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, Hirohito

doi:10.3390/ijms13010187

Cited by 12 publications

(10 citation statements)

References 56 publications

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“…Toxicity testing using embryonic stem cells (ESCs) has been developed as an efficient approach to assess the effect of environmental chemicals on neurodevelopment (Seiler et al, 2006 ). We have previously reported a mouse embryonic stem cell (mESC) neural differentiation protocol and showed that it could be used as an efficient tool to evaluate the toxic effects of environmental chemicals on neurodevelopment (Nagano et al, 2012 ). Furthermore, we have previously developed a method to quantitatively and statistically analyze microarray gene expression data using Bayesian networks with a log-linear functional relationship between genes (Toyoshiba et al, 2004 , 2006 ).…”

Section: Introductionmentioning

confidence: 99%

Identification of Stage-Specific Gene Expression Signatures in Response to Retinoic Acid during the Neural Differentiation of Mouse Embryonic Stem Cells

Akanuma¹,

Qin²,

Nagano³

et al. 2012

Front. Gene.

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We have previously established a protocol for the neural differentiation of mouse embryonic stem cells (mESCs) as an efficient tool to evaluate the neurodevelopmental toxicity of environmental chemicals. Here, we described a multivariate bioinformatic approach to identify the stage-specific gene sets associated with neural differentiation of mESCs. We exposed mESCs (B6G-2 cells) to 10−8 or 10−7 M of retinoic acid (RA) for 4 days during embryoid body formation and then performed morphological analysis on day of differentiation (DoD) 8 and 36, or genomic microarray analysis on DoD 0, 2, 8, and 36. Three gene sets, namely a literature-based gene set (set 1), an analysis-based gene set (set 2) using self-organizing map and principal component analysis, and an enrichment gene set (set 3), were selected by the combined use of knowledge from literatures and gene information selected from the microarray data. A gene network analysis for each gene set was then performed using Bayesian statistics to identify stage-specific gene expression signatures in response to RA during mESC neural differentiation. Our results showed that RA significantly increased the size of neurosphere, neuronal cells, and glial cells on DoD 36. In addition, the gene network analysis showed that glial fibrillary acidic protein, a neural marker, remarkably up-regulates the other genes in gene set 1 and 3, and Gbx2, a neural development marker, significantly up-regulates the other genes in gene set 2 on DoD 36 in the presence of RA. These findings suggest that our protocol for identification of developmental stage-specific gene expression and interaction is a useful method for the screening of environmental chemical toxicity during neurodevelopmental periods.

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Section: Introductionmentioning

confidence: 99%

Identification of Stage-Specific Gene Expression Signatures in Response to Retinoic Acid during the Neural Differentiation of Mouse Embryonic Stem Cells

Akanuma¹,

Qin²,

Nagano³

et al. 2012

Front. Gene.

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“…Due to statistical limitations, BNs are characterized by the well-known constraint that they generate only non-cyclic (acyclic) graphs. For BN reconstruction, we used the previously described inference algorithm TAO-Gen ( 26 , 27 ), which was developed using the Gibbs sampling method. As the details of the original TAO-Gen algorithm can be downloaded from our website ( http://stemcellinformatics.org/toxicology/ ), we describe only the additional improved algorithm here.…”

Section: Methodsmentioning

confidence: 99%

Prediction of developmental chemical toxicity based on gene networks of human embryonic stem cells

et al. 2016

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Predictive toxicology using stem cells or their derived tissues has gained increasing importance in biomedical and pharmaceutical research. Here, we show that toxicity category prediction by support vector machines (SVMs), which uses qRT-PCR data from 20 categorized chemicals based on a human embryonic stem cell (hESC) system, is improved by the adoption of gene networks, in which network edge weights are added as feature vectors when noisy qRT-PCR data fail to make accurate predictions. The accuracies of our system were 97.5–100% for three toxicity categories: neurotoxins (NTs), genotoxic carcinogens (GCs) and non-genotoxic carcinogens (NGCs). For two uncategorized chemicals, bisphenol-A and permethrin, our system yielded reasonable results: bisphenol-A was categorized as an NGC, and permethrin was categorized as an NT; both predictions were supported by recently published papers. Our study has two important features: (i) as the first study to employ gene networks without using conventional quantitative structure-activity relationships (QSARs) as input data for SVMs to analyze toxicogenomics data in an hESC validation system, it uses additional information of gene-to-gene interactions to significantly increase prediction accuracies for noisy gene expression data; and (ii) using only undifferentiated hESCs, our study has considerable potential to predict late-onset chemical toxicities, including abnormalities that occur during embryonic development.

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“…Gene expression changes during in vitro neural differentiation of ESCs were used to identifiy a principal component of approximately 4,000 genes that described degree of neural commitment 103 . Subsequently, Bayesian network analysis of ESC neural differentiation found that GFAP upregulates genes in a neural gene set created through principal component analysis 104 . Additionally, the gene expression profiles of NSPCs derived from human ESCs, human fetal NSPCs, oligodendrocyte precursor cells and astrocyte precursor cells were compared in order to identify common and unique characteristics of each examined NSPC population 105 .…”

Section: Systems Biology Approaches To Understanding Nspc Regulationmentioning

confidence: 99%

“…103 Subsequently, Bayesian network analysis of ESC neural differentiation found that GFAP upregulates genes in a neural gene set created through principal component analysis. 104 Additionally, the gene expression profiles of NSPCs derived from human ESCs, human fetal NSPCs, oligodendrocyte precursor cells and astrocyte precursor cells were compared in order to identify common and unique characteristics of each examined NSPC population. 105 Although ESC NSPC samples were generated through different methods in multiple labs, Shin et al identified a distinct ESC NSPC gene expression profile and concluded that ESC NSPCs had limited overall Volume 5, November/December 2013 similarity to fetal NSPCs.…”

Section: Systems Biology Approaches To Understanding Nspc Regulationmentioning

confidence: 99%

Neural stem and progenitor cells in health and disease

Ladran

Tran

Topol

et al. 2013

WIREs Mechanisms of Disease

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Neural stem/progenitor cells (NSPCs) have the potential to differentiate into neurons, astrocytes, and/or oligodendrocytes. Because these cells can be expanded in culture, they represent a vast source of neural cells. With the recent discovery that patient fibroblasts can be reprogrammed directly into induced NSPCs, the regulation of NSPC fate and function, in the context of cell-based disease models and patient-specific cell-replacement therapies, warrants review.

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

Cited by 12 publications

References 56 publications

Identification of Stage-Specific Gene Expression Signatures in Response to Retinoic Acid during the Neural Differentiation of Mouse Embryonic Stem Cells

Identification of Stage-Specific Gene Expression Signatures in Response to Retinoic Acid during the Neural Differentiation of Mouse Embryonic Stem Cells

Prediction of developmental chemical toxicity based on gene networks of human embryonic stem cells

Neural stem and progenitor cells in health and disease

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