DCHS1 DNA copy number loss associated with pediatric urinary tract infection risk

Human neutrophil peptides 1-3 protect the murine urinary tract from uropathogenic Escherichia coli challenge

Antimicrobial peptides (AMPs) are critical to the protection of the urinary tract of humans and other animals from pathogenic microbial invasion. AMPs rapidly destroy pathogens by disrupting microbial membranes and/or augmenting or inhibiting the host immune system through a variety of signaling pathways. We have previously demonstrated that alpha-defensins 1-3 (DEFA1A3) are AMPs expressed in the epithelial cells of the human kidney collecting duct in response to uropathogens. We also demonstrated that DNA copy number variations in the DEFA1A3 locus are associated with UTI and pyelonephritis risk. Because DEFA1A3 is not expressed in mice, we utilized human DEFA1A3 gene transgenic mice (DEFA^4/4) to further elucidate the biological relevance of this locus in the murine urinary tract. We demonstrate that the kidney transcriptional and translational expression pattern is similar in humans and the human gene transgenic mouse upon uropathogenic Escherichia coli (UPEC) stimulus in vitro and in vivo. We also demonstrate transgenic human DEFA^4/4 gene mice are protected from UTI and pyelonephritis under various UPEC challenges. This study serves as the foundation to start the exploration of manipulating the DEFA1A3 locus and alpha-defensins 1-3 expression as a potential therapeutic target for UTIs and other infectious diseases.

Tumor Expression Profile Analysis Developed and Validated a Prognostic Model Based on Immune-Related Genes in Bladder Cancer

Background: Bladder cancer (BLCA) ranks 10th in incidence among malignant tumors and 6th in incidence among malignant tumors in males. With the application of immune therapy, the overall survival (OS) rate of BLCA patients has greatly improved, but the 5-year survival rate of BLCA patients is still low. Furthermore, not every BLCA patient benefits from immunotherapy, and there are a limited number of biomarkers for predicting the immunotherapy response. Therefore, novel biomarkers for predicting the immunotherapy response and prognosis of BLCA are urgently needed.

Methods: The RNA sequencing (RNA-seq) data, clinical data and gene annotation files for The Cancer Genome Atlas (TCGA) BLCA cohort were extracted from the University of California, Santa Cruz (UCSC) Xena Browser. The BLCA datasets GSE31684 and GSE32894 from the Gene Expression Omnibus (GEO) database were extracted for external validation. Immune-related genes were extracted from InnateDB. Significant differentially expressed genes (DEGs) were identified using the R package “limma,” and Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis for the DEGs were performed using R package “clusterProfiler.” Least absolute shrinkage and selection operator (LASSO) regression analysis were used to construct the signature model. The infiltration level of each immune cell type was estimated using the single-sample gene set enrichment analysis (ssGSEA) algorithm. The performance of the model was evaluated with receiver operating characteristic (ROC) curves and calibration curves.

Results: In total, 1,040 immune-related DEGs were identified, and eight signature genes were selected to construct a model using LASSO regression analysis. The risk score of BLCA patients based on the signature model was negatively correlated with OS and the immunotherapy response. The ROC curve for OS revealed that the model had good accuracy. The calibration curve showed good agreement between the predictions and actual observations.

Conclusions: Herein, we constructed an immune-related eight-gene signature that could be a potential biomarker to predict the immunotherapy response and prognosis of BLCA patients.