Stochastic modeling reveals kinetic heterogeneity in post-replication DNA methylation

Abstract

DNA methylation is a heritable epigenetic modification that plays an essential role in mammalian development. Genomic methylation patterns are dynamically maintained, with DNA methyltransferases mediating inheritance of methyl marks onto nascent DNA over cycles of replication. A recently developed experimental technique employing immunoprecipitation of bromodeoxyuridine labeled nascent DNA followed by bisulfite sequencing (Repli-BS) measures post-replication temporal evolution of cytosine methylation, thus enabling genomewide monitoring of methylation maintenance. In this work, we combine statistical analysis and stochastic mathematical modeling to analyze Repli-BS data from human embryonic stem cells. We estimate site-specific kinetic rate constants for the restoration of methyl marks on >10 million uniquely mapped cytosines within the CpG (cytosine-phosphate-guanine) dinucleotide context across the genome using Maximum Likelihood Estimation. We find that post-replication remethylation rate constants span approximately two orders of magnitude, with half-lives of per-site recovery of steady-state methylation levels ranging from shorter than ten minutes to five hours and longer. Furthermore, we find that kinetic constants of maintenance methylation are correlated among neighboring CpG sites. Stochastic mathematical modeling provides insight to the biological mechanisms underlying the inference results, suggesting that enzyme processivity and/or collaboration can produce the observed kinetic correlations. Our combined statistical/mathematical modeling approach expands the utility of genomic datasets and disentangles heterogeneity in methylation patterns arising from replication-associated temporal dynamics versus stable cell-to-cell differences.

Local CpG density affects the trajectory of age-associated epigenetic changes

“…DNMT1 and DNMT3B both methylate processively along DNA strands whereas DNMT3A methylates in a distributive manner but can form multimers along the DNA fibre 49 . A computational analysis of DNA re-methylation dynamics suggests these processes are required to explain the observed rates of DNA re-methylation following replication 50 . The efficiency of DNMTs is also influenced by bases surrounding CpGs in vitro 51,52 and in vivo 53 .…”

A DNA methylation state transition model reveals the programmed epigenetic heterogeneity in human pre-implantation embryos

“…Despite its unique features, MethylTransition has two technical limitations. First, due to the high dropout rates of public scBS-seq data, MethylTransition relies on the assumption that the activity levels of DNA methylation-modifying enzymes in a cell are constant across the genome, which is inconsistent with numerous reports that both sequence and epigenetic features affect the activity of these enzymes [ 25 , 46 – 49 ]. This limitation could be partially resolved by applying this framework to a subset of promoters with similar sequence or epigenetic features.…”

“…McGovern et al and von Meyenn et al further incorporated 5-hydroxymethylcytosine (5hmC) into models to reflect the contribution of active demethylation to the DNA methylation state transition [ 23 , 24 ]. Recently, Busto-Moner et al enabled genome-wide quantification of subcell cycle kinetics of methylation maintenance using replication-associated bisulfite sequencing (Repli-BS-seq) data [ 25 ]. Researchers have proposed two types of methods to estimate the parameters of the models.…”

“…von Meyenn et al applied partial differential equations to estimate the parameters by using reduced representation bisulfite sequencing (RRBS) data, hairpin-BS-seq data, and TET-assisted bisulfite sequencing (TAB-seq) data [ 24 ]. Busto-Moner et al used maximum likelihood estimation (MLE) to estimate the parameters quantifying the remethylation kinetics of nascent DNA post-replication relied on Repli-BS-seq data [ 25 ].…”

Staying true to yourself: mechanisms of DNA methylation maintenance in mammals

“…Statistical analysis of repli-BS data also supports that CpG re-methylation occurs with very different speeds in distinct genomic regions, which correlate with the local density of CpGs. Mathematical modelling revealed that the kinetics of restoration between the neighboring CpG motifs in the genome is correlated ( 137 ), suggesting that either DNMT1 is processive through stretches of chromatinized template, or that the recruitment of DNMT1 in nearby loci is coregulated.…”