A dynamic multi-compartmental model of DNA methylation with demonstrable predictive value in hematological malignancies

A mathematical model which examines age-related stochastic fluctuations in DNA maintenance methylation

Due to its complexity and its ubiquitous nature the ageing process remains an enduring biological puzzle. Many molecular mechanisms and biochemical process have become synonymous with ageing. However, recent findings have pinpointed epigenetics as having a key role in ageing and healthspan. In particular age related changes to DNA methylation offer the possibility of monitoring the trajectory of biological ageing and could even be used to predict the onset of diseases such as cancer, Alzheimer's disease and cardiovascular disease. At the molecular level emerging evidence strongly suggests the regulatory processes which govern DNA methylation are subject to intracellular stochasticity. It is challenging to fully understand the impact of stochasticity on DNA methylation levels at the molecular level experimentally. An ideal solution is to use mathematical models to capture the essence of the stochasticity and its outcomes. In this paper we present a novel stochastic model which accounts for specific methylation levels within a gene promoter. Uncertainty of the eventual site-specific methylation levels for different values of methylation age, depending on the initial methylation levels were analysed. Our model predicts the observed bistable levels in CpG islands. In addition, simulations with various levels of noise indicate that uncertainty predominantly spreads through the hypermethylated region of stability, especially for large values of input noise. A key outcome of the model is that CpG islands with high to intermediate methylation levels tend to be more susceptible to dramatic DNA methylation changes due to increasing methylation age.

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

BACKGROUND

During mammalian early embryogenesis, expression and epigenetic heterogeneity emerge before the first cell fate determination, but the programs causing such determinate heterogeneity are largely unexplored.

RESULTS

Here, we present MethylTransition, a novel DNA methylation state transition model, for characterizing methylation changes during one or a few cell cycles at single-cell resolution. MethylTransition involves the creation of a transition matrix comprising three parameters that represent the probabilities of DNA methylation-modifying activities in order to link the methylation states before and after a cell cycle. We apply MethylTransition to single-cell DNA methylome data from human pre-implantation embryogenesis and elucidate that the DNA methylation heterogeneity that emerges at promoters during this process is largely an intrinsic output of a program with unique probabilities of DNA methylation-modifying activities. Moreover, we experimentally validate the effect of the initial DNA methylation on expression heterogeneity in pre-implantation mouse embryos.

CONCLUSIONS

Our study reveals the programmed DNA methylation heterogeneity during human pre-implantation embryogenesis through a novel mathematical model and provides valuable clues for identifying the driving factors of the first cell fate determination during this process.

Brain aging and garbage cleaning : Modelling the role of sleep, glymphatic system, and microglia senescence in the propagation of inflammaging

Brain aging is a complex process involving many functions of our body and described by the interplay of a sleep pattern and changes in the metabolic waste concentration regulated by the microglial function and the glymphatic system. We review the existing modelling approaches to this topic and derive a novel mathematical model to describe the crosstalk between these components within the conceptual framework of inflammaging. Analysis of the model gives insight into the dynamics of garbage concentration and linked microglial senescence process resulting from a normal or disrupted sleep pattern, hence, explaining an underlying mechanism behind healthy or unhealthy brain aging. The model incorporates accumulation and elimination of garbage, induction of glial activation by garbage, and glial senescence by over-activation, as well as the production of pro-inflammatory molecules by their senescence-associated secretory phenotype (SASP). Assuming that insufficient sleep leads to the increase of garbage concentration and promotes senescence, the model predicts that if the accumulation of senescent glia overcomes an inflammaging threshold, further progression of senescence becomes unstoppable even if a normal sleep pattern is restored. Inverting this process by "rejuvenating the brain" is only possible via a reset of concentration of senescent glia below this threshold. Our model approach enables analysis of space-time dynamics of senescence, and in this way, we show that heterogeneous patterns of inflammation will accelerate the propagation of senescence profile through a network, confirming a negative effect of heterogeneity.

