Metabolomics

, Volume 10, Issue 5, pp 897–908 | Cite as

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-Robertson
  • Young-Mo Kim
  • Erika M. Zink
  • Katherine A. Hallaian
  • Qibin Zhang
  • Ramana Madupu
  • Katrina M. Waters
  • Thomas O. Metz
Original Article

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.

Keywords

Urease Urine Gas chromatography–mass spectrometry Metabolomics Statistics 

Notes

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.

Informed consent

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.

Supplementary material

11306_2014_642_MOESM1_ESM.docx (59.8 mb)
Supplementary material 1 (DOCX 61252 kb)
11306_2014_642_MOESM2_ESM.xlsx (133 kb)
Supplementary material 2 (XLSX 133 kb)
11306_2014_642_MOESM3_ESM.xlsx (124 kb)
Supplementary material 3 (XLSX 124 kb)
11306_2014_642_MOESM4_ESM.xlsx (388 kb)
Supplementary material 4 (XLSX 387 kb)

References

  1. 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.CrossRefPubMedGoogle Scholar
  2. Broadhurst, D. I., & Kell, D. B. (2006). Statistical strategies for avoiding false discoveries in metabolomics and related experiments. Metabolomics, 2(4), 171–196.CrossRefGoogle Scholar
  3. 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.CrossRefPubMedGoogle Scholar
  4. 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.CrossRefPubMedGoogle Scholar
  5. 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.CrossRefPubMedPubMedCentralGoogle Scholar
  6. Ganti, S., & Weiss, R. H. (2011). Urine metabolomics for kidney cancer detection and biomarker discovery. Urologic Oncology, 29(5), 551–557.CrossRefPubMedPubMedCentralGoogle Scholar
  7. Guyton, A. C. (1981). Textbook of medical physiology (6th ed.). Philadelphia, PA: W. B. Saunders Company.Google Scholar
  8. Hecht, S. S. (2002). Human urinary carcinogen metabolites: biomarkers for investigating tobacco and cancer. Carcinogenesis, 23(6), 907–922.CrossRefPubMedGoogle Scholar
  9. 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.CrossRefPubMedGoogle Scholar
  10. Hollander, M., & Wolfe, D. A. (1999). Nonparametric statistical methods. Hoboken, NJ: John Wiley & Sons Inc.Google Scholar
  11. 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.CrossRefPubMedPubMedCentralGoogle Scholar
  12. 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.CrossRefPubMedPubMedCentralGoogle Scholar
  13. 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.CrossRefPubMedGoogle Scholar
  14. 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.CrossRefPubMedPubMedCentralGoogle Scholar
  15. 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.CrossRefGoogle Scholar
  16. Kussmann, M., Raymond, F., & Affolter, M. (2006). OMICS-driven biomarker discovery in nutrition and health. Journal of Biotechnology, 124(4), 758–787.CrossRefPubMedGoogle Scholar
  17. 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.CrossRefPubMedGoogle Scholar
  18. 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.Google Scholar
  19. 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.CrossRefPubMedPubMedCentralGoogle Scholar
  20. 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.CrossRefPubMedPubMedCentralGoogle Scholar
  21. 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.CrossRefPubMedPubMedCentralGoogle Scholar
  22. Ott, R. L., & Longnecker, M. (2008). An Introduction to statistical methods and data analysis. Belmont, CA: Brooks/Cole.Google Scholar
  23. 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.CrossRefPubMedGoogle Scholar
  24. 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.CrossRefPubMedGoogle Scholar
  25. Putnam, D. F. (1971). Composition and Concentrative Properties of Human Urine. (pp. 112). Huntington Beach, CA: National Aeronautics and Space Administration.Google Scholar
  26. 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.CrossRefPubMedGoogle Scholar
  27. 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.CrossRefGoogle Scholar
  28. 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.CrossRefPubMedGoogle Scholar
  29. 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.CrossRefPubMedGoogle Scholar
  30. 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.CrossRefPubMedGoogle Scholar
  31. 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.CrossRefPubMedPubMedCentralGoogle Scholar
  32. 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.CrossRefPubMedPubMedCentralGoogle Scholar
  33. 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.CrossRefPubMedGoogle Scholar
  34. 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.CrossRefPubMedPubMedCentralGoogle Scholar
  35. Wells, W. W., Chin, T., & Weber, B. (1964). Quantitative analysis of serum and urine sugars by gas chromatography. Clinica Chimica Acta, 10, 352–359.CrossRefGoogle Scholar
  36. Wilkins, J. N. (1997). Quantitative urine levels of cocaine and other substances of abuse. NIDA Research Monograph, 175, 235–252.PubMedGoogle Scholar

Copyright information

© Springer Science+Business Media New York (outside the USA)  2014

Authors and Affiliations

  • Bobbie-Jo Webb-Robertson
    • 1
  • Young-Mo Kim
    • 1
  • Erika M. Zink
    • 1
  • Katherine A. Hallaian
    • 1
  • Qibin Zhang
    • 1
  • Ramana Madupu
    • 2
  • Katrina M. Waters
    • 1
  • Thomas O. Metz
    • 1
  1. 1.Fundamental and Computational Sciences DirectoratePacific Northwest National LaboratoryRichlandUSA
  2. 2.J. Craig Venter InstituteRockvilleUSA

Personalised recommendations