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.
Keywords: Bayesian network; mouse embryonic stem cells; neural differentiation; retinoic acid; toxicity screening.