregulation. Serum SNHG3 was considered a potential diagnostic biomarker for RA from healthy individuals. SNHG3 regulated inflammatory response and oxidative stress by negatively modulating miR-128-3p.

Serum and urine lipidomic profiles identify biomarkers diagnostic for seropositive and seronegative rheumatoid arthritis.

Seronegative rheumatoid arthritis (RA) is defined as RA without circulating autoantibodies such as rheumatoid factor and anti-citrullinated protein antibodies; thus, early diagnosis of seronegative RA can be challenging. Here, we aimed to identify diagnostic biomarkers for seronegative RA by performing lipidomic analyses of sera and urine samples from patients with RA. We performed untargeted lipidomic analysis of sera and urine samples from 111 RA patients, 45 osteoarthritis (OA) patients, and 25 healthy controls (HC). These samples were divided into a discovery cohort (n = 97) and a validation cohort (n = 84). Serum samples from 20 patients with systemic lupus erythematosus (SLE) were also used for validation. The serum lipidome profile of RA was distinguishable from that of OA and HC. We identified a panel of ten serum lipids and three urine lipids in the discovery cohort that showed the most significant differences. These were deemed potential lipid biomarker candidates for RA. The serum lipid panel was tested using a validation cohort; the results revealed an accuracy of 79%, a sensitivity of 71%, and a specificity of 86%. Both seropositive and seronegative RA patients were differentiated from patients with OA, SLE, and HC. Three urinary lipids showing differential expression between RA from HC were identified with an accuracy of 84%, but they failed to differentiate RA from OA. There were five lipid pathways that differed between seronegative and seropositive RA. Here, we identified a panel of ten serum lipids as potential biomarkers that can differentiate RA from OA and SLE, regardless of seropositivity. In addition, three urinary lipids had diagnostic utility for differentiating RA from HC.

Autoimmune biomarkers in Rheumatoid arthritis: A clinical and environmental approach

Autoimmune biomarkers in Rheumatoid arthritis: A clinical and environmental approach

Clinical Phenotypes, Serological Biomarkers, and Synovial Features Defining Seropositive and Seronegative Rheumatoid Arthritis: A Literature Review.

Rheumatoid arthritis (RA) is a chronic autoimmune disorder which can lead to long-term joint damage and significantly reduced quality of life if not promptly diagnosed and adequately treated. Despite significant advances in treatment, about 40% of patients with RA do not respond to individual pharmacological agents and up to 20% do not respond to any of the available medications. To address this large unmet clinical need, several recent studies have focussed on an in-depth histological and molecular characterisation of the synovial tissue to drive the application of precision medicine to RA. Currently, RA patients are clinically divided into "seropositive" or "seronegative" RA, depending on the presence of routinely checked antibodies. Recent work has suggested that over the last two decades, long-term outcomes have improved significantly in seropositive RA but not in seronegative RA. Here, we present up-to-date differences in epidemiology, clinical features, and serological biomarkers in seronegative versus seropositive RA and discuss how histological and molecular synovial signatures, revealed by recent large synovial biopsy-based clinical trials, may be exploited to refine the classification of RA patients, especially in the seronegative group.

Application of Salivary Alpha-1 Antitrypsin in the Diagnosis of Rheumatoid Arthritis: A Pilot Study.

Background and Objective: Rheumatoid arthritis (RA) is an autoimmune disease in which joints are gradually destroyed. Early diagnosis and treatment before joint deformation or destruction is important. The detection of novel RA biomarkers in saliva may facilitate early detection of RA before disease onset. This study aimed to evaluate salivary concentration of α1-antitrypsin (A1AT) in healthy patients and those with RA, and to assess the diagnostic value of salivary A1AT. Materials and Methods: In total, 80 participants were included: 20 healthy participants, and 60 patients with RA. Saliva and serum samples were obtained from all the patients. Levels of A1AT and cytokines, including interleukin-1 beta (IL-1β), IL-6, and IL-10 in saliva and serum, were evaluated using an enzyme-linked immunosorbent assay kit and Luminex assay. Data were analyzed using SPSS for Windows. Results: There was a higher level of A1AT in the saliva of patients with RA (median: 2388.66 ng/mL) than that in healthy controls (1579.06 ng/mL). There was a positive mild-to-moderate accuracy (area under the curve: 0.57-0.85) of A1AT in saliva to diagnose RA. The cut-off level (ng/mL) of A1AT in saliva for detecting RA was 1689.0. Conclusions: The obtained data can promote the application of the measurements of A1AT in saliva to diagnose RA.

