Age-associated epigenetic drift: implications, and a case of epigenetic thrift?

It is now well established that the genomic landscape of DNA methylation gets altered as a function of age, a process we here call "epigenetic drift". The biological, functional, clinical and evolutionary significance of this epigenetic drift however remains unclear. We here provide a brief review of epigenetic drift, focusing on the potential implications for ageing, stem-cell biology and disease risk prediction. It has been demonstrated that epigenetic drift affects most of the genome, suggesting a global deregulation of DNA methylation patterns with age. A component of this drift is tissue specific, allowing remarkably accurate age-predictive models to be constructed. Another component is tissue-independent, targeting stem-cell differentiation pathways and affecting stem cells, which may explain the observed decline of stem cell function with age. Age-associated increases in DNA methylation target developmental genes, overlapping those associated with environmental disease risk factors and with disease itself, notably cancer. In particular, cancers and precursor cancer lesions exhibit aggravated age DNA methylation signatures. Epigenetic drift is also influenced by genetic factors. Thus, drift emerges as a promising biomarker for premature or biological ageing, and could potentially be used in geriatrics for disease risk prediction. Finally, we propose, in the context of human evolution, that epigenetic drift may represent a case of epigenetic thrift, or bet-hedging. In summary, this review demonstrates the growing importance of the "ageing epigenome", with potentially far reaching implications for understanding the effect of age on stem cell function and differentiation, as well as for disease prevention

A comparison of reference-based algorithms for correcting cell-type heterogeneity in Epigenome-Wide Association Studies.

BACKGROUND: Intra-sample cellular heterogeneity presents numerous challenges to the identification of biomarkers in large Epigenome-Wide Association Studies (EWAS). While a number of reference-based deconvolution algorithms have emerged, their potential remains underexplored and a comparative evaluation of these algorithms beyond tissues such as blood is still lacking. RESULTS: Here we present a novel framework for reference-based inference, which leverages cell-type specific DNAse Hypersensitive Site (DHS) information from the NIH Epigenomics Roadmap to construct an improved reference DNA methylation database. We show that this leads to a marginal but statistically significant improvement of cell-count estimates in whole blood as well as in mixtures involving epithelial cell-types. Using this framework we compare a widely used state-of-the-art reference-based algorithm (called constrained projection) to two non-constrained approaches including CIBERSORT and a method based on robust partial correlations. We conclude that the widely-used constrained projection technique may not always be optimal. Instead, we find that the method based on robust partial correlations is generally more robust across a range of different tissue types and for realistic noise levels. We call the combined algorithm which uses DHS data and robust partial correlations for inference, EpiDISH (Epigenetic Dissection of Intra-Sample Heterogeneity). Finally, we demonstrate the added value of EpiDISH in an EWAS of smoking. CONCLUSIONS: Estimating cell-type fractions and subsequent inference in EWAS may benefit from the use of non-constrained reference-based cell-type deconvolution methods

Tissue-independent and tissue-specific patterns of DNA methylation alteration in cancer

BACKGROUND: There is growing evidence that DNA methylation alterations contribute to carcinogenesis. While cancer tissue exhibits widespread DNA methylation changes, the proportion of tissue-specific versus tissue-independent DNA methylation alterations in cancer is unclear. In addition, it is unknown which factors determine the patterns of aberrant DNA methylation in cancer. RESULTS: Using HumanMethylation450 BeadChips (450k), we here analyze genome-wide DNA methylation patterns of ten types of fetal tissue, in addition to matched normal-cancer data for corresponding tissue types, encompassing over 3000 samples. We demonstrate that the level of aberrant cancer DNA methylation in gene promoters and gene bodies is highly correlated between cancer types. We estimate that up to 60 % of the DNA methylation variation in a cancer genome of a given tissue type is explained by the corresponding variation in a cancer genome of another type, implying that much of the cancer DNA methylation landscape is tissue independent. We further show that histone marks in normal cells are better predictors of aberrant cancer DNA methylation than the corresponding signals in human embryonic stem cells. We build predictors of cancer DNA methylation patterns and show that although inclusion of three histone marks (H3K4me3, H3K27me3 and H3K36me3) improves model accuracy, the bivalent marks are the most predictive. Finally, we show that chromatin accessibility of gene promoters in normal tissue dictates the promoter's propensity to acquire aberrant DNA methylation in cancer in so far as it determines its level of DNA methylation in normal tissue. CONCLUSIONS: Our data show that a considerable fraction of the aberrant cancer DNA methylation landscape results from a mechanism that is largely tissue specific. Histone marks as specified in the normal cell of origin provide highly predictive models of aberrant cancer DNA methylation and outperform those derived from the same marks in hESCs

