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Animal models have suggested that prenatal ethanol exposure (PEE) alters the κ opioid receptor system. The present study investigated the brain expression of dynorphin and nociceptin/orphanin FQ related genes and assessed anxiety-like behavior in the light-dark box (LDB), shelter-seeking and risk-taking behaviors in the concentric square field (CSF) test, and ethanol-induced locomotion in the open field (OF), in infant or adolescent Wistar rats that were exposed to PEE (0.0 or 2.0 g/kg, intragastrically, gestational days 17-20). We measured brain mRNA levels of prodynorphin (PDYN), κ opioid receptors (KOR), the nociceptin/orphanin FQ opioid peptide precursor prepronociceptin (ppN/OFQ) and nociceptine/orphanin FQ receptors (NOR). Prenatal ethanol exposure upregulated PDYN and KOR mRNA levels in the ventral tegmental area (VTA) in infant and adolescent rats and KOR mRNA levels in the prefrontal cortex in infant rats. The changes in gene expression in the VTA were accompanied by a reduction of DNA methylation at the PDYN gene promoter, and by a reduction of DNA methylation at the KOR gene promoter. The PEE-induced upregulation of PDYN/KOR in the VTA was accompanied by lower NOR gene expression in the VTA, and lower PDYN gene expression in the nucleus accumbens. PEE rats exhibited hypolocomotion in the OF, greater avoidance of the white and brightly lit areas in the LDB and CSF, and greater preference for the sheltered area in the CSF test. These results suggest that PEE upregulates the dynorphin system, resulting in an anxiety-prone phenotype and triggering compensatory responses in the nociceptin/orphanin FQ system. These findings may help elucidate the mechanisms that underlie the effects of PEE and suggest that the dynorphin and nociceptin/orphanin FQ systems may be possible targets for the prevention and treatment of PEE-induced alterations.
Pubmed ID: 29678771
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Software package to test for differentially expressed genes with microarray data. It can be used with both cDNA microarrays or Affymetrix chip. The packge fits a robust Bayesian hierarchical model for testing for differential expression. Outliers are modeled explicitly using a $t$-distribution. The model includes an exchangeable prior for the variances which allow different variances for the genes but still shrink extreme empirical variances. The model can be used for testing for differentially expressed genes among multiple samples, and can distinguish between the different possible patterns of differential expression when there are three or more samples. Parameter estimation is carried out using a novel version of Markov Chain Monte Carlo that is appropriate when the model puts mass on subspaces of the full parameter space.
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