, Volume 11, Issue 1, pp 184–197 | Cite as

Global urine fingerprinting by LC-ESI(+)-HRMS for better characterization of metabolic pathway disruption upon anabolic practices in bovine

  • Cristina C. Jacob
  • Gaud Dervilly-Pinel
  • Giancarlo Biancotto
  • Fabrice Monteau
  • Bruno Le Bizec
Original Article


The use of anabolic agents in meat producing animals is forbidden in Europe since 1988. Nevertheless, the possibility of widespread abuse of hormonal substances still exists, mainly due to economical benefits. The use of “omics” approach is a promising strategy to highlight anabolic hormone abuse by indirectly detecting their physiological action. In this context, the aim of this work was to set up a liquid chromatography – high resolution mass spectrometry based metabolomics workflow for the screening of a combined trenbolone acetate/estradiol implant abuse in cattle urine. Therefore, an untargeted metabolomics approach combining the information provided by reverse-phase liquid chromatography and hydrophilic interaction chromatography both coupled to high resolution mass spectrometry was developed and applied to characterize and compare cattle urinary metabolic profiles from non-implanted and implanted animals. The combination of both separation modes improved the metabolite information richness. Discrimination between treated and untreated animals was observed by application of multivariate statistical analysis. Moreover, OPLS models permitted to highlight the candidate biomarkers appearing as the ions which contribute the most in the observed discrimination. From the results obtained, metabolomics approaches can be considered as a powerful strategy for the detection of fraudulent anabolic treatments in cattle since global urinary metabolic response provides helpful discrimination.


Metabolomics Trenbolone acetate Estradiol-17β Screening LC-HRMS HILIC 



This work was financially supported by the Italian Ministry of Health (RF-IZV-2008-1175188). Authors thank Merck Animal Health for kindly providing the implant Revalor-XS®.

Conflict of interest

The authors have declared no conflicts of interest in the submission of this manuscript.