A Mathematical Model for Inheritance of DNA Methylation Patterns in Somatic Cells

DNA methylation is an essential epigenetic mechanism used by cells to regulate gene expression. Interestingly, DNA replication, a function necessary for cell division, disrupts the methylation pattern. Since perturbed methylation patterns are associated with aberrant gene expression and many diseases, including cancer, restoration of the correct pattern following DNA replication is crucial. However, the exact mechanisms of this restoration remain under investigation. DNA methyltransferases (Dnmts) perform methylation by adding a methyl group to cytosines at CpG sites in the DNA. These CpG sites are found in regions of high density, termed CpG islands (CGIs), and regions of low density in the genome. Nearly, every CpG site in a CGI has the same state, either methylated or unmethylated, and almost all CpG sites in regions of low CpG density are methylated. We propose a stochastic model for the dynamics of the post-replicative restoration of methylation patterns. The model considers the recruitment of Dnmts and demethylating enzymes to regions of hyper- and hypomethylation, respectively. The model also includes the interaction between Dnmt1 and PCNA, an enzyme that localizes Dnmt1 to the replication complex. Using our model, we predict that the methylation of regions of DNA can be bistable. Further, we predict that recruitment mechanisms maintain methylation in CGIs, whereas the Dnmt1-PCNA interaction maintains methylation in low-density regions.

Stochastic modeling reveals kinetic heterogeneity in post-replication DNA methylation

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 genome-wide 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.

Computational modelling folate metabolism and DNA methylation: implications for understanding health and ageing

Dietary folates have a key role to play in health, as deficiencies in the intake of these B vitamins have been implicated in a wide variety of clinical conditions. The reason for this is folates function as single carbon donors in the synthesis of methionine and nucleotides. Moreover, folates have a vital role to play in the epigenetics of mammalian cells by supplying methyl groups for DNA methylation reactions. Intriguingly, a growing body of experimental evidence suggests that DNA methylation status could be a central modulator of the ageing process. This has important health implications because the methylation status of the human genome could be used to infer age-related disease risk. Thus, it is imperative we further our understanding of the processes which underpin DNA methylation and how these intersect with folate metabolism and ageing. The biochemical and molecular mechanisms, which underpin these processes, are complex. However, computational modelling offers an ideal framework for handling this complexity. A number of computational models have been assembled over the years, but to date, no model has represented the full scope of the interaction between the folate cycle and the reactions, which governs the DNA methylation cycle. In this review, we will discuss several of the models, which have been developed to represent these systems. In addition, we will present a rationale for developing a combined model of folate metabolism and the DNA methylation cycle.

Data-driven selection and parameter estimation for DNA methylation mathematical models

Epigenetics is coming to the fore as a key process which underpins health. In particular emerging experimental evidence has associated alterations to DNA methylation status with healthspan and aging. Mammalian DNA methylation status is maintained by an intricate array of biochemical and molecular processes. It can be argued changes to these fundamental cellular processes ultimately drive the formation of aberrant DNA methylation patterns, which are a hallmark of diseases, such as cancer, Alzheimer's disease and cardiovascular disease. In recent years mathematical models have been used as effective tools to help advance our understanding of the dynamics which underpin DNA methylation. In this paper we present linear and nonlinear models which encapsulate the dynamics of the molecular mechanisms which define DNA methylation. Applying a recently developed Bayesian algorithm for parameter estimation and model selection, we are able to estimate distributions of parameters which include nominal parameter values. Using limited noisy observations, the method also identified which methylation model the observations originated from, signaling that our method has practical applications in identifying what models best match the biological data for DNA methylation.

Modeling Methylation Patterns with Long Read Sequencing Data

Variation in cytosine methylation at CpG dinucleotides is often observed in genomic regions, and analysis typically focuses on estimating the proportion of methylated sites observed in a given region and comparing these levels across samples to determine association with conditions of interest. While sites are tacitly treated as independent, when observed at the level of individual molecules methylation patterns exhibit strong evidence of local spatial dependence. We previously developed a neighboring sites model to account for correlation and clustering behavior observed in two tandem repeat regions in a collection of ovarian carcinomas. We now introduce extensions of the model that account for the effect of distance between sites as well as asymmetric correlation in de novo methylation and demethylation rates. We apply our models to published data from a whole genome bisulfite sequencing experiment using long reads, estimating model parameters for a selection of CpG-dense regions spanning between 21 and 67 sites. Our methods detect evidence of local spatial correlation as a function of site-to-site distance and demonstrate the added value of employing long read sequencing data in epigenetic research.

