Community profiling of the urinary microbiota: considerations for low-biomass samples

Abstract

Many studies have shown that the urinary tract harbours its own microbial community known as the urinary microbiota, which have been implicated in urinary tract disorders. This observation contradicts the long-held notion that urine is a sterile biofluid in the absence of acute infection of the urinary tract. In light of this new discovery, many basic questions that are crucial for understanding the role of the urinary microbiota in human health and disease remain unanswered. Given that the urinary microbiota is an emerging area of study, optimized techniques and protocols to identify microorganisms in the urinary tract are still being established. However, the low microbial biomass and close proximity to higher microbial biomass environments (for example, the vagina) present distinct methodological challenges for microbial community profiling of the urinary microbiota. A clear understanding of the unique technical considerations for obtaining and analysing low microbial biomass samples, as well the influence of key elements of experimental design and computational analysis on downstream interpretation, will improve our ability to interpret and compare results across methods and studies and is relevant for studies profiling the urinary microbiota and other sites of low microbial abundance.

Figure 1.

General overview of the workflow for 16S rRNA gene-based analysis of microbial communities, with specific considerations for urinary microbiome studies highlighted.

Figure 2.. Common errors in 16s rRNA sequencing-based microbiome studies.

Sequencing errors arise from incorrect base calling during sequencing and can be corrected when processing sequence reads with platform specific algorithms. Chimeric errors occur during the PCR amplification step and can be corrected for in the bioinformatics processing workflow. Contaminant errors arise from foreign bacteria being introduced to the sample throughout the sample collection and laboratory methods used to extract the bacterial DNA from the sample. Identifying and removing contaminant sequences is a particular challenge for samples with low microbial biomass.

Figure 3.

Typical bioinformatics pipeline for 16S rRNA sequencing based microbiome studies. Raw sequence reads are preprocessed typically by merging paired end reads and quality filtering to remove low quality sequences. The sequence reads passing this step are then grouped into operational taxonomic units (OTUs) or amplicon sequence variants (ASVs). This results in a data matrix of OTU/ASVs per sample that is annotated with taxonomic assignments. This table is used for downstream analysis to describe and understand the microbial communities.