Cooperative binding of transcription factors promotes bimodal gene expression response

Simplifications and approximations in a single-gene circuit modeling

The absence of detailed knowledge about regulatory interactions makes the use of phenomenological assumptions mandatory in cell biology modeling. Furthermore, the challenges associated with the analysis of these models compel the implementation of mathematical approximations. However, the constraints these methods introduce to biological interpretation are sometimes neglected. Consequently, understanding these restrictions is a very important task for systems biology modeling. In this article, we examine the impact of such simplifications, taking the case of a single-gene autoinhibitory circuit; however, our conclusions are not limited solely to this instance. We demonstrate that models grounded in the same biological assumptions but described at varying levels of detail can lead to different outcomes, that is, different and contradictory phenotypes or behaviors. Indeed, incorporating specific molecular processes like translation and elongation into the model can introduce instabilities and oscillations not seen when these processes are assumed to be instantaneous. Furthermore, incorporating a detailed description of promoter dynamics, usually described by a phenomenological regulatory function, can lead to instability, depending on the cooperative binding mechanism that is acting. Consequently, although the use of a regulating function facilitates model analysis, it may mask relevant aspects of the system's behavior. In particular, we observe that the two cooperative binding mechanisms, both compatible with the same sigmoidal function, can lead to different phenotypes, such as transcriptional oscillations with different oscillation frequencies.

BORIS/CTCFL epigenetically reprograms clustered CTCF binding sites into alternative transcriptional start sites

BACKGROUND

Pervasive usage of alternative promoters leads to the deregulation of gene expression in carcinogenesis and may drive the emergence of new genes in spermatogenesis. However, little is known regarding the mechanisms underpinning the activation of alternative promoters.

RESULTS

Here we describe how alternative cancer-testis-specific transcription is activated. We show that intergenic and intronic CTCF binding sites, which are transcriptionally inert in normal somatic cells, could be epigenetically reprogrammed into active de novo promoters in germ and cancer cells. BORIS/CTCFL, the testis-specific paralog of the ubiquitously expressed CTCF, triggers the epigenetic reprogramming of CTCF sites into units of active transcription. BORIS binding initiates the recruitment of the chromatin remodeling factor, SRCAP, followed by the replacement of H2A histone with H2A.Z, resulting in a more relaxed chromatin state in the nucleosomes flanking the CTCF binding sites. The relaxation of chromatin around CTCF binding sites facilitates the recruitment of multiple additional transcription factors, thereby activating transcription from a given binding site. We demonstrate that the epigenetically reprogrammed CTCF binding sites can drive the expression of cancer-testis genes, long noncoding RNAs, retro-pseudogenes, and dormant transposable elements.

CONCLUSIONS

Thus, BORIS functions as a transcription factor that epigenetically reprograms clustered CTCF binding sites into transcriptional start sites, promoting transcription from alternative promoters in both germ cells and cancer cells.

Multimerization of HIF enhances transcription of target genes containing the hypoxia ancillary sequence

Transcriptional activity of the hypoxia inducible factor (HIF) relies on the formation of a heterodimer composed of an oxygen-regulated α-subunit and a stably expressed β-subunit. Heterodimeric HIF activates expression by binding to RCGTG motifs within promoters of hypoxia-activated genes. Some hypoxia targets also possess an adjacent HIF ancillary sequence (HAS) reported to increase transcription but whose function remains obscure. Here, we investigate the contribution of the HAS element to the hypoxia response and its mechanism of action, using the HAS-containing prolyl 4-hydroxylase subunit α1 (P4HA1) as a gene model in NIH/3T3 mouse embryonic fibroblasts and HEK293 human embryonic kidney cells. Our HIF overexpression experiments demonstrate that the HAS motif is essential for full induction by hypoxia and that the presence of the tandem HAS/HIF, as opposed to HIF-only sequences, provides HIF proteins with the capacity to form complexes of stoichiometry beyond the classical heterodimer, likely tetramers, to cooperatively potentiate hypoxia-induced transcription. We also provide evidence of the crucial role played by the Fα helix of the PAS-B domain of the HIF1β subunit to support the interaction between heterodimers. Functional analysis showed that human genes containing the HAS/HIF motifs are better responders to hypoxia, and their promoters are enriched for specific transcription factor binding sites. Gene ontology enrichment revealed a predominance of HAS/HIF in genes primarily related to tissue formation and development. Our findings add an extra level of regulation of the hypoxia/HIF signaling through multimerization of HIF proteins on regulatory elements containing the HAS/HIF motifs.

