Systemic lupus erythematosus (SLE) characterized by immune dysfunction is possibly more vulnerable to herpes simplex virus (HSV) infection. The infection has been intensively considered a common onset and exacerbation of SLE. This study is aimed at elucidating the causal association between SLE and HSV. A bidirectional two‐sample Mendelian Randomization (TSMR) analysis was systematically conducted to explore the causal effect of SLE and HSV on each other. The causality was estimated by inverse variance weighted (IVW), MR‐Egger and weighted median methods based on the summary‐level genome‐wide association studies (GWAS) data from a publicly available database. Genetically proxied HSV infection exhibited no causal association with SLE in the forward MR analysis using IVW method (odds ratio [OR] = 0.987; 95% confidence interval [CI]: 0.891–1.093; p = 0.798), nor did HSV‐1 IgG (OR = 1.241; 95% CI: 0.874–1.762; p = 0.227) and HSV‐2 IgG (OR = 0.934; 95% CI: 0.821–1.062; p = 0.297). Similar null results with HSV infection (OR = 1.021; 95% CI: 0.986–1.057; p = 0.245), HSV‐1 IgG (OR = 1.003; 95% CI: 0.982–1.024; p = 0.788) and HSV‐2 IgG (OR = 1.034; 95% CI: 0.991–1.080; p = 0.121) were observed in the reverse MR where SLE served as the exposure. Our study demonstrated no causal association between the genetically predicted HSV and 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.