Environmental stress is among the most important contributors to increased susceptibility to develop psychiatric disorders. While it is well known that acute environmental stress alters gene expression, the molecular mechanisms underlying these changes remain largely unknown. 5-hydroxymethylcytosine (5hmC) is a novel environmentally sensitive epigenetic modification that is highly enriched in neurons and is associated with active neuronal transcription. Recently, we reported a genome-wide disruption of hippocampal 5hmC in male mice following acute stress that was correlated to altered transcript levels of genes in known stress related pathways. Since sex-specific endocrine mechanisms respond to environmental stimulus by altering the neuronal epigenome, we examined the genome-wide profile of hippocampal 5hmC in female mice following exposure to acute stress and identified 363 differentially hydroxymethylated regions (DhMRs) linked to known (e.g., Nr3c1 and Ntrk2) and potentially novel genes associated with stress response and psychiatric disorders. Integration of hippocampal expression data from the same female mice found stress-related hydroxymethylation correlated to altered transcript levels. Finally, characterization of stress-induced sex-specific 5hmC profiles in the hippocampus revealed 778 sex-specific acute stress-induced DhMRs some of which were correlated to altered transcript levels that produce sex-specific isoforms in response to stress. Together, the alterations in 5hmC presented here provide a possible molecular mechanism for the adaptive sex-specific response to stress that may augment the design of novel therapeutic agents that will have optimal effectiveness in each sex.
Posttraumatic stress disorder (PTSD) affects 5-10% percent of the US adult population with a higher prevalence among women compared with men. Although it remains unclear how biological sex associates with susceptibility to PTSD, one mechanism may involve a role for estrogen in a gene by environment interaction. We previously demonstrated a sex-dependent association between the pituitary adenylate cyclase-activating polypeptide type 1 receptor (PAC1) and PTSD, where carriers of a C allele at single-nucleotide polymorphism (SNP) rs2267735 within the PAC1 receptor gene (ADCYAP1R1) have increased symptoms of PTSD. This SNP is located within a predicted estrogen response element (ERE), which regulates gene transcription when bound to estradiol (E2) activated estrogen receptor alpha (ERα). In the current study, we examined E2 regulation of ADCYAP1R1 in vitro, in cell culture, and in vivo in mice and humans. We find in mice that fear conditioning and E2 additively increase ADCYAP1R1 expression. In vitro, we show that E2/ERα binds to the ADCYAP1R1 ERE, with less efficient binding to an ERE containing the C allele of rs2267735. In women with low serum E2, the CC genotype associates with lower ADCYAP1R1 expression, which further associates with higher PTSD symptoms. These findings lead to a model in which E2 induces the expression of ADCYAP1R1 through binding of ERα at the ERE as an adaptive response to stress. Inhibition of E2/ERα binding to the ERE containing the rs2267735 risk allele results in reduced expression of ADCYAP1R1, diminishing estrogen regulation as an adaptive stress response and increasing risk for PTSD.
DNA methylation is an important epigenetic mechanism that has been linked to complex diseases and is of great interest to researchers as a potential link between genome, environment, and disease. As the scale of DNA methylation association studies approaches that of genome-wide association studies, issues such as population stratification will need to be addressed. It is well-documented that failure to adjust for population stratification can lead to false positives in genetic association studies, but population stratification is often unaccounted for in DNA methylation studies. Here, we propose several approaches to correct for population stratification using principal components (PCs) from different subsets of genome-wide methylation data. We first illustrate the potential for confounding due to population stratification by demonstrating widespread associations between DNA methylation and race in 388 individuals (365 African American and 23 Caucasian). We subsequently evaluate the performance of our PC-based approaches and other methods in adjusting for confounding due to population stratification. Our simulations show that (1) all of the methods considered are effective at removing inflation due to population stratification, and (2) maximum power can be obtained with single-nucleotide polymorphism (SNP)-based PCs, followed by methylation-based PCs, which outperform both surrogate variable analysis and genomic control. Among our different approaches to computing methylation-based PCs, we find that PCs based on CpG sites chosen for their potential to proxy nearby SNPs can provide a powerful and computationally efficient approach to adjust for population stratification in DNA methylation studies when genome-wide SNP data are unavailable.