STATISTICAL INFERENCE OF TRANSMISSION FIDELITY OF DNA METHYLATION PATTERNS OVER SOMATIC CELL DIVISIONS IN MAMMALS

Non-homologous end joining induced alterations in DNA methylation: A source of permanent epigenetic change

In addition to genetic mutations, epigenetic revision plays a major role in the development and progression of cancer; specifically, inappropriate DNA methylation or demethylation of CpG residues may alter the expression of genes that promote tumorigenesis. We hypothesize that DNA repair, specifically the repair of DNA double strand breaks (DSB) by Non-Homologous End Joining (NHEJ) may play a role in this process. Using a GFP reporter system inserted into the genome of HeLa cells, we are able to induce targeted DNA damage that enables the cells, after successfully undergoing NHEJ repair, to express WT GFP. These GFP+ cells were segregated into two expression classes, one with robust expression (Bright) and the other with reduced expression (Dim). Using a DNA hypomethylating drug (AzadC) we demonstrated that the different GFP expression levels was due to differential methylation statuses of CpGs in regions on either side of the break site. Deep sequencing analysis of this area in sorted Bright and Dim populations revealed a collection of different epi-alleles that display patterns of DNA methylation following repair by NHEJ. These patterns differ between Bright and Dim cells which are hypo- and hypermethylated, respectively, and between the post-repair populations and the original, uncut cells. These data suggest that NHEJ repair facilitates a rewrite of the methylation landscape in repaired genes, elucidating a potential source for the altered methylation patterns seen in cancer cells, and understanding the mechanism by which this occurs could provide new therapeutic targets for preventing this process from contributing to tumorigenesis.

Epigenetic memory via concordant DNA methylation is inversely correlated to developmental potential of mammalian cells

In storing and transmitting epigenetic information, organisms must balance the need to maintain information about past conditions with the capacity to respond to information in their current and future environments. Some of this information is encoded by DNA methylation, which can be transmitted with variable fidelity from parent to daughter strand. High fidelity confers strong pattern matching between the strands of individual DNA molecules and thus pattern stability over rounds of DNA replication; lower fidelity confers reduced pattern matching, and thus greater flexibility. Here, we present a new conceptual framework, Ratio of Concordance Preference (RCP), that uses double-stranded methylation data to quantify the flexibility and stability of the system that gave rise to a given set of patterns. We find that differentiated mammalian cells operate with high DNA methylation stability, consistent with earlier reports. Stem cells in culture and in embryos, in contrast, operate with reduced, albeit significant, methylation stability. We conclude that preference for concordant DNA methylation is a consistent mode of information transfer, and thus provides epigenetic stability across cell divisions, even in stem cells and those undergoing developmental transitions. Broader application of our RCP framework will permit comparison of epigenetic-information systems across cells, developmental stages, and organisms whose methylation machineries differ substantially or are not yet well understood.

High-coverage methylation data of a gene model before and after DNA damage and homologous repair

Genome-wide methylation analysis is limited by its low coverage and the inability to detect single variants below 10%. Quantitative analysis provides accurate information on the extent of methylation of single CpG dinucleotide, but it does not measure the actual polymorphism of the methylation profiles of single molecules. To understand the polymorphism of DNA methylation and to decode the methylation signatures before and after DNA damage and repair, we have deep sequenced in bisulfite-treated DNA a reporter gene undergoing site-specific DNA damage and homologous repair. In this paper, we provide information on the data generation, the rationale for the experiments and the type of assays used, such as cytofluorimetry and immunoblot data derived during a previous work published in Scientific Reports, describing the methylation and expression changes of a model gene (GFP) before and after formation of a double-strand break and repair by homologous-recombination or non-homologous-end-joining. These data provide: 1) a reference for the analysis of methylation polymorphism at selected loci in complex cell populations; 2) a platform and the tools to compare transcription and methylation profiles.

DNA damage and Repair Modify DNA methylation and Chromatin Domain of the Targeted Locus: Mechanism of allele methylation polymorphism

We characterize the changes in chromatin structure, DNA methylation and transcription during and after homologous DNA repair (HR). We find that HR modifies the DNA methylation pattern of the repaired segment. HR also alters local histone H3 methylation as well chromatin structure by inducing DNA-chromatin loops connecting the 5' and 3' ends of the repaired gene. During a two-week period after repair, transcription-associated demethylation promoted by Base Excision Repair enzymes further modifies methylation of the repaired DNA. Subsequently, the repaired genes display stable but diverse methylation profiles. These profiles govern the levels of expression in each clone. Our data argue that DNA methylation and chromatin remodelling induced by HR may be a source of permanent variation of gene expression in somatic cells.

