Steroidogenesis studied in a human adrenocortical cell line : effects of single chemicals and mixtures

Sammanfattning: Steroidogenesis may be a target for endocrine disrupting chemicals; unfortunately data of such effects is limited. The aim of this thesis was to study effects on steroidogenesis of single chemicals and mixtures in the human adrenocarcinoma cell line H295R. Screening for effects on steroid secretion was performed by ELISA. Mechanisms were elucidated by analysing levels of steroid intermediates, gene expression and enzyme activity. Results from 30 tested chemicals showed qualitative and quantitative differences in effect on cortisol and aldosterone secretion; inhibition, stimulation and dissimilar effects on basal vs. induced steroid secretion. Mechanistic studies on the fungicide prochloraz revealed dose-dependent inhibition of cortisol secretion, in contrast to a biphasic effect, with low-dose stimulation and high-dose inhibition, of aldosterone secretion. The specific effects could be explained by inhibition of CYP17A1 and CYP21A2, and by down-regulation of steroidogenic genes. Effects of single chemicals and equimolar mixtures were compared to estimated effects from the concentration addition and independent action prediction models. The imidazole mixture of prochloraz, ketoconazole and imazalil caused additive effects similar to individual imidazole compounds, with inhibition of cortisol and biphasic effects on aldosterone secretion. A modification of the concentration addition model was required to predict the biphasic effect. The flavonoids daidzein, genistein and to a lesser extent apigenin inhibited cortisol, aldosterone and testosterone secretion. The flavonoid mixture inhibited the secretion of all tested steroids, including oestradiol, and in general acted in an additive way. The additive effects of the mixtures emphasize the need to assess chemicals together as a group. We conclude that the H295R cell line is a promising model for both screening of effects on steroidogenesis by single chemicals and mixtures, and for mechanistic analysis. The prediction models are valuable tools for assessment of mixture effects on steroidogenesis.

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