Computational inhibition of S100A8 (calgranulin A) as a potential non-invasive biomarker for rheumatoid arthritis.

Rheumatoid arthritis (RA) is an autoimmune disease characterized by chronic joint pain and inflammation, loss of mobility, which affects the quality of life. The etiology of RA is unknown, and there is no isolated cause for the development of this illness. Synovial inflammation, autoantibody generation and bone degradation leading to deformity are classic characteristics of RA. The search for a non-invasive biomarker is crucial for helping patients receive standard therapies leading to faster treatments, as diagnosis depends on invasive biopsies. Therefore, in the present investigation, using transcriptomics and proteomic approaches, potential genes involved in crucial networks in RA were identified. Gene expression datasets of two tissue types i.e. whole blood and synovial tissue were retrieved from the GEO database and used for transcriptomic analyses. Using SWATH-MS analysis, differentially expressed proteins were identified from collected saliva of RA patients. Through bioinformatics analysis, S100A8, also known as Calprotectin (a complex of S100A8/A9) or Calgranulin A, was found to be common among all tissue types. S100A8 was then further quantified in saliva of healthy volunteers and RA patients, where it was found that the protein's level in healthy controls was lowered when compared to RA patients. Through in-silico characterization, potential plant-based inhibitors of S100A8 were also identified, to reduce its pro-inflammatory effects. Thus, it may be important in the pathophysiology of RA and, in the future, might help guide targeted treatment. as well as act as a non-invasive diagnostic biomarker platform.

Serum miR-124a and miR-34a as Potential Biomarkers for Rheumatoid Arthritis

Abstract Background: MicroRNAs (miRs) are defined as noncoding small RNAs that are involved in the regulation of various immune functions, indicating they could be possible biomarkers for immune-mediated disorders. We aimed to evaluate miR-124a and miR-34a levels in serum as diagnostic biomarkers for rheumatoid arthritis (RA) and to investigate their correlation with the disease activity in RA patients. Methods: Our study consisted of 40 patients with RA and 40 controls. The disease activity for the RA patients was evaluated using the Disease Activity Score in 28 joints (DAS28). Relative quantification of miR-124a and miR-34a expressions in serum was conducted by reverse transcriptase quantitative real-time polymerase chain reaction. Results: Expression levels of miR-124a and miR-34a in serum were significantly lower in RA patients (median: 0.64 and 0.30, respectively) compared to controls (median: 3.12 and 1.88, respectively), P < 0.001. Their serum levels were negatively associated with disease activity and inversely correlated with DAS28, C-reactive protein, and erythrocyte sedimentation rate (P < 0.001). Conclusion: Serum miR-124a and miR-34a could serve as potential noninvasive biomarkers for RA diagnosis and reflect the disease activity in RA patients.

Comparison of Low-Dose Interleukin 2 Therapy in Conjunction With Standard Therapy in Patients With Systemic Lupus Erythematosus vs Rheumatoid Arthritis: A Systematic Review.

This systematic review aims to compare the efficacy and safety of a novel immunotherapy with low-dose interleukin 2 (IL2) across two of the most prevalent autoimmune diseases i.e. systemic lupus erythematosus (SLE) and rheumatoid arthritis (RA). Contemporary therapeutic practices have not been able to achieve complete remission from these autoimmune disorders. In contrast, low-dose IL2has shown promise in achieving this therapeutic goal via inducing self-tolerance in patients with autoimmune diseases; however, due to variable irregularities among autoimmune processes of variable diseases, the benefit of low-dose IL2 could not be determined among different autoimmune diseases. Therefore, we conducted a study to compare low-dose IL2 therapy effects on SLE and RA. We systematically screened four databases: PubMed, Medical Literature Analysis and Retrieval System Online (MEDLINE), PubMed Central (PMC), and Google Scholar. Inclusion and exclusion criteria were implemented. Quality appraisal of studies chosen for the review was done using the Cochrane Risk-of-Bias (RoB) assessment tool for randomized controlled trials, and the Newcastle-Ottawa Scale (NOS) and JBI critical appraisal tool for non-randomized clinical trials. Information was gathered from seven articles: three randomized controlled trials and four non-randomized clinical trials. Our review concluded that low-dose IL2 therapy in conjunction with respective standard therapies for SLE and RA has a higher efficacy and safety profile as compared to standard therapy alone and the therapeutic effects were comparable in both SLE and RA patients treated with low-dose IL2; however, this novel intervention does not seem to have a significant corrective effect on the biomarkers of RA as it does for SLE biomarkers.

