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Cell and Microvesicle Urine microRNA Deep Sequencing Profiles from Healthy Individuals: Observations with Potential Impact on Biomarker Studies

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  • Iddo Z Ben-Dov
  • Veronica M Whalen
  • Beatrice Goilav
  • Klaas E A Max
  • Thomas Tuschl

Abstract

Background: Urine is a potential source of biomarkers for diseases of the kidneys and urinary tract. RNA, including microRNA, is present in the urine enclosed in detached cells or in extracellular vesicles (EVs) or bound and protected by extracellular proteins. Detection of cell- and disease-specific microRNA in urine may aid early diagnosis of organ-specific pathology. In this study, we applied barcoded deep sequencing to profile microRNAs in urine of healthy volunteers, and characterized the effects of sex, urine fraction (cells vs. EVs) and repeated voids by the same individuals. Results: Compared to urine-cell-derived small RNA libraries, urine-EV-derived libraries were relatively enriched with miRNA, and accordingly had lesser content of other small RNA such as rRNA, tRNA and sn/snoRNA. Unsupervised clustering of specimens in relation to miRNA expression levels showed prominent bundling by specimen type (urine cells or EVs) and by sex, as well as a tendency of repeated (first and second void) samples to neighbor closely. Likewise, miRNA profile correlations between void repeats, as well as fraction counterparts (cells and EVs from the same specimen) were distinctly higher than correlations between miRNA profiles overall. Differential miRNA expression by sex was similar in cells and EVs. Conclusions: miRNA profiling of both urine EVs and sediment cells can convey biologically important differences between individuals. However, to be useful as urine biomarkers, careful consideration is needed for biofluid fractionation and sex-specific analysis, while the time of voiding appears to be less important.

Suggested Citation

  • Iddo Z Ben-Dov & Veronica M Whalen & Beatrice Goilav & Klaas E A Max & Thomas Tuschl, 2016. "Cell and Microvesicle Urine microRNA Deep Sequencing Profiles from Healthy Individuals: Observations with Potential Impact on Biomarker Studies," PLOS ONE, Public Library of Science, vol. 11(1), pages 1-10, January.
    • Handle: RePEc:plo:pone00:0147249
    DOI: 10.1371/journal.pone.0147249
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    1. John W. Emerson, 2008. "Interactive and Dynamic Graphics for Data Analysis: With R and GGobi by COOK, D. and SWAYNE, D," Biometrics, The International Biometric Society, vol. 64(4), pages 1301-1303, December.
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