Evolutionary innovation through transcription factor rewiring in microbes is shaped by levels of transcription factor activity, expression, and existing connectivity

The survival of a population during environmental shifts depends on whether the rate of phenotypic adaptation keeps up with the rate of changing conditions. A common way to achieve this is via change to gene regulatory network (GRN) connections-known as rewiring-that facilitate novel interactions and innovation of transcription factors. To understand the success of rapidly adapting organisms, we therefore need to determine the rules that create and constrain opportunities for GRN rewiring. Here, using an experimental microbial model system with the soil bacterium Pseudomonas fluorescens, we reveal a hierarchy among transcription factors that are rewired to rescue lost function, with alternative rewiring pathways only unmasked after the preferred pathway is eliminated. We identify 3 key properties-high activation, high expression, and preexisting low-level affinity for novel target genes-that facilitate transcription factor innovation. Ease of acquiring these properties is constrained by preexisting GRN architecture, which was overcome in our experimental system by both targeted and global network alterations. This work reveals the key properties that determine transcription factor evolvability, and as such, the evolution of GRNs.

Multifaceted Transcriptional Network of Estrogen-Related Receptor Alpha in Health and Disease

Estrogen-related receptors (ERRα, β and γ in mammals) are orphan members of the nuclear receptor superfamily acting as transcription factors. ERRs are expressed in several cell types and they display various functions in normal and pathological contexts. Amongst others, they are notably involved in bone homeostasis, energy metabolism and cancer progression. In contrast to other nuclear receptors, the activities of the ERRs are apparently not controlled by a natural ligand but they rely on other means such as the availability of transcriptional co-regulators. Here we focus on ERRα and review the variety of co-regulators that have been identified by various means for this receptor and their reported target genes. ERRα cooperates with distinct co-regulators to control the expression of distinct sets of target genes. This exemplifies the combinatorial specificity of transcriptional regulation that induces discrete cellular phenotypes depending on the selected coregulator. We finally propose an integrated view of the ERRα transcriptional network.

Concentration-dependent transcriptional switching through a collective action of cis-elements

Gene expression specificity of homeobox transcription factors has remained paradoxical. WUSCHEL activates and represses CLAVATA3 transcription at lower and higher concentrations, respectively. We use computational modeling and experimental analysis to investigate the properties of the cis-regulatory module. We find that intrinsically each cis-element can only activate CLAVATA3 at a higher WUSCHEL concentration. However, together, they repress CLAVATA3 at higher WUSCHEL and activate only at lower WUSCHEL, showing that the concentration-dependent interactions among cis-elements regulate both activation and repression. Biochemical experiments show that two adjacent functional cis-elements bind WUSCHEL with higher affinity and dimerize at relatively lower levels. Moreover, increasing the distance between cis-elements prolongs WUSCHEL monomer binding window, resulting in higher CLAVATA3 activation. Our work showing a constellation of optimally spaced cis-elements of defined affinities determining activation and repression thresholds in regulating CLAVATA3 transcription provides a previously unknown mechanism of cofactor-independent regulation of transcription factor binding in mediating gene expression specificity.

Robust and tunable signal processing in mammalian cells via engineered covalent modification cycles

Engineered signaling networks can impart cells with new functionalities useful for directing differentiation and actuating cellular therapies. For such applications, the engineered networks must be tunable, precisely regulate target gene expression, and be robust to perturbations within the complex context of mammalian cells. Here, we use bacterial two-component signaling proteins to develop synthetic phosphoregulation devices that exhibit these properties in mammalian cells. First, we engineer a synthetic covalent modification cycle based on kinase and phosphatase proteins derived from the bifunctional histidine kinase EnvZ, enabling analog tuning of gene expression via its response regulator OmpR. By regulating phosphatase expression with endogenous miRNAs, we demonstrate cell-type specific signaling responses and a new strategy for accurate cell type classification. Finally, we implement a tunable negative feedback controller via a small molecule-stabilized phosphatase, reducing output expression variance and mitigating the context-dependent effects of off-target regulation and resource competition. Our work lays the foundation for establishing tunable, precise, and robust control over cell behavior with synthetic signaling networks.

