Reversal of Cocaine-Conditioned Place Preference through Methyl Supplementation in Mice: Altering Global DNA Methylation in the Prefrontal Cortex

Analysis of global methylation in cells has revealed correlations between overall DNA methylation status and some biological states. Recent studies suggest that epigenetic regulation through DNA methylation could be responsible for neuroadaptations induced by addictive drugs. However, there is no investigation to determine global DNA methylation status following repeated exposure to addictive drugs. Using mice conditioned place preference (CPP) procedure, we measured global DNA methylation level in the nucleus accumbens (NAc) and the prefrontal cortex (PFC) associated with drug rewarding effects. We found that cocaine-, but not morphine- or food-CPP training decreased global DNA methylation in the PFC. Chronic treatment with methionine, a methyl donor, for 25 consecutive days prior to and during CPP training inhibited the establishment of cocaine, but not morphine or food CPP. We also found that both mRNA and protein level of DNMT (DNA methytransferase) 3b in the PFC were downregulated following the establishment of cocaine CPP, and the downregulation could be reversed by repeated administration of methionine. Our study indicates a crucial role of global PFC DNA hypomethylation in the rewarding effects of cocaine. Reversal of global DNA hypomethylation could significantly attenuate the rewarding effects induced by cocaine. Our results suggest that methionine may have become a potential therapeutic target to treat cocaine addiction

DNA methylation, the early-life social environment and behavioral disorders

One of the outstanding questions in behavioral disorders is untangling the complex relationship between nurture and nature. Although epidemiological data provide evidence that there is an interaction between genetics (nature) and the social and physical environments (nurture) in a spectrum of behavioral disorders, the main open question remains the mechanism. Emerging data support the hypothesis that DNA methylation, a covalent modification of the DNA molecule that is a component of its chemical structure, serves as an interface between the dynamic environment and the fixed genome. We propose that modulation of DNA methylation in response to environmental cues early in life serves as a mechanism of life-long genome adaptation. Under certain contexts, this adaptation can turn maladaptive resulting in behavioral disorders. This hypothesis has important implications on understanding, predicting, preventing, and treating behavioral disorders including autism that will be discussed

Predicting DNA Methylation State of CpG Dinucleotide Using Genome Topological Features and Deep Networks

The hypo- or hyper-methylation of the human genome is one of the epigenetic features of leukemia. However, experimental approaches have only determined the methylation state of a small portion of the human genome. We developed deep learning based (stacked denoising autoencoders, or SdAs) software named “DeepMethyl” to predict the methylation state of DNA CpG dinucleotides using features inferred from three-dimensional genome topology (based on Hi-C) and DNA sequence patterns. We used the experimental data from immortalised myelogenous leukemia (K562) and healthy lymphoblastoid (GM12878) cell lines to train the learning models and assess prediction performance. We have tested various SdA architectures with different configurations of hidden layer(s) and amount of pre-training data and compared the performance of deep networks relative to support vector machines (SVMs). Using the methylation states of sequentially neighboring regions as one of the learning features, an SdA achieved a blind test accuracy of 89.7% for GM12878 and 88.6% for K562. When the methylation states of sequentially neighboring regions are unknown, the accuracies are 84.82% for GM12878 and 72.01% for K562. We also analyzed the contribution of genome topological features inferred from Hi-C. DeepMethyl can be accessed at http://dna.cs.usm.edu/deepmethyl/