Meaghan J. Jones

DNA methylation and healthy human aging

The process of aging results in a host of changes at the cellular and molecular levels, which include senescence, telomere shortening, and changes in gene expression. Epigenetic patterns also change over the lifespan, suggesting that epigenetic changes may constitute an important component of the aging process. The epigenetic mark that has been most highly studied is DNA methylation, the presence of methyl groups at CpG dinucleotides. These dinucleotides are often located near gene promoters and associate with gene expression levels. Early studies indicated that global levels of DNA methylation increase over the first few years of life and then decrease beginning in late adulthood. Recently, with the advent of microarray and next‐generation sequencing technologies, increases in variability of DNA methylation with age have been observed, and a number of site‐specific patterns have been identified. It has also been shown that certain CpG sites are highly associated with age, to the extent that prediction models using a small number of these sites can accurately predict the chronological age of the donor. Together, these observations point to the existence of two phenomena that both contribute to age‐related DNA methylation changes: epigenetic drift and the epigenetic clock. In this review, we focus on healthy human aging throughout the lifetime and discuss the dynamics of DNA methylation as well as how interactions between the genome, environment, and the epigenome influence aging rates. We also discuss the impact of determining ‘epigenetic age’ for human health and outline some important caveats to existing and future studies.

Landscape of DNA methylation on the X chromosome reflects CpG density, functional chromatin state and X-chromosome inactivation

X-chromosome inactivation (XCI) achieves dosage compensation between males and females through the silencing of the majority of genes on one of the female X chromosomes. Thus, the female X chromosomes provide a unique opportunity to study euchromatin and heterochromatin of allelic regions within the same nuclear environment. We examined the interplay of DNA methylation (DNAm) with CpG density, transcriptional activity and chromatin state at genes on the X chromosome using over 1800 female samples analysed with the Illumina Infinium Human Methylation450 BeadChip. DNAm was used to predict an inactivation status for 63 novel transcription start sites (TSSs) across 27 tissues. There was high concordance of inactivation status across tissues, with 62% of TSSs subject to XCI in all 27 tissues examined, whereas 9% escaped from XCI in all tissues, and the remainder showed variable escape from XCI between females in subsets of tissues. Inter-female and twin data supported a model of predominately cis-acting influences on inactivation status. The level of expression from the inactive X relative to the active X correlated with the amount of female promoter DNAm to a threshold of ∼30%, beyond which genes were consistently subject to inactivation. The inactive X showed lower DNAm than the active X at intragenic and intergenic regions for genes subject to XCI, but not at genes that escape from inactivation. Our categorization of genes that escape from X inactivation provides candidates for sex-specific differences in disease.

Concordant and discordant DNA methylation signatures of aging in human blood and brain

BackgroundDNA methylation is an epigenetic mark that balances plasticity with stability. While DNA methylation exhibits tissue specificity, it can also vary with age and potentially environmental exposures. In studies of DNA methylation, samples from specific tissues, especially brain, are frequently limited and so surrogate tissues are often used. As yet, we do not fully understand how DNA methylation profiles of these surrogate tissues relate to the profiles of the central tissue of interest.ResultsWe have adapted principal component analysis to analyze data from the Illumina 450K Human Methylation array using a set of 17 individuals with 3 brain regions and whole blood. All of the top five principal components in our analysis were associated with a variable of interest: principal component 1 (PC1) differentiated brain from blood, PCs 2 and 3 were representative of tissue composition within brain and blood, respectively, and PCs 4 and 5 were associated with age of the individual (PC4 in brain and PC5 in both brain and blood). We validated our age-related PCs in four independent sample sets, including additional brain and blood samples and liver and buccal cells. Gene ontology analysis of all five PCs showed enrichment for processes that inform on the functions of each PC.ConclusionsPrincipal component analysis (PCA) allows simultaneous and independent analysis of tissue composition and other phenotypes of interest. We discovered an epigenetic signature of age that is not associated with cell type composition and required no correction for cellular heterogeneity.

DNA methylation, genotype and gene expression: who is driving and who is along for the ride?

A new study integrates genome-wide SNP genotyping, RNA-Seq and DNA methylation in human cells, revealing their relationships and posing new questions about causality.

