Literature DB >> 19618909

Metabolomics approach to anabolic steroid urine profiling of bovines treated with prohormones.

Jeroen C W Rijk1, Arjen Lommen, Martien L Essers, Maria J Groot, Johan M Van Hende, Timo G Doeswijk, Michel W F Nielen.   

Abstract

In livestock production, illegal use of natural steroids is hard to prove because metabolites are either unknown or not significantly above highly fluctuating endogenous levels. In this work we outlined for the first time a metabolomics based strategy for anabolic steroid urine profiling. Urine profiles of controls and bovines treated with the prohormones dehydroepiandrosterone (DHEA) and pregnenolone were analyzed with ultraperformance liquid chromatography in combination with time-of-flight accurate mass spectrometry (UPLC-TOFMS). The obtained full scan urinary profiles were compared using sophisticated preprocessing and alignment software (MetAlign) and multivariate statistics, revealing hundreds of mass signals which were differential between untreated control and prohormone-treated animals. Moreover, statistical testing of the individual accurate mass signals showed that several mass peak loadings could be used as biomarkers for DHEA and pregnenolone abuse. In addition, accurate mass derived elemental composition analysis and verification by standards or Orbitrap mass spectrometry demonstrated that the observed differential masses are most likely steroid phase I and glucuronide metabolites excreted as a direct result from the DHEA and pregnenolone administration, thus underlining the relevance of the findings from this untargeted metabolomics approach. It is envisaged that this approach can be used as a holistic screening tool for anabolic steroid abuse in bovines and possibly in sports doping as well.

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Year:  2009        PMID: 19618909     DOI: 10.1021/ac900874m

Source DB:  PubMed          Journal:  Anal Chem        ISSN: 0003-2700            Impact factor:   6.986


  8 in total

Review 1.  Hormones as doping in sports.

Authors:  Leonidas H Duntas; Vera Popovic
Journal:  Endocrine       Date:  2012-09-19       Impact factor: 3.633

2.  Multivariate statistical approach and machine learning for the evaluation of biogeographical ancestry inference in the forensic field.

Authors:  Eugenio Alladio; Brando Poggiali; Giulia Cosenza; Elena Pilli
Journal:  Sci Rep       Date:  2022-05-28       Impact factor: 4.996

3.  Feasibility of a liver transcriptomics approach to assess bovine treatment with the prohormone dehydroepiandrosterone (DHEA).

Authors:  Jeroen C W Rijk; Ad A C M Peijnenburg; Peter J M Hendriksen; Johan M Van Hende; Maria J Groot; Michel W F Nielen
Journal:  BMC Vet Res       Date:  2010-09-16       Impact factor: 2.741

4.  Ultra-fast searching assists in evaluating sub-ppm mass accuracy enhancement in U-HPLC/Orbitrap MS data.

Authors:  Arjen Lommen; Arjen Gerssen; J Efraim Oosterink; Harrie J Kools; Ainhoa Ruiz-Aracama; Ruud J B Peters; Hans G J Mol
Journal:  Metabolomics       Date:  2010-07-18       Impact factor: 4.290

5.  Profiling of Metabolomic Changes in Plasma and Urine of Pigs Caused by Illegal Administration of Testosterone Esters.

Authors:  Kamil Stastny; Kristina Putecova; Lenka Leva; Milan Franek; Petr Dvorak; Martin Faldyna
Journal:  Metabolites       Date:  2020-07-27

6.  MetAlign 3.0: performance enhancement by efficient use of advances in computer hardware.

Authors:  Arjen Lommen; Harrie J Kools
Journal:  Metabolomics       Date:  2011-10-08       Impact factor: 4.290

7.  MSClust: a tool for unsupervised mass spectra extraction of chromatography-mass spectrometry ion-wise aligned data.

Authors:  Y M Tikunov; S Laptenok; R D Hall; A Bovy; R C H de Vos
Journal:  Metabolomics       Date:  2011-10-15       Impact factor: 4.290

8.  Alterations of the volatile metabolome in mouse models of Alzheimer's disease.

Authors:  Bruce A Kimball; Donald A Wilson; Daniel W Wesson
Journal:  Sci Rep       Date:  2016-01-14       Impact factor: 4.379

  8 in total

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