Dysbiosis of urine microbiota in obstructive urinary retention patients revealed by next-generation sequencing

Shan Jiang, Saisai Lu, Xiaomin Chen, Fengxia Li, Chengwei Zhu, Yuancai Zheng, Xiaobing Wang, Shihao Xu
2021 Annals of Clinical Microbiology and Antimicrobials  
Background Urinary retention (UR) is a common urinary system disease can be caused by urinary tract obstruction with numerous reasons, however, the role of urine microbes in these disorders is still poorly understood. The aim of this study was to identify the urine microbial features of two common types of obstructive UR, caused by urinary stones or urinary tract tumors, with comparison to healthy controls. Methods Urine samples were collected from a cohort of 32 individuals with stone UR, 25
more » ... with stone UR, 25 subjects with tumor UR and 25 healthy controls. The urine microbiome of all samples was analyzed using high-throughput 16S rRNA (16S ribosomal RNA) gene sequencing. Results We observed dramatically increased urine microbial richness and diversity in both obstructive UR groups compared to healthy controls. Despite different origins of UR, bacteria such as Pseudomonas, Acinetobacter and Sphingomonas were enriched, while Lactobacillus, Streptococcus, Gardnerella, Prevotella and Atopobium were decreased in both UR groups in comparison with healthy controls, exhibited an approximate urine microbial community and functional characteristics of two types of obstructive UR. Furthermore, disease classifiers were constructed using specific enriched genera in UR, which can distinguish stone UR or tumor UR patients from healthy controls with an accuracy of 92.29% and 97.96%, respectively. Conclusion We presented comprehensive microbial landscapes of two common types of obstructive urinary retention and demonstrated that urine microbial features of these patients are significantly different from that of healthy people. The urine microbial signatures would shed light on the pathogenesis of these types of urinary retention and might be used as potential classification tools in the future.
doi:10.1186/s12941-020-00408-5 pmid:33407528 fatcat:xywqmrnwxja53h2ktqmpi6vp2m