Inhalation of diesel exhaust and allergen alters human bronchial epithelium DNA methylation

Background: Allergic disease affects 30% to 40% of the worlds population, and its development is determined by the interplay between environmental and inherited factors. Air pollution, primarily consisting of diesel exhaust emissions, has increased at a similar rate to allergic disease. Exposure to diesel exhaust may play a role in the development and progression of allergic disease, in particular allergic respiratory disease. One potential mechanism underlying the connection between air pollution and increased allergic disease incidence is DNA methylation, an epigenetic process with the capacity to integrate gene‐environment interactions. Objective: We sought to investigate the effect of allergen and diesel exhaust exposure on bronchial epithelial DNA methylation. Methods: We performed a randomized crossover‐controlled exposure study to allergen and diesel exhaust in humans, and measured single‐site (CpG) resolution global DNA methylation in bronchial epithelial cells. Results: Exposure to allergen alone, diesel exhaust alone, or allergen and diesel exhaust together (coexposure) led to significant changes in 7 CpG sites at 48 hours. However, when the same lung was exposed to allergen and diesel exhaust but separated by approximately 4 weeks, significant changes in more than 500 sites were observed. Furthermore, sites of differential methylation differed depending on which exposure was experienced first. Functional analysis of differentially methylated CpG sites found genes involved in transcription factor activity, protein metabolism, cell adhesion, and vascular development, among others. Conclusions: These findings suggest that specific exposures can prime the lung for changes in DNA methylation induced by a subsequent insult.

Discordance of DNA Methylation Variance Between two Accessible Human Tissues

Population epigenetic studies have been seeking to identify differences in DNA methylation between specific exposures, demographic factors, or diseases in accessible tissues, but relatively little is known about how inter-individual variability differs between these tissues. This study presents an analysis of DNA methylation differences between matched peripheral blood mononuclear cells (PMBCs) and buccal epithelial cells (BECs), the two most accessible tissues for population studies, in 998 promoter-located CpG sites. Specifically we compared probe-wise DNA methylation variance, and how this variance related to demographic factors across the two tissues. PBMCs had overall higher DNA methylation than BECs, and the two tissues tended to differ most at genomic regions of low CpG density. Furthermore, although both tissues showed appreciable probe-wise variability, the specific regions and magnitude of variability differed strongly between tissues. Lastly, through exploratory association analysis, we found indication of differential association of BEC and PBMC with demographic variables. The work presented here offers insight into variability of DNA methylation between individuals and across tissues and helps guide decisions on the suitability of buccal epithelial or peripheral mononuclear cells for the biological questions explored by epigenetic studies in human populations.

The epigenomic landscape of African rainforest hunter-gatherers and farmers

The genetic history of African populations is increasingly well documented, yet their patterns of epigenomic variation remain uncharacterized. Moreover, the relative impacts of DNA sequence variation and temporal changes in lifestyle and habitat on the human epigenome remain unknown. Here we generate genome-wide genotype and DNA methylation profiles for 362 rainforest hunter-gatherers and sedentary farmers. We find that the current habitat and historical lifestyle of a population have similarly critical impacts on the methylome, but the biological functions affected strongly differ. Specifically, methylation variation associated with recent changes in habitat mostly concerns immune and cellular functions, whereas that associated with historical lifestyle affects developmental processes. Furthermore, methylation variation—particularly that correlated with historical lifestyle—shows strong associations with nearby genetic variants that, moreover, are enriched in signals of natural selection. Our work provides new insight into the genetic and environmental factors affecting the epigenomic landscape of human populations over time.

Improving cell mixture deconvolution by identifying optimal DNA methylation libraries (IDOL).

