ArticleInternational journal of molecular sciences2012
Multi-parametric profiling network based on gene expression and phenotype data: a novel approach to developmental neurotoxicity testing.
Article in International journal of molecular sciences, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- The evolution of chemical biology into translational physiology and precision medicine.American journal of physiology. Cell physiology · 2025Review
- Oxidative Stress as a Common Key Event in Developmental Neurotoxicity.Oxidative medicine and cellular longevity · 2021Review
- Early Transcriptomic Changes upon Thalidomide Exposure Influence the Later Neuronal Development in Human Embryonic Stem Cell-Derived Spheres.International journal of molecular sciences · 2020Article
- Prediction of developmental chemical toxicity based on gene networks of human embryonic stem cells.Nucleic acids research · 2016Article
- Identification of Stage-Specific Gene Expression Signatures in Response to Retinoic Acid during the Neural Differentiation of Mouse Embryonic Stem Cells.Frontiers in genetics · 2012Article
- Neural stem and progenitor cells in health and disease.Wiley interdisciplinary reviews. Systems biology and medicineReview
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Authors and funding
8 authors.
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Abstract
The establishment of more efficient approaches for developmental neurotoxicity testing (DNT) has been an emerging issue for children's environmental health. Here we describe a systematic approach for DNT using the neuronal differentiation of mouse embryonic stem cells (mESCs) as a model of fetal programming. During embryoid body (EB) formation, mESCs were exposed to 12 chemicals for 24 h and then global gene expression profiling was performed using whole genome microarray analysis. Gene expression signatures for seven kinds of gene sets related to neuronal development and neuronal diseases were selected for further analysis. At the later stages of neuronal cell differentiation from EBs, neuronal phenotypic parameters were determined using a high-content image analyzer. Bayesian network analysis was then performed based on global gene expression and neuronal phenotypic data to generate comprehensive networks with a linkage between early events and later effects. Furthermore, the probability distribution values for the strength of the linkage between parameters in each network was calculated and then used in principal component analysis. The characterization of chemicals according to their neurotoxic potential reveals that the multi-parametric analysis based on phenotype and gene expression profiling during neuronal differentiation of mESCs can provide a useful tool to monitor fetal programming and to predict developmentally neurotoxic compounds.
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