Toxicogenomic analysis of physiologically important metals: An integrated in silico approach

Thyrotoxic Effects of Mixed Exposure to Perfluorinated Compounds: Integrating Population-Based, Toxicogenomic, Animal, and Cellular Evidence to Elucidate Molecular Mechanisms and Identify Potential Effector Targets

Perfluoroalkyl and polyfluoroalkyl substances (PFAS) are emerging environmental endocrine disruptors that may adversely affect the human endocrine system, particularly the thyroid gland, the largest endocrine gland in the human body. An epidemiologic survey was conducted involving 318 community residents in Shanghai, China, to assess PFAS exposure levels. The relationship between PFAS exposure and five thyroid function indicators was analyzed using Bayesian Kernel Regression (BKMR) and Weighted Quantile Sum Regression (WQS). Ten effector genes related to PFAS and thyroid diseases were identified through the Comparative Toxicogenomics Database (CTD) for bioinformatics analysis and pathways involved were explored through mediation analysis. In vivo validation of these effector genes was conducted using PCR, complemented by in vitro cellular experiments involving transcriptome sequencing and the construction of animal models to simulate mixed PFAS exposure in the general population. Mixed PFAS exposure was found to impact thyroid health primarily through pathways related to lipid metabolism in toxicogenomic studies and resulted in the upregulation of key genes associated with lipid metabolism in animal models. Our results demonstrate that PFAS exposure could affect the expression of lipid metabolism pathways through the modulation of transcription factors, contributing to the development of thyroid disease.

Predicting the Metal Mixture Toxicity with a Toxicokinetic-Toxicodynamic Model Considering the Time-Dependent Adverse Outcome Pathways

Chemicals mainly exist in ecosystems as mixtures, and understanding and predicting their effects are major challenges in ecotoxicology. While the adverse outcome pathway (AOP) and toxicokinetic-toxicodynamic (TK-TD) models show promise as mechanistic approaches in chemical risk assessment, there is still a lack of methodology to incorporate the AOP into a TK-TD model. Here, we describe a novel approach that integrates the AOP and TK-TD models to predict mixture toxicity using metal mixtures (specifically Cd-Cu) as a case study. We preliminarily constructed an AOP of the metal mixture through temporal transcriptome analysis together with confirmatory bioassays. The AOP revealed that prolonged exposure time activated more key events and adverse outcomes, indicating different modes of action over time. We selected a potential key event as a proxy for damage and used it as a measurable parameter to replace the theoretical parameter (scaled damage) in the TK-TD model. This refined model, which connects molecular responses to organism outcomes, effectively predicts Cd-Cu mixture toxicity over time and can be extended to other metal mixtures and even multicomponent mixtures. Overall, our results contribute to a better understanding of metal mixture toxicity and provide insights for integrating the AOP and TK-TD models to improve risk assessment for chemical mixtures.