Satoshi Imanishi

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

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 10−7u2009M of retinoic acid (RA) for 4u2009days 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.

Effects of methylmercury exposure on neuronal differentiation of mouse and human embryonic stem cells.

The establishment of more efficient in vitro approaches has been widely acknowledged as a critical need for toxicity testing. In this study, we examined the effects of methylmercury (MeHg), which is a well-known developmental neurotoxicant, in two neuronal differentiation systems of mouse and human embryonic stem cells (mESCs and hESCs, respectively). Embryoid bodies were generated from gathering of mESCs and hESCs using a micro-device and seeded onto ornithine-laminin-coated plates to promote proliferation and neuronal differentiation. The cells were exposed to MeHg from the start of neuronal induction until the termination of cultures, and significant reductions of mESCs and hESCs were observed in the cell viability assays at 1,10,100 and 1000nM, respectively. Although the mESC derivatives were more sensitive than the hESC derivatives to MeHg exposure in terms of cell viability, the morphological evaluation demonstrated that the neurite length and branch points of hESC derivatives were more susceptible to a low concentration of MeHg. Then, the mRNA levels of differentiation markers were examined using quantitative RT-PCR analysis and the interactions between MeHg exposure and gene expression levels were visualized using a network model based on a Bayesian algorithm. The Bayesian network analysis showed that a MeHg-node was located on the highest hierarchy in the hESC derivatives, but not in the mESC derivatives, suggesting that MeHg directly affect differentiation marker genes in hESCs. Taken together, effects of MeHg were observed in our neuronal differentiation systems of mESCs and hESCs using a combination of morphological and molecular markers. Our study provided possible, but limited, evidences that human ESC models might be more sensitive in particular endpoints in response to MeHg exposure than that in mouse ESC models. Further investigations that expand on the findings of the present paper may solve problems that occur when the outcomes from laboratory animals are extrapolated for human risk evaluation.

Prenatal exposure to permethrin influences vascular development of fetal brain and adult behavior in mice offspring

Pyrethroids are one of the most widely used classes of insecticides and show neurotoxic effects that induce oxidative stress in the neonatal rat brain. However, little is still known about effects of prenatal exposure to permethrin on vascular development in fetal brain, central nervous system development, and adult offspring behaviors. In this study, the effects of prenatal exposure to permethrin on the development of cerebral arteries in fetal brains, neurotransmitter in neonatal brains, and locomotor activities in offspring mice were investigated. Permethrin (0, 2, 10, 50, and 75 mg/kg) was orally administered to pregnant females once on gestation day 10.5. The brains of permethrin‐treated fetuses showed altered vascular formation involving shortened lengths of vessels, an increased number of small branches, and, in some cases, insufficient fusion of the anterior communicating arteries in the area of circle of Willis. The prenatal exposure to permethrin altered neocortical and hippocampus thickness in the mid brain and significantly increased norepinephrine and dopamine levels at postnatal day 7 mice. For spontaneous behavior, the standing ability test using a viewing jar and open‐field tests showed significant decrease of the standing ability and locomotor activity in male mice at 8 or 12 weeks of age, respectively. The results suggest that prenatal exposure to permethrin may affect insufficient development of the brain through alterations of vascular development.

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 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.

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

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.

Effect of low-dose thalidomide on dopaminergic neuronal differentiation of human neural progenitor cells: a combined study of metabolomics and morphological analysis.

Thalidomide is increasingly used in anticancer and anti-inflammation therapies. However, it is known for its teratogenicity and ability to induce peripheral neuropathy, although the mechanisms underlying its neurological effect in humans are unclear. In this study, we investigated the effect of thalidomide on the metabolism and neuronal differentiation of human neural progenitor cells. We found that levels of tyrosine, phenylalanine, methionine and glutathione, which are involved in dopamine and methionine metabolism, were decreased following thalidomide treatment. Morphological analysis revealed that treatment with 100 nM thalidomide, which is much lower than clinical doses, significantly decreased the number of dopaminergic (tyrosine hydroxylase-positive) neurons, compared with control cells. Our results suggest that these adverse neurological effects of thalidomide should be taken into consideration prior to its use for the treatment of neurodegenerative and other diseases.

Construction of a High-precision Chemical Prediction System Using Human ESCs

u3000Toxicity prediction based on stem cells and tissue derived from stem cells plays a very important role in the fields of biomedicine and pharmacology. Here we report on qRT-PCR data obtained by exposing 20 compounds to human embryonic stem (ES) cells. The data are intended to improve toxicity prediction, per category, of various compounds through the use of support vector machines, and by applying gene networks. The accuracy of our system was 97.5-100% in three toxicity categories: neurotoxins (NTs), genotoxic carcinogens (GCs), and non-genotoxic carcinogens (NGCs). We predicted that two uncategorized compounds (bisphenol-A and permethrin) should be classified as follows: bisphenol-A as a non-genotoxic carcinogen, and permethrin as a neurotoxin. These predictions are supported by recent reports, and as such constitute a good outcome. Our results include two important features: 1) The accuracy of prediction was higher when machine learning was carried out using gene networks and activity, rather than the normal quantitative structure-activity relationship (QSAR); and 2) By using undifferentiated ES cells, the late effect of chemical substances was predicted. From these results, we succeeded in constructing a highly effective and highly accurate system to predict the toxicity of compounds using stem cells.

Environmental Chemical Substances in Relation to Neurodevelopmental Disorders: A Systematic Literature Review

Since the 1950s-70s, increasing number of regulations have rapidly expanded for the global usage of industrial, agricultural and other environmental chemical substances (ECSs). It is believed that children are at high risk of exposure to ECSs, which are produced in quantities greater than one million tons per year and widely dispersed in air, water, food crops, communities, waste sites and homes (Landrigan et al. 2006). Prevalence rates of many common diseases in children, including certain childhood cancers (Devesa et al. 1995; Robison et al. 1995; Schechter 1999; Supriyadi et al. 2011), birth defects (Ananth et al. 2005; Gilboa et al. 2010) and neurodevelopmental disorders (Malik et al. 2011), have been increased or maintained at high levels in industrialized countries. Although primary etiologic factors contributing to these diseases are unknown yet, accumulating evidences indicate that exposure to ECSs are partially responsible for the developmental disabilities, such as autism spectrum disorders (ASDs), attention deficit hyperactivity disorder (ADHD), and other developmental delays (Boyle et al. 2011; Larson et al. 2001) (Figure 1). In this chapter, we conducted a systematic literature review for neurotoxic agents in environment to elucidate the relationship between exposure to ECSs and neurodevelopment disorders in children.

Development of an image profiling system to evaluate for the effects of chemicals in neural differentiation from mES cells

Magnetoencephalography (MEG) directly measures the magnetic field caused by neural current activity, with a high temporal resolution. However, its amplitude is very weak and contaminated by various artifacts. One of the such an artifact is caused by heartbeat. In this study, we measured MEG and electrocardiogram (ECG) simultaneously. MEG was averaged with respect to an onset of ECG, that is, the peak of R-wave. Then, we applied equivalent current dipole (ECD) method to estimate current sources of artifacts caused by heartbeat. In addition, we propose a probabilistic model to remove such artifacts and applied to artificial data in order to confirm the efficiency of the method.