Uropathogenic Escherichia coli (UPEC), a leading cause of urinary tract infections, is associated with prostate and bladder cancers. Cytotoxic necrotizing factor 1 (CNF1) is a key UPEC toxin; however, its role in bladder cancer is unknown. In the present study, we found CNF1 induced bladder cancer cells to secrete vascular endothelial growth factor (VEGF) through activating Ras homolog family member C (RhoC), leading to subsequent angiogenesis in the bladder cancer microenvironment. We then investigated that CNF1‐mediated RhoC activation modulated the stabilization of hypoxia‐inducible factor 1α (HIF1α) to upregulate the VEGF. We demonstrated in vitro that active RhoC increased heat shock factor 1 (HSF1) phosphorylation, which induced the heat shock protein 90α (HSP90α) expression, leading to stabilization of HIF1α. Active RhoC elevated HSP90α, HIF1α, VEGF expression, and angiogenesis in the human bladder cancer xenografts. In addition, HSP90α, HIF1α, and VEGF expression were also found positively correlated with the human bladder cancer development. These results provide a potential mechanism through which UPEC contributes to bladder cancer progression, and may provide potential therapeutic targets for bladder cancer.

Objective: Trained immunity of natural killer (NK) cells has shown great potential in the treatment of cancers by eliciting enhanced effector responses to restimulation by cytokines or cancer cells for long time periods after preactivation. However, the human NK cells responsible for the generation and maintenance of trained immunity are largely unknown. We hypothesized that heterogeneous human NK cells would respond differentially to stimulation with a combination of IL-12, IL-15, and IL-18, and that an NK cell subset might exist that is mainly responsible for the induction of trained immunity. On the basis of our hypothesis, we aimed to identify the subset from which cytokine-trained human NK cells originate and to explore possible regulatory targets for drug intervention. Methods: Flow cytometry assays were performed to analyze the functions of cytokine-trained NK cells and examine cell division and protein expression in NK cell subsets. Single-cell RNA sequencing (scRNA-seq) plus TotalSeq™ technology was used to track the heterogeneity of NK cells during the induction of trained immunity. Results: Traditional developmental markers for peripheral NK cells were unable to identify the precursors of human NK cells with trained immunity. Therefore, we used scRNA-seq plus TotalSeq™ technology to track the heterogeneity of NK cells during the induction of trained immunity and identified a unique cluster of CD57-NKG2A+EZH2+IFNG+MKI67+IL12R+IL15R+IL18R+ NK cells. Enrichment and pseudotime trajectory analyses suggested that this cluster of NK cells contained the precursor of trained NK cells. We then used flow cytometry to further investigate the role of EZH2 in trained NK precursors and found that CD57-NKG2A+EZH2+ NK cells had faster cell cycles and an enhanced trained phenotype, and EZH2 inhibition significantly impaired the induction of trained immunity in NK cells. These results suggested that EZH2 is a unique epigenetic marker of precursors of human NK cells with trained immunity. Conclusions: Our work revealed human NK heterogeneity in the induction of trained immunity, identified the precursor subset for trained NK cells, and demonstrated the critical role of EZH2 in the induction of trained immunity in human NK cells.

Urinary tract infections (UTIs) are one of the most common infectious diseases. UTIs are mainly caused by uropathogenic Escherichia coli (UPEC), and are either upper or lower according to the infection site. Fimbriae are necessary for UPEC to adhere to the host uroepithelium, and are abundant and diverse in UPEC strains. Although great progress has been made in determining the roles of different types of fimbriae in UPEC colonization, the contributions of multiple fimbriae to site-specific attachment also need to be considered. Therefore, the distribution patterns of 22 fimbrial genes in 90 UPEC strains from patients diagnosed with upper or lower UTIs were analyzed using PCR. The distribution patterns correlated with the infection sites, an XGBoost model with a mean accuracy of 83.33% and a mean area under the curve (AUC) of the receiver operating characteristic (ROC) of 0.92 demonstrated that fimbrial gene distribution patterns could predict the localization of upper and lower UTIs.