The multi-omic landscape of transcription factor inactivation in cancer

BACKGROUND: Hypermethylation of transcription factor promoters bivalently marked in stem cells is a cancer hallmark. However, the biological significance of this observation for carcinogenesis is unclear given that most of these transcription factors are not expressed in any given normal tissue. METHODS: We analysed the dynamics of gene expression between human embryonic stem cells, fetal and adult normal tissue, as well as six different matching cancer types. In addition, we performed an integrative multi-omic analysis of matched DNA methylation, copy number, mutational and transcriptomic data for these six cancer types. RESULTS: We here demonstrate that bivalently and PRC2 marked transcription factors highly expressed in a normal tissue are more likely to be silenced in the corresponding tumour type compared with non-housekeeping genes that are also highly expressed in the same normal tissue. Integrative multi-omic analysis of matched DNA methylation, copy number, mutational and transcriptomic data for six different matching cancer types reveals that in-cis promoter hypermethylation, and not in-cis genomic loss or genetic mutation, emerges as the predominant mechanism associated with silencing of these transcription factors in cancer. However, we also observe that some silenced bivalently/PRC2 marked transcription factors are more prone to copy number loss than promoter hypermethylation, pointing towards distinct, mutually exclusive inactivation patterns. CONCLUSIONS: These data provide statistical evidence that inactivation of cell fate-specifying transcription factors in cancer is an important step in carcinogenesis and that it occurs predominantly through a mechanism associated with promoter hypermethylation

An integrative network algorithm identifies age-associated differential methylation interactome hotspots targeting stem-cell differentiation pathways

Epigenetic changes have been associated with ageing and cancer. Identifying and interpreting epigenetic changes associated with such phenotypes may benefit from integration with protein interactome models. We here develop and validate a novel integrative epigenome-interactome approach to identify differential methylation interactome hotspots associated with a phenotype of interest. We apply the algorithm to cancer and ageing, demonstrating the existence of hotspots associated with these phenotypes. Importantly, we discover tissue independent age-associated hotspots targeting stem-cell differentiation pathways, which we validate in independent DNA methylation data sets, encompassing over 1000 samples from different tissue types. We further show that these pathways would not have been discovered had we used a non-network based approach and that the use of the protein interaction network improves the overall robustness of the inference procedure. The proposed algorithm will be useful to any study seeking to identify interactome hotspots associated with common phenotypes

EpiDISH web server: Epigenetic Dissection of Intra-Sample-Heterogeneity with online GUI

It is well recognized that cell-type heterogeneity hampers the interpretation of Epigenome-Wide Association Studies (EWAS). Many tools have emerged to address this issue, including several R/Bioconductor packages that infer cell-type composition. Here we present a web application for cell-type deconvolution, which offers the functionality of our EpiDISH Bioconductor/R package in a user-friendly GUI environment. Users can upload their data to infer cell-type composition and differentially methylated cytosines in individual cell-types (DMCTs) for a range of different tissues. Availability and implementation EpiDISH web server is implemented with Shiny in R, and is freely available at https://www.biosino.org/EpiDISH/

A novel cell-type deconvolution algorithm reveals substantial contamination by immune cells in saliva, buccal and cervix

AIM: An outstanding challenge in epigenome studies is the estimation of cell-type proportions in complex epithelial tissues. MATERIALS & METHODS: Here, we construct and validate a DNA methylation reference and algorithm for complex tissues that contain epithelial, immune and nonimmune stromal cells. RESULTS: Using this reference, we show that easily accessible tissues such as saliva, buccal and cervix exhibit substantial variation in immune cell (IC) contamination. We further validate our reference in the context of oral cancer, where it correctly predicts an increased IC infiltration in cancer but suppressed in patients with highest smoking exposure. Finally, our method can improve the specificity of differentially methylated CpG calls in epithelial cancer. CONCLUSION: The degree and variation of IC contamination in complex epithelial tissues is substantial. We provide a valuable resource and tool for assessing the epithelial purity and IC contamination of samples and for identifying differential methylation in such complex tissues