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      An analysis of the effect of mu-opioid receptor gene ( OPRM1) promoter region DNA methylation on the response of naltrexone treatment of alcohol dependence

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          Abstract

          This study explored the effect of OPRM1 promoter region DNA methylation on the outcome of treatment with the opioid antagonist naltrexone (NTX) for alcohol dependence (AD). Ninety-three patients with DSM-IV AD [41 African Americans (AAs) and 52 European Americans (EAs)] received double-blind treatment with NTX or placebo for at least three months. Relapse to heavy drinking was assessed during the first 13 weeks of the trial. Peripheral blood methylation levels of 33 CpG units in the OPRM1 promoter region were quantified using Sequenom EpiTYPER technology. Bayesian logistic regression was used to analyze the effects of NTX treatment, CpG methylation, CpG methylation×NTX treatment, and age on AD relapse. The Random Forest machine learning algorithm was applied to select AD relapse predictors. No significant effect of individual OPRM1 promoter CpG units on AD relapse was observed in either AAs or EAs. Age was significantly associated with AD relapse in EAs, among whom older subjects had a lower relapse rate. Random forest analyses revealed that the prediction rate for AD relapse reached 66.0% with five top variables (age and four CpG units; ranked by their importance to AD relapse) in the prediction model. These findings suggest that methylation levels of individual OPRM1 promoter CpG units do not contribute significantly to inter-individual variation in NTX response. However, the age of subjects in combination with a cluster of specific OPRM1 promoter CpG units may affect NTX treatment outcome. Additional studies of OPRM1 DNA methylation changes during and after NTX treatment of AD are needed.

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          Most cited references45

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          DNA methylome profiling of human tissues identifies global and tissue-specific methylation patterns

          Background DNA epigenetic modifications, such as methylation, are important regulators of tissue differentiation, contributing to processes of both development and cancer. Profiling the tissue-specific DNA methylome patterns will provide novel insights into normal and pathogenic mechanisms, as well as help in future epigenetic therapies. In this study, 17 somatic tissues from four autopsied humans were subjected to functional genome analysis using the Illumina Infinium HumanMethylation450 BeadChip, covering 486 428 CpG sites. Results Only 2% of the CpGs analyzed are hypermethylated in all 17 tissue specimens; these permanently methylated CpG sites are located predominantly in gene-body regions. In contrast, 15% of the CpGs are hypomethylated in all specimens and are primarily located in regions proximal to transcription start sites. A vast number of tissue-specific differentially methylated regions are identified and considered likely mediators of tissue-specific gene regulatory mechanisms since the hypomethylated regions are closely related to known functions of the corresponding tissue. Finally, a clear inverse correlation is observed between promoter methylation within CpG islands and gene expression data obtained from publicly available databases. Conclusions This genome-wide methylation profiling study identified tissue-specific differentially methylated regions in 17 human somatic tissues. Many of the genes corresponding to these differentially methylated regions contribute to tissue-specific functions. Future studies may use these data as a reference to identify markers of perturbed differentiation and disease-related pathogenic mechanisms.
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            Distinct DNA methylation changes highly correlated with chronological age in the human brain.

            Methylation at CpG sites is a critical epigenetic modification in mammals. Altered DNA methylation has been suggested to be a central mechanism in development, some disease processes and cellular senescence. Quantifying the extent and identity of epigenetic changes in the aging process is therefore potentially important for understanding longevity and age-related diseases. In the current study, we have examined DNA methylation at >27,000 CpG sites throughout the human genome, in frontal cortex, temporal cortex, pons and cerebellum from 387 human donors between the ages of 1 and 102 years. We identify CpG loci that show a highly significant, consistent correlation between DNA methylation and chronological age. The majority of these loci are within CpG islands and there is a positive correlation between age and DNA methylation level. Lastly, we show that the CpG sites where the DNA methylation level is significantly associated with age are physically close to genes involved in DNA binding and regulation of transcription. This suggests that specific age-related DNA methylation changes may have quite a broad impact on gene expression in the human brain.
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              Blood-based profiles of DNA methylation predict the underlying distribution of cell types: a validation analysis.

              The potential influence of underlying differences in relative leukocyte distributions in studies involving blood-based profiling of DNA methylation is well recognized and has prompted development of a set of statistical methods for inferring changes in the distribution of white blood cells using DNA methylation signatures. However, the extent to which this methodology can accurately predict cell-type proportions based on blood-derived DNA methylation data in a large-scale epigenome-wide association study (EWAS) has yet to be examined. We used publicly available data deposited in the Gene Expression Omnibus (GEO) database (accession number GSE37008), which consisted of both blood-derived epigenome-wide DNA methylation data assayed using the Illumina Infinium HumanMethylation27 BeadArray and complete blood cell (CBC) counts among a community cohort of 94 non-diseased individuals. Constrained projection (CP) was used to obtain predictions of the proportions of lymphocytes, monocytes and granulocytes for each of the study samples based on their DNA methylation signatures. Our findings demonstrated high consistency between the average CBC-derived and predicted percentage of monocytes and lymphocytes (17.9% and 17.6% for monocytes and 82.1% and 81.4% for lymphocytes), with root mean squared error (rMSE) of 5% and 6%, for monocytes and lymphocytes, respectively. Similarly, there was moderate-high correlation between the CP-predicted and CBC-derived percentages of monocytes and lymphocytes (0.60 and 0.61, respectively), and these results were robust to the number of leukocyte differentially methylated regions (L-DMRs) used for CP prediction. These results serve as further validation of the CP approach and highlight the promise of this technique for EWAS where DNA methylation is profiled using whole-blood genomic DNA.
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                Author and article information

                Journal
                101083949
                22416
                Pharmacogenomics J
                Pharmacogenomics J.
                The pharmacogenomics journal
                1470-269X
                1473-1150
                7 February 2020
                07 February 2020
                October 2020
                23 September 2020
                : 20
                : 5
                : 672-680
                Affiliations
                [1 ]Department of Psychiatry, Boston University School of Medicine, Boston, MA, USA
                [2 ]Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania and VISN4 MIRECC, Crescenz VAMC, Philadelphia, PA, USA
                [3 ]Department of Medicine (Biomedical Genetics), Boston University School of Medicine, Boston, MA, USA
                [4 ]Department of Hepatology, the First Hospital of Jilin University, Jilin University, Changchun, China
                Author notes
                [* ]Correspondence to: Huiping Zhang, Ph.D., Departments of Psychiatry and Medicine (Biomedical Genetics), Boston University School of Medicine, 72 East Concord Street, Boston, MA 02118-2526, USA, Tel: (617) 358-3689, Fax: (617) 414-1996, huipingz@ 123456bu.edu
                Article
                NIHMS1552776
                10.1038/s41397-020-0158-1
                7415483
                32029903
                06132b40-e75e-452d-88e9-1d34bb2708a5

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                Categories
                Article

                Pharmacology & Pharmaceutical medicine
                pharmacoepigenetics,alcohol dependence,naltrexone treatment,mu-opioid receptor gene,dna methylation,sequenom’s epityper

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