Updating urinary microbiome analyses to enhance biologic interpretation

Abstract

Objective: An approach for assessing the urinary microbiome is 16S rRNA gene sequencing, where a segment of the bacterial genome is amplified and sequenced. Methods used to analyze these data are rapidly evolving, although the research implications are not known. This re-analysis of an existing dataset aimed to determine the impact of updated bioinformatic and statistical techniques. Methods: A prior Pelvic Floor Disorders Network (PFDN) study compared the urinary microbiome in 123 women with mixed urinary incontinence (MUI) and 84 controls. We used the PFDN unprocessed sequencing data of V1-V3 and V4-V6 16S variable regions, processed operational taxonomic unit (OTU) tables, and de-identified clinical data. We processed sequencing data with an updated bioinformatic pipeline, which used DADA2 to generate amplicon sequence variant (ASV) tables. Taxa from ASV tables were compared to OTU tables generated from the original processing; taxa from different variable regions (e.g., V1-V3 versus V4-V6) after updated processing were also compared. After updated processing, data were analyzed with multiple filtering thresholds. Several techniques were tested to cluster samples into microbial communities. Multivariable regression was used to test for associations between microbial communities and MUI, while controlling for potentially confounding variables. Results: Of taxa identified through updated bioinformatic processing, only 40% were identified originally, though taxa identified through both methods represented >99% of sequencing data in terms of relative abundance. When different 16S rRNA gene regions were sequenced from the same samples, there were differences noted in recovered taxa. When the original clustering methods were applied to reprocessed sequencing data, we confirmed differences in microbial communities associated with MUI. However, when samples were clustered with a different methodology, microbial communities were no longer associated with MUI. Conclusions: Updated bioinformatic processing techniques recover many different taxa compared to prior techniques, though most of these differences exist in low abundance taxa that occupy a small proportion of the overall microbiome. Detection of high abundance taxa are not significantly impacted by bioinformatic strategy. However, there are different biases for less abundant taxa; these differences as well as downstream clustering methodology and filtering thresholds may affect interpretation of overall results.