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

Current Trends, Advances, and Challenges of Tissue Engineering-Based Approaches of Tooth Regeneration: A Review of the Literature

INTRODUCTION

Tooth loss is a significant health issue. Currently, this situation is often treated with the use of synthetic materials such as implants and prostheses. However, these treatment modalities do not fully meet patients' biological and mechanical needs and have limited longevity. Regenerative medicine focuses on the restoration of patients' natural tissues via tissue engineering techniques instead of rehabilitating with artificial appliances. Therefore, a tissue-engineered tooth regeneration strategy seems like a promising option to treat tooth loss.

OBJECTIVE

This review aims to demonstrate recent advances in tooth regeneration strategies and discoveries about underlying mechanisms and pathways of tooth formation.

RESULTS AND DISCUSSION

Whole tooth regeneration, tooth root formation, and dentin-pulp organoid generation have been achieved by using different seed cells and various materials for scaffold production. Bioactive agents are critical elements for the induction of cells into odontoblast or ameloblast lineage. Some substantial pathways enrolled in tooth development have been figured out, helping researchers design their experiments more effectively and aligned with the natural process of tooth formation.

CONCLUSION

According to current knowledge, tooth regeneration is possible in case of proper selection of stem cells, appropriate design and manufacturing of a biocompatible scaffold, and meticulous application of bioactive agents for odontogenic induction. Understanding innate odontogenesis pathways play a crucial role in accurately planning regenerative therapeutic interventions in order to reproduce teeth.

Promoter methylation in a mixed feedback loop circadian clock model

We investigate how epigenetic modifications to clock gene promoters affect transcriptomic activity in the circadian clock. Motivated by experimental observations that link DNA methylation with the behavior of the clock, we introduce and analyze an extension of the mixed feedback loop (MFL) model of François and Hakim. We extend the original model to include an additional methylated promoter state and allow for reversible protein sequestration, an important feature for circadian applications. First, working with the general form of the MFL model, we find that the qualitative behavior of the model is dictated by the promoter state with the highest transcription rate. We then build on the work of Kim and Forger, who analyzed the stability of the mammalian circadian clock by using a reduced form of the MFL model. We present a rigorous procedure for translating between the MFL model and the reduction of Kim and Forger. We then propose a model reduction more appropriate for the study of oscillatory promoter states, making use of a fully coupled quasi-steady-state approximation rather than the standard partially uncoupled quasi-steady-state approach. Working with the novel reduced form of the model, we find substantial differences in the transcription function and show that, although methylation contributes to period control, excessive methylation can abolish rhythmicity. Altogether our results show that even in a minimal clock model, DNA methylation has a nontrivial influence on the system's ability to oscillate.

Cluster mean-field theory accurately predicts statistical properties of large-scale DNA methylation patterns

The accurate establishment and maintenance of DNA methylation patterns is vital for mammalian development and disruption to these processes causes human disease. Our understanding of DNA methylation mechanisms has been facilitated by mathematical modelling, particularly stochastic simulations. Megabase-scale variation in DNA methylation patterns is observed in development, cancer and ageing and the mechanisms generating these patterns are little understood. However, the computational cost of stochastic simulations prevents them from modelling such large genomic regions. Here, we test the utility of three different mean-field models to predict summary statistics associated with large-scale DNA methylation patterns. By comparison to stochastic simulations, we show that a cluster mean-field model accurately predicts the statistical properties of steady-state DNA methylation patterns, including the mean and variance of methylation levels calculated across a system of CpG sites, as well as the covariance and correlation of methylation levels between neighbouring sites. We also demonstrate that a cluster mean-field model can be used within an approximate Bayesian computation framework to accurately infer model parameters from data. As mean-field models can be solved numerically in a few seconds, our work demonstrates their utility for understanding the processes underpinning large-scale DNA methylation patterns.

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.

Melatonin-induced suppression of DNA methylation promotes odontogenic differentiation in human dental pulp cells

Differentiation potency of human dental pulp cells (hDPCs) is essential for dentin regeneration. DNA methylation is one of the major epigenetic mechanisms and is suggested to involve in differentiation of hDPCs, the machinery of which includes DNA methyltransferase enzymes (DNMTs) and methyl-CpG-binding domain proteins (MBDs). Our previous study has found that melatonin (MT) promoted hDPC differentiation, but its mechanism remains elusive. We aimed to investigate the role of DNA methylation in the promotion of MT to differentiation of hDPCs in vitro. hDPCs were cultured in basal growth medium (CO) or odontogenic medium (OM) exposed to MT at different concentrations (0, 10-12, 10-10, 10-8, 10-6, 10-4 M). The cell growth was analyzed using Cell Counting Kit-8 assay, and mineralized tissue formation was measured using Alizarin red staining. The expression of the 10 genes (DNMT1, DNMT3A, DNMT3B, MBD1-6, MeCP2) was determined using real-time qPCR and western blotting. The abundance of MeCP2 in the nuclei was evaluated using immunofluorescence analysis. Global methylation level was tested using ELISA. We found that mineralized tissue formation significantly increased in OM with MT at 10-4 M, while the levels of MeCP2 and global DNA methylation level declined. The expression of MBD1, MBD3, and MBD4 significantly increased in OM alone, and the expession of DNMT1 and MBD2 was decreased. These results indicate that MT promotes odontogenic differentiation of hDPCs in vitro by regulating the levels of DNMT1, MeCP2, and global DNA methylation, suggesting that MT-induced DNA methylation machinery may play an important role in tooth regeneration.