Short- and mid-term temporal variability of the human urinary microbiota: a prospective observational cohort study
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Background
Understanding the temporal variability of the microbiome is critical for translating associations of the microbiome with health and disease into clinical practice. The aim of this study is to assess the extent of temporal variability of the human urinary microbiota. A pair of urine samples were collected from study participants at 3–40-month interval. DNA was extracted and the bacterial V4 hypervariable region of the 16S rRNA gene was sequenced on the Illumina MiSeq platform. The alpha diversity of paired samples was analyzed using Chao1 and Shannon indices and PERMANOVA was used to test the factors influencing beta diversity.
Results
A total of 63 participants (43 men and 20 women with a mean age of 63.0 and 57.1 years, respectively) were included in the final analysis. An average of 152 ± 128 bacterial operational taxonomic units (OTUs) were identified in each urine sample from the entire cohort. There was an average of 41 ± 32 overlapping OTUs in each sample pair, accounting for 66.3 ± 29.4% of the relative abundance. There was a clear correlation between the number of overlapping OTUs and the relative abundance covered. The difference in Chao1 index between paired samples was statistically significant; the difference in Shannon index was not. Beta diversity did not differ significantly within the paired samples. Neither age nor sex of the participants influenced the variation in community composition. With a longer interval between the collections, the relative abundance covered by the overlapping OTUs changed significantly but not the number of OTUs.
Conclusion
Our findings demonstrated that, while the relative abundance of dominant bacteria varied, repeated collections generally shared more than 60% of the bacterial community. Furthermore, we observed little variation in the alpha and beta diversity of the microbial community in human urine. These results help to understand the dynamics of human urinary microbiota and enable interpretation of future studies.
