Background: Previous studies have demonstrated that the gut microbiota (GM) and rheumatoid arthritis (RA) are significantly associated, but the causal relationship has not been fully elucidated. Methods: We investigated the association between GM and RA using Mendelian randomization (MR) with two independent samples. Our study aimed to determine the causal relationship between gut microbiota and RA, including its seronegative and seropositive subtypes. Using data from a genome-wide association study (GWAS), we identified instrumental variables for 211 gut bacteria types. We then analyzed the FinnGen GWAS dataset, which included 3877 seronegative RA cases and 285,035 controls, along with 4290 seropositive RA cases and 368,362 controls, employing the inverse variance weighted (IVW) method and rigorous tests for pleiotropy and heterogeneity to ensure reliability. Results: The IVW results revealed that Prevotella 9, Sutterella, and Christensenellaceae R.7 exhibited an adverse correlation with seronegative RA (p < 0.05). Additionally, Lachnospira, Slackia, Roseburia, Barnesiella, and Prevotella 7 were associated with a reduced occurrence of seropositive RA (p < 0.05). Conversely, Ruminococcaceae UCG002 and Ruminococcus gauvreauii were linked to an increased susceptibility to seropositive RA (p < 0.05). Notably, no significant heterogeneity (p > 0.05) or pleiotropy (p > 0.05) was detected in the analysis of the significant MR estimates. Conclusions: Our study suggested significant associations between several gut bacteria and RA subtypes, indicating a potential microbial influence on RA development. These findings enhance our understanding of the gut-joint axis in RA and highlight promising targets for future microbiota-based therapies.

Objectives. This study aimed to establish a rat experimental membranous nephropathy model, investigate the phenotypic characteristics of Tfh cells, and analyze a clinically significant correlation between Tfh cells. Materials and methods. Passive Heymann nephritis (PHN) rats were induced by immunizing Sprague Dawley rats with anti-Fx1A serum. The frequency of Tfh and B cell subsets was analyzed with flow cytometry (FC). The serum concentration of interleukin-21 (IL-21), the relative mRNA expression levels of IL-21 and B cell lymphoma 6 (Bcl-6) in spleen mononuclear cells (MNCs), and the kidney infiltration of CD4(+) T cells and IL-21 were assessed. The potential correlations among these measures were analyzed. Results. In comparison with the control group, significantly increased percentages of Tfh cells, inducible T cell co-stimulator-positive (ICOS+) Tfh cells, and mRNA expression of Bcl-6 were detected in the spleen of PHN rats. Elevated IL-21 expression was detected in the serum and kidneys. Remarkably, the percentage of splenic ICOS+ Tfh cells was positively correlated with 24 h urine protein concentrations (r = 0.676, p = 0.011) in PHN rats. Conclusions. These data indicate that ICOS+ Tfh cells contribute to development of IMN, and they might be potential therapeutic targets for IMN.

Background/Objectives: Myasthenia gravis (MG) is an immune-mediated neuromuscular disorder for which antibody-based assays have limited sensitivity, particularly in double-seronegative MG (dsNMG), highlighting the need for complementary biomarkers. Given their roles in immune regulation, membrane integrity, and metabolic stress responses, lipids represent promising candidates for biomarker discovery. Methods: We designed a prospective case–control study and systematically stratified 68 patients with myasthenia gravis (MG) according to clinical classification and autoantibody status. Using LC–MS/MS, we quantified 824 lipids in 136 serum samples collected from these patients and 68 healthy controls. The analyzed subtypes included ocular MG (OMG), generalized MG (GMG), acetylcholine receptor antibody-positive MG (AChR-MG), and dsNMG. Differential lipid analysis, correlation network construction, KEGG pathway enrichment, and multivariable logistic regression were performed. Diagnostic and subtype prediction models were developed using LASSO with 10 × 10 repeated cross-validation and interpreted using Shapley Additive exPlanations (SHAP) analysis. A longitudinal follow-up analysis was conducted to assess dynamic associations between lipid signatures and disease activity. Results: In total, 240 lipids were significantly altered in MG compared with controls. Lipids distinguishing GMG from OMG were enriched in ether lipid metabolism, necroptosis, and sphingolipid signaling pathways. AChR-MG and dsNMG shared lipid networks related to membrane remodeling and signaling regulation, whereas dsNMG exhibited marked elevations in acylcarnitines and bile acid-related metabolites, potentially reflecting a distinct phenotype characterized by altered energy metabolism. The lipid-based model achieved an AUC of 0.917 for distinguishing MG from controls, and AUCs of 0.77 and 0.71 for differentiating AChR-MG from dsNMG and GMG from OMG, respectively. Longitudinal analyses showed that SM(d18:1/23:0) and Cer(d24:1/18:0(2OH)) displayed dynamic changes consistent with disease activity. Conclusions: Serum lipidomics revealed subtype-specific metabolic features of MG, with stable disease-associated remodeling and dynamic sphingolipid changes potentially reflecting disease activity. By integrating systematic clinical and antibody-based subtype stratification with longitudinal follow-up, this study supports lipidomics as a complementary tool for precision diagnosis and disease stratification, particularly in antibody-negative dsNMG.