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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...

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Autores principales: Nagano, Reiko, Akanuma, Hiromi, Qin, Xian-Yang, Imanishi, Satoshi, Toyoshiba, Hiroyoshi, Yoshinaga, Jun, Ohsako, Seiichiroh, Sone, Hideko
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Molecular Diversity Preservation International (MDPI) 2011
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.
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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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