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Guignard D, Canlet C, Tremblay-Franco M, Chaillou E, Gautier R, Gayrard V, Picard-Hagen N, Schroeder H, Jourdan F, Zalko D, Viguié C, Cabaton NJ. Gestational exposure to bisphenol A induces region-specific changes in brain metabolomic fingerprints in sheep. ENVIRONMENT INTERNATIONAL 2022; 165:107336. [PMID: 35700571 DOI: 10.1016/j.envint.2022.107336] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/10/2022] [Revised: 06/02/2022] [Accepted: 06/02/2022] [Indexed: 06/15/2023]
Abstract
Fetal brain development depends on maternofetal thyroid function. In rodents and sheep, perinatal BPA exposure is associated with maternal and/or fetal thyroid disruption and alterations in central nervous system development as demonstrated by metabolic modulations in the encephala of mice. We hypothesized that a gestational exposure to a low dose of BPA affects maternofetal thyroid function and fetal brain development in a region-specific manner. Pregnant ewes, a relevant model for human thyroid and brain development, were exposed to BPA (5 µg/kg bw/d, sc). The thyroid status of ewes during gestation and term fetuses at delivery was monitored. Fetal brain development was assessed by metabolic fingerprints at birth in 10 areas followed by metabolic network-based analysis. BPA treatment was associated with a significant time-dependent decrease in maternal TT4 serum concentrations. For 8 fetal brain regions, statistical models allowed discriminating BPA-treated from control lambs. Metabolic network computational analysis revealed that prenatal exposure to BPA modulated several metabolic pathways, in particular excitatory and inhibitory amino-acid, cholinergic, energy and lipid homeostasis pathways. These pathways might contribute to BPA-related neurobehavioral and cognitive disorders. Discrimination was particularly clear for the dorsal hippocampus, the cerebellar vermis, the dorsal hypothalamus, the caudate nucleus and the lateral part of the frontal cortex. Compared with previous results in rodents, the use of a larger animal model allowed to examine specific brain areas, and generate evidence of the distinct region-specific effects of fetal BPA exposure on the brain metabolome. These modifications occur concomitantly to subtle maternal thyroid function alteration. The functional link between such moderate thyroid changes and fetal brain metabolomic fingerprints remains to be determined as well as the potential implication of other modes of action triggered by BPA such as estrogenic ones. Our results pave the ways for new scientific strategies aiming at linking environmental endocrine disruption and altered neurodevelopment.
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Affiliation(s)
- Davy Guignard
- Toxalim (Research Centre in Food Toxicology), Université de Toulouse, INRAE, ENVT, INP-Purpan, UPS, Toulouse, France
| | - Cécile Canlet
- Toxalim (Research Centre in Food Toxicology), Université de Toulouse, INRAE, ENVT, INP-Purpan, UPS, Toulouse, France; Metatoul-AXIOM Platform, National Infrastructure for Metabolomics and Fluxomics: MetaboHUB, Toxalim, INRAE, Toulouse, France
| | - Marie Tremblay-Franco
- Toxalim (Research Centre in Food Toxicology), Université de Toulouse, INRAE, ENVT, INP-Purpan, UPS, Toulouse, France; Metatoul-AXIOM Platform, National Infrastructure for Metabolomics and Fluxomics: MetaboHUB, Toxalim, INRAE, Toulouse, France
| | - Elodie Chaillou
- CNRS, IFCE, INRAE, Université de Tours, PRC, Nouzilly, France
| | - Roselyne Gautier
- Toxalim (Research Centre in Food Toxicology), Université de Toulouse, INRAE, ENVT, INP-Purpan, UPS, Toulouse, France; Metatoul-AXIOM Platform, National Infrastructure for Metabolomics and Fluxomics: MetaboHUB, Toxalim, INRAE, Toulouse, France
| | - Véronique Gayrard
- Toxalim (Research Centre in Food Toxicology), Université de Toulouse, INRAE, ENVT, INP-Purpan, UPS, Toulouse, France
| | - Nicole Picard-Hagen
- Toxalim (Research Centre in Food Toxicology), Université de Toulouse, INRAE, ENVT, INP-Purpan, UPS, Toulouse, France
| | - Henri Schroeder
- Université de Lorraine, INSERM U1256, NGERE, Nutrition Génétique et Exposition aux Risques Environnementaux, 54000 Nancy, France
| | - Fabien Jourdan
- Toxalim (Research Centre in Food Toxicology), Université de Toulouse, INRAE, ENVT, INP-Purpan, UPS, Toulouse, France
| | - Daniel Zalko
- Toxalim (Research Centre in Food Toxicology), Université de Toulouse, INRAE, ENVT, INP-Purpan, UPS, Toulouse, France
| | - Catherine Viguié
- Toxalim (Research Centre in Food Toxicology), Université de Toulouse, INRAE, ENVT, INP-Purpan, UPS, Toulouse, France.
