Model Of Gene Regulation Research Articles

On the impact of discreteness and abstractions on modelling noise in gene regulatory networks

In this paper, we explore the impact of different forms of model abstraction and the role of discreteness on the dynamical behaviour of a simple model of gene regulation where a transcriptional repressor negatively regulates its own expression. We first investigate the relation between a minimal set of parameters and the system dynamics in a purely discrete stochastic framework, with the twofold purpose of providing an intuitive explanation of the different behavioural patterns exhibited and of identifying the main sources of noise. Then, we explore the effect of combining hybrid approaches and quasi-steady state approximations on model behaviour (and simulation time), to understand to what extent dynamics and quantitative features such as noise intensity can be preserved.

Hypoxia-Sensitive Epigenetic Regulation of an Antisense-Oriented lncRNA Controls WT1 Expression in Myeloid Leukemia Cells

WT1 is a transcription factor expressed in hematopoietic stem cells and in most cases of myeloid leukemia. We investigated the roles of hypoxia and epigenetics in the regulation of WT1 expression in myeloid leukemia cells. WT1 expression correlates with hypomethylation of the CpG island in Intron 1, and pharmacologic demethylation of this CpG island induces WT1 mRNA expression. Hypoxia causes decreases in DNMT expression and activity and increased expression and activity of TET2 and TET3, resulting in demethylation of this CpG island and expression of WT1 mRNA. Demethylation of the CpG island, either from pharmacologic treatment or induction of hypoxia, results in transcription of an antisense-oriented lncRNA, and inhibiting lncRNA expression with shRNA blocks WT1 mRNA expression. These results reveal a novel model of hypoxia-mediated epigenetic gene regulation. In addition, this is the first report that TET2 and TET3, increasingly recognized as important epigenetic regulators of gene expression in stem cells and in cancer cells, can be regulated by hypoxia, providing a solid mechanistic link between hypoxia and epigenetic regulation of gene expression with important implications for the role of hypoxia in stem cell function.

A Boolean gene regulatory model of heterosis and speciation.

BackgroundModelling genetic phenomena affecting biological traits is important for the development of agriculture as it allows breeders to predict the potential of breeding for certain traits. One such phenomenon is heterosis or hybrid vigor: crossing individuals from genetically distinct populations often results in improvements in quantitative traits, such as growth rate, biomass production and stress resistance. Heterosis has become a very useful tool in global agriculture, but its genetic basis remains controversial and its effects hard to predict. We have taken a computational approach to studying heterosis, developing a simulation of evolution, independent reassortment of alleles and hybridization of Gene Regulatory Networks (GRNs) in a Boolean framework. These artificial regulatory networks exhibit topological properties that reflect those observed in biology, and fitness is measured as the ability of a network to respond to external inputs in a pre-defined way.ResultsOur model reproduced common experimental observations on heterosis using only biologically justified parameters, such as mutation rates. Hybrid vigor was observed and its extent was seen to increase as parental populations diverged, up until a point of sudden collapse of hybrid fitness. Thus, the model also describes a process akin to speciation due to genetic incompatibility of the separated populations. We also reproduce, for the first time in a model, the fact that hybrid vigor cannot easily be fixed by within a breeding line, currently an important limitation of the use of hybrid crops. The simulation allowed us to study the effects of three standard models for the genetic basis of heterosis: dominance, over-dominance, and epistasis.ConclusionThis study describes the most detailed simulation of heterosis using gene regulatory networks to date and reproduces several phenomena associated with heterosis for the first time in a model. The level of detail in our model allows us to suggest possible warning signs of the impending collapse of hybrid vigor in breeding. In addition, the simulation provides a framework that can be extended to study other aspects of heterosis and alternative evolutionary scenarios.Electronic supplementary materialThe online version of this article (doi:10.1186/s12862-015-0298-0) contains supplementary material, which is available to authorized users.

Mittag–Leffler stability and generalized Mittag–Leffler stability of fractional-order gene regulatory networks

Gene regulatory networks have attracted much attention, and the networks with integer-order have been well studied. Fractional-order gene regulatory networks have been shown that it is more suitable to model gene regulation mechanism, but has rarely been researched. In this paper, a class of fractional-order gene regulatory networks is studied. Some criteria on the Mittag–Leffler stability and generalized Mittag–Leffler stability are established by using the fractional Lyapunov method for these networks. The existence of the equilibrium point is also considered. One illustrative example is provided to show the effectives of the obtained results.

