Variation In DNA Methylation Levels Research Articles

Epigenetic Regulation of Subgenomic Gene Expression in Allotetraploid Brassica napus.

The allotetraploid Brasscia napus has now been extensively utilized to reveal the genetic processes involved in hybridization and polyploidization. Here, transcriptome, WGBS, and Chip-Seq sequencing data were obtained to explore the regulatory consequences of DNA methylation and histone modifications on gene expression in B. napus. When compared with diploid parents, the expression levels of 14,266 (about 32%) and 17,054 (about 30%) genes were altered in the An and Cn subgenomes, respectively, and a total of 4982 DEGs were identified in B. napus. Genes with high or no expression in diploid parents often shifted to medium or low expression in B. napus. The number of genes with elevated methylation levels in gene promoters and gene body regions has increased in An and Cn subgenomes. The peak number of H3K4me3 modification increased, while the peak number of H3K27ac and H3K27me3 decreased in An and Cn subgenomes, and more genes that maintained parental histone modifications were identified in Cn subgenome. The differential multiples of DEGs in B. napus were positively correlated with DNA methylation levels in promoters and the gene body, and the differential multiples of these DEGs were also affected by the degree of variation in DNA methylation levels. Further analysis revealed that about 99% of DEGs were of DNA methylation, and about 68% of DEGs were modified by at least two types of DNA methylation and H3K4me3, H3K27ac, and H3K27me3 histone modifications. These results demonstrate that DNA methylation is crucial for gene expression regulation, and different epigenetic modifications have an essential function in regulating the differential expression of genes in B. napus.

Longitudinal data reveal strong genetic and weak non-genetic components of ethnicity-dependent blood DNA methylation levels

ABSTRACT Epigenetic architecture is influenced by genetic and environmental factors, but little is known about their relative contributions or longitudinal dynamics. Here, we studied DNA methylation (DNAm) at over 750,000 CpG sites in mononuclear blood cells collected at birth and age 7 from 196 children of primarily self-reported Black and Hispanic ethnicities to study race-associated DNAm patterns. We developed a novel Bayesian method for high-dimensional longitudinal data and showed that race-associated DNAm patterns at birth and age 7 are nearly identical. Additionally, we estimated that up to 51% of all self-reported race-associated CpGs had race-dependent DNAm levels that were mediated through local genotype and, quite surprisingly, found that genetic factors explained an overwhelming majority of the variation in DNAm levels at other, previously identified, environmentally-associated CpGs. These results indicate that race-associated blood DNAm patterns in particular, and blood DNAm levels in general, are primarily driven by genetic factors, and are not as sensitive to environmental exposures as previously suggested, at least during the first 7 years of life.

Identifying blood-specific age-related DNA methylation markers on the Illumina MethylationEPIC® BeadChip

The past decade has seen rapid development in DNA methylation (DNAm) microarrays, including the Illumina HumanMethylation27 and HumanMethylation450 (450K) chips, which have played an essential role in identifying and evaluating age-related (AR) DNAm markers in different tissues. Recently, a new array, the Illumina MethylationEPIC (EPIC) was introduced, with nearly double the number of probes as the 450K (∼850,000 probes). In this study, we test these newly added probes for age association using a large cohort of 754 DNAm profiles from blood samples assayed on the EPIC BeadChip, for individuals aged 0–88 years old. 52 AR CpG sites (Spearman’s abs(rho) >0.6 and P-value <10−83) were identified, 21 of which were novel sites and mapped to 18 genes, nine of which (LHFPL4, SLC12A8, EGFEM1P, GPR158, TAL1, KIAA1755, LOC730668, DUSP16, and FAM65C) have never previously been reported to be associated with age. The data were subsequently split into a 527-sample training set and a 227-sample testing set to build and validate two age prediction models using elastic net regression and multivariate regression. Elastic net regression selected 425 CpG markers with a mean absolute deviation (MAD) of 2.6 years based on the testing set. To build a multivariate linear regression model, AR CpG sites with R2 > 0.5 at FDR < 0.05 were input into stepwise regression to select the best subset for age prediction. The resulting six CpG markers were linearly modelled with age and explained 81% of age-correlated variation in DNAm levels. Age estimation accuracy using bootstrap analysis was 4.5 years, with 95% confidence intervals of 4.56 to 4.57 years based on the testing set. These results suggest that EPIC BeadChip probes for age estimation fall within the range of probes found on the previous Illumina HumanMethylation platforms in terms of their age-prediction ability.

