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

Regulation of Gonadotropin-Releasing Hormone (GnRH)-Receptor Gene Expression in Tilapia: Effect of GnRH and Dopamine

The present work was designed to study certain aspects of the endocrine regulation of gonadotropin-releasing hormone receptor (GnRH-R) in the pituitary of the teleost fish tilapia. A GnRH-R was cloned from the pituitary of hybrid tilapia (taGnRH-R) and was identified as a typical seven-transmembrane receptor. Northern blot analysis revealed a single GnRH-R transcript in the pituitary of approximately 2.3 kilobases. The taGnRH-R mRNA levels were significantly higher in females than in males. Injection of the salmon GnRH analog (sGnRHa; 5–50 μg/kg) increased the steady-state levels of taGnRH-R mRNA, with the highest response recorded at 25 μg/kg and at 36 h. At the higher dose of sGnRHa (50 μg/kg), taGnRH-R transcript appeared to be down-regulated. Exposure of tilapia pituitary cells in culture to graded doses (0.1–100 nM) of seabream (sbGnRH = GnRH I), chicken II (cGnRH II), or salmon GnRH (sGnRH = GnRH III) resulted in a significant increase in taGnRH-R mRNA levels. The highest levels of both LH release and taGnRH-R mRNA levels were recorded after exposure to cGnRH II and the lowest after exposure to sbGnRH. The dopamine-agonist quinpirole suppressed LH release and mRNA levels of taGnRH-R, indicating an inhibitory effect on GnRH-R synthesis. Collectively, these data provide evidence that GnRH in tilapia can up- regulate, whereas dopamine down-regulates, taGnRH-R mRNA levels

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

Integrated signaling pathway and gene expression regulatory model to dissect dynamics of <em>Escherichia coli </em>challenged mammary epithelial cells

AbstractCells transform external stimuli, through the activation of signaling pathways, which in turn activate gene regulatory networks, in gene expression. As more omics data are generated from experiments, eliciting the integrated relationship between the external stimuli, the signaling process in the cell and the subsequent gene expression is a major challenge in systems biology. The complex system of non-linear dynamic protein interactions in signaling pathways and gene networks regulates gene expression.The complexity and non-linear aspects have resulted in the study of the signaling pathway or the gene network regulation in isolation. However, this limits the analysis of the interaction between the two components and the identification of the source of the mechanism differentiating the gene expression profiles. Here, we present a study of a model of the combined signaling pathway and gene network to highlight the importance of integrated modeling.Based on the experimental findings we developed a compartmental model and conducted several simulation experiments. The model simulates the mRNA expression of three different cytokines (RANTES, IL8 and TNFα) regulated by the transcription factor NFκB in mammary epithelial cells challenged with E. coli. The analysis of the gene network regulation identifies a lack of robustness and therefore sensitivity for the transcription factor regulation. However, analysis of the integrated signaling and gene network regulation model reveals distinctly different underlying mechanisms in the signaling pathway responsible for the variation between the three cytokine's mRNA expression levels. Our key findings reveal the importance of integrating the signaling pathway and gene expression dynamics in modeling. Modeling infers valid research questions which need to be verified experimentally and can assist in the design of future biological experiments

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

Interaction and cross-talk between non-coding RNAs.

Non-coding RNA (ncRNA) has been shown to regulate diverse cellular processes and functions through controlling gene expression. Long non-coding RNAs (lncRNAs) act as a competing endogenous RNAs (ceRNAs) where microRNAs (miRNAs) and lncRNAs regulate each other through their biding sites. Interactions of miRNAs and lncRNAs have been reported to trigger decay of the targeted lncRNAs and have important roles in target gene regulation. These interactions form complicated and intertwined networks. Certain lncRNAs encode miRNAs and small nucleolar RNAs (snoRNAs), and may regulate expression of these small RNAs as precursors. SnoRNAs have also been reported to be precursors for PIWI-interacting RNAs (piRNAs) and thus may regulate the piRNAs as a precursor. These miRNAs and piRNAs target messenger RNAs (mRNAs) and regulate gene expression. In this review, we will present and discuss these interactions, cross-talk, and co-regulation of ncRNAs and gene regulation due to these interactions

Canonical transforming growth factor-β signaling regulates disintegrin metalloprotease expression in experimental renal fibrosis via miR-29

Fibrosis pathophysiology is critically regulated by Smad 2– and Smad 3–mediated transforming growth factor-β (TGF-β) signaling. Disintegrin metalloproteases (Adam) can manipulate the signaling environment, however, the role and regulation of ADAMs in renal fibrosis remain unclear. TGF-β stimulation of renal cells results in a significant up-regulation of Adams 10, 17, 12, and 19. The selective Smad2/3 inhibitor SB 525334 reversed these TGF-β–induced changes. In vivo, using ureteral obstruction to model renal fibrosis, we observed increased Adams gene expression that was blocked by oral administration of SB 525334. Similar increases in Adam gene expression also occurred in preclinical models of hypertension-induced renal damage and glomerulonephritis. miRNAs are a recently discovered second level of regulation of gene expression. Analysis of 3′ untranslated regions of Adam12 and Adam19 mRNAs showed multiple binding sites for miR-29a, miR-29b, and miR-29c. We show that miR-29 family expression is decreased after unilateral ureter obstruction and this significant decrease in miR-29 family expression was observed consistently in preclinical models of renal dysfunction and correlated with an increase in Adam12 and Adam19 expression. Exogenous overexpression of the miR-29 family blocked TGF-β–mediated up-regulation of Adam12 and Adam19 gene expression. This study shows that Adams are involved in renal fibrosis and are regulated by canonical TGF-β signaling and miR-29. Therefore, both Adams and the miR-29 family represent therapeutic targets for renal fibrosis