Jiachen Wang

Background Rheumatoid arthritis (RA) is a chronic autoimmune disorder characterized by synovial hyperplasia, persistent inflammation, and progressive joint destruction. Ginkgetin (GK), a biflavonoid derived from Ginkgo biloba, exhibits strong anti-inflammatory and antioxidant properties. However, its therapeutic potential and underlying molecular mechanisms in RA remain insufficiently understood. Methods An integrative strategy combining network pharmacology, transcriptomic profiling, molecular docking, and in vitro experiments was applied to elucidate the mechanisms of GK against RA. Differentially expressed genes (DEGs) were identified from four GEO datasets of RA synovial tissues, and overlapping targets between GK and RA were screened. Gene Ontology (GO), KEGG pathway enrichment, and protein–protein interaction (PPI) analyses were conducted to identify key pathways and hub genes. Molecular docking and single-cell transcriptomic analyses were performed to predict binding interactions and cellular localization. Finally, MH7A synovial fibroblasts were used to validate GK’s effects on inflammation, proliferation, migration, and apoptosis. Results Thirty-two potential therapeutic targets of GK in RA were identified, among which six hub genes (CXCR4, HIF1A, STAT1, VEGFA, CDK1, and CCNB1) were highlighted. Enrichment analyses indicated that these targets were primarily involved in the HIF-1, VEGF, and cell cycle signaling pathways. Molecular docking demonstrated strong binding affinities between GK and key proteins. In vitro, GK inhibited LPS-induced proliferation and migration of MH7A cells, reduced IL-6 and IL-1β expression, and promoted apoptosis. qRT-PCR analysis confirmed that GK downregulated all six hub genes, indicating its multi-target regulatory activity on hypoxia, angiogenesis, and inflammatory signaling. Conclusion GK exerts anti-inflammatory, anti-proliferative, and pro-apoptotic effects in RA synovial fibroblasts by modulating multiple signaling pathways, particularly the HIF-1α/VEGFA, STAT1, and CXCR4 axes. These findings provide mechanistic insight into GK’s pharmacological actions and support its potential as a novel multi-target therapeutic candidate for RA. Key Points •Network pharmacology identified six hub genes (CXCR4, HIF1A, STAT1, VEGFA, CDK1, CCNB1) linking Ginkgetin (GK) to rheumatoid arthritis pathology. •Molecular docking confirmed strong binding affinities of GK with key targets, supporting its multitarget pharmacological potential. •GK inhibits proliferation and migration, suppresses inflammatory cytokines, and promotes apoptosis in LPS-stimulated MH7A synovial fibroblasts. •These findings provide mechanistic evidence supporting GK as a promising multi-target candidate for rheumatoid arthritis therapy.

Objective: Ankylosing spondylitis (AS) is associated with a variety of gut microbiotas. We aim to analyze the causal relationship between the two at the genetic level. Methods: Mendelian randomization (MR) is a type of instrumental variables (IVs) analysis; MR follows the Mendelian genetic rule of “parental alleles are randomly assigned to offspring” and takes genetic variation as IVs to infer the causal association between exposure factors and study outcome in observational studies. Genome-wide association study (GWAS) summary data of AS were from the FinnGen consortium, and the gut microbiota (Bacteroides, Streptococcus, Proteobacteria, Lachnospiraceae) were from the MiBioGen consortium. The TwoSampleMR and MRPRESSO packages of the R were used to perform a two-sample MR study. Random-effects inverse variance weighted (IVW) was the main analysis method, and MR Egger, weighted median, simple mode, and weighted mode were used as supplementary methods. We examined heterogeneity and horizontal pleiotropy, and examined whether the analysis results were influenced by a single SNP. We applied radial variants of the IVW and MR-Egger model for the improved visualization of the causal estimate. We further examined the causal relationship between AS and gut microbiota, and the robustness of the analysis results. Finally, we performed maximum likelihood, penalized weighted median, and IVW (fixed effects) to further identify the potential causal association. Results: The random-effects IVW results showed that Bacteroides (p = 0.965, OR 95% confidence interval [CI] = 0.990 [0.621–1.579]), Streptococcus (p = 0.591, OR 95% CI = 1.120 [0.741–1.692]), Proteobacteria (p = 0.522, OR 95% CI = 1.160 [0.737–1.826]), and Lachnospiraceae (p = 0.717, OR 95% CI = 1.073 [0.732–1.574]) have no genetic causal relationship with AS. There was no heterogeneity, horizontal pleiotropy or outliers, and results were normally distributed. The MR analysis results were not driven by a single SNP. Conclusions: This study showed that Bacteroides, Streptococcus, Proteobacteria and Lachnospiraceae, four common gut microbiotas associated with AS, had no causal relationship with AS at the genetic level. This study makes a positive contribution to the genetics of AS, but the insufficient number of gut microbiota included is a limitation.