Non-Coding RNAs in Transcriptional Regulation: The review for Current Molecular Biology Reports.

Transcriptional gene silencing guided by small RNAs is a process conserved from protozoa to mammals. Small RNAs loaded into Argonaute family proteins direct repressive histone modifications or DNA cytosine methylation to homologous regions of the genome. Small RNA-mediated transcriptional silencing is required for many biological processes, including repression of transposable elements, maintaining the genome stability/integrity, and epigenetic inheritance of gene expression. Here we will summarize the current knowledge about small RNA biogenesis and mechanisms of transcriptional regulation in plants, Drosophila, C. elegans and mice. Furthermore, a rapidly growing number long non-coding RNAs (lncRNAs) have been implicated as important players in transcription regulation. We will discuss current models for long non-coding RNA-mediated gene regulation.

Spatial enhancer clustering and regulation of enhancer-proximal genes by cohesin

In addition to mediating sister chromatid cohesion during the cell cycle, the cohesin complex associates with CTCF and with active gene regulatory elements to form long-range interactions between its binding sites. Genome-wide chromosome conformation capture had shown that cohesin's main role in interphase genome organization is in mediating interactions within architectural chromosome compartments, rather than specifying compartments per se. However, it remains unclear how cohesin-mediated interactions contribute to the regulation of gene expression. We have found that the binding of CTCF and cohesin is highly enriched at enhancers and in particular at enhancer arrays or “super-enhancers” in mouse thymocytes. Using local and global chromosome conformation capture, we demonstrate that enhancer elements associate not just in linear sequence, but also in 3D, and that spatial enhancer clustering is facilitated by cohesin. The conditional deletion of cohesin from noncycling thymocytes preserved enhancer position, H3K27ac, H4K4me1, and enhancer transcription, but weakened interactions between enhancers. Interestingly, ∼50% of deregulated genes reside in the vicinity of enhancer elements, suggesting that cohesin regulates gene expression through spatial clustering of enhancer elements. We propose a model for cohesin-dependent gene regulation in which spatial clustering of enhancer elements acts as a unified mechanism for both enhancer-promoter “connections” and “insulation.”

Keeping up with the 'omics: non-equilibrium models of gene regulation.

Non-equilibrium processes are vital features of biological systems. Despite this universally accepted fact, gene regulation is typically formalized into models that assume thermodynamic equilibrium. As experimental evidence expands the repertoire of non-equilibrium genome regulatory mechanisms, theoreticians are challenged to devise general approaches to accommodate and suggest functions for non-equilibrium processes. Ahsendorf et al. provide one such framework, which is discussed in the context of the growing complexity of eukaryotic gene regulation.

A gene expression atlas of a bicoid-depleted Drosophila embryo reveals early canalization of cell fate.

In developing embryos, gene regulatory networks drive cells towards discrete terminal fates, a process called canalization. We studied the behavior of the anterior-posterior segmentation network in Drosophila melanogaster embryos by depleting a key maternal input, bicoid (bcd), and measuring gene expression patterns of the network at cellular resolution. This method results in a gene expression atlas containing the levels of mRNA or protein expression of 13 core patterning genes over six time points for every cell of the blastoderm embryo. This is the first cellular resolution dataset of a genetically perturbed Drosophila embryo that captures all cells in 3D. We describe the technical developments required to build this atlas and how the method can be employed and extended by others. We also analyze this novel dataset to characterize the degree and timing of cell fate canalization in the segmentation network. We find that in two layers of this gene regulatory network, following depletion of bcd, individual cells rapidly canalize towards normal cell fates. This result supports the hypothesis that the segmentation network directly canalizes cell fate, rather than an alternative hypothesis whereby cells are initially mis-specified and later eliminated by apoptosis. Our gene expression atlas provides a high resolution picture of a classic perturbation and will enable further computational modeling of canalization and gene regulation in this transcriptional network.

Inference of quantitative models of bacterial promoters from time-series reporter gene data.