Computational identification of new potential transcriptional partners of ERRα in breast cancer cells: specific partners for specific targets

Estrogen related receptors are orphan members of the nuclear receptor superfamily acting as transcription factors (TFs). In contrast to classical nuclear receptors, the activities of the ERRs are not controlled by a natural ligand. Regulation of their activities thus relies on availability of transcriptional co-regulators. In this paper, we focus on ERRα, whose involvement in cancer progression has been broadly demonstrated. We propose a new approach to identify potential co-activators, starting from previously identified ERRα-activated genes in a breast cancer (BC) cell line. Considering mRNA gene expression from two sets of human BC cells as major endpoint, we used sparse partial least squares modeling to uncover new transcriptional regulators associated with ERRα. Among them, DDX21, MYBBP1A, NFKB1, and SETD7 are functionally relevant in MDA-MB-231 cells, specifically activating the expression of subsets of ERRα-activated genes. We studied SET7 in more details and showed its co-localization with ERRα and its ERRα-dependent transcriptional and phenotypic effects. Our results thus demonstrate the ability of a modeling approach to identify new transcriptional partners from gene expression. Finally, experimental results show that ERRα cooperates with distinct co-regulators to control the expression of distinct sets of target genes, thus reinforcing the combinatorial specificity of transcription.

Liquid-Liquid Phase Separation in Chromatin

In eukaryotic cells, protein and RNA factors involved in genome activities like transcription, RNA processing, DNA replication, and repair accumulate in self-organizing membraneless chromatin subcompartments. These structures contribute to efficiently conduct chromatin-mediated reactions and to establish specific cellular programs. However, the underlying mechanisms for their formation are only partly understood. Recent studies invoke liquid-liquid phase separation (LLPS) of proteins and RNAs in the establishment of chromatin activity patterns. At the same time, the folding of chromatin in the nucleus can drive genome partitioning into spatially distinct domains. Here, the interplay between chromatin organization, chromatin binding, and LLPS is discussed by comparing and contrasting three prototypical chromatin subcompartments: the nucleolus, clusters of active RNA polymerase II, and pericentric heterochromatin domains. It is discussed how the different ways of chromatin compartmentalization are linked to transcription regulation, the targeting of soluble factors to certain parts of the genome, and to disease-causing genetic aberrations.

Efficient Framework Analysis for Targeted Drug Delivery Based on Internet of Bio-NanoThings

The Internet of Bio-NanoThings (IoBNTs) is a novel paradigm that derives from synthetic biology and advances in nanotechnology for controlling the embedded nanodevices in various medical applications. However, numerous studies have focused on communication efficiency among the nanodevices in a given network, the challenges such as the design and the development of the nanodevices, and the coordination of molecular communication within the wireless body area network (BAN), and the interface connection between the BAN and the Internet are yet to be addressed. Therefore, in this study, we present a framework analysis comprising of the compartmental model, for studying the effects and variances in drug concentration that occur inside intra-body nanonetworks through IoBNT, while taking into account the properties of target cells as well as the ligand-receptor binding mechanism. A performance analysis of the proposed framework for the forward link (i.e., from the Internet to the intra-body nanonetwork) and reverse link (i.e., from the intra-body nanonetwork to the Internet) is presented. The simulation results of the developed framework reveal its ability to enhance the delivery of therapeutic drugs to the target cell while minimizing the side effects in healthy cells.

Decoupled molecules with binding polynomials of bidegree (n, 2)

We present a result on the number of decoupled molecules for systems binding two different types of ligands. In the case of n and 2 binding sites respectively, we show that there are \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$2(n!)^{2}$$\end{document}2(n!)2 decoupled molecules to a generic binding polynomial. For molecules with more binding sites for the second ligand, we provide computational results.

Detection of cooperatively bound transcription factor pairs using ChIP-seq peak intensities and expectation maximization

Transcription factors (TFs) often work cooperatively, where the binding of one TF to DNA enhances the binding affinity of a second TF to a nearby location. Such cooperative binding is important for activating gene expression from promoters and enhancers in both prokaryotic and eukaryotic cells. Existing methods to detect cooperative binding of a TF pair rely on analyzing the sequence that is bound. We propose a method that uses, instead, only ChIP-seq peak intensities and an expectation maximization (CPI-EM) algorithm. We validate our method using ChIP-seq data from cells where one of a pair of TFs under consideration has been genetically knocked out. Our algorithm relies on our observation that cooperative TF-TF binding is correlated with weak binding of one of the TFs, which we demonstrate in a variety of cell types, including E. coli, S. cerevisiae and M. musculus cells. We show that this method performs significantly better than a predictor based only on the ChIP-seq peak distance of the TFs under consideration. This suggests that peak intensities contain information that can help detect the cooperative binding of a TF pair. CPI-EM also outperforms an existing sequence-based algorithm in detecting cooperative binding. The CPI-EM algorithm is available at https://github.com/vishakad/cpi-em.

