System size reduction in stochastic simulations of the facilitated diffusion mechanism.

BACKGROUND: Site-specific Transcription Factors (TFs) are proteins that bind to specific sites on the DNA and control the activity of a target gene by enhancing or decreasing the rate at which the gene is transcribed by RNA polymerase. The process by which TF molecules locate their target sites is a key component of transcriptional regulation. Therefore it is essential to gain insight into the mechanisms by which TFs search for the target sites.Research in this area uses experimental and analytical approaches, but also stochastic simulations of the search process. Previous work based on stochastic simulations focussed only on short sequences, primarily for reasons of technical feasibility. Many of these studies had to disregard possible biases introduced by reducing a genome-wide system to a smaller subsystem. In particular, we identified crucial parameters that require adjustment, which were not adequately changed in these previous studies. RESULTS: We investigated several methods that adequately adapt the parameters of stochastic simulations of the facilitated diffusion, when the full sequence space is reduced to smaller regions of interest. We found two methods that scale the system accordingly: the copy number model and the association rate model. We systematically compared the results produced by simulations of the subsystem with respect to the original system. Our results confirmed that the copy number model is adequate only for high abundance TFs, while for low abundance TFs the association rate model is the only one that reproduces with high accuracy the results of the full system. CONCLUSIONS: We propose a strategy to reduce the size of the system that adequately adapts important parameters to capture the behaviour of the full system. This enables correct simulations of a smaller sequence space (which can be as small as 100 Kbp) and, thus, provides independence from computationally intensive genome-wide simulations of the facilitated diffusion mechanism.RIGHTS : This article is licensed under the BioMed Central licence at http://www.biomedcentral.com/about/license which is similar to the 'Creative Commons Attribution Licence'. In brief you may : copy, distribute, and display the work; make derivative works; or make commercial use of the work - under the following conditions: the original author must be given credit; for any reuse or distribution, it must be made clear to others what the license terms of this work are

GRiP: a computational tool to simulate transcription factor binding in prokaryotes

Motivation: Transcription factors (TFs) are proteins that regulate gene activity by binding to specific sites on the DNA. Understanding the way these molecules locate their target site is of great importance in understanding gene regulation. We developed a comprehensive computational model of this process and estimated the model parameters in (N.R.Zabet and B.Adryan, submitted for publication)

Facilitated diffusion buffers noise in gene expression.

Transcription factors perform facilitated diffusion [three-dimensional (3D) diffusion in the cytosol and 1D diffusion on the DNA] when binding to their target sites to regulate gene expression. Here, we investigated the influence of this binding mechanism on the noise in gene expression. Our results showed that, for biologically relevant parameters, the binding process can be represented by a two-state Markov model and that the accelerated target finding due to facilitated diffusion leads to a reduction in both the mRNA and the protein noise.The following article has been published by Physical Review E. It can be found at: https://journals.aps.org/pre/abstract/10.1103/PhysRevE.90.032701. Copyright 2014 American Physical Society

Homotypic clusters of transcription factor binding sites: A model system for understanding the physical mechanics of gene expression.

The organization of binding sites in cis-regulatory elements (CREs) can influence gene expression through a combination of physical mechanisms, ranging from direct interactions between TF molecules to DNA looping and transient chromatin interactions. The study of simple and common building blocks in promoters and other CREs allows us to dissect how all of these mechanisms work together. Many adjacent TF binding sites for the same TF species form homotypic clusters, and these CRE architecture building blocks serve as a prime candidate for understanding interacting transcriptional mechanisms. Homotypic clusters are prevalent in both bacterial and eukaryotic genomes, and are present in both promoters as well as more distal enhancer/silencer elements. Here, we review previous theoretical and experimental studies that show how the complexity (number of binding sites) and spatial organization (distance between sites and overall distance from transcription start sites) of homotypic clusters influence gene expression. In particular, we describe how homotypic clusters modulate the temporal dynamics of TF binding, a mechanism that can affect gene expression, but which has not yet been sufficiently characterized. We propose further experiments on homotypic clusters that would be useful in developing mechanistic models of gene expression.This is the published version of the manuscript. It was first published by Elsevier in Computational and Structural Biotechnology Journal here: http://www.sciencedirect.com/science/article/pii/S2001037014000142

Современные аспекты правовой защиты персональной информации в РФ

Motivation: Much is now known about the mechanistic details of gene translation. There are also rapid advances in high-throughput technologies to determine quantitative aspects of the system. As a consequence-realistic and system-wide simulation models of translation are now feasible. Such models are also needed as devices to integrate a large volume of highly fragmented data known about translation. Software: In this application note, we present a novel, highly efficient software tool to model translation. The tool represents the main aspects of translation. Features include a representation of exhaustible tRNA pools, ribosome–ribosome interactions and differential initiation rates for different mRNA species. The tool is written in Java, and is hence portable and can be parameterized for any organism. Availability: The model can be obtained from the authors or directly downloaded from the authors' home-page (http://goo.gl/JUWvI)

Chromatin architecture reorganisation during neuronal cell differentiation in Drosophila genome