The inference of regulatory interactions and quantitative models of gene regulation from time-series transcriptomics data has been extensively studied and applied to a range of problems in drug discovery, cancer research, and biotechnology. The application of existing methods is commonly based on implicit assumptions on the biological processes under study. First, the measurements of mRNA abundance obtained in transcriptomics experiments are taken to be representative of protein concentrations. Second, the observed changes in gene expression are assumed to be solely due to transcription factors and other specific regulators, while changes in the activity of the gene expression machinery and other global physiological effects are neglected. While convenient in practice, these assumptions are often not valid and bias the reverse engineering process. Here we systematically investigate, using a combination of models and experiments, the importance of this bias and possible corrections. We measure in real time and in vivo the activity of genes involved in the FliA-FlgM module of the E. coli motility network. From these data, we estimate protein concentrations and global physiological effects by means of kinetic models of gene expression. Our results indicate that correcting for the bias of commonly-made assumptions improves the quality of the models inferred from the data. Moreover, we show by simulation that these improvements are expected to be even stronger for systems in which protein concentrations have longer half-lives and the activity of the gene expression machinery varies more strongly across conditions than in the FliA-FlgM module. The approach proposed in this study is broadly applicable when using time-series transcriptome data to learn about the structure and dynamics of regulatory networks. In the case of the FliA-FlgM module, our results demonstrate the importance of global physiological effects and the active regulation of FliA and FlgM half-lives for the dynamics of FliA-dependent promoters.

Data Requirements for Model-Based Cancer Prognosis Prediction.

Cancer prognosis prediction is typically carried out without integrating scientific knowledge available on genomic pathways, the effect of drugs on cell dynamics, or modeling mutations in the population. Recent work addresses some of these problems by formulating an uncertainty class of Boolean regulatory models for abnormal gene regulation, assigning prognosis scores to each network based on intervention outcomes, and partitioning networks in the uncertainty class into prognosis classes based on these scores. For a new patient, the probability distribution of the prognosis class was evaluated using optimal Bayesian classification, given patient data. It was assumed that (1) disease is the result of several mutations of a known healthy network and that these mutations and their probability distribution in the population are known and (2) only a single snapshot of the patient’s gene activity profile is observed. It was shown that, even in ideal settings where cancer in the population and the effect of a drug are fully modeled, a single static measurement is typically not sufficient. Here, we study what measurements are sufficient to predict prognosis. In particular, we relax assumption (1) by addressing how population data may be used to estimate network probabilities, and extend assumption (2) to include static and time-series measurements of both population and patient data. Furthermore, we extend the prediction of prognosis classes to optimal Bayesian regression of prognosis metrics. Even when time-series data is preferable to infer a stochastic dynamical network, we show that static data can be superior for prognosis prediction when constrained to small samples. Furthermore, although population data is helpful, performance is not sensitive to inaccuracies in the estimated network probabilities.

Molecular Origins of Bimodal mRNA Copy Number Distribution

Stochastic gene expression contributes to the variability of genetically identical cells in identical environments, giving rise to significant consequences in many biological processes. Generally, stochastic gene expression is explained by a two-state model of gene regulation, where a gene switches between on and off states. Transcription kinetics obtained from in vivo imaging is consistent with the two-state model. A theoretical model predicts that the distribution of mRNA copy numbers can be transitioned from a Poisson distribution to a bimodal distribution by modulating kon and koff. This transition is considered the origin of cell-to-cell variability. However, despite the versatility of the two-state model of gene regulation, little is known about the dynamics of the on-off switching and modulation. Many mechanisms of gene regulation have been proposed, including promoter binding of repressors and activators and, more recently, structural changes of DNA by a DNA binding protein.In this study, we reconstructed prokaryote transcription and transcriptional regulation in vitro by using an E coli RNA polymerase (RNAP) and a λ phage promoter, and visualized mRNA production at the single molecule level by fast fluorescence in situ hybridization (fastFISH) to analyze the dynamics of mRNA production and its modulation.

Differential spatial and structural organization of the X chromosome underlies dosage compensation in C. elegans.

The adjustment of X-linked gene expression to the X chromosome copy number (dosage compensation [DC]) has been widely studied as a model of chromosome-wide gene regulation. In Caenorhabditis elegans, DC is achieved by twofold down-regulation of gene expression from both Xs in hermaphrodites. We show that in males, the single X chromosome interacts with nuclear pore proteins, while in hermaphrodites, the DC complex (DCC) impairs this interaction and alters X localization. Our results put forward a structural model of DC in which X-specific sequences locate the X chromosome in transcriptionally active domains in males, while the DCC prevents this in hermaphrodites.

PPARγ gene expression is autoregulated in primary adipocytes: ligand, sumoylation, and isoform specificity.