Modelling the molecular mechanisms of aging

The aging process is driven at the cellular level by random molecular damage that slowly accumulates with age. Although cells possess mechanisms to repair or remove damage, they are not 100% efficient and their efficiency declines with age. There are many molecular mechanisms involved and exogenous factors such as stress also contribute to the aging process. The complexity of the aging process has stimulated the use of computational modelling in order to increase our understanding of the system, test hypotheses and make testable predictions. As many different mechanisms are involved, a wide range of models have been developed. This paper gives an overview of the types of models that have been developed, the range of tools used, modelling standards and discusses many specific examples of models that have been grouped according to the main mechanisms that they address. We conclude by discussing the opportunities and challenges for future modelling in this field.

Impairment of DNA Methylation Maintenance Is the Main Cause of Global Demethylation in Naive Embryonic Stem Cells

Global demethylation is part of a conserved program of epigenetic reprogramming to naive pluripotency. The transition from primed hypermethylated embryonic stem cells (ESCs) to naive hypomethylated ones (serum-to-2i) is a valuable model system for epigenetic reprogramming. We present a mathematical model, which accurately predicts global DNA demethylation kinetics. Experimentally, we show that the main drivers of global demethylation are neither active mechanisms (Aicda, Tdg, and Tet1-3) nor the reduction of de novo methylation. UHRF1 protein, the essential targeting factor for DNMT1, is reduced upon transition to 2i, and so is recruitment of the maintenance methylation machinery to replication foci. Concurrently, there is global loss of H3K9me2, which is needed for chromatin binding of UHRF1. These mechanisms synergistically enforce global DNA hypomethylation in a replication-coupled fashion. Our observations establish the molecular mechanism for global demethylation in naive ESCs, which has key parallels with those operating in primordial germ cells and early embryos.

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Impaired DNA methylation maintenance is the cause of global demethylation in naive ESCs

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Loss of H3K9me2 and UHRF1 lead to impaired maintenance targeting to replication foci

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TET enzymes are not required for global demethylation

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Mathematical model accurately predicts global 5mC and 5hmC during epigenetic resetting

Aging and computational systems biology

Aging research is undergoing a paradigm shift, which has led to new and innovative methods of exploring this complex phenomenon. The systems biology approach endeavors to understand biological systems in a holistic manner, by taking account of intrinsic interactions, while also attempting to account for the impact of external inputs, such as diet. A key technique employed in systems biology is computational modeling, which involves mathematically describing and simulating the dynamics of biological systems. Although a large number of computational models have been developed in recent years, these models have focused on various discrete components of the aging process, and to date no model has succeeded in completely representing the full scope of aging. Combining existing models or developing new models may help to address this need and in so doing could help achieve an improved understanding of the intrinsic mechanisms which underpin aging.

Epigenetic control of HIV-1 post integration latency: implications for therapy

With the development of effective combined anti-retroviral therapy (cART), there is significant reduction in deaths associated with human immunodeficiency virus type 1 (HIV-1) infection. However, the complete cure of HIV-1 infection is difficult to achieve without the elimination of latent reservoirs which exist in the infected individuals even under cART regimen. These latent reservoirs established during early infection have long life span, include resting CD4⁺ T cells, macrophages, central nervous system (CNS) resident macrophage/microglia, and gut-associated lymphoid tissue/macrophages, and can actively produce virus upon interruption of the cART. Several epigenetic and non-epigenetic mechanisms have been implicated in the regulation of viral latency. Epigenetic mechanisms such as histone post translational modifications (e.g., acetylation and methylation) and DNA methylation of the proviral DNA and microRNAs are involved in the establishment of HIV-1 latency. The better understanding of epigenetic mechanisms modulating HIV-1 latency could give clues for the complete eradication of these latent reservoirs. Several latency-reversing agents (LRA) have been found effective in reactivating HIV-1 reservoirs in vitro, ex vivo, and in vivo. Some of these agents target epigenetic modifications to elicit viral expression in order to kill latently infected cells through viral cytopathic effect or host immune response. These therapeutic approaches aimed at achieving a sterilizing cure (elimination of HIV-1 from the human body). In the present review, we will discuss our current understanding of HIV-1 epigenomics and how this information can be moved from the laboratory bench to the patient’s bedside.