Big data analysis using modern statistical and machine learning methods in medicine

In this article we introduce modern statistical machine learning and bioinformatics approaches that have been used in learning statistical relationships from big data in medicine and behavioral science that typically include clinical, genomic (and proteomic) and environmental variables. Every year, data collected from biomedical and behavioral science is getting larger and more complicated. Thus, in medicine, we also need to be aware of this trend and understand the statistical tools that are available to analyze these datasets. Many statistical analyses that are aimed to analyze such big datasets have been introduced recently. However, given many different types of clinical, genomic, and environmental data, it is rather uncommon to see statistical methods that combine knowledge resulting from those different data types. To this extent, we will introduce big data in terms of clinical data, single nucleotide polymorphism and gene expression studies and their interactions with environment. In this article, we will introduce the concept of well-known regression analyses such as linear and logistic regressions that has been widely used in clinical data analyses and modern statistical models such as Bayesian networks that has been introduced to analyze more complicated data. Also we will discuss how to represent the interaction among clinical, genomic, and environmental data in using modern statistical models. We conclude this article with a promising modern statistical method called Bayesian networks that is suitable in analyzing big data sets that consists with different type of large data from clinical, genomic, and environmental data. Such statistical model form big data will provide us with more comprehensive understanding of human physiology and disease.

The dynamics of DNA methylation fidelity during mouse embryonic stem cell self-renewal and differentiation

The faithful transmission of DNA methylation patterns through cell divisions is essential for the daughter cells to retain a proper cell identity. To achieve a comprehensive assessment of methylation fidelity, we implemented a genome-scale hairpin bisulfite sequencing approach to generate methylation data for DNA double strands simultaneously. We show here that methylation fidelity increases globally during differentiation of mouse embryonic stem cells (mESCs), and is particularly high in the promoter regions of actively expressed genes and positively correlated with active histone modification marks and binding of transcription factors. The majority of intermediately (40%–60%) methylated CpG dinucleotides are hemi-methylated and have low methylation fidelity, particularly in the differentiating mESCs. While 5-hmC and 5-mC tend to coexist, there is no significant correlation between 5-hmC levels and methylation fidelity. Our findings may shed new light on our understanding of the origins of methylation variations and the mechanisms underlying DNA methylation transmission.

Targeted DNA methylation by homology-directed repair in mammalian cells. Transcription reshapes methylation on the repaired gene

We report that homology-directed repair of a DNA double-strand break within a single copy Green Fluorescent Protein (GFP) gene in HeLa cells alters the methylation pattern at the site of recombination. DNA methyl transferase (DNMT)1, DNMT3a and two proteins that regulate methylation, Np95 and GADD45A, are recruited to the site of repair and are responsible for selective methylation of the promoter-distal segment of the repaired DNA. The initial methylation pattern of the locus is modified in a transcription-dependent fashion during the 15–20 days following repair, at which time no further changes in the methylation pattern occur. The variation in DNA modification generates stable clones with wide ranges of GFP expression. Collectively, our data indicate that somatic DNA methylation follows homologous repair and is subjected to remodeling by local transcription in a discrete time window during and after the damage. We propose that DNA methylation of repaired genes represents a DNA damage code and is source of variation of gene expression.

Statistical inference of in vivo properties of human DNA methyltransferases from double-stranded methylation patterns

