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 pre-treatment enhanced the ability to distinguish between volume-based and biological sample types compared to no treatment. Taken together, these results show that urease pre-treatment of urine offers multiple beneficial effects that outweigh any artifacts that may be introduced to the data in urinary metabolomics analyses.
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Barr, D. B., Wilder, L. C., Caudill, S. P., Gonzalez, A. J., Needham, L. L., & Pirkle, J. L. (2005). Urinary creatinine concentrations in the U.S. population: implications for urinary biologic monitoring measurements. Environmental Health Perspectives, 113(2), 192–200.
Broadhurst, D. I., & Kell, D. B. (2006). Statistical strategies for avoiding false discoveries in metabolomics and related experiments. Metabolomics, 2(4), 171–196.
Chan, E. C., Pasikanti, K. K., & Nicholson, J. K. (2011). Global urinary metabolic profiling procedures using gas chromatography–mass spectrometry. Nature Protocols, 6(10), 1483–1499.
Clements, R. S, Jr, & Starnes, W. R. (1975). An improved method for the determination of urinary myoinositol by gas-liquid chromatography. Biochemical Medicine, 12(2), 200–204.
Egeghy, P. P., Cohen Hubal, E. A., Tulve, N. S., Melnyk, L. J., Morgan, M. K., Fortmann, R. C., et al. (2011). Review of pesticide urinary biomarker measurements from selected US EPA children’s observational exposure studies. International Journal of Environmental Research and Public Health, 8(5), 1727–1754.
Ganti, S., & Weiss, R. H. (2011). Urine metabolomics for kidney cancer detection and biomarker discovery. Urologic Oncology, 29(5), 551–557.
Guyton, A. C. (1981). Textbook of medical physiology (6th ed.). Philadelphia, PA: W. B. Saunders Company.
Hecht, S. S. (2002). Human urinary carcinogen metabolites: biomarkers for investigating tobacco and cancer. Carcinogenesis, 23(6), 907–922.
Hiller, K., Hangebrauk, J., Jager, C., Spura, J., Schreiber, K., & Schomburg, D. (2009). MetaboliteDetector: comprehensive analysis tool for targeted and nontargeted GC/MS based metabolome analysis. Analytical Chemistry, 81(9), 3429–3439.
Hollander, M., & Wolfe, D. A. (1999). Nonparametric statistical methods. Hoboken, NJ: John Wiley & Sons Inc.
Kim, Y. M., Metz, T. O., Hu, Z., Wiedner, S. D., Kim, J. S., Smith, R. D., et al. (2011). Formation of dehydroalanine from mimosine and cysteine: artifacts in gas chromatography/mass spectrometry based metabolomics. Rapid Communications in Mass Spectrometry, 25(17), 2561–2564.
Kim, Y. M., Schmidt, B. J., Kidwai, A. S., Jones, M. B., Deatherage Kaiser, B. L., Brewer, H. M., et al. (2013). Salmonella modulates metabolism during growth under conditions that induce expression of virulence genes. Molecular BioSystems, 9, 1522.
Kind, T., Tolstikov, V., Fiehn, O., & Weiss, R. H. (2007). A comprehensive urinary metabolomic approach for identifying kidney cancerr. Analytical Biochemistry, 363(2), 185–195.
Kind, T., Wohlgemuth, G., Lee do, Y., Lu, Y., Palazoglu, M., Shahbaz, S., et al. (2009). FiehnLib: mass spectral and retention index libraries for metabolomics based on quadrupole and time-of-flight gas chromatography/mass spectrometry. Analytical Chemistry, 81(24), 10038–10048.
Kuhara, T. (2007). Noninvasive human metabolome analysis for differential diagnosis of inborn errors of metabolism. Journal of Chromatography B Analytical Technology Biomedical Life Science, 855(1), 42–50.
Kussmann, M., Raymond, F., & Affolter, M. (2006). OMICS-driven biomarker discovery in nutrition and health. Journal of Biotechnology, 124(4), 758–787.
Matsumoto, I., & Kuhara, T. (1996). A new chemical diagnostic method for inborn errors of metabolism by mass spectrometry: Rapid, practical, and simultaneous urinary metabolites analysis. Mass Spectrometry Reviews, 15(1), 43–57.
Matsumoto, M., Zhang, C., Shinka, T., Inoue, Y., Furumoto, T., Kuhara, T., et al. (1994). The chemical diagnosis of the metabolic disorders 1. Chemical diagnosis of propionic acidemia. Journal of Kanazawa Medical University, 19, 213–219.
Matzke, M. M., Waters, K. M., Metz, T. O., Jacobs, J. M., Sims, A. C., Baric, R. S., et al. (2011). Improved quality control processing of peptide-centric LC-MS proteomics data. Bioinformatics, 27(20), 2866–2872.
Meeker, J. D., Sathyanarayana, S., & Swan, S. H. (2009). Phthalates and other additives in plastics: human exposure and associated health outcomes. Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences, 364(1526), 2097–2113.
