Multi-parametric profiling network based on gene expression and phenotype data: a novel approach to developmental neurotoxicity testing

Oxidative Stress as a Common Key Event in Developmental Neurotoxicity

The developing brain is extremely sensitive to many chemicals. Perinatal exposure to neurotoxicants has been implicated in several neurodevelopmental disorders, including autism spectrum disorder, attention-deficit hyperactive disorder, and schizophrenia. Studies of the molecular and cellular events related to developmental neurotoxicity have identified a number of “adverse outcome pathways,” many of which share oxidative stress as a key event. Oxidative stress occurs when the balance between the production of free oxygen radicals and the activity of the cellular antioxidant system is dysregulated. In this review, we describe some of the developmental neurotoxins that target the antioxidant system and the mechanisms by which they elicit stress, including oxidative phosphorylation in mitochondria and plasma membrane redox system in rodent models. We also discuss future directions for identifying adverse outcome pathways related to oxidative stress and developmental neurotoxicity, with the goal of improving our ability to quickly and accurately screen chemicals for their potential developmental neurotoxicity.

Early Transcriptomic Changes upon Thalidomide Exposure Influence the Later Neuronal Development in Human Embryonic Stem Cell-Derived Spheres

Stress in early life has been linked with the development of late-life neurological disorders. Early developmental age is potentially sensitive to several environmental chemicals such as alcohol, drugs, food contaminants, or air pollutants. The recent advances using three-dimensional neural sphere cultures derived from pluripotent stem cells have provided insights into the etiology of neurological diseases and new therapeutic strategies for assessing chemical safety. In this study, we investigated the neurodevelopmental effects of exposure to thalidomide (TMD); 2,2′,4,4′-tetrabromodiphenyl ether; bisphenol A; and 4-hydroxy-2,2′,3,4′,5,5′,6-heptachlorobiphenyl using a human embryonic stem cell (hESC)-derived sphere model. We exposed each chemical to the spheres and conducted a combinational analysis of global gene expression profiling using microarray at the early stage and morphological examination of neural differentiation at the later stage to understand the molecular events underlying the development of hESC-derived spheres. Among the four chemicals, TMD exposure especially influenced the differentiation of spheres into neuronal cells. Transcriptomic analysis and functional annotation identified specific genes that are TMD-induced and associated with ERK and synaptic signaling pathways. Computational network analysis predicted that TMD induced the expression of DNA-binding protein inhibitor ID2, which plays an important role in neuronal development. These findings provide direct evidence that early transcriptomic changes during differentiation of hESCs upon exposure to TMD influence neuronal development in the later stages.

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

　Toxicity 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.

Stem Cell-Based Methods to Predict Developmental Chemical Toxicity

Human pluripotent stem cells such as embryonic stem (ES) and induced pluripotent stem (iPS) cells, combined with sophisticated bioinformatics methods, are powerful tools to predict developmental chemical toxicity. Because cell differentiation is not necessary, these cells can facilitate cost-effective assays, thus providing a practical system for the toxicity assessment of various types of chemicals. Here we describe how to apply machine learning techniques to different types of data, such as qRT-PCRs, gene networks, and molecular descriptors, for toxic chemicals, as well as how to integrate these data to predict toxicity categories. Interestingly, our results using 20 chemical data for neurotoxins (NTs), genotoxic carcinogens (GCs), and nongenotoxic carcinogens (NGCs) demonstrated that the highest and most robust prediction performance was obtained by using gene networks as the input. We also observed that qRT-PCR and molecular descriptors tend to contribute to specific toxicity categories.

Single Cell and Population Level Analysis of HCA Data

High Content Analysis instrumentation has undergone tremendous hardware advances in recent years. It is now possible to obtain images of hundreds of thousands to millions of individual objects, across multiple wells, channels, and plates, in a reasonable amount of time. In addition, it is possible to extract dozens, or hundreds, of features per object using commonly available software tools. Analyzing this data provides new challenges to the scientists. The magnitude of these numbers is reminiscent of flow cytometer, where practitioners have long been taking what effectively amounted to very low resolution, multi-parametric measurements from individual cells for many decades. Flow cytometrists have developed a wide range of tools to effectively analyze and interpret these types of data. This chapter will review the techniques used in flow cytometry and show how they can easily and effectively be applied to High Content Analysis.

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.

Bayesian Network Inference Enables Unbiased Phenotypic Anchoring of Transcriptomic Responses to Cigarette Smoke in Humans

Microarray-based transcriptomic analysis has been demonstrated to hold the opportunity to study the effects of human exposure to, e.g., chemical carcinogens at the whole genome level, thus yielding broad-ranging molecular information on possible carcinogenic effects. Since genes do not operate individually but rather through concerted interactions, analyzing and visualizing networks of genes should provide important mechanistic information, especially upon connecting them to functional parameters, such as those derived from measurements of biomarkers for exposure and carcinogenic risk. Conventional methods such as hierarchical clustering and correlation analyses are frequently used to address these complex interactions but are limited as they do not provide directional causal dependence relationships. Therefore, our aim was to apply Bayesian network inference with the purpose of phenotypic anchoring of modified gene expressions. We investigated a use case on transcriptomic responses to cigarette smoking in humans, in association with plasma cotinine levels as biomarkers of exposure and aromatic DNA-adducts in blood cells as biomarkers of carcinogenic risk. Many of the genes that appear in the Bayesian networks surrounding plasma cotinine, and to a lesser extent around aromatic DNA-adducts, hold biologically relevant functions in inducing severe adverse effects of smoking. In conclusion, this study shows that Bayesian network inference enables unbiased phenotypic anchoring of transcriptomics responses. Furthermore, in all inferred Bayesian networks several dependencies are found which point to known but also to new relationships between the expression of specific genes, cigarette smoke exposure, DNA damaging-effects, and smoking-related diseases, in particular associated with apoptosis, DNA repair, and tumor suppression, as well as with autoimmunity.

Neural stem and progenitor cells in health and disease

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.

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 10⁻⁷ 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.

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.