| | - Nicolas J Cabaton
- Toxalim (Research Centre in Food Toxicology), Université de Toulouse, INRAE, ENVT, INP-Purpan, UPS, Toulouse, France
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Abstract
Untargeted metabolomics refers to the high-throughput analysis of the metabolic state of a biological system (e.g., tissue, biological fluid, cell culture) based on the concentration profile of all measurable free low molecular weight metabolites. Gas chromatography-mass spectrometry (GC-MS), being a highly sensitive and high-throughput analytical platform, has been proven a useful tool for untargeted studies of primary metabolism in a variety of applications. As an omic analysis, GC-MS metabolomics is a multistep procedure; thus, standardization of an untargeted GC-MS metabolomics protocol requires the integrated optimization of pre-analytical, analytical, and computational steps. The main difference of GC-MS metabolomics compared to other metabolomics analytical platforms, including liquid chromatography-MS, is the need for the derivatization of the metabolite extracts into volatile and thermally stable derivatives, the latter being quantified in the metabolic profiles. This analytical step requires special care in the optimization of the untargeted GC-MS metabolomics experimental protocol. Moreover, both the derivatization of the original sample and the compound fragmentation that takes place in GC-MS impose specialized GC-MS metabolomic data identification, quantification, normalization and filtering methods. In this chapter, we describe the integrated protocol of untargeted GC-MS metabolomics with both the analytical and computational steps, focusing on the GC-MS specific parts, and provide details on any sample depending differences.
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Affiliation(s)
- Matthaios-Emmanouil P Papadimitropoulos
- Metabolic Engineering and Systems Biology Laboratory, Institute of Chemical Engineering Sciences, Foundation for Research & Technology - Hellas (FORTH/ICE-HT), Patras, 26504, Greece
- Division of Genetics, Cell & Developmental Biology, Department of Biology, University of Patras, Patras, 26500, Greece
| | - Catherine G Vasilopoulou
- Metabolic Engineering and Systems Biology Laboratory, Institute of Chemical Engineering Sciences, Foundation for Research & Technology - Hellas (FORTH/ICE-HT), Patras, 26504, Greece
- Human and Animal Physiology Laboratory, Department of Biology, University of Patras, Patras, 26500, Greece
| | - Christoniki Maga-Nteve
- Metabolic Engineering and Systems Biology Laboratory, Institute of Chemical Engineering Sciences, Foundation for Research & Technology - Hellas (FORTH/ICE-HT), Patras, 26504, Greece
- School of Medicine, University of Patras, Patras, 26500, Greece
| | - Maria I Klapa
- Metabolic Engineering and Systems Biology Laboratory, Institute of Chemical Engineering Sciences, Foundation for Research & Technology - Hellas (FORTH/ICE-HT), Patras, 26504, Greece.
- Department of Chemical and Biomolecular Engineering, University of Maryland, College Park, MD 20742, USA.
- Department of Bioengineering, University of Maryland, College Park, MD 20742, USA.
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