Whole-genome Methylation Profiling Research Articles

Methylation Abnormalities in Mammary Carcinoma: The Methylation Suicide Hypothesis.

Promoter silencing by ectopic de novo methylation of tumor suppressor genes has been proposed as comparable or equivalent to inactivating mutations as a factor in carcinogenesis. However, this hypotheses had not previously been tested by high resolution, high-coverage whole-genome methylation profiling in primary carcinomas. We have determined the genomic methylation status of a series of primary mammary carcinomas and matched control tissues by examination of more than 2.7 billion CpG dinucleotides. Most of the tumors showed variable losses of DNA methylation from all sequence compartments, but increases in promoter methylation were infrequent, very small in extent, and were observed largely at CpG-poor promoters. De novo methylation at the promoters of proto-oncogenes and tumor suppressor genes occurred at approximately the same frequency. The findings indicate that tumor suppressor silencing by de novo methylation is much less common than currently believed. We put forward a hypothesis under which the demethylation commonly observed in carcinomas is a manifestation of a defensive system that kills incipient cancer cells.

An optimized algorithm for detecting and annotating regional differential methylation

BackgroundDNA methylation profiling reveals important differentially methylated regions (DMRs) of the genome that are altered during development or that are perturbed by disease. To date, few programs exist for regional analysis of enriched or whole-genome bisulfate conversion sequencing data, even though such data are increasingly common. Here, we describe an open-source, optimized method for determining empirically based DMRs (eDMR) from high-throughput sequence data that is applicable to enriched whole-genome methylation profiling datasets, as well as other globally enriched epigenetic modification data.ResultsHere we show that our bimodal distribution model and weighted cost function for optimized regional methylation analysis provides accurate boundaries of regions harboring significant epigenetic modifications. Our algorithm takes the spatial distribution of CpGs into account for the enrichment assay, allowing for optimization of the definition of empirical regions for differential methylation. Combined with the dependent adjustment for regional p-value combination and DMR annotation, we provide a method that may be applied to a variety of datasets for rapid DMR analysis. Our method classifies both the directionality of DMRs and their genome-wide distribution, and we have observed that shows clinical relevance through correct stratification of two Acute Myeloid Leukemia (AML) tumor sub-types.ConclusionsOur weighted optimization algorithm eDMR for calling DMRs extends an established DMR R pipeline (methylKit) and provides a needed resource in epigenomics. Our method enables an accurate and scalable way of finding DMRs in high-throughput methylation sequencing experiments. eDMR is available for download at http://code.google.com/p/edmr/.

Conservation and divergence in eukaryotic DNA methylation

Cytosine methylation is a common DNA modification found in most eukaryotic organisms including plants, animals, and fungi (1, 2). The addition of a methyl group to cytosine nucleotides in DNA does not change the primary DNA sequence, but the covalent modification of DNA by methylation can impact gene expression and activity in a heritable fashion. This type of epigenetic regulation through DNA methylation appears to be a critical process, in an evolutionary sense, with highly conserved enzymes mediating the process (3). In humans, aberrant DNA methylation has been associated with diseases, including cancer (4). To study cytosine methylation patterns across the genome, researchers have used microarray hybridization or direct sequencing of bisulfite-treated DNA (5). However, mapping methylation of individual cytosines in a given genome has been a challenging task and, accordingly, comparative analysis of genome methylation patterns across species has not been performed. Several new studies have addressed this gap in our understanding of the evolution of cytosine methylation (6, 7). Feng et al. in PNAS (7) used next-generation sequencing to investigate the DNA methylation patterns in eight divergent species, including green algae, flowering plants, insects, and vertebrates. Their data allowed a comprehensive comparison of whole-genome methylation profiles across the plant and animal kingdoms, revealing both conserved and divergent features of DNA methylation in eukaryotes.