Chronic High-Fat Diet Drives Postnatal Epigenetic Regulation of μ-Opioid Receptor in the Brain

Opioid system dysregulation has been observed in both genetic and high-fat diet (HFD)-induced models of obesity. An understanding of the molecular mechanisms of MOR transcriptional regulation, particularly within an in vivo context, is lacking. Using a diet-induced model of obesity (DIO), mice were fed a high-fat diet (60% calories from fat) from weaning to >18 weeks of age. Compared with mice fed the control diet, DIO mice had a decreased preference for sucrose. MOR mRNA expression was decreased in reward-related circuitry (ventral tegmental area (VTA), nucleus accumbens (NAc), and prefrontal cortex (PFC)) but not the hypothalamus, important in the homeostatic regulation of feeding. DNA methylation is an epigenetic modification that links environmental exposures to altered gene expression. We found a significant increase in DNA methylation in the MOR promoter region within the reward-related brain regions. Methyl CpG-binding protein 2 (MeCP2) can bind methylated DNA and repress transcription, and DIO mice showed increased binding of MeCP2 to the MOR promoter in reward-related regions of the brain. Finally, using ChIP assays we examined H3K9 methylation (inactive chromatin) and H3 acetylation (active chromatin) within the MOR promoter region and found increased H3K9 methylation and decreased H3 acetylation. These data are the first to identify DNA methylation, MeCP2 recruitment, and chromatin remodeling as mechanisms leading to transcriptional repression of MOR in the brains of mice fed a high-fat diet

Towards automated detection of depression from brain structural magnetic resonance images

Introduction : Depression is a major issue worldwide and is seen as a significant health problem. Stigma and patient denial, clinical experience, time limitations, and reliability of psychometrics are barriers to the clinical diagnoses of depression. Thus, the establishment of an automated system that could detect such abnormalities would assist medical experts in their decision-making process. This paper reviews existing methods for the automated detection of depression from brain structural magnetic resonance images (sMRI).Methods : Relevant sources were identified from various databases and online sites using a combination of keywords and terms including depression, major depressive disorder, detection, classification, and MRI databases. Reference lists of chosen articles were further reviewed for associated publications.Results : The paper introduces a generic structure for representing and describing the methods developed for the detection of depression from sMRI of the brain. It consists of a number of components including acquisition and preprocessing, feature extraction, feature selection, and classification.Conclusion : Automated sMRI-based detection methods have the potential to provide an objective measure of depression, hence improving the confidence level in the diagnosis and prognosis of depression