Abstract
DNA methylation is considered a stable epigenetic mark, yet methylation patterns can vary during differentiation and in diseases such as cancer. Local levels of DNA methylation result from opposing enzymatic activities, the rates of which remain largely unknown. Here we developed a theoretical and experimental framework enabling us to infer methylation and demethylation rates at 860,404 CpGs in mouse embryonic stem cells. We find that enzymatic rates can vary as much as two orders of magnitude between CpGs with identical steady-state DNA methylation. Unexpectedly, de novo and maintenance methylation activity is reduced at transcription factor binding sites, while methylation turnover is elevated in transcribed gene bodies. Furthermore, we show that TET activity contributes substantially more than passive demethylation to establishing low methylation levels at distal enhancers. Taken together, our work unveils a genome-scale map of methylation kinetics, revealing highly variable and context-specific activity for the DNA methylation machinery.
Highlights
DNA methylation is considered a stable epigenetic mark, yet methylation patterns can vary during differentiation and in diseases such as cancer
DNA methylation is a dynamic process, resulting in the average methylation patterns observed in various cell types
Studying methylation as a continuous process reveals that methylation levels do not predict methylation turnover, which can differ over two orders of magnitude
Summary
DNA methylation is considered a stable epigenetic mark, yet methylation patterns can vary during differentiation and in diseases such as cancer. Previous work has sought to determine methylation activities empirically at CpG sites in vitro[35] and in cultured cells[34,36], as well as theoretically[37,38,39] These studies have revealed several properties of the enzymes responsible for depositing these marks, from presence of non-CpG methylation[34,35] to the inference of methylation and maintenance rates for individual CpGs37, as well as DNMT1 processivity[38]. These models have been extended and adapted with the aim of describing population methylation dynamics[40,41,42] While informative in their own right, their genomic scope is limited or they do not quantitatively infer the rates of all three processes at the individual CpG level, including de novo and maintenance methylation, as well as active demethylation
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