Characterization of noise in multistable genetic circuits reveals ways to modulate heterogeneity

Random fluctuations in the amount of cellular components like mRNA and protein molecules are inevitable due to the stochastic and discrete nature of biochemical reactions. If large enough, this so-called “cellular noise” can lead to random transitions between the expression states of a multistable genetic circuit. That way, heterogeneity within isogenic populations is created. Our aim is to understand which dynamical features of a simple autoregulatory system determine its intrinsic noise level, and how they can be modified in order to regulate state-transitions. To that end, novel mathematical methods for the state-dependent characterization and prediction of noise in multistable systems are developed. First, we introduce the hybrid LNA, a modified version of the Linear Noise Approximation. It yields good predictions on variances of mRNA and protein fluctuations, even for reaction systems comprising low-copy-number components (e.g. mRNA) and highly nonlinear reaction rates. Furthermore, the temporal structure of fluctuations and the skewness of the protein distribution are characterized via state-dependent protein burst sizes and burst frequencies. Based on this mathematical framework, we develop graphical methods which support the intuitive design of regulatory circuits with a desired noise pattern. The methods are then used to predict how overall noise in the system can be adapted, and how state-specific noise modifications are possible that allow, e.g., the generation of unidirectional transitions. Our considerations are validated by stochastic simulations. This way, a design of genetic circuits is possible that takes population heterogeneity into account and is valuable in applications of synthetic biology and biotechnology. Moreover, natural phenomena like the bimodal development of genetic competence can be studied.

Information propagation in a noisy gene cascade

We use information theory to study the information transmission through a simple gene cascade where the product of an unregulated gene regulates the expression activity of a cooperative genetic switch. While the input signal is provided by the upstream gene with two states, we consider that the expression of downstream gene is controlled by a cis-regulatory system with three binding sites for the regulator product, which can bind cooperatively. By computing exactly the associated probability distributions, we estimate information transmission thought the mutual information measure. We found that the mutual information associated with unimodal input signal is lower than the associated with bimodal inputs. We also observe that mutual information presents a maximum in the cooperativity intensity, and the position of this maximum depends on the kinetic rates of the promoter. Furthermore, we found that the bursting dynamics of the input signal can enhance the information transmission capacity.

A measure to quantify the degree of cooperativity in overall titration curves

In the framework of the grand canonical ensemble, different definitions of cooperativity commonly used in the context of ligand binding are not equivalent. A unifying definition is the existence of non-real roots of the binding polynomial. Using this qualitative criterion, an open question is how to quantify the degree of cooperativity. In this work, we introduce a theoretical measure to quantify the degree of cooperativity of a titriation curve. Its definition is based on a minimal energy approach mapping a given binding polynomial to the minimal interaction energy which is required to generate it. We show that the degree of cooperativity can be calculated easily, if the molecule under consideration is assumed to consist of energetically identical binding sites. Moreover, the property of sub-multiplicativity allows us to determine upper bounds for the degree of cooperativity in asymmetric systems. The approach is consistent with the qualitative definition of cooperativity based on the existence of non-real roots of the binding polynomial, and thus helps to put the concept of cooperativity on a solid theoretical ground. It connects macro- and microstates, but takes here also into account that an infinite number of different molecules can cause the same macroscopic ligand binding behavior, which means that the underlying microsystem cannot be uniquely identified based on the titration curve only.

Multiple binding sites for transcriptional repressors can produce regular bursting and enhance noise suppression

Cells may control fluctuations in protein levels by means of negative autoregulation, where transcription factors bind DNA sites to repress their own production. Theoretical studies have assumed a single binding site for the repressor, while in most species it is found that multiple binding sites are arranged in clusters. We study a stochastic description of negative autoregulation with multiple binding sites for the repressor. We find that increasing the number of binding sites induces regular bursting of gene products. By tuning the threshold for repression, we show that multiple binding sites can also suppress fluctuations. Our results highlight possible roles for the presence of multiple binding sites of negative autoregulators.

Gene switching rate determines response to extrinsic perturbations in the self-activation transcriptional network motif

Gene switching dynamics is a major source of randomness in genetic networks, also in the case of large concentrations of the transcription factors. In this work, we consider a common network motif - the positive feedback of a transcription factor on its own synthesis - and assess its response to extrinsic noises perturbing gene deactivation in a variety of settings where the network might operate. These settings are representative of distinct cellular types, abundance of transcription factors and ratio between gene switching and protein synthesis rates. By investigating noise-induced transitions among the different network operative states, our results suggest that gene switching rates are key parameters to shape network response to external perturbations, and that such response depends on the particular biological setting, i.e. the characteristic time scales and protein abundance. These results might have implications on our understanding of irreversible transitions for noise-related phenomena such as cellular differentiation. In addition these evidences suggest to adopt the appropriate mathematical model of the network in order to analyze the system consistently to the reference biological setting.