BackgroundConfounding due to cellular heterogeneity represents one of the foremost challenges currently facing Epigenome-Wide Association Studies (EWAS). Statistical methods leveraging the tissue-specificity of DNA methylation for deconvoluting the cellular mixture of heterogenous biospecimens offer a promising solution, however the performance of such methods depends entirely on the library of methylation markers being used for deconvolution. Here, we introduce a novel algorithm for Identifying Optimal Libraries (IDOL) that dynamically scans a candidate set of cell-specific methylation markers to find libraries that optimize the accuracy of cell fraction estimates obtained from cell mixture deconvolution.ResultsApplication of IDOL to training set consisting of samples with both whole-blood DNA methylation data (Illumina HumanMethylation450 BeadArray (HM450)) and flow cytometry measurements of cell composition revealed an optimized library comprised of 300 CpG sites. When compared existing libraries, the library identified by IDOL demonstrated significantly better overall discrimination of the entire immune cell landscape (p = 0.038), and resulted in improved discrimination of 14 out of the 15 pairs of leukocyte subtypes. Estimates of cell composition across the samples in the training set using the IDOL library were highly correlated with their respective flow cytometry measurements, with all cell-specific R2>0.99 and root mean square errors (RMSEs) ranging from [0.97 % to 1.33 %] across leukocyte subtypes. Independent validation of the optimized IDOL library using two additional HM450 data sets showed similarly strong prediction performance, with all cell-specific R2>0.90 and RMSE<4.00 %. In simulation studies, adjustments for cell composition using the IDOL library resulted in uniformly lower false positive rates compared to competing libraries, while also demonstrating an improved capacity to explain epigenome-wide variation in DNA methylation within two large publicly available HM450 data sets.ConclusionsDespite consisting of half as many CpGs compared to existing libraries for whole blood mixture deconvolution, the optimized IDOL library identified herein resulted in outstanding prediction performance across all considered data sets and demonstrated potential to improve the operating characteristics of EWAS involving adjustments for cell distribution. In addition to providing the EWAS community with an optimized library for whole blood mixture deconvolution, our work establishes a systematic and generalizable framework for the assembly of libraries that improve the accuracy of cell mixture deconvolution.

BECon: a tool for interpreting DNA methylation findings from blood in the context of brain

Tissue differences are one of the largest contributors to variability in the human DNA methylome. Despite the tissue-specific nature of DNA methylation, the inaccessibility of human brain samples necessitates the frequent use of surrogate tissues such as blood, in studies of associations between DNA methylation and brain function and health. Results from studies of surrogate tissues in humans are difficult to interpret in this context, as the connection between blood–brain DNA methylation is tenuous and not well-documented. Here, we aimed to provide a resource to the community to aid interpretation of blood-based DNA methylation results in the context of brain tissue. We used paired samples from 16 individuals from three brain regions and whole blood, run on the Illumina 450 K Human Methylation Array to quantify the concordance of DNA methylation between tissues. From these data, we have made available metrics on: the variability of cytosine-phosphate-guanine dinucleotides (CpGs) in our blood and brain samples, the concordance of CpGs between blood and brain, and estimations of how strongly a CpG is affected by cell composition in both blood and brain through the web application BECon (Blood–Brain Epigenetic Concordance; https://redgar598.shinyapps.io/BECon/). We anticipate that BECon will enable biological interpretation of blood-based human DNA methylation results, in the context of brain.

Genome-wide Interaction Analysis of Air Pollution Exposure and Childhood Asthma with Functional Follow-up

Rationale: The evidence supporting an association between traffic‐related air pollution exposure and incident childhood asthma is inconsistent and may depend on genetic factors. Objectives: To identify gene‐environment interaction effects on childhood asthma using genome‐wide single‐nucleotide polymorphism (SNP) data and air pollution exposure. Identified loci were further analyzed at epigenetic and transcriptomic levels. Methods: We used land use regression models to estimate individual air pollution exposure (represented by outdoor NO2 levels) at the birth address and performed a genome‐wide interaction study for doctors’ diagnoses of asthma up to 8 years in three European birth cohorts (n = 1,534) with look‐up for interaction in two separate North American cohorts, CHS (Childrens Health Study) and CAPPS/SAGE (Canadian Asthma Primary Prevention Study/Study of Asthma, Genetics and Environment) (n = 1,602 and 186 subjects, respectively). We assessed expression quantitative trait locus effects in human lung specimens and blood, as well as associations among air pollution exposure, methylation, and transcriptomic patterns. Measurements and Main Results: In the European cohorts, 186 SNPs had an interaction P < 1 × 10−4 and a look‐up evaluation of these disclosed 8 SNPs in 4 loci, with an interaction P < 0.05 in the large CHS study, but not in CAPPS/SAGE. Three SNPs within adenylate cyclase 2 (ADCY2) showed the same direction of the interaction effect and were found to influence ADCY2 gene expression in peripheral blood (P = 4.50 × 10−4). One other SNP with P < 0.05 for interaction in CHS, rs686237, strongly influenced UDP‐Gal:betaGlcNAc &bgr;‐1,4‐galactosyltransferase, polypeptide 5 (B4GALT5) expression in lung tissue (P = 1.18 × 10−17). Air pollution exposure was associated with differential discs, large homolog 2 (DLG2) methylation and expression. Conclusions: Our results indicated that gene‐environment interactions are important for asthma development and provided supportive evidence for interaction with air pollution for ADCY2, B4GALT5, and DLG2.