Background: Rheumatoid arthritis (RA) increases the risk of depression. However, studies on the effects of RA on the dose of depression medications are limited. Therefore, in this study, we used two-sample Mendelian randomization (MR) to explore whether RA increases the dose of depression medications and gain a more comprehensive understanding of the relationship between RA and depression. Methods: Two-sample MR was used to evaluate the causal effect of RA on the dose of depression medications. The aggregated data on RA originated from extensive genome-wide association studies (GWASs) of European descent (14,361 cases and 42,923 controls). The summary GWAS data for the dose of depression medications were derived from the FinnGen consortium (58,842 cases and 59,827 controls). Random effects inverse-variance weighted (IVW), MR-Egger regression, weighted median, and fixed effects IVW methods were used for the MR analysis. Random effects IVW was the primary method. The heterogeneity of the MR results was detected using the IVW Cochran’s Q test. The pleiotropy of the MR results was detected using MR-Egger regression and the MR pleiotropy residual sum and outlier (MR-PRESSO) test. Finally, a leave-one-out analysis was performed to determine whether the MR results were affected by a specific single-nucleotide polymorphism (SNP). Results: The primary method, random effects IVW, revealed that genetically predicted RA had a positive causal association with the dose of depression medications (Beta, 0.035; 95% confidence interval (CI), 0.007–0.064; p = 0.015). The IVW Cochran’s Q test results revealed no heterogeneity in the MR analysis (p > 0.05). The MR-Egger regression and MR-PRESSO tests revealed that there was no pleiotropy in our MR analysis. The leave-one-out analysis confirmed that a single SNP did not affect the MR results, indicating the study’s robustness. Conclusion: Using MR techniques, we discovered that having RA increases the dose of depression medications; however, the exact mechanisms and pathways still need to be further explored.

Based on previous observational studies, the causal association between circulating adiponectin (CA) levels and ankylosing spondylitis (AS) risk remains unclear. Therefore, this study aims to investigate whether CA levels are related to the risk of AS. We carried out a bidirectional two-sample Mendelian randomization (MR) analysis to examine the causal correlation between CA levels and AS via published genome-wide association study (GWAS) datasets. Single-nucleotide polymorphisms (SNPs) related to CA levels were derived from a large GWAS that included 39,883 individuals of European descent. SNPs related to AS were obtained from the FinnGen consortium (2252 cases and 227,338 controls). The random-effects inverse variance weighted (IVW) method was the primary method utilized in our research. We also used four complementary approaches to improve the dependability of this study (MR–Egger regression, Weighted median, Weighted mode, and Simple mode). Random-effects IVW (odds ratio [OR], 1.00; 95% confidence interval [CI], 0.79–1.27, p = 0.984) and four complementary methods all indicated that genetically predicted CA levels were not causally related to the risk of AS. In reverse MR analysis, there is little evidence to support the genetic causality between the risk of AS and CA levels.