Résumé
Complex mixtures of contaminants with potential adverse effects for human health are found in the environment and in the food chain, including natural and synthetic molecules able to act as endocrine disruptors. The structural variety of these compounds, but also their biotic or abiotic transformation, render these mixtures even more complex (parent compounds +- metabolites). Endocrine Disruptor Chemicals can interfere with a variety of nuclear receptors (NR), urging the need to identify the structure of known and unknown NR ligands present in complex matrices. New strategies need to be developed in order to address this question. We have previously shown that NR-based affinity columns are a useful tool for the isolation and characterization of bioactive compounds from complex food matrices such as infant formulas [1]. Here, we extended this approach with the aim to develop and validate the possibility to use NR-based affinity columns, to highlight the presence of known or unknown bioactive molecules (i.e. endocrine disruptors) out of different complex mixtures such as environmental samples. We used recombinant estrogen receptor alpha (ER?)-based affinity columns, for the isolation and characterization of estrogenic substances present in surface sediment samples collected in a French river under mixed anthropogenic pressure. We combined biological, biochemical and analytical techniques (HPLC, LC-MS) as well as High Resolution Mass Spectrometry (HRMS), to characterize the structure of ligands retained on the NR, and demonstrated by LC-HRMS the presence of several active molecules, including bisphenol A, octylphenol, paraben and hydroxymethylbenzofuranone. ER? affinity columns can be used for the isolation, purification and identification of known and unknown estrogenic contaminants present in various complex matrices.