Abstract
Traditional approaches in evaluating the hazard of drug candidates on the developing offspring are often time-consuming and cost-intensive. Moreover, variations in the toxicological response of different animal species to the tested substance cause severe problems when extrapolating safety dosages for humans. Therefore, more predictive and relevant toxicological systems based on human cell models are required. In the presented study the environmental toxicant methylmercury chloride (MeHgCl), known to cause structural developmental abnormalities in the brain, was used as reference compound to develop a concept contributing to a mechanistic understanding of the toxicity of an investigated substance. Despite the fact, that there are significant data available from animal studies and from poisonings in Japan and Iraq, uncertainties on the mechanism of MeHgCl during human development are still remaining and qualify the substance for further analysis. Transcriptomics analysis in combination with a human cell based in vitro model has been used in order to elucidate the toxicity of MeHgCl at molecular level. Differentiating neural precursor cells that have been exposed continuously to non- and low-cytotoxic concentrations of MeHgCl were investigated. Quantitative change in the mRNA expression profiles of selected genes demonstrated the sensitivity of the cell model and its qualification for a transcriptomics study screening changes in the expression profile of the complete human genome of MeHgCl-treated human neural cells. Potential biomarkers were identified and these candidate marker genes as well as their involvement in a possible toxic mechanism of MeHgCl during the human neurulation process are hereby introduced. The study confirmed the hypothesis that a cellular model based on a human stem cell line can be applied for elucidating unknown mode of actions of developmental toxicants.
Keywords: Biomarker identification, stem cells, In vitro toxicity test, methymercury chloride, molecular pathways, neuronal precursor differentiation, ESNATS
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