Dataset Information

Isogenic mice exhibit sexually-dimorphic DNA methylation patterns across multiple tissues.

ABSTRACT: Cytosine methylation is a stable epigenetic modification of DNA that plays an important role in both normal physiology and disease. Most diseases exhibit some degree of sexual dimorphism, but the extent to which epigenetic states are influenced by sex is understudied and poorly understood. To address this deficit we studied DNA methylation patterns across multiple reduced representation bisulphite sequencing datasets (from liver, heart, brain, muscle and spleen) derived from isogenic male and female mice.DNA methylation patterns varied significantly from tissue to tissue, as expected, but they also varied between the sexes, with thousands of sexually dimorphic loci identified. The loci affected were largely autonomous to each tissue, even within tissues derived from the same germ layer. At most loci, differences between genders were driven by females exhibiting hypermethylation relative to males; a proportion of these differences were independent of the presence of testosterone in males. Loci harbouring gender differences were clustered in ontologies related to tissue function.Our findings suggest that gender is underwritten in the epigenome in a tissue-specific and potentially sex hormone-independent manner. Gender-specific epigenetic states are likely to have important implications for understanding sexually dimorphic phenotypes in health and disease.

SUBMITTER: McCormick H

PROVIDER: S-EPMC5729250 | biostudies-literature | 2017 Dec

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Isogenic mice exhibit sexually-dimorphic DNA methylation patterns across multiple tissues.

McCormick Helen H Young Paul E PE Hur Suzy S J SSJ Booher Keith K Chung Hunter H Cropley Jennifer E JE Giannoulatou Eleni E Suter Catherine M CM

BMC genomics 20171213 1

<h4>Background</h4>Cytosine methylation is a stable epigenetic modification of DNA that plays an important role in both normal physiology and disease. Most diseases exhibit some degree of sexual dimorphism, but the extent to which epigenetic states are influenced by sex is understudied and poorly understood. To address this deficit we studied DNA methylation patterns across multiple reduced representation bisulphite sequencing datasets (from liver, heart, brain, muscle and spleen) derived from i ...[more]

PMID: 29237414

Similar Datasets

Project description:BackgroundFetal sex and placental development impact pregnancy outcomes and fetal-maternal health, but the critical timepoint of placenta establishment in first trimester is understudied in human pregnancies.MethodsPregnant subjects were recruited in late first trimester (weeks 10-14) at time of chorionic villus sampling, a prenatal diagnostic test. Leftover placenta tissue was collected and stored until birth outcomes were known, then DNA and RNA were isolated from singleton, normal karyotype pregnancies resulting in live births. DNA methylation was measured with the Illumina Infinium MethylationEPIC BeadChip array (n = 56). Differential methylation analysis compared 25 females versus 31 males using a generalized linear model on 743,461 autosomal probes. Gene expression sex differences were analyzed with RNA-sequencing (n = 74). An integrated analysis was performed using linear regression to correlate gene expression and DNA methylation in 51 overlapping placentas.ResultsMethylation analysis identified 151 differentially methylated probes (DMPs) significant at false discovery rate < 0.05, including 89 (59%) hypermethylated in females. Probe cg17612569 (GABPA, ATP5J) was the most significant CpG site, hypermethylated in males. There were 11 differentially methylated regions affected by fetal sex, with transcription factors ZNF300 and ZNF311 most significantly hypermethylated in males and females, respectively. RNA-sequencing identified 152 genes significantly sexually dimorphic at false discovery rate < 0.05. The 151 DMPs were associated with 18 genes with gene downregulation (P < 0.05) in the direction of hypermethylation, including 2 genes significant at false discovery rate < 0.05 (ZNF300 and CUB and Sushi multiple domains 1, CSMD1). Both genes, as well as Family With Sequence Similarity 228 Member A (FAM228A), showed significant correlation between DNA methylation and sexually dimorphic gene expression, though FAM228A DNA methylation was less sexually dimorphic. Comparison with other sex differences studies found that cg17612569 is male-hypermethylated across gestation in placenta and in human blood up to adulthood.ConclusionsOverall, sex dimorphic differential methylation with associated differential gene expression in the first trimester placenta is small, but there remain significant genes that may be regulated through methylation leading to differences in the first trimester placenta.

Project description:BackgroundDNA methylation is an important tissue-specific epigenetic event that influences transcriptional regulation of gene expression. Differentially methylated CpG sites may act as mediators between genetic variation and gene expression, and this relationship can be exploited while mapping multi-tissue expression quantitative trait loci (eQTL). Current multi-tissue eQTL mapping techniques are limited to only exploiting gene expression patterns across multiple tissues either in a joint tissue or tissue-by-tissue frameworks. We present a new statistical approach that enables us to model the effect of germ-line variation on tissue-specific gene expression in the presence of effects due to DNA methylation.ResultsOur method efficiently models genetic and epigenetic variation to identify genomic regions of interest containing combinations of mRNA transcripts, CpG sites, and SNPs by jointly testing for genotypic effect and higher order interaction effects between genotype, methylation and tissues. We demonstrate using Monte Carlo simulations that our approach, in the presence of both genetic and DNA methylation effects, gives an improved performance (in terms of statistical power) to detect eQTLs over the current eQTL mapping approaches. When applied to an array-based dataset from 150 neuropathologically normal adult human brains, our method identifies eQTLs that were undetected using standard tissue-by-tissue or joint tissue eQTL mapping techniques. As an example, our method identifies eQTLs by leveraging methylated CpG sites in a LIM homeobox member gene (LHX9), which may have a role in the neural development.ConclusionsOur score test-based approach does not need parameter estimation under the alternative hypothesis. As a result, our model parameters are estimated only once for each mRNA - CpG pair. Our model specifically studies the effects of non-coding regions of DNA (in this case, CpG sites) on mapping eQTLs. However, we can easily model micro-RNAs instead of CpG sites to study the effects of post-transcriptional events in mapping eQTL. Our model's flexible framework also allows us to investigate other genomic events such as alternative gene splicing by extending our model to include gene isoform-specific data.

Dataset Information

Isogenic mice exhibit sexually-dimorphic DNA methylation patterns across multiple tissues.

Publications

Isogenic mice exhibit sexually-dimorphic DNA methylation patterns across multiple tissues.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets