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Int. J. Mol. Sci. 2012, 13(1), 187-207; doi:10.3390/ijms13010187
Article

Multi-Parametric Profiling Network Based on Gene Expression and Phenotype Data: A Novel Approach to Developmental Neurotoxicity Testing

1,†
, 1,†
, 1,2,†
, 3
, 1
, 2
, 3
 and 1,*
1 Health Risk Research Section, Research Center for Environmental Risk, National Institute for Environmental Studies, 16-2 Onogawa, Tsukuba 305-8506, Japan 2 Department of Environmental Studies, Graduate School of Frontier Science, The University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa, Chiba 270-8563, Japan 3 Center for Disease Biology and Integrative Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8654, Japan These authors contributed equally to this work.
* Author to whom correspondence should be addressed.
Received: 3 October 2011 / Revised: 14 November 2011 / Accepted: 30 November 2011 / Published: 23 December 2011
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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.
Keywords: developmental neurotoxicity; embryonic stem cells; high-content screening; Bayesian network modeling; gene expression; multi-parametric analysis developmental neurotoxicity; embryonic stem cells; high-content screening; Bayesian network modeling; gene expression; multi-parametric analysis
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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MDPI and ACS Style

Nagano, R.; Akanuma, H.; Qin, X.-Y.; Imanishi, S.; Toyoshiba, H.; Yoshinaga, J.; Ohsako, S.; Sone, H. Multi-Parametric Profiling Network Based on Gene Expression and Phenotype Data: A Novel Approach to Developmental Neurotoxicity Testing. Int. J. Mol. Sci. 2012, 13, 187-207.

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