High Growth Differentiation Factor-15 (GDF-15) in Rheumatoid Arthritis Patients Potential Risk for Cardiovascular Disease

Rheumatoid arthritis (RA) is a chronic autoimmune disorder primarily affecting joints, leading to pain, stiffness, and functional disability. The inflammatory mechanisms underlying RA also impact various organ systems, with significant implications for morbidity and mortality. In this study, we analyzed the growth differentiation factor-15 (GDF-15), a cytokine implicated in inflammation and associated with RA and cardiovascular diseases. We conducted a case-control study involving 150 RA patients and 150 healthy individuals, assessing various biomarkers (ACPA, CRP, ESR, and RF) including GDF-15, lipid profile, and inflammatory markers. Our results demonstrated significantly elevated levels of GDF-15 in RA patients (309.44 pg/ml) compared to controls (64.40 pg/ml), indicating its potential role as a biomarker for RA and cardiovascular risk (p<0.001). Furthermore, RA patients exhibited dyslipidemia characterized by elevated total cholesterol, triglycerides, LDL cholesterol, and atherogenic indices, along with decreased HDL cholesterol levels, predisposing them to a higher risk of atherosclerosis and cardiovascular complications (p<0.001). Correlation analyses revealed associations between GDF-15 levels, lipid profile parameters, and disease severity markers, highlighting the intricate interplay between inflammation, lipid metabolism, and RA progression. These findings demonstrated the importance of early detection and management of dyslipidemia in RA patients to mitigate cardiovascular risk. Overall, our study contributes to understanding the pathophysiology of RA and identifies potential biomarkers for disease monitoring and risk stratification.

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

ObjectivesRheumatoid 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. MethodsThe 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. ResultsThe 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. ConclusionsThis 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.

Osteoimmunology: The Crosstalk between T Cells, B Cells, and Osteoclasts in Rheumatoid Arthritis.

Rheumatoid arthritis (RA) is an ongoing inflammatory condition that affects the joints and can lead to severe damage to cartilage and bones, resulting in significant disability. This condition occurs when the immune system becomes overactive, causing osteoclasts, cells responsible for breaking down bone, to become more active than necessary, leading to bone breakdown. RA disrupts the equilibrium between osteoclasts and osteoblasts, resulting in serious complications such as localized bone erosion, weakened bones surrounding the joints, and even widespread osteoporosis. Antibodies against the receptor activator of nuclear factor-κB ligand (RANKL), a crucial stimulator of osteoclast differentiation, have shown great effectiveness both in laboratory settings and actual patient cases. Researchers are increasingly focusing on osteoclasts as significant contributors to bone erosion in RA. Given that RA involves an overactive immune system, T cells and B cells play a pivotal role by intensifying the immune response. The imbalance between Th17 cells and Treg cells, premature aging of T cells, and excessive production of antibodies by B cells not only exacerbate inflammation but also accelerate bone destruction. Understanding the connection between the immune system and osteoclasts is crucial for comprehending the impact of RA on bone health. By delving into the immune mechanisms that lead to joint damage, exploring the interactions between the immune system and osteoclasts, and investigating new biomarkers for RA, we can significantly improve early diagnosis, treatment, and prognosis of this condition.

LncRNA expression profiling in exosomes derived from synovial fluid of patients with rheumatoid arthritis

