Literature DB >> 25254001

A Statistical Analysis of the Effects of Urease Pre-treatment on the Measurement of the Urinary Metabolome by Gas Chromatography-Mass Spectrometry.

Bobbie-Jo Webb-Robertson1, Young-Mo Kim1, Erika M Zink1, Katherine A Hallaian1, Qibin Zhang1, Ramana Madupu2, Katrina M Waters1, Thomas O Metz1.   

Abstract

Urease pre-treatment of urine has been utilized since the early 1960s to remove high levels of urea from samples prior to further processing and analysis by gas chromatography-mass spectrometry (GC-MS). Aside from the obvious depletion or elimination of urea, the effect, if any, of urease pre-treatment on the urinary metabolome has not been studied in detail. Here, we report the results of three separate but related experiments that were designed to assess possible indirect effects of urease pre-treatment on the urinary metabolome as measured by GC-MS. In total, 235 GC-MS analyses were performed and over 106 identified and 200 unidentified metabolites were quantified across the three experiments. The results showed that data from urease pre-treated samples 1) had the same or lower coefficients of variance among reproducibly detected metabolites, 2) more accurately reflected quantitative differences and the expected ratios among different urine volumes, and 3) increased the number of metabolite identifications. Overall, we observed no negative consequences of urease pre-treatment. In contrast, urease pretreatment enhanced the ability to distinguish between volume-based and biological sample types compared to no treatment. Taken together, these results show that urease pretreatment of urine offers multiple beneficial effects that outweigh any artifacts that may be introduced to the data in urinary metabolomics analyses.

Entities:  

Keywords:  gas chromatography-mass spectrometry; metabolomics; statistics; urease; urine

Year:  2014        PMID: 25254001      PMCID: PMC4169993          DOI: 10.1007/s11306-014-0642-1

Source DB:  PubMed          Journal:  Metabolomics        ISSN: 1573-3882            Impact factor:   4.290


  29 in total

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