Being a key-factor in glucose homeostasis, PPARγ transcriptional activity (TA) is of high importance. However, its mediation by ligands and post-translational modifications in insulin target tissues are unclear. We investigated effects of rosiglitazone (Rg) and sumoylation on PPARγ-TA by overexpressing expression vectors and promoter-reporters for PPARγ1 and PPARγ2 in primary rat adipocytes. Wild type (WT) PPARγ1 and PPARγ2 dose-dependently repressed transcription from their promoters to a maximum of 40-50%. PPARγ2 mutants defective in either MAP-kinase phosphorylation (S112A) or the ligand-binding domain (LBD; P495L, L496A/E499A) exhibited decreased repression of PPARγ2 promoter. Rg enhanced repression by S112A, but not by LBD-defective mutants. Sumoylation-defective PPARγ1 mutants K77R and K365R repressed PPARγ2 promoter activity similar to WT, while Rg enhanced repression by K77R but not by K365R. Sumoylation-defective PPARγ2 mutants K107R and K395R exhibited impaired TA and impaired responsiveness to Rg. GLUT4 promoter, previously shown by us to be repressed by WT-PPARγ1 and WT-PPARγ2, was similarly repressed by both sumoylation-defective PPARγ1 mutants, while both sumoylation-defective PPARγ2 mutants exerted reduced repression. Surprisingly, Rg alleviated only WT-PPARγ2-induced repression, while augmenting that induced by WT-PPARγ1 and all sumoylation-defective mutants. Promoter and chromatin immunoprecipitation analyses revealed that PPARγ2 autorepression involves its direct binding to its promoter. In concert with effects at promoter level, Rg decreased endogenous level of PPARγ2 mRNA, while increasing that of GLUT4. We suggest a hypothetical model for PPARγ gene regulation in primary adipocytes that is isoform-specific and Rg/sumoylation-dependent. These findings are important due to the role of PPARγ and Rg in insulin sensitivity.

Multiple dimensions of epigenetic gene regulation in the malaria parasite Plasmodium falciparum: gene regulation via histone modifications, nucleosome positioning and nuclear architecture in P. falciparum.

Plasmodium falciparum is the most deadly human malarial parasite, responsible for an estimated 207 million cases of disease and 627,000 deaths in 2012. Recent studies reveal that the parasite actively regulates a large fraction of its genes throughout its replicative cycle inside human red blood cells and that epigenetics plays an important role in this precise gene regulation. Here, we discuss recent advances in our understanding of three aspects of epigenetic regulation in P. falciparum: changes in histone modifications, nucleosome occupancy and the three-dimensional genome structure. We compare these three aspects of the P. falciparum epigenome to those of other eukaryotes, and show that large-scale compartmentalization is particularly important in determining histone decomposition and gene regulation in P. falciparum. We conclude by presenting a gene regulation model for P. falciparum that combines the described epigenetic factors, and by discussing the implications of this model for the future of malaria research.

Mixed Poisson distributions in exact solutions of stochastic autoregulation models.

In this paper we study the interplay between stochastic gene expression and system design using simple stochastic models of autoactivation and autoinhibition. Using the Poisson representation, a technique whose particular usefulness in the context of nonlinear gene regulation models we elucidate, we find exact results for these feedback models in the steady state. Further, we exploit this representation to analyze the parameter spaces of each model, determine which dimensionless combinations of rates are the shape determinants for each distribution, and thus demarcate where in the parameter space qualitatively different behaviors arise. These behaviors include power-law-tailed distributions, bimodal distributions, and sub-Poisson distributions. We also show how these distribution shapes change when the strength of the feedback is tuned. Using our results, we reexamine how well the autoinhibition and autoactivation models serve their conventionally assumed roles as paradigms for noise suppression and noise exploitation, respectively.

Genetic and epigenetic fine mapping of causal autoimmune disease variants.

SummaryGenome-wide association studies have identified loci underlying human diseases, but the causal nucleotide changes and mechanisms remain largely unknown. Here we developed a fine-mapping algorithm to identify candidate causal variants for 21 autoimmune diseases from genotyping data. We integrated these predictions with transcription and cis-regulatory element annotations, derived by mapping RNA and chromatin in primary immune cells, including resting and stimulated CD4+ T-cell subsets, regulatory T-cells, CD8+ T-cells, B-cells, and monocytes. We find that ~90% of causal variants are noncoding, with ~60% mapping to immune-cell enhancers, many of which gain histone acetylation and transcribe enhancer-associated RNA upon immune stimulation. Causal variants tend to occur near binding sites for master regulators of immune differentiation and stimulus-dependent gene activation, but only 10–20% directly alter recognizable transcription factor binding motifs. Rather, most noncoding risk variants, including those that alter gene expression, affect non-canonical sequence determinants not well-explained by current gene regulatory models.