Integrative single-cell analysis of transcriptome, DNA methylome and chromatin accessibility in mouse oocytes

Oocyte growth is a key step in forming mature eggs that are ready to be fertilized. The states and modifications of chromatin represent critical sources of information for this process. However, the dynamics and interrelations of these chromatin characteristics remain elusive. In this study, we developed an improved scCOOL-seq technique (iscCOOL-seq), which is a multi-omics, single-cell and single-base resolution method with high mapping rates, and explored the chromatin accessibility landscape and its relationship to DNA methylation in growing mouse oocytes. The most dramatic change in chromatin accessibility occurs during oocyte growth initiation, accompanied with prominent transcriptome alterations and an elevated variation in DNA methylation levels among individual oocytes. Unlike CpG islands (CGIs), partially methylated domains (PMDs) are associated with a low density of nucleosome-depleted regions (NDRs) during the whole maturation period. Surprisingly, highly expressed genes are usually associated with NDRs at their transcriptional end sites (TESs). In addition, genes with de novo methylated gene bodies during oocyte maturation are already open at their promoters before oocyte growth initiation. Furthermore, epigenetic and transcription factors that might be involved in oocyte maturation are identified. Our work paves the way for dissecting the complex, yet highly coordinated, epigenetic alterations during mouse oocyte growth and the establishment of totipotency.

Meffil: efficient normalization and analysis of very large DNA methylation datasets.

MotivationDNA methylation datasets are growing ever larger both in sample size and genome coverage. Novel computational solutions are required to efficiently handle these data.ResultsWe have developed meffil, an R package designed for efficient quality control, normalization and epigenome-wide association studies of large samples of Illumina Methylation BeadChip microarrays. A complete re-implementation of functional normalization minimizes computational memory without increasing running time. Incorporating fixed and random effects within functional normalization, and automated estimation of functional normalization parameters reduces technical variation in DNA methylation levels, thus reducing false positive rates and improving power. Support for normalization of datasets distributed across physically different locations without needing to share biologically-based individual-level data means that meffil can be used to reduce heterogeneity in meta-analyses of epigenome-wide association studies.Availability and implementation https://github.com/perishky/meffil/ Supplementary information Supplementary data are available at Bioinformatics online.

Current Understanding of DNA Methylation and Age-related Disease

DNA methylation involves the covalent transfer of a methyl group to the C-5 position of the cytosine ring on a DNA strand. DNA methylation is both heritable and modifiable and can affect gene expression. In recent years, epigenome-wide association studies using high-throughput technologies have associated variation in DNA methylation levels with normal and pathological aging processes in human populations. DNA methylation patterns have been used to construct epigenetic clocks which can serve as potential biomarkers of age-related diseases. Age acceleration, as determined using these epigenetic clocks, has been strongly linked to common diseases including cancer, neurodegenerative diseases, metabolic diseases, and cardiovascular diseases. Identification of these robust associations between DNA methylation and aging may provide new potential therapeutic avenues for preventing and treating age-related diseases. This review focuses on the role of DNA methylation in aging processes and recent advances in epigenome-wide association studies (EWASs) reporting associations between DNA methylation and age-related diseases.

Current Understanding of DNA Methylation and Age-related Disease

DNA methylation involves the covalent transfer of a methyl group to the C-5 position of the cytosine ring on a DNA strand which can affect gene transcription. DNA methylation is both heritable and modifiable. In recent years, epigenome-wide association studies using high-throughput technologies have associated variation in DNA methylation levels with normal and pathological aging processes in human populations. Consequently, DNA methylation patterns have been used to construct epigenetic clocks which can serve as potential biomarkers of age-related diseases. To date, age acceleration, as determined using these epigenetic clocks has been strongly linked to diseases including cancer, neurodegenerative diseases, metabolic diseases, and cardiovascular diseases. Identification of these robust associations between DNA methylation and aging provide new potential therapeutic avenues for the understanding, preventing and treating age-related diseases.

PPARGC1α gene DNA methylation variations in human placenta mediate the link between maternal hyperglycemia and leptin levels in newborns.

