Information Thermodynamics of Cytosine DNA Methylation

Abstract

Cytosine DNA methylation (CDM) is a stable epigenetic modification to the genome and a widespread regulatory process in living organisms that involves multicomponent molecular machines. Genome-wide cytosine methylation patterning participates in the epigenetic reprogramming of a cell, suggesting that the biological information contained within methylation positions may be amenable to decoding. Adaptation to a new cellular or organismal environment also implies the potential for genome-wide redistribution of CDM changes that will ensure the stability of DNA molecules. This raises the question of whether or not we would be able to sort out the regulatory methylation signals from the CDM background (“noise”) induced by thermal fluctuations. Here, we propose a novel statistical and information thermodynamic description of the CDM changes to address the last question. The physical basis of our statistical mechanical model was evaluated in two respects: 1) the adherence to Landauer’s principle, according to which molecular machines must dissipate a minimum energy ε = kBT ln2 at each logic operation, where kB is the Boltzmann constant, and T is the absolute temperature and 2) whether or not the binary stretch of methylation marks on the DNA molecule comprise a language of sorts, properly constrained by thermodynamic principles. The study was performed for genome-wide methylation data from 152 ecotypes and 40 trans-generational variations of Arabidopsis thaliana and 93 human tissues. The DNA persistence length, a basic mechanical property altered by CDM, was estimated with values from 39 to 66.9 nm. Classical methylome analysis can be retrieved by applying information thermodynamic modelling, which is able to discriminate signal from noise. Our finding suggests that the CDM signal comprises a language scheme properly constrained by molecular thermodynamic principles, which is part of an epigenomic communication system that obeys the same thermodynamic rules as do current human communication systems.

Genome-wide Discriminatory Information Patterns of Cytosine DNA Methylation

“…In this scenario, genome-wide CDM methylation changes would not show strong correlation with SNPs unless filtering the signal from the methylation background noise. However, the reported correlation of 0.4 in reference [11] is not too small and it is significant. The reason for that resides in that this correlation was not derived from arbitrary genome-wide methylation data, but from DMPs in the CG methylation context; i.e., the original data was previously filtered.…”

“…The physical basis of the amount of information I R has been described in the Introduction and is documented in a recent publication [11]. The physical foundation of LC R is given in Section 4.4.…”

“…The challenge is to sort out the regulatory methylation signals from the CDM noise induced by thermal fluctuations and random mutational events. This signal-to-noise challenge has already been confronted (see [11,[55][56][57]), and a concrete application in the context of CDM is illustrated in Figure 10. It is not possible to fully separate the regulatory methylation signal from the CDM background; even a simple regulatory methylation change could alter the mechanical properties of the DNA molecule [2,33,36] and thus could require additional local readjustment.…”

Example of Methylome Analysis with MethylIT using Cancer Datasets

“…MethylIT is an R package for methylome analysis based on information thermodynamics and signal detection. The information thermodynamics-based approach is postulated to provide greater sensitivity for resolving true signal from the thermodynamic background within the methylome (Sanchez and Mackenzie 2016). Because the biological signal created within the dynamic methylome environment characteristic of plants is not free from background noise, the approach, designated Methyl-IT, includes the application of signal detection theory (Greiner, Pfeiffer, and Smith 2000;Carter et al 2016;Harpaz et al 2013;Kruspe et al 2017).…”

“…A basic requirement for the application of signal detection is the knowledge of the probability distribution of the background noise. Probability distribution, as a Weibull distribution model, can be deduced on a statistical mechanical/thermodynamics basis for DNA methylation induced by thermal fluctuations (Sanchez and Mackenzie 2016). Assuming that this background methylation variation is consistent with a Poisson process, it can be distinguished from variation associated with methylation regulatory machinery, which is non-independent for all genomic regions (Sanchez and Mackenzie 2016).…”