Metz, T. O., Zhang, Q., Page, J. S., Shen, Y., Callister, S. J., Jacobs, J. M., et al. (2007). The future of liquid chromatography-mass spectrometry (LC-MS) in metabolic profiling and metabolomic studies for biomarker discovery. Biomarkers in Medicine, 1(1), 159–185.
Ott, R. L., & Longnecker, M. (2008). An Introduction to statistical methods and data analysis. Belmont, CA: Brooks/Cole.
Pasikanti, K. K., Ho, P. C., & Chan, E. C. (2008). Development and validation of a gas chromatography/mass spectrometry metabonomic platform for the global profiling of urinary metabolites. Rapid Communications in Mass Spectrometry, 22(19), 2984–2992.
Psihogios, N. G., Gazi, I. F., Elisaf, M. S., Seferiadis, K. I., & Bairaktari, E. T. (2008). Gender-related and age-related urinalysis of healthy subjects by NMR-based metabonomics. NMR in Biomedicine, 21(3), 195–207.
Putnam, D. F. (1971). Composition and Concentrative Properties of Human Urine. (pp. 112). Huntington Beach, CA: National Aeronautics and Space Administration.
Roberts, L. J., & Morrow, J. D. (2000). Measurement of F(2)-isoprostanes as an index of oxidative stress in vivo. Free Radical Biology and Medicine, 28(4), 505–513.
Saude, E. J., Adamko, D., Rowe, B. H., Marrie, T., & Sykes, B. D. (2007). Variation of metabolites in normal human urine. Metabolomics, 3(4), 439–451.
Shelby, M. K., Crouch, D. J., Black, D. L., Robert, T. A., & Heltsley, R. (2011). Screening indicators of dehydroepiandosterone, androstenedione, and dihydrotestosterone use: a literature review. Journal of Analytical Toxicology, 35(9), 638–655.
Shoemaker, J. D., & Elliott, W. H. (1991). Automated screening of urine samples for carbohydrates, organic and amino acids after treatment with urease. Journal of Chromatography, 562(1–2), 125–138.
Slupsky, C. M., Rankin, K. N., Wagner, J., Fu, H., Chang, D., Weljie, A. M., et al. (2007). Investigations of the effects of gender, diurnal variation, and age in human urinary metabolomic profiles. Analytical Chemistry, 79(18), 6995–7004.
Sumner, L. W., Amberg, A., Barrett, D., Beale, M. H., Beger, R., Daykin, C. A., et al. (2007). Proposed minimum reporting standards for chemical analysis. Metabolomics, 3(3), 211–221.
Webb-Robertson, B. J., Matzke, M. M., Jacobs, J. M., Pounds, J. G., & Waters, K. M. (2011). A statistical selection strategy for normalization procedures in LC-MS proteomics experiments through dataset-dependent ranking of normalization scaling factors. Proteomics, 11(24), 4736–4741.
Webb-Robertson, B. J., Matzke, M. M., Metz, T. O., McDermott, J. E., Walker, H., Rodland, K. D., et al. (2013). Sequential projection pursuit principal component analysis: Dealing with missing data associated with new-omics technologies. BioTechniques, 54(3), 165–168.
Webb-Robertson, B. J., McCue, L. A., Waters, K. M., Matzke, M. M., Jacobs, J. M., Metz, T. O., et al. (2010). Combined statistical analyses of peptide intensities and peptide occurrences improves identification of significant peptides from MS-based proteomics data. Journal of Proteome Research, 9(11), 5748–5756.
Wells, W. W., Chin, T., & Weber, B. (1964). Quantitative analysis of serum and urine sugars by gas chromatography. Clinica Chimica Acta, 10, 352–359.
Wilkins, J. N. (1997). Quantitative urine levels of cocaine and other substances of abuse. NIDA Research Monograph, 175, 235–252.
Acknowledgments
This work was funded by NIH NIDDK Grant DP3 DK094343. Significant portions of the work were performed at the Environmental Molecular Sciences Laboratory, a national scientific user facility sponsored by the Department of Energy’s (DOE) Office of Biological and Environmental Research and located at Pacific Northwest National Laboratory (PNNL) in Richland, Washington. PNNL is a multi-program national laboratory operated by Battelle for the DOE under Contract DE-AC05-76RLO 1830.
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All procedures followed were in accordance with the ethical standards of the responsible committee on human experimentation (institutional and national) and with the Helsinki Declaration of 1975, as revised in 2000. Informed consent was obtained from all patients before being included in the study.
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Bobbie-Jo Webb-Robertson and Young-Mo Kim have contributed equally to this work.
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Webb-Robertson, BJ., Kim, YM., Zink, E.M. et al. A statistical analysis of the effects of urease pre-treatment on the measurement of the urinary metabolome by gas chromatography–mass spectrometry. Metabolomics 10, 897–908 (2014). https://doi.org/10.1007/s11306-014-0642-1
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DOI: https://doi.org/10.1007/s11306-014-0642-1