BackgroundChildren exposed to gestational diabetes mellitus (GDM) are at a higher risk of developing obesity and type 2 diabetes. This susceptibility might involve brown adipose tissue (BAT), which is suspected to protect against obesity. The objective of this study is to assess whether fetal exposure to maternal hyperglycemia is associated with DNA methylation variations in genes involved in BAT genesis and activation.MethodsDNA methylation levels at the PRDM16, BMP7, CTBP2, and PPARGC1α gene loci were measured in placenta samples using bisulfite pyrosequencing in E-21 (n = 133; 33 cases of GDM) and the HumanMethylation450 array in Gen3G (n = 172, all from non-diabetic women) birth cohorts. Glucose tolerance was assessed in all women using an oral glucose tolerance test at the second trimester of pregnancy. Participating women were extensively phenotyped throughout pregnancy, and placenta and cord blood samples were collected at birth.ResultsWe report that maternal glycemia at the second and third trimester of pregnancy are correlated with variations in DNA methylation levels at PRDM16, BMP7, and PPARGC1α and with cord blood leptin levels. Variations in PRDM16 and PPARGC1α DNA methylation levels were also correlated with cord blood leptin levels. Mediation analyses support that DNA methylation variations at the PPARGC1α gene locus explain 0.8 % of the cord blood leptin levels variance independently of maternal fasting glucose levels (p = 0.05).ConclusionsThese results suggest that maternal glucose in pregnancy could produce variations in DNA methylation in BAT-related genes and that some of these DNA methylation marks seem to mediate the impact of maternal glycemia on cord blood leptin levels, an adipokine regulating body weight.Electronic supplementary materialThe online version of this article (doi:10.1186/s13148-016-0239-9) contains supplementary material, which is available to authorized users.

Species-wide patterns of DNA methylation variation in Quercus lobata and their association with climate gradients.

DNA methylation in plants affects transposon silencing, transcriptional regulation and thus phenotypic variation. One unanswered question is whether DNA methylation could be involved in local adaptation of plant populations to their environments. If methylation alters phenotypes to improve plant response to the environment, then methylation sites or the genes that affect them could be a target of natural selection. Using reduced-representation bisulphite sequencing (RRBS) data, we assessed whether climate is associated with variation in DNA methylation levels among 58 naturally occurring, and species-wide samples of valley oak (Quercus lobata) collected across climate gradients. We identified the genomic context of these variants referencing a new draft valley oak genome sequence. Methylation data were obtained for 341107 cytosines, of which we deemed 57488 as single-methylation variants (SMVs), found in the CG, CHG and CHH sequence contexts. Environmental association analyses revealed 43 specific SMVs that are significantly associated with any of four climate variables, the majority of which are associated with mean maximum temperature. The 43 climate-associated SMVs tend to occur in or near genes, several of which have known involvement in plant response to environment. Multivariate analyses show that climate and spatial variables explain more overall variance in CG-SMVs among individuals than in SNPs, CHG-SMVs or CHH-SMVs. Together, these results from natural oak populations provide initial evidence for a role of CG methylation in locally adaptive evolution or plasticity in plant response.

DNA Methylation and Expression Patterns of Selected Genes in First-Trimester Placental Tissue from Pregnancies with Small-for-Gestational-Age Infants at Birth1

Variations in DNA methylation levels in the placenta are thought to influence gene expression and are associated with complications of pregnancy, like fetal growth restriction (FGR). The most important cause for FGR is placental dysfunction. Here, we examined whether changes in DNA methylation, followed by gene expression changes, are mechanistically involved in the etiology of FGR. In this retrospective case-control study, we examined the association between small-for-gestational-age (SGA) children and both DNA methylation and gene expression levels of the genes WNT2, IGF2/H19, SERPINA3, HERVWE1, and PPARG in first-trimester placental tissue. We also examined the repetitive element LINE-1. These candidate genes have been reported in the literature to be associated with SGA. We used first-trimester placental tissue from chorionic villus biopsies. A total of 35 SGA children (with a birth weight below the 10th percentile) were matched to 70 controls based on their gestational age. DNA methylation levels were analyzed by pyrosequencing and mRNA levels were analyzed by real-time PCR. None of the average DNA methylation levels, measured for each gene, showed a significant difference between SGA placental tissue compared to control tissue. However, hypermethylation of WNT2 was detected on two CpG positions in SGA. This was not associated with changes in gene expression. Apart from two CpG positions of the WNT2 gene, in early placenta samples, no evident changes in DNA methylation or expression were found. This indicates that the already reported changes in term placenta are not present in the early placenta, and therefore must arise after the first trimester.

A Flexible, Efficient Binomial Mixed Model for Identifying Differential DNA Methylation in Bisulfite Sequencing Data.

