Computational micromodel for epigenetic mechanisms

Definition and characterization of the role of Epigenetic mechanisms have gained immense momentum since the completion of the Human Genome Project. The human epigenetic layer, made up of DNA methylation and multiple histone protein modifications, (the key elements of epigenetic mechanisms), is known to act as a switchboard that regulates the occurrence of most cellular events. In multicellular organisms such as humans, all cells have identical genomic contents but vary in DNA Methylation (DM) profile with the result that different types of cells perform a spectrum of functions. DM within the genome is associated with tight control of gene expression, parental imprinting, X-chromosome inactivation, long-term silencing of repetitive elements and chromatin condensation. Recently, considerable evidence has been put forward to demonstrate that environmental stress implicitly alters normal interactions among key epigenetic elements inside the genome. Aberrations in the spread of DM especially hypo/hyper methylation supported by an abnormal landscape of histone modifications have been strongly associated with Cancer initiation and development. While new findings on the impact of these key elements are reported regularly, precise information on how DM is controlled and its relation to networks of histone modifications is lacking. This has motivated modelling of DNA methylation and histone modifications and their interdependence. We describe initial computational methods used to investigate these key elements of epigenetic change, and to assess related information contained in DNA sequence patterns. We then describe attempts to develop a phenomenological epigenetic "micromodel", based on Markov-Chain Monte Carlo principles. This theoretical micromodel ("EpiGMP") aims to explore the effect of histome modifications and gene expression for defined levels of DNA methylation. We apply this micromodel to (i) test networks of genes in colon cancer (extracted from an in-house database, StatEpigen), and (ii) to help define an agent-based modelling framework to explore chromatin remodelling (or the dynamics of physical rearrangements), inside the human genome. Parallelization techniques to address issues of scale during the application of this micromodel have been adopted as well. A generic tool of this kind can potentially be applied to predict molecular events that affect the state of expression of any gene during the onset or progress of cancer. Ultimately, the goal is to provide additional information on ways in which these low level molecular changes determine physical traits for mormal and disease conditions in an organism

Computational Micromodel for Epigenetic Mechanisms

Characterization of the epigenetic profile of humans since the initial breakthrough on the human genome project has strongly established the key role of histone modifications and DNA methylation. These dynamic elements interact to determine the normal level of expression or methylation status of the constituent genes in the genome. Recently, considerable evidence has been put forward to demonstrate that environmental stress implicitly alters epigenetic patterns causing imbalance that can lead to cancer initiation. This chain of consequences has motivated attempts to computationally model the influence of histone modification and DNA methylation in gene expression and investigate their intrinsic interdependency. In this paper, we explore the relation between DNA methylation and transcription and characterize in detail the histone modifications for specific DNA methylation levels using a stochastic approach

Computational Micromodel for Epigenetic Mechanisms

Characterization of the epigenetic profile of humans since the initial breakthrough on the human genome project has strongly established the key role of histone modifications and DNA methylation. These dynamic elements interact to determine the normal level of expression or methylation status of the constituent genes in the genome. Recently, considerable evidence has been put forward to demonstrate that environmental stress implicitly alters epigenetic patterns causing imbalance that can lead to cancer initiation. This chain of consequences has motivated attempts to computationally model the influence of histone modification and DNA methylation in gene expression and investigate their intrinsic interdependency. In this paper, we explore the relation between DNA methylation and transcription and characterize in detail the histone modifications for specific DNA methylation levels using a stochastic approach

Computational analysis of epigenetic information in human DNA sequences

Over the last few years, investigations of human epigenetic profiles have identified key elements of change to be Histone Modifications, stable and heritable DNA methylation and Chromatin remodeling. These factors determine gene expression levels and characterise conditions leading to disease. In order to extract information embedded in long DNA sequences, data mining and pattern recognition tools are widely used, but efforts have been limited to date with respect to analyzing epigenetic changes, and their role as catalysts in disease onset. Useful insight, however, can be gained by investigation of associated dinucleotide distributions. The focus of this paper is to explore specific dinucleotides frequencies across defined regions within the human genome, and to identify new patterns between epigenetic mechanisms and DNA content. Signal processing methods, including Fourier and Wavelet Transformations, are employed and principal results are reported

Analysis of Average percentage visits of H3 histone states containing <i>Lysine Methylation i.e. K5</i> in 16 promoters after 5000 iterations (for high levels of DNA Methylation (<0.85 or 85%)).

<p>States containing Lysine Methylation are visited the most. Hence we analyse the average of percentage visitation of model to all other modifications (except Lysine Methylation K5) during the simulation. Each unit in the X-axis represents an amino acid–position in H3 array–number of Amino acids changeable–Modification. The Y axis elaborates on the average percentage visitation of H3 states that contain the modification given on the X axis.</p

Analysis of Average percentage visits of H3 histone states containing <i>Lysine Acetylation .i.e K6</i> in 16 promoters after 5000 iterations (for low levels of DNA Methylation (<0.15 or 15%)).

<p>States containing Lysine acetylation are visited the most. Hence we analyse the the average of percentage visitation of model to all other modifications (except Lysine Acetylation - K6 in Figure) during the simulation. Here each unit in the X-axis represents an amino acid–position in H3 array–number of Amino acids–Modification possible. The Y axis elaborates on the average percentage visitation of H3 states that contain the modification depicted in each unit of X axis.</p

Evolution of H4 (H4-1 and H4-2) histone states in the 16 promoters for 10 different datasets during high DNA methylation levels (<0.85 or 85%).

<p>H4-1 and H4-2 histone states were tested with 10 dataset of random probability values (represented by colors in the graph).</p

A Comparison between the average (of all 20 test results obtained for H4-1 and H4-2) preferences of H4 states for high and low DNA Methylation Levels.

<p>Error bars represent the standard deviation calculated from the total number of visits, for every H4 histone state (occupancy) during the simulation.</p

General representation of histone <i>states</i> in our model.

<p>The number of modifiable amino acids chosen for each histone type differs. In general, each modification is encoded as a number - Acetylation as “1”, Methylation as “2”, Phosphorylation as “3” and no modifications as “0”. The string of numbers or the current Histone <i>state</i> represents the possible combination of modifications within that particular histone type.</p