ObjectiveTo identify the long non-coding RNA (lncRNA) expression profiling in exosomes derived from synovial fluid of rheumatoid arthritis (RA) patients, and carry out bioinformatics analysis on target genes of differentially expressed lncRNAs. MethodsExosomes were isolated from synovial fluid via ultracentrifugation. RNAs were extracted from exosomes by using HiPure Liquid RNA/miRNA kits, followed by lncRNA sequencing. Differentially expressed lncRNAs in RA were screened, and bioinformatics analysis of their target genes was carried out. qRT-PCR was used to verify the lncRNA expression levels. ResultsCompared with osteoarthritis (OA), 347 lncRNAs were found differentially expressed in RA. Compared with gout, 805 lncRNAs were found differentially expressed in RA. Compared with both OA and gout, 85 lncRNAs were found specially expressed in RA (65 were upregulated (including ENST00000433825.1)). Functional analysis of target genes of the specially expressed lncRNAs revealed significant enrichment of “autophagy” and “mTOR signaling pathway”. The qRT-PCR results indicated that ENST00000433825.1 was highly expressed in RA, compared with both OA and gout (P < 0.05), which matched the lncRNA sequencing results. Correlation analysis showed that the level of ENST00000433825.1 in RA patients was significantly and positively correlated with the level of C-reactive protein (CRP) (P < 0.001). ConclusionsThe lncRNA expression profiling in exosomes derived from synovial fluid of RA was significantly different from OA and gout. ENST00000433825.1 was highly and uniquely expressed in RA and significantly and positively correlated with CRP, which might provide a diagnostic and therapeutic biomarker for RA.

Potential mechanisms and drug prediction of Rheumatoid Arthritis and primary Sjögren's Syndrome: A public databases-based study.

Rheumatoid arthritis (RA) and primary Sjögren's syndrome (pSS) are the most common systemic autoimmune diseases, and they are increasingly being recognized as occurring in the same patient population. These two diseases share several clinical features and laboratory parameters, but the exact mechanism of their co-pathogenesis remains unclear. The intention of this study was to investigate the common molecular mechanisms involved in RA and pSS using integrated bioinformatic analysis. RNA-seq data for RA and pSS were picked up from the Gene Expression Omnibus (GEO) database. Co-expression genes linked with RA and pSS were recognized using weighted gene co-expression network analysis (WGCNA) and differentially expressed gene (DEG) analysis. Then, we screened two public disease-gene interaction databases (GeneCards and Comparative Toxicogenomics Database) for common targets associated with RA and pSS. The DGIdb database was used to predict therapeutic drugs for RA and pSS. The Human microRNA Disease Database (HMDD) was used to screen out the common microRNAs associated with RA and pSS. Finally, a common miRNA-gene network was created using Cytoscape. Four hub genes (CXCL10, GZMA, ITGA4, and PSMB9) were obtained from the intersection of common genes from WGCNA, differential gene analysis and public databases. Twenty-four drugs corresponding to hub gene targets were predicted in the DGIdb database. Among the 24 drugs, five drugs had already been reported for the treatment of RA and pSS. Other drugs, such as bortezomib, carfilzomib, oprozomib, cyclosporine and zidovudine, may be ideal drugs for the future treatment of RA patients with pSS. According to the miRNA-gene network, hsa-mir-21 may play a significant role in the mechanisms shared by RA and pSS. In conclusion, we identified commom targets as potential biomarkers in RA and pSS from publicly available databases and predicted potential drugs based on the targets. A new understanding of the molecular mechanisms associated with RA and pSS is provided according to the miRNA-gene network.

Exploring candidate biomarkers for rheumatoid arthritis through cardiovascular and cardiometabolic serum proteome profiling.

RA patients are at higher risk of cardiovascular disease, influenced by therapies. Studying their cardiovascular and cardiometabolic proteome can unveil biomarkers and insights into related biological pathways. This study included two cohorts of RA patients: newly diagnosed individuals (n=25) and those with established RA (disease duration >25 years, n=25). Both cohorts were age and sex-matched with a control group (n=25). Additionally, a longitudinal investigation was conducted on a cohort of 25 RA patients treated with methotrexate and another cohort of 25 RA patients treated with tofacitinib for 6 months. Clinical and analytical variables were recorded, and serum profiling of 184 proteins was performed using the Olink technology platform. RA patients exhibited elevated levels of 75 proteins that might be associated with cardiovascular disease. In addition, 24 proteins were increased in RA patients with established disease. Twenty proteins were commonly altered in both cohorts of RA patients. Among these, elevated levels of CTSL1, SORT1, SAA4, TNFRSF10A, ST6GAL1 and CCL18 discriminated RA patients and HDs with high specificity and sensitivity. Methotrexate treatment significantly reduced the levels of 13 proteins, while tofacitinib therapy modulated the expression of 10 proteins. These reductions were associated with a decrease in DAS28. Baseline levels of SAA4 and high levels of BNP were associated to the non-response to methotrexate. Changes in IL6 levels were specifically linked to the response to methotrexate. Regarding tofacitinib, differences in baseline levels of LOX1 and CNDP1 were noted between non-responder and responder RA patients. In addition, response to tofacitinib correlated with changes in SAA4 and TIMD4 levels. In summary, this study pinpoints molecular changes linked to cardiovascular disease in RA and proposes candidate protein biomarkers for distinguishing RA patients from healthy individuals. It also highlights how methotrexate and tofacitinib impact these proteins, with distinct alterations corresponding to each drug's response, identifying potential candidates, as SAA4, for the response to these therapies.

