Background The interferon-gamma (IFN-γ) signaling pathway is activated in Systemic lupus erythematosus (SLE). This study aims to assess the causal association between IFN-γ, IFN-γR1, and IFN-γR2 and SLE within a bidirectional Mendelian-randomization design.Methods Genetic instruments of exposure to IFN-γ, IFN-γR1, and IFN-γR2 were derived from the large genome-wide association study (GWAS), including 3,301 sample size. Instrumental variables for SLE were selected from another independent GWAS analysis comprising 7,219 cases and 15,991 controls with European ancestry. Bi-directional two-sample MR was performed using inverse variance weighting (IVW), MR-Egger regression, and weighted median methods. A series of sensitivity analyses were conducted to assess the robustness of the results.Results The IVW showed IFN-γ had a positive causal association with the risk of SLE [OR 1.24 (95% CI 0.85, 2.26), P = 0.018]. IFN-γR2 was found to have a negative correlation with the onset of SLE [OR 0.85 (95% CI 0.73, 0.99), P = 0.034]. However, no genetic association was detected between IFN-γR1 and SLE [OR 0.97 (95% CI 0.79, 1.19), P = 0.768]. Evidence from bidirectional MR did not support reverse causality. Weighted median regression also showed directionally similar estimates.Conclusion Higher levels of IFN-γ or lower levels of IFN-γR2 are significantly associated with an increased risk of SLE, providing insights into the pathogenesis of SLE.
Background and Aims Ulcerative colitis [UC] is a complex heterogeneous disease. This study aims to reveal the underlying molecular features of UC using genome-scale transcriptomes of patients with UC and develop and validate a novel stratification scheme. Methods A normalized compendium was created using colon tissue samples [455 patients with UC and 147 healthy controls [HCs]], covering genes from 10 microarray datasets. Up-regulated differentially expressed genes [DEGs] were subjected to functional network analysis, wherein samples were grouped using unsupervised clustering. Additionally, the robustness of subclustering was further assessed by two RNA sequencing datasets [100 patients with UC and 16 HCs]. Finally, the Xgboost classifier was applied to the independent datasets to evaluate the efficacy of different biologics in patients with UC. Results Based on 267 up-regulated DEGs of the transcript profiles, UC patients were classified into three subtypes [subtype A-C] with distinct molecular and cellular signatures. Epithelial activation-related pathways were significantly enriched in subtype A [named epithelial proliferation], whereas subtype C was characterized as the immune activation subtype with prominent immune cells and proinflammatory signatures. Subtype B [named mixed] was modestly activated in all the signalling pathways. Notably, subtype A showed a stronger association with the superior response of biologics such as golimumab, infliximab, vedolizumab and ustekinumab compared to subtype C. Conclusions We conducted a deep stratification of mucosal tissue using the most comprehensive microarray and RNA sequencing data, providing critical insights into pathophysiological features of UC, which could serve as a template for stratified treatment approaches.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.