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Towards high-throughput identification of endocrine disrupting compounds with mass spectrometry
Authors:Cédric Bovet  Benoit Plet  Marc Ruff  Sylvia Eiler  Florence Granger  Andreas Panagiotidis  Ryan Wenzel  Alexis Nazabal  Dino Moras  Renato Zenobi
Affiliation:1. Department of Chemistry and Applied Biosciences, ETH Zurich, HCI E 329, 8093 Zurich, Switzerland;2. CovalX AG, Technoparkstrasse 1, 8005 Zurich, Switzerland;3. IGBMC (Institut de Génétique et de Biologie Moléculaire et Cellulaire), Département de Biologie et Génomique Structurales, Université Louis Pasteur, U596 INSERM, UMR7104 CNRS, 1 rue Laurent Fries, 67404 Illkirch, France;4. Institute of Molecular Systems Biology, ETH Zurich, 8093 Zurich, Switzerland
Abstract:
High-mass matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS) combined with chemical cross-linking has the ability to monitor the ligand-dependent dimerization of the human estrogen receptor α ligand binding domain (hERα LBD) in solution. Because only ER ligands enhance the homodimer abundance, we evaluated the ability of this label-free approach for identifying endocrine disrupting compounds (EDCs) in a high-throughput manner. This was achieved by combining an automated liquid handler with an automated MS acquisition procedure, which allowed a five-fold gain in operator time compared to a fully manual approach. To detect ligand binding with enough confidence, the receptor has to be incubated with at least a 10 μM concentration of the test compound. Based on the increase of the measured homodimer intensity, eight compounds with a relative binding affinity (RBA, relative to the natural hormone estradiol) >7% were identified as ER ligands among the 28 chemicals tested. Two other compounds, quercetin and 4-tert-amylphenol, were also identified as ER ligands, although their RBAs have been reported to be only 0.01% and 0.000055%, respectively. This suggests that these two ligands have a higher affinity for hERα LBD than reported in the literature. The high-mass MALDI approach thus allows identifying high affinity EDCs in an efficient way.
Keywords:
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