Validation of new immune and inflammation-related diagnostic biomarkers for RA.

Rheumatoid arthritis (RA) is a chronic, systemic autoimmune disease, whose development is associated with immune cells and persistent inflammation. Exploring the biomarkers of RA holds immense significance in terms of the prevention, diagnosis, and treatment of RA. The differentially expressed genes (DEGs) in RA patients and the control group were screened by limma package. Through DEGs intersection overlapping 200 inflammatory response-related genes and 2498 immune-related genes, differentially expressed immune and inflammation-related genes (DE-IIRGs) were identified. Lasso regression analysis screened RA diagnostic biomarkers and constructed PPI networks. Finally, immune infiltration analysis and drug prediction were performed. A total of 20 DE-IIRGs were identified by overlapping DEGs with 2498 immune-related genes and 200 inflammatory response-related genes. These DE-IIRGs were primarily enriched in the cytokine-cytokine receptor interaction and other biological processes, and then five biomarker genes (TNFSF10, IL1R1, CXCL9, ACVR1B, and IL15) were identified. It was found that the expression levels of CXCL9, IL15, and TNFSF10 in the disease samples were significantly higher than those in the control group. These biomarker genes have more effective diagnostic potential. The RA samples exhibited significantly higher levels of cell infiltration compared to the control samples. hsa-miR-199a-5p's connections to the ACVR1B and CCR7 genes were identified by creating ceRNA networks from 20 screened DE-IIRGs. There was a connection between CCL5 and AEMA4D and hsa-miR-214-3p. We identified immune- and inflammation-related biomarkers in RA based on bioinformatics analysis and screened TNFSF10, IL1R1, CXCL9, ACVR1B, and IL15 as diagnostic markers for RA. Key Points • TNFSF10, IL1R1, CXCL9, ACVR1B, and IL15 may be new diagnostic biomarkers for RA. • These findings may provide a theoretical basis for early RA diagnosis.

USEFULLNESS OF SERUM LEUCINE-RICH ALPHA2-GLYCOPROTEIN AND FECAL CALPROTECTIN ASSURROGATE MARKERS IN ULCERATIVE COLITIS

Abstract BACKGROUND AND AIM Serum Leucine-rich alpha-2 glycoprotein (LRG) was identified as a novel biomarker for rheumatoid arthritis and IBD by using a proteomics approach. Several studies have suggested that serum LRG levels and fecal calprotectin (fCal) were correlated with clinical activities in patients with ulcerative colitis (UC). We compared utility of these biomarkers for monitoring disease activity in UC. METHODS A single-center observational study was conducted at Kyushu University Hospital. A total of 101 patients who underwent colonoscopy during the period from February to December 2021 were enrolled. The associations between endoscopic and histological activity and biomarkers were investigated. Endoscopic activity was defined as Mayo endoscopic score (MES) of 1 or less for mucosal healing and 0 for complete mucosal healing. For histological activity (n=90), Geboes score of 2 or less was defined as histological remission. RESULTS Subjects ranged in age from 16 to 80 years (35 males and 66 females). There were 63 cases of total colitistype, 32 cases of left-sided colitis type, and 6 cases of proctitis type. The MES at enrollment were as follows: MES 0 in 41 cases, MES 1 in 33 cases, MES 2 in 19 cases, and MES 3 in 8 cases. In comparison between the mucosal healing and non-mucosal healing groups, LRG was significantly lower in the mucosal healing group (p&amp;lt;0.0001). The correlation coefficient of LRG with MES was r=0.614 (p&amp;lt;0.0001), which was higher than that of fCal, r=0.414 (p&amp;lt;0.0001). In ROC analysis with mucosal healing as the outcome, the AUC and cut-off value of LRG were 0.781 and 17.9 μg/ml, respectively, and for fCal were 0.850 and 396 μg/g, respectively. In the ROC analysis with complete mucosal healing as the outcome, the AUC for LRG was 0.690, which was lower than the AUC of 0.846 for fCal . When compared by histological remission, LRG was lower in the histological remission group (p&amp;lt;0.001). In the ROC analysis with histological remission as the outcome, the AUC for LRG was 0.722 (cutoff value 14.2 μg/ml) and for fCal was 0.837 (cutoff value 119 μg/g). CONCLUSION Both LRG and fCal are valuable biomarkers reflecting disease activity in UC.

