Impact of epidemiologically identified mixtures of endocrine disrupting chemicals on metabolic programming

Abstract: We are ubiquitously exposed to a plethora of endocrine-disrupting chemicals (EDCs), i.e. substances that alter the function(s) of the endocrine system. While ample evidence show individual EDC's influence on developmental processes resulting in adverse health outcomes, less is known about the effects of human-relevant EDC mixtures exposure. Additionally, there is a lack of appropriate methodology to assess the hazard and risk of complex mixtures.This doctoral project aimed to examine the effects of two EDC mixtures and to compare individual components to its mixture, on the developing metabolic system. And to investigate additivity approach for predicting effects of complex mixtures. Studied EDC mixtures (G and G1) were previously identified using Swedish Environmental Longitudinal Mother and Child Asthma and Allergy (SELMA) study data, based on their association with lower birth weight. In this thesis, these mixtures, and mixture G1’s components, were tested for effects on adipogenesis and underlying epigenetic and transcriptional changes in human mesenchymal stem cells (hMSCs) and on metabolic rate in zebrafish larvae.In hMSCs, both mixtures induced adipogenesis at concentrations corresponding to SELMA cohort measured levels. Mixture G induced early transcriptional changes of over 1000 genes in a dose-dependent manner. These genes significantly overlapped with glucocorticoid-regulated genes and were involved in early osteogenesis. Mixture G1 induced significant DNA methylation changes at 713 positions and in six genomic regions in genes whose expression or methylation was previously associated with obesity or MSC differentiation. In zebrafish larvae, mixture G1 increased oxygen consumption rate. Compared to mixture G1, none of its individual components showed equally large effects on adipogenesis or metabolic rate. However, mixture G1 effect on both endpoints could be adequately predicted by the additivity model using experimental data from its constituents.In conclusion, this doctoral project showed that mixtures corresponding to human real-life exposures, in terms of proportions and concentrations, can induce molecular, cellular, and whole-organism changes relevant to developmental metabolic programming, which could underlie adverse outcomes later in life. The results emphasise that mixtures matter and should be accounted for in regulatory risk assessments, and provide support for additivity models as a pragmatic approach to mixture risk assessment.