Urinary stones and urinary tract infection (UTI) are the most common diseases in urology and they are characterized by high incidence and high recurrence rate in China. Previous studies have shown that urinary stones are closely associated with gut or urine microbiota. Calcium oxalate stones are the most common type of urinary stones. However, the profile of urinary tract microorganisms of calcium oxalate stones with UTI is not clear. In this research, we firstly found two novel clusters in patients with calcium oxalate stones (OA) that were associated with the WBC/HP (white blood cells per high-power field) level in urine. Two clusters in the OA group (OA1 and OA2) were distinguished by the key microbiota Firmicutes and Enterobacteriaceae. We found that Enterobacteriaceae enriched in OA1 cluster was positively correlated with several infection-related pathways and negatively correlated with a few antibiotics-related pathways. Meantime, some probiotics with higher abundance in OA2 cluster such as Bifidobacterium were positively correlated with antibiotics-related pathways, and some common pathogens with higher abundance in OA2 cluster such as Enterococcus were positively correlated with infection-related pathways. Therefore, we speculated that as a sub-type of OA disease, OA1 was caused by Enterobacteriaceae and the lack of probiotics compared with OA2 cluster. Moreover, we also sequenced urine samples of healthy individuals (CK), patients with UTI (I), patients with uric acid stones (UA), and patients with infection stones (IS). We identified the differentially abundant taxa among all groups. We hope the findings will be helpful for clinical treatment and diagnosis of urinary stones.
Frontiers in cellular and infection microbiology. 2021 Nov 17*** epublish ***
Chen Shen, Qianhui Zhu, Fan Dong, Wei Wang, Bo Fan, Kexin Li, Jun Chen, Songnian Hu, Zilong He, Xiancheng Li
Departmant of Urology, The Second Affiliated Hospital of Dalian Medical University, Dalian, China., State Key Laboratory of Microbial Resources, Institute of Microbiology, Chinese Academy of Sciences, Beijing, China., Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, MN, United States., Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, Interdisciplinary Innovation Institute of Medicine and Engineering, Beihang University, Beijing, China.