Analysis of Potential Vitamin D Molecule Biomarkers: Association of Calcitriol and Its Hydroxylation Efficiency Ratio with Cardiovascular Disease Risk in Rheumatoid Arthritis Patients.

Rheumatoid arthritis (RA) is a multifactorial autoimmune disease in which hypovitaminosis D by calcidiol quantification has been associated with disease severity. However, other vitamin D molecules could be implicated in RA pathophysiology and its comorbidities such as cardiovascular disease (CVD), which impacts the severity and mortality of RA patients. This study aimed to assess the relationship between calcidiol, calcitriol, its hydroxylation efficiency ratio, and the soluble vitamin D receptor (sVDR) and clinical and CVD risk variables to propose potential vitamin D molecule biomarkers for RA. A cross-sectional study of females was conducted on 154 RA patients and 201 healthy subjects (HS). Calcidiol, calcitriol, and the sVDR were measured in blood serum, and vitamin D hydroxylation efficiency was estimated using the calcitriol/calcidiol ratio score. CVD risk was calculated by the high-sensitivity C-reactive protein (hs-CRP) cutoff values. Disease activity was evaluated with the Disease Activity Score for 28 standard joints (DAS28-CRP). Results: The hydroxylation efficiency ratio and calcitriol serum levels were higher in RA patients with hypovitaminosis D (p < 0.001). Moreover, RA patients had a higher probability of a high hydroxylation efficiency ratio (OR = 2.02; p = 0.02), calcitriol serum levels (OR = 2.95; p < 0.001), and sVDR serum levels (OR = 5.57; p < 0.001) than HS. This same pattern was also observed in RA patients with high CVD risk using CRP serum levels; they showed a higher hydroxylation efficiency ratio (OR = 4.51; p = 0.04) and higher calcitriol levels (OR = 5.6; p < 0.01). Calcitriol correlates positively with the sVDR (r = 0.21, p = 0.03), CRP (r = 0.28, p < 0.001), and cardiometabolic indexes (p < 0.001) also showed discrimination capacity for CVD risk in RA patients with CRP ≥ 3 mg/L (AUC = 0.72, p < 0.01). In conclusion, hypovitaminosis D in RA patients was characterized by a pattern of a higher hydroxylation efficiency ratio and higher calcitriol and sVDR serum levels. Notably, higher calcitriol serum levels and a higher vitamin D hydroxylation efficiency ratio were associated with higher CVD risk in RA patients.

Identification of CD8+ T cell-related biomarkers and immune infiltration characteristic of rheumatoid arthritis.

Rheumatoid arthritis (RA) is an autoimmune rheumatic disease, which do not respond well to current treatment partially. Therefore, further in-depth elucidation of the molecular mechanism and pathogenesis of RA is urgently needed for the diagnosis, personalized therapy and drug development. Herein, we collected 111 RA samples from Gene Expression Omnibus (GEO) database, and conducted differentially expressed genes and GESA analysis. Abnormal activation and imbalance of immune cells in RA were observed. WGCNA was utilized to explore the gene modules and CD8+ T cell-related genes (CRGs) were chosen for KEGG and GO analysis. Besides, to explore biomarkers of RA in depth, machine learning algorithms and bioinformatics analysis were used, and we identified GDF15, IGLC1, and IGHM as diagnostic markers of RA, which was confirmed by clinical samples. Next, ssGSEA algorithms were adopted to investigate the differences in immune infiltration of 23 immune cell subsets between RA and healthy control group. Finally, optimal classification analysis based on consensus clustering combined with ssGSEA algorithms were conducted. GDF15 was revealed that to be positively correlated with mast cells and type 2 T helper cells, but negatively correlated with most other immune cells. On the other hand, IGHM and IGLC1 were negatively correlated with CD56dim natural killer cells, while positively associated with other immune cells. Finally, RA samples in subtype A exhibited a higher immune infiltration status. This study could provide guidance for individualized treatment of RA patients and provide new targets for drug design.

