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Multi-Parametric Profiling Network Based on Gene Expression and Phenotype Data: A Novel Approach to Developmental Neurotoxicity Testing
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 pro...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Molecular Diversity Preservation International (MDPI)
2011
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3269681/ https://www.ncbi.nlm.nih.gov/pubmed/22312247 http://dx.doi.org/10.3390/ijms13010187 |
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author | Nagano, Reiko Akanuma, Hiromi Qin, Xian-Yang Imanishi, Satoshi Toyoshiba, Hiroyoshi Yoshinaga, Jun Ohsako, Seiichiroh Sone, Hideko |
author_facet | Nagano, Reiko Akanuma, Hiromi Qin, Xian-Yang Imanishi, Satoshi Toyoshiba, Hiroyoshi Yoshinaga, Jun Ohsako, Seiichiroh Sone, Hideko |
author_sort | Nagano, Reiko |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-3269681 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2011 |
publisher | Molecular Diversity Preservation International (MDPI) |
record_format | MEDLINE/PubMed |
spelling | pubmed-32696812012-02-06 Multi-Parametric Profiling Network Based on Gene Expression and Phenotype Data: A Novel Approach to Developmental Neurotoxicity Testing Nagano, Reiko Akanuma, Hiromi Qin, Xian-Yang Imanishi, Satoshi Toyoshiba, Hiroyoshi Yoshinaga, Jun Ohsako, Seiichiroh Sone, Hideko Int J Mol Sci Article 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. Molecular Diversity Preservation International (MDPI) 2011-12-23 /pmc/articles/PMC3269681/ /pubmed/22312247 http://dx.doi.org/10.3390/ijms13010187 Text en © 2012 by the authors; licensee Molecular Diversity Preservation International, Basel, Switzerland. http://creativecommons.org/licenses/by/3.0 This article is an open-access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/3.0/). |
spellingShingle | Article Nagano, Reiko Akanuma, Hiromi Qin, Xian-Yang Imanishi, Satoshi Toyoshiba, Hiroyoshi Yoshinaga, Jun Ohsako, Seiichiroh Sone, Hideko Multi-Parametric Profiling Network Based on Gene Expression and Phenotype Data: A Novel Approach to Developmental Neurotoxicity Testing |
title | Multi-Parametric Profiling Network Based on Gene Expression and Phenotype Data: A Novel Approach to Developmental Neurotoxicity Testing |
title_full | Multi-Parametric Profiling Network Based on Gene Expression and Phenotype Data: A Novel Approach to Developmental Neurotoxicity Testing |
title_fullStr | Multi-Parametric Profiling Network Based on Gene Expression and Phenotype Data: A Novel Approach to Developmental Neurotoxicity Testing |
title_full_unstemmed | Multi-Parametric Profiling Network Based on Gene Expression and Phenotype Data: A Novel Approach to Developmental Neurotoxicity Testing |
title_short | Multi-Parametric Profiling Network Based on Gene Expression and Phenotype Data: A Novel Approach to Developmental Neurotoxicity Testing |
title_sort | multi-parametric profiling network based on gene expression and phenotype data: a novel approach to developmental neurotoxicity testing |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3269681/ https://www.ncbi.nlm.nih.gov/pubmed/22312247 http://dx.doi.org/10.3390/ijms13010187 |
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