  1. Anizan, S., Bichon, E., Monteau, F., Cesbron, N., Antignac, J.-P., & Le Bizec, B. (2010). A new reliable sample preparation for high throughput focused steroid profiling by gas chromatography-mass spectrometry. Journal of Chromatography A, 1217, 6652–6660.PubMedCrossRefGoogle Scholar
  2. Bartle, S. J., Preston, R. L., Brown, R. E., & Grant, R. J. (1992). Trenbolone acetate/estradiol combinations in feedlot steers: dose-response and implant carrier effects. Journal of Animal Science, 70, 1326–1332.PubMedGoogle Scholar
  3. Blanco CE1, Popper P, Micevych P. (1997). Anabolic-androgenic steroid induced alterations in choline acetyltransferase messenger RNA levels of spinal cord motoneurons in the male rat. Neuroscience. 78(3):873-82.Google Scholar
  4. Cone, E. J., Caplan, Y. H., Moser, F., Robert, T., Shelby, M. K., & Black, D. L. (2009). Normalization of urinary drug concentrations with specific gravity and creatinine. Journal of Analytical Toxicology, 33(1), 1–7.PubMedCrossRefGoogle Scholar
  5. Council Directive 2003/74/EC (2003). Amending Council Directive 96/22/EC concerning the prohibition on the use in stockfarming of certain substances having a hormonal or thyrostatic action anf of beta-agonists. Official Journal of European Communities, L262, 17–21.Google Scholar
  6. Council Directive 2008/97/EC (2008). Amending Council Directive 96/22/EC concerning the prohibition on the use in stockfarming of certain substances having a hormonal or thyrostatic action and of beta-agonists. Official Journal of European Communities, L318, 9–11.Google Scholar
  7. Council Directive 96/22/EC (1996). Concerning the prohibition on the use in stockfarming of certain substances having a hormonal or thyrostatic action and of beta-agonists, and repealing Directives 81/602/EEC, 88/146/EEC and 88/299/EEC. Official Journal of European Communities, L125, 3–9.Google Scholar
  8. Courant, F., Pinel, G., Bichon, E., Monteau, F., Antignac, J.-P., & Le Bizec, B. (2009). Development of a metabolomic approach based on liquid chromatography-high resolution mass spectrometry to screen for a clenbuterol abuse in calves. Analyst, 134, 1637–1646.PubMedCrossRefGoogle Scholar
  9. Dervilly-Pinel, G., Courant, F., Chereau, S., Royer, A. L., Boyard-Kieken, F., Antignac, J. P., et al. (2012). Metabolomics in food analysis: application to the control of forbidden substances. Drug Testing and Analysis, 4, 59–69. doi:10.1002/dta.1349.PubMedCrossRefGoogle Scholar
  10. FDA (2007). Freedom of information summary original new animal drug administration. NADA 141-269. REVALOR-XS (Trenbolone acetate and Estradiol) Implant (pellets) for cattle (Steers fed in confinement for slaughter). Accessed July 13, 2012.
  11. Jacob, C. C., Dervilly-Pinel, G., Biancotto, G., & Le Bizec, B. (2013). Evaluation of specific gravity as normalization strategy for cattle urinary metabolome analysis. Metabolomics, 1–11, doi:10.1007/s11306-013-0604-z.
  12. Johnson, B. J., Anderson, P. T., Meiske, J. C., & Dayton, W. R. (1996). Effect of a combined trenbolone acetate and estradiol implant on feedlot performance, carcass characteristics and carcass composition of feedlot steers. Journal of Animal Science, 74, 363–371.PubMedGoogle Scholar
  13. Kind, T., & Fiehn, O. (2007). Seven golden rules for heuristic filtering of molecular formulas obtained by accurate mass spectrometry. BMC bioinformatics, 8, doi:10.1186/1471-2105-8-105.
  14. MacDonald, M. A., Krueger, H., & Bogart, R. (1960). Rate and Efficiency of Gains in Beef Cattle. 8. Urinary Specific Gravity, pH, and Buffer Capacity in Beef Cattle (Vol. 54). Oregon State University: Technical Bulletin. Oregon Agricultural Experiment Station.Google Scholar
  15. Pampusch, M. S., Johnson, B. J., White, M. E., Hathaway, M. R., Dunn, J. D., Waylan, A. T., et al. (2003). Time course of changes in growth factor mRNA levels in muscle of steroid-implanted and nonimplanted steers. Journal of Animal Science, 81, 2733–2740.PubMedGoogle Scholar
  16. Patti, G. J., Tautenhahn, R., & Siuzdak, G. (2012). Meta-analysis of untargeted metabolomic data from multiple profiling experiments. Nature Protocols, 7(3), 508–516. doi:10.1038/nprot.2011.454.PubMedCentralPubMedCrossRefGoogle Scholar
  17. Pinel, G., Weigel, S., Antignac, J.-P., Mooney, M. H., Elliot, C., Nielen, M. W. F., et al. (2010). Targeted and untargeted profiling of biological fluids to screen for anabolic practices in cattle. Trends in Analytical Chemistry, 29, 1269–1280.CrossRefGoogle Scholar
  18. Pinel, G., Weigel, S., Lommen, A., Chereau, S., Rambaud, L., Essers, M. L., et al. (2011). Assessment of two complementary LC-HRMS metabolomics strategies for the screening of anabolic steroid treatment in calves. Analytica Chimica Acta, 700, 144–154.CrossRefGoogle Scholar
  19. Ridgway, N. D. (2013). The role of phosphatidylcholine and choline metabolites to cell proliferation and survival. Critical Reviews in Biochemistry and Molecular Biology, 48(1), 20–38.PubMedCrossRefGoogle Scholar
  20. Rijk, J. C. W., Lommen, A., Essers, M. L., Groot, M. J., Van Hende, J. M., Doeswijk, T. G., et al. (2009). Metabolomics approach to anabolic steroid urine profiling of bovines treated with prohormones. Analytical Chemistry, 81(16), 6879–6888.PubMedCrossRefGoogle Scholar
  21. Smith, C. A., Want, E. J., O’Maille, G., Abagyan, R., & Siuzdak, G. (2006). XCMS: processing mass spectrometry data for metabolite profiling using nonlinear peak alignment, matching, and identification. Analytical Chemistry, 78(3), 779–787. doi:10.1021/ac051437y.PubMedCrossRefGoogle Scholar
  22. Tautenhahn, R., Bottcher, C., & Neumann, S. (2008). Highly sensitive feature detection for high resolution LC/MS. BMC Bioinformatics, 9, 504. doi:10.1186/1471-2105-9-504.PubMedCentralPubMedCrossRefGoogle Scholar
  23. Tautenhahn, R., Patti, G. J., Kalisiak, E., Miyamoto, T., Schmidt, M., Lo, F. Y., et al. (2011). metaXCMS: Second-order analysis of untargeted metabolomics data. Analytical Chemistry, 83(3), 696–700. doi:10.1021/ac102980g.PubMedCentralPubMedCrossRefGoogle Scholar
  24. Trygg, J., Holmes, E., & Lundstedt, T. (2007). Chemometrics in metabonomics. Journal of Proteome Research, 6, 469–479.PubMedCrossRefGoogle Scholar
  25. van der Kloet, F. M., Bobeldijk, I., Verheij, E. R., & Jellema, R. H. (2009). Analytical Error Reduction Using Single Point Calibration for Accurate and Precise Metabolomic Phenotyping. Journal of Proteome Research, 8, 5132–5141.PubMedCrossRefGoogle Scholar
  26. Werner, E., Heilier, J.-F., Ducruix, C., Ezan, E., Junot, C., & Tabet, J.-C. (2008). Mass spectrometry for the identification of the discriminating signals from metabolomics: Current status and future trends. Journal of Chromatography B-Analytical Technologies in the Biomedical and Life Sciences, 871(2), 143–163. doi:10.1016/j.jchromb.2008.07.004.CrossRefGoogle Scholar
  27. Westerhuis, J. A., Hoefsloot, H. C. J., Smit, S., Vis, D. J., Smilde, A. K., van Velzen, E. J. J., et al. (2008). Assessment of PLSDA cross validation. Metabolomics, 4, 81–89.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Cristina C. Jacob
    • 1
  • Gaud Dervilly-Pinel
    • 1
  • Giancarlo Biancotto
    • 2
  • Fabrice Monteau
    • 1
  • Bruno Le Bizec
    • 1
  1. 1.LUNAM Université, ONIRIS, École nationale vétérinaire, agroalimentaire et de l’alimentation Nantes-AtlantiqueLaboratoire d’Étude des Résidus et Contaminants dans les Aliments (LABERCA)NantesFrance
  2. 2.Department of ChemistryIstituto Zooprofilattico Sperimentale delle VenezieLegnaroItaly

Personalised recommendations