DNA methyltransferases establish methylation patterns in cells and transmit these patterns over cell generations, thereby influencing each cell's epigenetic states. Three primary DNA methyltransferases have been identified in mammals: DNMT1, DNMT3A and DNMT3B. Extensive in vitro studies have investigated key properties of these enzymes, namely their substrate specificity and processivity. Here we study these properties in vivo, by applying novel statistical analysis methods to double-stranded DNA methylation patterns collected using hairpin-bisulfite PCR. Our analysis fits a novel Hidden Markov Model (HMM) to the observed data, allowing for potential bisulfite conversion errors, and yields statistical estimates of parameters that quantify enzyme processivity and substrate specificity. We apply this model to methylation patterns established in vivo at three loci in humans: two densely methylated inactive X (Xi)-linked loci (FMR1 and G6PD), and an autosomal locus (LEP), where methylation densities are tissue-specific but moderate. We find strong evidence for a high level of processivity of DNMT1 at FMR1 and G6PD, with the mean association tract length being a few hundred base pairs. Regardless of tissue types, methylation patterns at LEP are dominated by DNMT1 maintenance events, similar to the two Xi-linked loci, but are insufficiently informative regarding processivity to draw any conclusions about processivity at that locus. At all three loci we find that DNMT1 shows a strong preference for adding methyl groups to hemi-methylated CpG sites over unmethylated sites. The data at all three loci also suggest low (possibly 0) association of the de novo methyltransferases, the DNMT3s, and are consequently uninformative about processivity or preference of these enzymes. We also extend our HMM to reanalyze published data on mouse DNMT1 activities in vitro. The results suggest shorter association tracts (and hence weaker processivity), and much longer non-association tracts than human DNMT1 in vivo.

Assisted reproduction treatment and epigenetic inheritance

BACKGROUND

The subject of epigenetic risk of assisted reproduction treatment (ART), initiated by reports on an increase of children with the Beckwith–Wiedemann imprinting disorder, is very topical. Hence, there is a growing literature, including mouse studies.

METHODS

In order to gain information on transgenerational epigenetic inheritance and epigenetic effects induced by ART, literature databases were searched for papers on this topic using relevant keywords.

RESULTS

At the level of genomic imprinting involving CpG methylation, ART-induced epigenetic defects are convincingly observed in mice, especially for placenta, and seem more frequent than in humans. Data generally provide a warning as to the use of ovulation induction and in vitro culture. In human sperm from compromised spermatogenesis, sequence-specific DNA hypomethylation is observed repeatedly. Transmittance of sperm and oocyte DNA methylation defects is possible but, as deduced from the limited data available, largely prevented by selection of gametes for ART and/or non-viability of the resulting embryos. Some evidence indicates that subfertility itself is a risk factor for imprinting diseases. As in mouse, physiological effects from ART are observed in humans.

In the human, indications for a broader target for changes in CpG methylation than imprinted DNA sequences alone have been found. In the mouse, a broader range of CpG sequences has not yet been studied. Also, a multigeneration study of systematic ART on epigenetic parameters is lacking.

CONCLUSIONS

The field of epigenetic inheritance within the lifespan of an individual and between generations (via mitosis and meiosis, respectively) is growing, driven by the expansion of chromatin research. ART can induce epigenetic variation that might be transmitted to the next generation.

STATISTICAL INFERENCE OF TRANSMISSION FIDELITY OF DNA METHYLATION PATTERNS OVER SOMATIC CELL DIVISIONS IN MAMMALS

We develop Bayesian inference methods for a recently-emerging type of epigenetic data to study the transmission fidelity of DNA methylation patterns over cell divisions. The data consist of parent-daughter double-stranded DNA methylation patterns with each pattern coming from a single cell and represented as an unordered pair of binary strings. The data are technically difficult and time-consuming to collect, putting a premium on an efficient inference method. Our aim is to estimate rates for the maintenance and de novo methylation events that gave rise to the observed patterns, while accounting for measurement error. We model data at multiple sites jointly, thus using whole-strand information, and considerably reduce confounding between parameters. We also adopt a hierarchical structure that allows for variation in rates across sites without an explosion in the effective number of parameters. Our context-specific priors capture the expected stationarity, or near-stationarity, of the stochastic process that generated the data analyzed here. This expected stationarity is shown to greatly increase the precision of the estimation. Applying our model to a data set collected at the human FMR1 locus, we find that measurement errors, generally ignored in similar studies, occur at a non-trivial rate (inappropriate bisulfite conversion error: 1.6% with 80% CI: 0.9-2.3%). Accounting for these errors has a substantial impact on estimates of key biological parameters. The estimated average failure of maintenance rate and daughter de novo rate decline from 0.04 to 0.024 and from 0.14 to 0.07, respectively, when errors are accounted for. Our results also provide evidence that de novo events may occur on both parent and daughter strands: the median parent and daughter de novo rates are 0.08 (80% CI: 0.04-0.13) and 0.07 (80% CI: 0.04-0.11), respectively.