The organization of the genome into topologically associating domains (TADs) was shown to have a regulatory role in development and cellular functioning, but the mechanism involved in TAD establishment is still unclear. Here, we presented the first high-resolution contact map of Drosophila neuronal cells (BG3) and identified different classes of TADs by comparing this to genome organization in embryonic cells (Kc167). We find that only some TADs are conserved in both cell lines, whereas the rest are cell-specific TADs. This is supported by a change in the enrichment of architectural proteins at TAD borders, with BEAF-32 present in embryonic cells and CTCF in neuronal cells. Furthermore, we observed strong divergent transcription, together with RNA Polymerase II occupancy, and an increase in DNA accessibility at the TAD borders. TAD borders that are specific to neuronal cells are enriched in enhancers controlled by neuronal-specific transcription factors. Our results suggest that TADs are dynamic across developmental stages and reflect the interplay between insulators, transcriptional states and enhancer activities

An explainable artificial intelligence approach for decoding the enhancer histone modifications code and identification of novel enhancers in Drosophila

Background Enhancers are non-coding regions of the genome that control the activity of target genes. Recent efforts to identify active enhancers experimentally and in silico have proven effective. While these tools can predict the locations of enhancers with a high degree of accuracy, the mechanisms underpinning the activity of enhancers are often unclear. Results Using machine learning (ML) and a rule-based explainable artificial intelligence (XAI) model, we demonstrate that we can predict the location of known enhancers in Drosophila with a high degree of accuracy. Most importantly, we use the rules of the XAI model to provide insight into the underlying combinatorial histone modifications code of enhancers. In addition, we identified a large set of putative enhancers that display the same epigenetic signature as enhancers identified experimentally. These putative enhancers are enriched in nascent transcription, divergent transcription and have 3D contacts with promoters of transcribed genes. However, they display only intermediary enrichment of mediator and cohesin complexes compared to previously characterised active enhancers. We also found that 10–15% of the predicted enhancers display similar characteristics to super enhancers observed in other species. Conclusions Here, we applied an explainable AI model to predict enhancers with high accuracy. Most importantly, we identified that different combinations of epigenetic marks characterise different groups of enhancers. Finally, we discovered a large set of putative enhancers which display similar characteristics with previously characterised active enhancers

A comprehensive computational model of facilitated diffusion in prokaryotes

Motivation: Gene activity is mediated by site-specific transcription factors (TFs). Their binding to defined regions in the genome determines the rate at which their target genes are transcribed

DNA sequence properties that predict susceptibility to epiallelic switching.

Transgenerationally heritable epialleles are defined by the stable propagation of alternative transcriptional states through mitotic and meiotic cell cycles. Given that the propagation of DNA methylation at CpG sites, mediated in Arabidopsis by MET1, plays a central role in epigenetic inheritance, we examined genomewide DNA methylation in partial and complete loss-of-function met1 mutants. We interpreted the data in relation to transgenerational epiallelic stability, which allowed us to classify chromosomal targets of epigenetic regulation into (i) single copy and methylated exclusively at CpGs, readily forming epialleles, and (ii) transposon-derived, methylated at all cytosines, which may or may not form epialleles. We provide evidence that DNA sequence features such as density of CpGs and genomic repetitiveness of the loci predispose their susceptibility to epiallelic switching. The importance and predictive power of these genetic features were confirmed by analyses of common epialleles in natural Arabidopsis accessions, epigenetic recombinant inbred lines (epiRILs) and also verified in rice.Transgenerationally heritable epialleles are defined by the stable propagation of alternative transcriptional states through mitotic and meiotic cell cycles. Given that the propagation of DNA methylation at CpG sites, mediated in Arabidopsis by MET1, plays a central role in epigenetic inheritance, we examined genomewide DNA methylation in partial and complete loss‐of‐function met1 mutants. We interpreted the data in relation to transgenerational epiallelic stability, which allowed us to classify chromosomal targets of epigenetic regulation into (i) single copy and methylated exclusively at CpGs, readily forming epialleles, and (ii) transposon‐derived, methylated at all cytosines, which may or may not form epialleles. We provide evidence that DNA sequence features such as density of CpGs and genomic repetitiveness of the loci predispose their susceptibility to epiallelic switching. The importance and predictive power of these genetic features were confirmed by analyses of common epialleles in natural Arabidopsis accessions, epigenetic recombinant inbred lines (epiRILs) and also verified in rice

DMRcaller: a versatile R/Bioconductor package for detection and visualization of differentially methylated regions in CpG and non-CpG contexts

DNA methylation has been associated with transcriptional repression and detection of differential methylation is important in understanding the underlying causes of differential gene expression. Bisulfite-converted genomic DNA sequencing is the current gold standard in the field for building genome-wide maps at a base pair resolution of DNA methylation. Here we systematically investigate the underlying features of detecting differential DNA methylation in CpG and non-CpG contexts, considering both the case of mammalian systems and plants. In particular, we introduce DMRcaller, a highly efficient R/Bioconductor package, which implements several methods to detect Differentially Methylated Regions (DMRs) between two samples. Most importantly, we show that different algorithms are required to compute DMRs and the most appropriate algorithm in each case depends on the sequence context and levels of methylation. Furthermore, we show that DMRcaller outperforms other available packages and we propose a new method to select the parameters for this tool and for other available tools. DMRcaller is a comprehensive tool for differential methylation analysis which displays high sensitivity and specificity for the detection of DMRs and performs entire genome wide analysis within a few hours