Impaired immune tolerance mediated by reduced Tfr cells in rheumatoid arthritis linked to gut microbiota dysbiosis and altered metabolites

BackgroundPatients with rheumatoid arthritis (RA) showed impaired immune tolerance characterized by reduced follicular regulatory T (Tfr) cells, and they also exhibited altered gut microbiotas and their metabolites in RA. However, the association of gut microbiotas and their metabolites with the immune tolerance mediated by Tfr cells in RA remains unclear.MethodsPeripheral blood and stool samples were collected from 32 new-onset RA patients and 17 healthy controls (HCs) in the Second Hospital of Shanxi Medical University between January 2022 and June 2022. The peripheral blood was used to detect the circulating regulatory T (Treg), helper T(Th)17, Tfr, and follicular helper T (Tfh) cells by modified flow cytometry. The stool samples were used to analyze the gut microbiotas and their metabolites via 16S rDNA sequencing and metabolomic profiling. We aimed to characterize the gut microbiotas and their metabolites in RA and identified their association with Tfr cell-mediated immune tolerance.ResultsThe new-onset RA demonstrated reduced Treg and Tfr cells, associated with the disease activity and autoantibodies. There were significant differences in gut microbiotas between the two groups as the results of β diversity analysis (P = 0.039) including 21 differential gut microbiotas from the phylum to genus levels. In which, Ruminococcus 2 was associated with the disease activity and autoantibodies of RA, and it was identified as the potential biomarker of RA [area under curve (AUC) = 0.782, 95% confidence interval (CI) = 0.636–0.929, P = 0.001]. Eleven differential metabolites were identified and participated in four main pathways related to RA. Arachidonic acid might be the potential biomarker of RA (AUC = 0.724, 95% CI = 0.595–0.909, P = 0.038), and it was the core metabolite as the positive association with six gut microbiotas enriched in RA. The reduced Tfr cells were associated with the altered gut microbiotas and their metabolites including the Ruminococcus 2, the arachidonic acid involved in the biosynthesis of unsaturated fatty acid pathway and the 3-methyldioxyindole involved in the tryptophan metabolism pathway.ConclusionThe breakdown of immune tolerance mediated by reduced Tfr cells was associated with the altered gut microbiotas and their metabolites implying the possible mechanism of RA pathogenesis from the perspective of microecology-metabolism-immune.

Mechanism of lysine oxidase-like 1 promoting synovial inflammation mediating rheumatoid arthritis development.

Rheumatoid arthritis (RA) is a chronic inflammatory joint disease that causes great distress to patients and society. Early diagnosis is the key to the successful treatment of RA. The basement membrane, one of the oldest tissue structures, is localized under the epithelium. Its complex composition and rich biological functions have made it a focus of research in recent years, while basement membrane-associated genetic variants are involved in most human disease processes. The aim of this study is to find new diagnostic biomarkers for RA and explore their role and possible mechanism in rheumatoid arthritis. The GSE12021, GSE55235 and GSE55457 datasets were downloaded from the GEO database. Their fraction associated with basement membrane genes was analyzed and differentially expressed genes between the disease and normal groups were explored. We identified two basement membrane-associated genes, lysine oxidase-like 1 (LOXL1) and discoid peptide receptor 2 (DDR2). Focusing on the more interesting LOXL1, we found that LOXL1 expression was significantly elevated in the synovium of patients with rheumatoid arthritis, and LOXL1 mRNA and protein levels were elevated in tumor necrosis factor α-stimulated human synovial sarcoma cells (SW982). And LOXL1 knockdown inhibited tumor necrosis factor α-induced inhibition in SW982 cells expression of inducible nitric oxide synthase (INOS), cyclooxygenase-2 (COX2), and interleukin-6 (IL-6). Interestingly, knockdown of LOXL1 inhibited the phosphorylation of PI3K and AKT. In summary, LOXL1 may become a novel diagnostic gene for RA, and knockdown of LoxL1 may inhibit synovial inflammation by affecting PI3K/AKT pathway.