Identifying sources of variation in DNA methylation levels is important for understanding gene regulation. Recently, bisulfite sequencing has become a popular tool for investigating DNA methylation levels. However, modeling bisulfite sequencing data is complicated by dramatic variation in coverage across sites and individual samples, and because of the computational challenges of controlling for genetic covariance in count data. To address these challenges, we present a binomial mixed model and an efficient, sampling-based algorithm (MACAU: Mixed model association for count data via data augmentation) for approximate parameter estimation and p-value computation. This framework allows us to simultaneously account for both the over-dispersed, count-based nature of bisulfite sequencing data, as well as genetic relatedness among individuals. Using simulations and two real data sets (whole genome bisulfite sequencing (WGBS) data from Arabidopsis thaliana and reduced representation bisulfite sequencing (RRBS) data from baboons), we show that our method provides well-calibrated test statistics in the presence of population structure. Further, it improves power to detect differentially methylated sites: in the RRBS data set, MACAU detected 1.6-fold more age-associated CpG sites than a beta-binomial model (the next best approach). Changes in these sites are consistent with known age-related shifts in DNA methylation levels, and are enriched near genes that are differentially expressed with age in the same population. Taken together, our results indicate that MACAU is an efficient, effective tool for analyzing bisulfite sequencing data, with particular salience to analyses of structured populations. MACAU is freely available at www.xzlab.org/software.html.

Intraindividual variation and short-term temporal trend in DNA methylation of human blood.

Between- and within-person variation in DNA methylation levels are important parameters to be considered in epigenome-wide association studies. Temporal change is one source of within-person variation in DNA methylation that has been linked to aging and disease. We analyzed CpG-site-specific intraindividual variation and short-term temporal trend in leukocyte DNA methylation among 24 healthy Chinese women, with blood samples drawn at study entry and after 9 months. Illumina HumanMethylation450 BeadChip was used to measure methylation. Intraclass correlation coefficients (ICC) and trend estimates were summarized by genomic location and probe type. The median ICC was 0.36 across nonsex chromosomes and 0.80 on the X chromosome. There was little difference in ICC profiles by genomic region and probe type. Among CpG loci with high variability between participants, more than 99% had ICC > 0.8. Statistically significant trend was observed in 10.9% CpG loci before adjustment for cell-type composition and in 3.4% loci after adjustment. For CpG loci differentially methylated across subjects, methylation levels can be reliably assessed with one blood sample. More samples per subject are needed for low-variability and unmethylated loci. Temporal changes are largely driven by changes in cell-type composition of blood samples, but temporal trend unrelated to cell types is detected in a small percentage of CpG sites. This study shows that one measurement can reliably assess methylation of differentially methylated CpG loci. Cancer Epidemiol Biomarkers Prev; 24(3); 490-7. ©2014 AACR.

Abstract 288: DNA methylation patterns in peripheral blood and the relationship with cancer susceptibility loci at chromosome 8q24

Abstract Chromosome 8q24 has emerged as an important region for genetic susceptibility to several cancers, but little is known about the contribution of DNA methylation in this region. To explore the extent of variation in DNA methylation at 8q24 in peripheral blood and its relationship with genetic variation in the region, we conducted a cross-sectional study using blood samples from 80 non-Hispanic Caucasian males in the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial. Specifically, we aimed to evaluate between-individual variation in DNA methylation levels at specific CpG sites at 8q24 and to investigate the underlying genetic structure in the region by examining correlations for DNA methylation levels with each other and with the established cancer susceptibility SNPs at 8q24. We quantified DNA methylation levels at 145 CpG sites nearby cancer susceptibility single nucleotide polymorphisms (SNPs) at 8q24 or the MYC oncogene using pyrosequencing of bisulfite-treated DNA, which is considered a highly sensitive method to detect differences in DNA methylation levels between individuals. We calculated pairwise Spearman correlations (rho), adjusting for multiple testing using the False Discovery Rate (FDR) method. We identified a large number of CpG sites that were reproducible and demonstrated moderate to high between-individual variation in our study population. Among these CpG sites, some sites within or nearby MYC and POU5F1B were strongly correlated with one another (highest rho=0.74), suggesting a coordination of DNA methylation levels in gene regions. Moreover, we observed strong correlations between several CpG sites and some of the known cancer susceptibility SNPs at 8q24. Some of the correlations remained statistically significant after adjustment for multiple comparisons, including a CpG site and an established prostate cancer SNP in the long non-coding RNA PRNCR1 (Chr8:128167809 and rs1456315, rho=0.52; p-value=1.4x10-6; FDR-adjusted p-value=0.002) and a CpG site (Chr8:128498051) in POU5F1B and the known prostate/colorectal cancer SNP rs6983267 (rho=0.46; p-value=2.0x10-5; FDR-adjusted p-value=0.01). This is the first study to report associations between DNA methylation levels at 8q24 in peripheral blood and 8q24 cancer susceptibility SNPs, suggesting that DNA methylation at this important susceptibility locus may contribute to risk. Additional studies are needed to clarify the relationship between genetic and epigenetic variation at 8q24 and cancer risk. Citation Format: Kathryn Hughes Barry, Lee Moore, Joshua Sampson, Liying Yan, Ann Meyer, Charles C. Chung, Meredith Yeager, Laufey Amundadottir, Sonja I. Berndt. DNA methylation patterns in peripheral blood and the relationship with cancer susceptibility loci at chromosome 8q24. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 288. doi:10.1158/1538-7445.AM2014-288