The hypomethylating agent Decitabine causes a paradoxical increase in 5-hydroxymethylcytosine in human leukemia cells

The USFDA approved "epigenetic drug", Decitabine, exerts its effect by hypomethylating DNA, demonstrating the pivotal role aberrant genome-wide DNA methylation patterns play in cancer ontology. Using sensitive technologies in a cellular model of Acute Myeloid Leukemia, we demonstrate that while Decitabine reduces the global levels of 5-methylcytosine (5mC), it results in paradoxical increase of 5-hydroxymethylcytosine (5hmC), 5-formylcytosine (5fC) and 5-carboxylcytosine (5caC) levels. Hitherto, the only biological mechanism known to generate 5hmC, 5fC and 5caC, involving oxidation of 5mC by members of Ten-Eleven-Translocation (TET) dioxygenase family, was not observed to undergo any alteration during DAC treatment. Using a multi-compartmental model of DNA methylation, we show that partial selectivity of TET enzymes for hemi-methylated CpG dinucleotides could lead to such alterations in 5hmC content. Furthermore, we investigated the binding of TET1-catalytic domain (CD)-GFP to DNA by Fluorescent Correlation Spectroscopy in live cells and detected the gradual increase of the DNA bound fraction of TET1-CD-GFP after treatment with Decitabine. Our study provides novel insights on the therapeutic activity of DAC in the backdrop of the newly discovered derivatives of 5mC and suggests that 5hmC has the potential to serve as a biomarker for monitoring the clinical success of patients receiving DAC.

Association between MTHFR C677T polymorphism and risk of acute lymphoblastic leukemia: a meta-analysis based on 51 case-control studies

Background

Studies and systematic reviews have reached inconsistent conclusions on the role of 5, 10-methylenetetrahydrofolate reductase (MTHFR) polymorphism C677T in acute lymphoblastic leukemia (ALL) risk.

Material/Methods

The present meta-analysis comprising of 51 case-control studies, including 7892 cases and 14 280 controls was performed to reevaluate the association between MTHFR C677T polymorphism and ALL risk.

Results

Statistical differences were found in the dominant model (TT+CT vs. CC, odd ratio (OR)=0.89, 95% CI, 0.79–1.00, P=0.04) and the CT vs. CC (OR=0.89, 95% CI, 0.80–1.00, P=0.05), but not in the allele contrast model (T vs. C, OR=0.92, 95% CI, 0.84–1.01, P=0.08), additive model (TT vs. CC, OR=0.87, 95% CI, 0.73–1.05, P=0.15), or recessive model (TT vs. CT+CC, OR=0.94, 95% CI, 0.81–1.10, P=0.44) in overall populations. In the subgroup analyses stratified by age (children and adults) and ethnicity (Asian and Caucasian), no significant associations between MTHFR C677T polymorphism and ALL risk were observed.

Conclusions

The current study found no sufficient evidence of a protective role of MTHFR C677T polymorphism in ALL susceptibility.

Epigenetics and colorectal cancer pathogenesis

Colorectal cancer (CRC) develops through a multistage process that results from the progressive accumulation of genetic mutations, and frequently as a result of mutations in the Wnt signaling pathway. However, it has become evident over the past two decades that epigenetic alterations of the chromatin, particularly the chromatin components in the promoter regions of tumor suppressors and oncogenes, play key roles in CRC pathogenesis. Epigenetic regulation is organized at multiple levels, involving primarily DNA methylation and selective histone modifications in cancer cells. Assessment of the CRC epigenome has revealed that virtually all CRCs have aberrantly methylated genes and that the average CRC methylome has thousands of abnormally methylated genes. Although relatively less is known about the patterns of specific histone modifications in CRC, selective histone modifications and resultant chromatin conformation have been shown to act, in concert with DNA methylation, to regulate gene expression to mediate CRC pathogenesis. Moreover, it is now clear that not only DNA methylation but also histone modifications are reversible processes. The increased understanding of epigenetic regulation of gene expression in the context of CRC pathogenesis has led to development of epigenetic biomarkers for CRC diagnosis and epigenetic drugs for CRC therapy.

Trials with 'epigenetic' drugs: an update

Epigenetic inactivation of pivotal genes involved in correct cell growth is a hallmark of human pathologies, in particular cancer. These epigenetic mechanisms, including crosstalk between DNA methylation, histone modifications and non-coding RNAs, affect gene expression and are associated with disease progression. In contrast to genetic mutations, epigenetic changes are potentially reversible. Re-expression of genes epigenetically inactivated can result in the suppression of disease state or sensitization to specific therapies. Small molecules that reverse epigenetic inactivation, so-called epi-drugs, are now undergoing clinical trials. Accordingly, the Food and Drug Administration (FDA) and the European Medicines Agency (EMA) for cancer treatment have approved some of these drugs. Here, we focus on the biological features of epigenetic molecules, analyzing the mechanism(s) of action and their current use in clinical practice.