Functional Characteristics of Gene Expression Motifs with Single and Dual Strategies of Regulation

Transcriptional regulation by transcription factors and post-transcriptional regulation by microRNAs constitute two major modes of regulation of gene expression. While gene expression motifs incorporating solely transcriptional regulation are well investigated, the dynamics of motifs with dual strategies of regulation, i.e., both transcriptional and post-transcriptional regulation, have not been studied as extensively. In this paper, we probe the dynamics of a four-gene motif with dual strategies of regulation of gene expression. Some of the functional characteristics are compared with those of a two-gene motif, the genetic toggle, employing only transcriptional regulation. Both the motifs define positive feedback loops with the potential for bistability and hysteresis. The four-gene motif, contrary to the genetic toggle, is found to exhibit bistability even in the absence of cooperativity in the regulation of gene expression. The four-gene motif further exhibits a novel dynamical feature in which two regions of monostability with linear threshold response are separated by a region of bistability with digital response. Using the linear noise approximation (LNA), we further show that the coefficient of variation (a measure of noise), associated with the protein levels in the steady state, has a lower magnitude in the case of the four-gene motif as compared to the case of the genetic toggle. We next compare transcriptional with post-transcriptional regulation from an information theoretic perspective. We focus on two gene expression motifs, Motif 1 with transcriptional regulation and Motif 2 with post-transcriptional regulation. We show that amongst the two motifs, Motif 2 has a greater capacity for information transmission for an extended range of parameter values

Associative memory in gene regulation networks

The pattern of gene expression in the phenotype of an organism is determined in part by the dynamical attractors of the organism’s gene regulation network. Changes to the connections in this network over evolutionary time alter the adult gene expression pattern and hence the fitness of the organism. However, the evolution of structure in gene expression networks (potentially reflecting past selective environments) and its affordances and limitations with respect to enhancing evolvability is poorly understood in general. In this paper we model the evolution of a gene regulation network in a controlled scenario. We show that selected changes to connections in the regulation network make the currently selected gene expression pattern more robust to environmental variation. Moreover, such changes to connections are necessarily ‘Hebbian’ – ‘genes that fire together wire together’ – i.e. genes whose expression is selected for in the same selective environments become co-regulated. Accordingly, in a manner formally equivalent to well-understood learning behaviour in artificial neural networks, a gene expression network will therefore develop a generalised associative memory of past selected phenotypes. This theoretical framework helps us to better understand the relationship between homeostasis and evolvability (i.e. selection to reduce variability facilitates structured variability), and shows that, in principle, a gene regulation network has the potential to develop ‘recall’ capabilities normally reserved for cognitive systems

A linear mixed model approach to gene expression-tumor aneuploidy association studies.

Aneuploidy, defined as abnormal chromosome number or somatic DNA copy number, is a characteristic of many aggressive tumors and is thought to drive tumorigenesis. Gene expression-aneuploidy association studies have previously been conducted to explore cellular mechanisms associated with aneuploidy. However, in an observational setting, gene expression is influenced by many factors that can act as confounders between gene expression and aneuploidy, leading to spurious correlations between the two variables. These factors include known confounders such as sample purity or batch effect, as well as gene co-regulation which induces correlations between the expression of causal genes and non-causal genes. We use a linear mixed-effects model (LMM) to account for confounding effects of tumor purity and gene co-regulation on gene expression-aneuploidy associations. When applied to patient tumor data across diverse tumor types, we observe that the LMM both accounts for the impact of purity on aneuploidy measurements and identifies a new association between histone gene expression and aneuploidy

Stochastic modeling of regulation of gene expression by multiple small RNAs

A wealth of new research has highlighted the critical roles of small RNAs (sRNAs) in diverse processes such as quorum sensing and cellular responses to stress. The pathways controlling these processes often have a central motif comprising of a master regulator protein whose expression is controlled by multiple sRNAs. However, the regulation of stochastic gene expression of a single target gene by multiple sRNAs is currently not well understood. To address this issue, we analyze a stochastic model of regulation of gene expression by multiple sRNAs. For this model, we derive exact analytic results for the regulated protein distribution including compact expressions for its mean and variance. The derived results provide novel insights into the roles of multiple sRNAs in fine-tuning the noise in gene expression. In particular, we show that, in contrast to regulation by a single sRNA, multiple sRNAs provide a mechanism for independently controlling the mean and variance of the regulated protein distribution

Origins of Binary Gene Expression in Post-transcriptional Regulation by MicroRNAs

MicroRNA-mediated regulation of gene expression is characterised by some distinctive features that set it apart from unregulated and transcription factor-regulated gene expression. Recently, a mathematical model has been proposed to describe the dynamics of post-transcriptional regulation by microRNAs. The model explains the observations made in single cell experiments quite well. In this paper, we introduce some additional features into the model and consider two specific cases. In the first case, a non-cooperative positive feedback loop is included in the transcriptional regulation of the target gene expression. In the second case, a stochastic version of the original model is considered in which there are random transitions between the inactive and active expression states of the gene. In the first case we show that bistability is possible in a parameter regime, due to the presence of a non-linear protein decay term in the gene expression dynamics. In the second case, we derive the conditions for obtaining stochastic binary gene expression. We find that this type of gene expression is more favourable in the case of regulation by microRNAs as compared to the case of unregulated gene expression. The theoretical predictions relating to binary gene expression are experimentally testable.Comment: 10 Pages, 5 Figure