40 VARIOUS DNA METHYLATION LEVELS OF IMPRINTED GENES IN CLONED COWS FROM THE SAME DONOR CELLS

Somatic cell nuclear transferred (SCNT) animals are genetically identical to the donors; however, because of epigenetic abnormalities caused by incomplete reprogramming during nuclear transfer, the efficiency of SCNT is still very low. Monozygotic twins are also genetically identical, but it is reported that their epigenetic patterns on the genome, the so-called epigenome, are different. The epigenome is easily influenced by aging, environmental changes and nutrients, therefore these effects can be predicted by comparing epigenetic differences between genetically identical animals. Here we analysed DNA methylation levels of imprinted genes, which express in a parent-of-origin specific manner, in various tissues of cloned cows derived from the same donor cells. Imprinted gene expression is controlled by DNA methylation and other epigenetic modifications and abnormal expression/methylation patterns of imprinted genes have been observed in cloned animals. These alterations also occur during in vitro development of preimplantation embryos, which suggests that imprinted genes are easily influenced by environmental changes. Therefore, we chose H19 and PEG3 imprinted genes for the analysis to determine the epigenetic differences between individual cloned cows derived from the same donor cells. From 5 cloned and 5 non-cloned cows, we isolated DNA from 8 tissues (heart, lung, liver, kidney, spleen, intestine, muscle, and spinal cord) and analysed DNA methylation levels by bisulfite sequencing method. Briefly, genomic DNA was isolated by QIAGEN DNeasy Blood &amp; Tissue Kit and bisulfite converted by QIAGEN EpiTect Bisulfite Kits (Qiagen, Valencia, CA). After amplification, the PCR products were cloned into TA vector and at least 10 clones were sequenced in each gene/sample. In every tissue analysed, the methylation levels largely differ among tissues and individuals. On average, the paternally imprinted gene H19 was 9.4 to 47.9% methylated (average 27.6 ± 10.3%) in clones and 0.5 to 69.8% methylated (average 29.0 ± 16.8%) in non-clones. The maternally imprinted gene PEG3 was 18.8 to 82.2% methylated (average 43.5 ± 15.8%) in clones and 8.0 to 98.7% (average 48.2 ± 18.8%) in non-clones. Even though there were large variations in DNA methylation levels, the variability tends to be low in clones compared to non-clones. More specifically, the variabilities of H19 methylation levels in spleen and intestine were significantly lower in clones than those in non-clones (32.3 ± 5.4% v. 27.0 ± 19.0% and 25.1 ± 4.2% v. 45.1 ± 14.3%, respectively, F-test; P &lt; 0.05). These results suggest for the first time that epigenetic patterns in some tissues of both clones and non-clones are influenced by genetic background; however, mostly they are varied depending on non-genetic factors.

Tissue-specific variation in DNA methylation levels along human chromosome 1.

BackgroundDNA methylation is a major epigenetic modification important for regulating gene expression and suppressing spurious transcription. Most methods to scan the genome in different tissues for differentially methylated sites have focused on the methylation of CpGs in CpG islands, which are concentrations of CpGs often associated with gene promoters.ResultsHere, we use a methylation profiling strategy that is predominantly responsive to methylation differences outside of CpG islands. The method compares the yield from two samples of size-selected fragments generated by a methylation-sensitive restriction enzyme. We then profile nine different normal tissues from two human donors relative to spleen using a custom array of genomic clones covering the euchromatic portion of human chromosome 1 and representing 8% of the human genome. We observe gross regional differences in methylation states across chromosome 1 between tissues from the same individual, with the most striking differences detected in the comparison of cerebellum and spleen. Profiles of the same tissue from different donors are strikingly similar, as are the profiles of different lobes of the brain. Comparing our results with published gene expression levels, we find that clones exhibiting extreme ratios reflecting low relative methylation are statistically enriched for genes with high expression ratios, and vice versa, in most pairs of tissues examined.ConclusionThe varied patterns of methylation differences detected between tissues by our methylation profiling method reinforce the potential functional significance of regional differences in methylation levels outside of CpG islands.