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

Nature Reviews Urology volume 15, pages 735–749 (2018)Cite this article

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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.

Key points

  • Similar to other areas of the human body, the urinary tract and bladder are inhabited by commensal microorganisms that are collectively referred to as the urinary microbiota.

  • The composition of the urinary microbiota has been associated with response to treatments, risk of infection and urological disorders.

  • Studying the urinary microbiota presents many challenges owing to its low microbial biomass and proximity to other body sites with rich microbial environments (such as the vulva and vagina).

  • Further research to understand these microbial communities and their relationship with urological disorders is warranted and will require careful sample collection and data analysis to draw robust and reproducible conclusions.

  • DNA sequencing techniques enable the culture-independent identification of bacteria but have limitations such as poor taxonomic resolution, inability to distinguish between living and dead bacteria and lack of functional annotation.

  • Despite outstanding advances in microbiome bioinformatics that have improved the information gained from 16S ribosomal RNA gene sequencing studies, adoption of these methods is still lagging.

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Fig. 1: Considerations for 16S rRNA gene sequencing-based urinary microbiome analysis.
Fig. 2: Common errors in 16S rRNA gene sequencing-based microbiome studies.
Fig. 3: Typical bioinformatics pipeline for 16S rRNA gene sequencing-based microbiome studies.

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Acknowledgements

The authors thank R. Searles and L. Brubaker for helpful discussions. The authors also thank their funders who supported this work: L.K. is funded by the Oregon BIRCWH (Building Interdisciplinary Research Careers in Women’s Health) programme (US NIH award number K12HD043488), the NIH (award number (K01DK116706) and the Rheumatology Research Foundation. M.A. is funded by the Rheumatology Research Foundation and the Spondylitis Association of America. J.T.R. is funded by the NIH (award number R01EY029266), Research to Prevent Blindness (RPB), the Rheumatology Research Foundation, the Stan and Madelle Rosenfeld Family Trust, the William and Mary Bauman Family Foundation and the Spondylitis Association of America. J.B. is funded by the NIH (award numbers P01DK46763, P30CA016042 and UL1TR001881). D.A.F. is funded by the NIH (award numbers R01MH115357, R01MH105538, R01MH096773 and R00MH091238). S.K.M. is funded by the NIH (award numbers UL1TR002369 and U24TR002306). R.N. is funded by the Society of Urodynamics, Female Pelvic Medicine & Urogenital Reconstruction (Overactive Bladder Syndrome Urgency Urinary Incontinence grant). The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH or any other funding agency.

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Nature Reviews Urology thanks J. P. Burton, A. Wolfe and C. Putonti for their contribution to the peer review of this work.

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Authors and Affiliations

  1. Division of Bioinformatics and Computational Biology, Oregon Health & Science University, Portland, OR, USA

    Lisa Karstens, Vincent Caruso & Shannon K. McWeeney

  2. Division of Urogynecology, Oregon Health & Science University, Portland, OR, USA

    Lisa Karstens, W. Thomas Gregory & Rahel Nardos

  3. Division of Arthritis and Rheumatic Diseases, Oregon Health & Science University, Portland, OR, USA

    Mark Asquith & James T. Rosenbaum

  4. Devers Eye Institute, Oregon Health & Science University, Portland, OR, USA

    James T. Rosenbaum

  5. Department of Behavioral Neuroscience, Oregon Health & Science University, Portland, OR, USA

    Damien A. Fair

  6. Department of Psychiatry, Oregon Health & Science University, Portland, OR, USA

    Damien A. Fair

  7. Advanced Imaging Research Center, Oregon Health & Science University, Portland, OR, USA

    Damien A. Fair

  8. Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California–Los Angeles, Los Angeles, CA, USA

    Jonathan Braun

  9. Kaiser Permanente Northwest, Portland, OR, USA

    Rahel Nardos

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Contributions

L.K., R.N., M.A. and V.C. researched data for article. L.K. and M.A. wrote the manuscript. All authors made a substantial contribution to discussion of content and reviewed and/or edited the manuscript before submission.

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Correspondence to Lisa Karstens.

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Related links

QIIME: https://qiime2.org/

Mothur: https://mothur.org/

DADA2: https://benjjneb.github.io/dada2/index.html

Jupyter Notebook: http://jupyter.org/

R Markdown: https://rmarkdown.rstudio.com/

Earth Microbiome Project: http://www.earthmicrobiome.org/

Human Microbiome Project: https://hmpdacc.org/

Glossary

Microbiota

The microorganisms (bacteria, archaea, fungi and viruses) present in a defined environment.

Catheter-collected urine

Urine collected via the insertion of a catheter into the urethra to directly collect urine from the bladder.

Clean-catch midstream urine

Voided urine collected by instructing individuals to clean the urethral area with disinfectant wipes, begin to void and then place a specimen cup into the urinary stream to collect urine.

Microbiome

Includes the microorganisms (bacteria, archaea, fungi and viruses), their genomes (that is, genes) and the surrounding environmental conditions in a defined environment.

Transurethral catheterization

The insertion of a catheter into the urethra (for example, for catheter-collected urine).

Suprapubic aspiration

A procedure during which a needle is used to puncture the skin and abdomen to aspirate urine directly from the bladder.

Whole-genome shotgun sequencing

A DNA sequencing technique in which all of the DNA from a sample is sequenced.

Marker gene sequencing

A DNA sequencing technique in which a specific gene or gene region is targeted for sequencing.

Expanded quantitative urine culture

(EQUC). A set of culturing techniques under a variety of nonstandard conditions (such as in an increased CO2 environment or prolonged incubation time) to grow and isolate a large variety of bacteria from urine.

Chimeric sequences

DNA sequences that arise during PCR amplification when two distinct DNA sequences anneal to form a new, non-biological sequence, which is subsequently amplified.

454 pyrosequencing

A sequencing-by-synthesis DNA sequencing method based on pyrosequencing technology.

HiSeq

An Illumina sequencing platform that (in high-throughput mode) has eight independent lanes (each optimally clustering at 280 million templates per lane) and read lengths up to 125 bp.

MiSeq

An Illumina sequencing platform that has a dual-lane (single-sample) configuration with lower output (~20–25 million single reads (raw)) but longer read length (up to 300 bp) than HiSeq.

Quality trimming

A data processing procedure in which low-quality sections of sequenced reads are removed.

Denoising algorithms

Algorithms that attempt to resolve errors introduced by the DNA sequencer.

Operational taxonomic unit

(OTU). A group of similar sequences (often with 97% similarity).

Amplicon sequence variant

(ASV). Groups of error-resolved DNA sequences that can be used in place of operational taxonomic units (OTUs) for analysis. Also referred to as exact sequence variants or sub-OTUs.

Demultiplexing

Ungrouping reads from a sequencing run so that reads are associated with specific samples.

Quality filtering

Removing reads that contain errors above a user-defined threshold.

Posterior quality scores

Scores from the sequencer that indicate the probability of an individual nucleotide being correctly called.

k-mer matching

An algorithmic approach to identify sequences of length k from a read that match a reference sequence in a database.

Alpha diversity

Measures of within-sample diversity (often involves richness and/or evenness).

Beta diversity

Measures of between-sample similarity or dissimilarity.

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Karstens, L., Asquith, M., Caruso, V. et al. Community profiling of the urinary microbiota: considerations for low-biomass samples. Nat Rev Urol 15, 735–749 (2018). https://doi.org/10.1038/s41585-018-0104-z

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