Metabolomic based on liquid chromatography coupled to high resolution mass spectrometry as new investigation tool for assessing and characterising endocrine disruption situations
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Jean-Philippe ANTIGNAC Nantes, France |
Biography
Dr Jean-Philippe ANTIGNAC is a Research Engineer and scientific officer of LABERCA, the French National Reference Laboratory for growth promoters and various classes of environmental contaminants. His competence areas lie in the analysis of residue and contaminants in biological matrices (sample treatment and identification based on mass spectrometry), the validation of analytical methods and the statistical analysis of spectrometric data using multivariate techniques. His specific research field is currently the study of endocrine disruptors in food products of animal origin.
Compounds studied include for instance phytoestrogens and brominated flame retardants (PBDE, TBBP-A, HBCD), but also steroids (natural hormones or xenobiotics). The general purpose of this research lies both in the assessment of human exposure to these substances, the study of their transfer and metabolism through the food chain up to the final foodstuffs, as well as the development of new analytical strategies based on metabolomics and mass spectrometric fingerprinting in order to assess the eventual link between these chemical substances and some endocrine disruption effects in human.
Abstract
Metabolomic based on liquid chromatography coupled to high resolution mass spectrometry as new investigation tool for assessing and characterising endocrine disruption situations.
Jean-Philippe ANTIGNACa, Frédérique COURANTa, Gaud PINELa, Sébastien ANIZANa, Niels JORGENSENb, Anders JUULb, Fabrice MONTEAUa, Niels SKAKKEBAEKb, and Bruno LE BIZECa
a Laboratoire d’Etude des Résidus et Contaminants dans les Aliments (LABERCA), Ecole Nationale Vétérinaire, Agroalimentaire et de l’Alimentation (ONIRIS), USC INRA 2013, BP 50707, 44307 Nantes, France
b Department of Growth and Reproduction, Copenhagen University Hospital, Blegdamsvej 9, DK-2100 Copenhagen, Denmark
In recent years, the so-called ’omic’ techniques (transcriptomics, proteomics, metabolomics) have emerged as powerful tools in the fields of biology and chemistry to profile “life complexity” using unrestricted descriptive methodologies. These approaches are all based on the simultaneous generation of a large set of descriptors that characterise the biological system under study (cell, tissue, organ, or entire organism). These descriptors are then referred to genomic, proteomic or metabolomic species. In metabolomics, the monitored signals correspond to chemical substances (so-called metabolites) which are produced after the full complexity of absorption, distribution, regulation, and cell metabolism processes.
Basically, the general principle of this approach is then to characterise the studied biological systems by generating “metabolic fingerprints”. When performed on large sample sets, the comparison of such multi-endpoint measurements is expected to reveal potential similarities or differences between the analysed samples, and further research ways focusing on relevant metabolites/biomarkers of interest to be finally used for predictive diagnostic and/or clinical purposes. The generated fingerprints are then expected to reflect either the possible presence of chemical pollutants (as well as their eventual degradation and/or biotransformation products), and their potential impact on normal cell metabolism. Particularly, this approach may permits to overcome some limitation faced by conventional method such as mixtures or long-term and low-dose effect issues. Thus, metabolomics today appears as a new valuable methodological approach in the field of clinical diagnosis or toxicology in relation to a chemical exposure, especially in the endocrine disruption field. The untargeted metabolomic approach developed in this study enables to examine small molecules including primary and secondary metabolites up to 800 Da in liquid (urine, serum) or solid (tissue) biological matrices. The developed methodology is based on a minimal sample preparation (filtration) associated to a last generation of HPLC separation (1.9 mm particle size) coupled to a high resolution mass analyser (linear trap – orbital trap, R=30,000). Appropriate tools were also developed and/or implemented either for raw data pre-processing (xcms software) and statistical analyses (Simca-P software). This methodology was then applied to various research thematics. A first one is the development of new screening strategies for the control of the illegal use of growth promoters in cattle based on the monitoring of the metabolic impact induced by the administered drug. A second one is the characterisation of metabolomic perturbations associated to a reproductive endocrine situation in human.