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Retinoic acid (RA) is a critical signaling molecule regulating aspects of proliferation and differentiation during the development of numerous organ systems including the CNS, eye, testis and heart. In utero exposure to 2,3,7,8-tetrachlorodibenzop-dioxin (TCDD) can cause neurobehavioral deficits in learning and memory in numerous species, including humans. Mechanistic research on TCDD-induced toxicity has implicated perturbation of RA metabolism and RA-induced gene expression. Here we explore the hypothesis that TCDD-induced neurodevelopmental toxicity is regulated through perturbation of RA signaling. During forebrain development, proliferation and differentiation of neural stem cells is orchestrated by numerous key regulatory transcription factors (TFs) including homeobox and zinc-finger family members such as Pax6, Emx2, Gli1, Gli2, and the basic helix-loop-helix TFs, Ngn1 and Ngn2. RA signaling plays a critical role in the normal induction of these TFs, most likely through regulation of two key signaling pathways, namely the sonic hedgehog (SHH) and Fibroblast Growth Factor 8 (FGF8) pathways. We have developed a hypothetical regulatory gene network based on published research using various methodologies including transgenic technology, exogenous application of known RA, SHH, and FGF8 agonists and antagonists, and in situ hybridization experiments. We then quantitatively test these hypothetical linkages through development of a Bayesian gene network model based on microarray gene expression data from mouse neural stem cells. Our model results support linkage between the RA, SHH and FGF8 pathways and subsequent control of the expression of key TFs during forebrain development. Using an existing gene network model, we apply theoretical TCDDinduced changes to the RA-regulated gene network of forebrain development to examine the quantitative implications of this possible mechanism on neurodevelopmental anomalies associated with exposure to TCDD. The results suggest further experimental research of this mechanistic hypothesis is warranted. Finally, this analysis suggests Bayesian network modeling serves as a robust methodology for testing hypotheses of specific gene interactions based on quantitative gene expression data.
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