A theoretical framework for the regulation of Shh morphogen-controlled gene expression.

How morphogen gradients govern the pattern of gene expression in developing tissues is not well understood. Here, we describe a statistical thermodynamic model of gene regulation that combines the activity of a morphogen with the transcriptional network it controls. Using Sonic hedgehog (Shh) patterning of the ventral neural tube as an example, we show that the framework can be used together with the principled parameter selection technique of approximate Bayesian computation to obtain a dynamical model that accurately predicts tissue patterning. The analysis indicates that, for each target gene regulated by Gli, which is the transcriptional effector of Shh signalling, there is a neutral point in the gradient, either side of which altering the Gli binding affinity has opposite effects on gene expression. This explains recent counterintuitive experimental observations. The approach is broadly applicable and provides a unifying framework to explain the temporospatial pattern of morphogen-regulated gene expression.

The transcription factor p53: Not a repressor, solely an activator

The predominant function of the tumor suppressor p53 is transcriptional regulation. It is generally accepted that p53-dependent transcriptional activation occurs by binding to a specific recognition site in promoters of target genes. Additionally, several models for p53-dependent transcriptional repression have been postulated. Here, we evaluate these models based on a computational meta-analysis of genome-wide data. Surprisingly, several major models of p53-dependent gene regulation are implausible. Meta-analysis of large-scale data is unable to confirm reports on directly repressed p53 target genes and falsifies models of direct repression. This notion is supported by experimental re-analysis of representative genes reported as directly repressed by p53. Therefore, p53 is not a direct repressor of transcription, but solely activates its target genes. Moreover, models based on interference of p53 with activating transcription factors as well as models based on the function of ncRNAs are also not supported by the meta-analysis. As an alternative to models of direct repression, the meta-analysis leads to the conclusion that p53 represses transcription indirectly by activation of the p53-p21-DREAM/RB pathway.

Non-equilibrium thermodynamics analysis of transcriptional regulation kinetics

Abstract Gene expression in eukaryotic cells is an extremely complex and interesting phenomenon whose dynamics are controlled by a large number of subtle physicochemical processes commonly described by means of gene regulatory networks. Such networks consist in a series of coupled chemical reactions, conformational changes, and other biomolecular processes involving the interaction of the DNA molecule itself with a number of proteins usually called transcription factors as well as enzymes and other components. The kinetics behind the functioning of such gene regulatory networks are largely unknown, though its description in terms of non-equilibrium thermodynamics has been discussed recently. In this work we will derive general kinetic equations for a gene regulatory network from a non-equilibrium thermodynamical description and discuss its use in understanding the free energy constrains imposed in the network structure. We also will discuss explicit expressions for the kinetics of a simple model of gene regulation and show that the kinetic role of mRNA decay during the RNA synthesis stage (or transcription) is somehow limited due to the comparatively low values of decay rates. At the level discussed here, this implies a decoupling of the kinetics of mRNA synthesis and degradation a fact that may become quite useful when modeling gene regulatory networks from experimental data on whole genome gene expression.

A high-resolution network model for global gene regulation in Mycobacterium tuberculosis.

The resilience of Mycobacterium tuberculosis (MTB) is largely due to its ability to effectively counteract and even take advantage of the hostile environments of a host. In order to accelerate the discovery and characterization of these adaptive mechanisms, we have mined a compendium of 2325 publicly available transcriptome profiles of MTB to decipher a predictive, systems-scale gene regulatory network model. The resulting modular organization of 98% of all MTB genes within this regulatory network was rigorously tested using two independently generated datasets: a genome-wide map of 7248 DNA-binding locations for 143 transcription factors (TFs) and global transcriptional consequences of overexpressing 206 TFs. This analysis has discovered specific TFs that mediate conditional co-regulation of genes within 240 modules across 14 distinct environmental contexts. In addition to recapitulating previously characterized regulons, we discovered 454 novel mechanisms for gene regulation during stress, cholesterol utilization and dormancy. Significantly, 183 of these mechanisms act uniquely under conditions experienced during the infection cycle to regulate diverse functions including 23 genes that are essential to host-pathogen interactions. These and other insights underscore the power of a rational, model-driven approach to unearth novel MTB biology that operates under some but not all phases of infection.