Optimal first-passage time in gene regulatory networks

The inherent probabilistic nature of the biochemical reactions, and low copy number of species can lead to stochasticity in gene expression across identical cells. As a result, after induction of gene expression, the time at which a specific protein count is reached is stochastic as well. Therefore events taking place at a critical protein level will see stochasticity in their timing. First-passage time (FPT), the time at which a stochastic process hits a critical threshold, provides a framework to model such events. Here, we investigate stochasticity in FPT. Particularly, we consider events for which controlling stochasticity is advantageous. As a possible regulatory mechanism, we also investigate effect of auto-regulation, where the transcription rate of gene depends on protein count, on stochasticity of FPT. Specifically, we investigate for an optimal auto-regulation which minimizes stochasticity in FPT, given fixed mean FPT and threshold. For this purpose, we model the gene expression at a single cell level. We find analytic formulas for statistical moments of the FPT in terms of model parameters. Moreover, we examine the gene expression model with auto-regulation. Interestingly, our results show that the stochasticity in FPT, for a fixed mean, is minimized when the transcription rate is independent of protein count. Further, we discuss the results in context of lysis time of an \textit{E. coli} cell infected by a $\lambda$ phage virus. An optimal lysis time provides evolutionary advantage to the $\lambda$ phage, suggesting a possible regulation to minimize its stochasticity. Our results indicate that there is no auto-regulation of the protein responsible for lysis. Moreover, congruent to experimental evidences, our analysis predicts that the expression of the lysis protein should have a small burst size.Comment: 8 pages, 3 figures, Submitted to Conference on Decision and Control 201

Metabolite-dependent regulation of gene expression in trypanosoma brucei

Mechanisms regulating gene expression in trypanosomatid protozoa differ significantly from those in other eukaryotes. Transcription of the genome appears to be more or less constitutive with the polyadenylation and trans-splicing of large polycistronic RNAs producing monocistronic RNAs whose translation may then depend upon information within their 3′ untranslated regions (3′UTRs). Various 3′UTR sequences involved in life-cycle stage-dependent differential gene expression have been described. Moreover, several RNA-binding proteins have been implicated in regulating expression of these transcripts through altering either their stability or their ability to interact with ribosomes. In this issue of Molecular Microbiology Xiao et al. report on a regulatory element within the 3′UTR of the transcript that encodes the polyamine pathway regulatory protein called prozyme. It appears that the RNA element controls translation of the prozyme RNA causing expression to be upregulated when levels of decarboxylated S-adenosylmethionine (dcAdoMet) are depleted. Since prozyme activates the enzyme S-adenosylmethionine decarboxylase (AdoMetDC), which is responsible for the production of dcAdoMet, losing this metabolite leads to upregulation of prozyme, activation of AdoMetDC and restoration of optimal levels of dcAdomet. The system thus represents a novel metabolite-sensing regulatory circuit that maintains polyamine homeostasis in these cells

Stochastic neural network models for gene regulatory networks

Recent advances in gene-expression profiling technologies provide large amounts of gene expression data. This raises the possibility for a functional understanding of genome dynamics by means of mathematical modelling. As gene expression involves intrinsic noise, stochastic models are essential for better descriptions of gene regulatory networks. However, stochastic modelling for large scale gene expression data sets is still in the very early developmental stage. In this paper we present some stochastic models by introducing stochastic processes into neural network models that can describe intermediate regulation for large scale gene networks. Poisson random variables are used to represent chance events in the processes of synthesis and degradation. For expression data with normalized concentrations, exponential or normal random variables are used to realize fluctuations. Using a network with three genes, we show how to use stochastic simulations for studying robustness and stability properties of gene expression patterns under the influence of noise, and how to use stochastic models to predict statistical distributions of expression levels in population of cells. The discussion suggest that stochastic neural network models can give better description of gene regulatory networks and provide criteria for measuring the reasonableness o mathematical models

Genome-wide co-expression analysis in multiple tissues

Expression quantitative trait loci (eQTLs) represent genetic control points of gene expression, and can be categorized as cis- and trans-acting, reflecting local and distant regulation of gene expression respectively. Although there is evidence of co-regulation within clusters of trans-eQTLs, the extent of co-expression patterns and their relationship with the genotypes at eQTLs are not fully understood. We have mapped thousands of cis- and trans-eQTLs in four tissues (fat, kidney, adrenal and left ventricle) in a large panel of rat recombinant inbred (RI) strains. Here we investigate the genome-wide correlation structure in expression levels of eQTL transcripts and underlying genotypes to elucidate the nature of co-regulation within cis- and trans-eQTL datasets. Across the four tissues, we consistently found statistically significant correlations of cis-regulated gene expression to be rare (<0.9% of all pairs tested). Most (>80%) of the observed significant correlations of cis-regulated gene expression are explained by correlation of the underlying genotypes. In comparison, co-expression of trans-regulated gene expression is more common, with significant correlation ranging from 2.9%-14.9% of all pairs of trans-eQTL transcripts. We observed a total of 81 trans-eQTL clusters (hot-spots), defined as consisting of > or =10 eQTLs linked to a common region, with very high levels of correlation between trans-regulated transcripts (77.2-90.2%). Moreover, functional analysis of large trans-eQTL clusters (> or =30 eQTLs) revealed significant functional enrichment among genes comprising 80% of the large clusters. The results of this genome-wide co-expression study show the effects of the eQTL genotypes on the observed patterns of correlation, and suggest that functional relatedness between genes underlying trans-eQTLs is reflected in the degree of co-expression observed in trans-eQTL clusters. Our results demonstrate the power of an integrative, systematic approach to the analysis of a large gene expression dataset to uncover underlying structure, and inform future eQTL studies