Promoters Architecture-Based Mechanism for Noise-Induced Oscillations in a Single-Gene Circuit

It is well known that single-gene circuits with negative feedback loop can lead to oscillatory gene expression when they operate with time delay. In order to generate these oscillations many processes can contribute to properly timing such delay. Here we show that the time delay coming from the transitions between internal states of the cis-regulatory system (CRS) can drive sustained oscillations in an auto-repressive single-gene circuit operating in a small volume like a cell. We found that the cooperative binding of repressor molecules is not mandatory for a oscillatory behavior if there are enough binding sites in the CRS. These oscillations depend on an adequate balance between the CRS kinetic, and the synthesis/degradation rates of repressor molecules. This finding suggest that the multi-site CRS architecture can play a key role for oscillatory behavior of gene expression. Finally, our results can also help to synthetic biologists on the design of the promoters architecture for new genetic oscillatory circuits.

Cooperative binding: a multiple personality

Cooperative binding has been described in many publications and has been related to or defined by several different properties of the binding behavior of the ligand to the target molecule. In addition to the commonly used Hill coefficient, other characteristics such as a sigmoidal shape of the overall titration curve in a linear plot, a change of ligand affinity of the other binding sites when a site of the target molecule becomes occupied, or complex roots of the binding polynomial have been used to define or to quantify cooperative binding. In this work, we analyze how the different properties are related in the most general model for binding curves based on the grand canonical partition function and present several examples which highlight differences between the cooperativity characterizing properties which are discussed. Our results mainly show that among the presented definitions there are not two which fully coincide. Moreover, this work poses the question whether it can make sense to distinguish between positive and negative cooperativity based on the macroscopic binding isotherm only. This article shall emphasize that scientists who investigate cooperative effects in biological systems could help avoiding misunderstandings by stating clearly which kind of cooperativity they discuss.

Cooperativity to increase Turing pattern space for synthetic biology

It is hard to bridge the gap between mathematical formulations and biological implementations of Turing patterns, yet this is necessary for both understanding and engineering these networks with synthetic biology approaches. Here, we model a reaction-diffusion system with two morphogens in a monostable regime, inspired by components that we recently described in a synthetic biology study in mammalian cells.1 The model employs a single promoter to express both the activator and inhibitor genes and produces Turing patterns over large regions of parameter space, using biologically interpretable Hill function reactions. We applied a stability analysis and identified rules for choosing biologically tunable parameter relationships to increase the likelihood of successful patterning. We show how to control Turing pattern sizes and time evolution by manipulating the values for production and degradation relationships. More importantly, our analysis predicts that steep dose-response functions arising from cooperativity are mandatory for Turing patterns. Greater steepness increases parameter space and even reduces the requirement for differential diffusion between activator and inhibitor. These results demonstrate some of the limitations of linear scenarios for reaction-diffusion systems and will help to guide projects to engineer synthetic Turing patterns.

Noise and information transmission in promoters with multiple internal States

Based on the measurements of noise in gene expression performed during the past decade, it has become customary to think of gene regulation in terms of a two-state model, where the promoter of a gene can stochastically switch between an ON and an OFF state. As experiments are becoming increasingly precise and the deviations from the two-state model start to be observable, we ask about the experimental signatures of complex multistate promoters, as well as the functional consequences of this additional complexity. In detail, we i), extend the calculations for noise in gene expression to promoters described by state transition diagrams with multiple states, ii), systematically compute the experimentally accessible noise characteristics for these complex promoters, and iii), use information theory to evaluate the channel capacities of complex promoter architectures and compare them with the baseline provided by the two-state model. We find that adding internal states to the promoter generically decreases channel capacity, except in certain cases, three of which (cooperativity, dual-role regulation, promoter cycling) we analyze in detail.

A computational method for automated characterization of genetic components

The ability to design and construct synthetic biological systems with predictable behavior could enable significant advances in medical treatment, agricultural sustainability, and bioenergy production. However, to reach a stage where such systems can be reliably designed from biological components, integrated experimental and computational techniques that enable robust component characterization are needed. In this paper we present a computational method for the automated characterization of genetic components. Our method exploits a recently developed multichannel experimental protocol and integrates bacterial growth modeling, Bayesian parameter estimation, and model selection, together with data processing steps that are amenable to automation. We implement the method within the Genetic Engineering of Cells modeling and design environment, which enables both characterization and design to be integrated within a common software framework. To demonstrate the application of the method, we quantitatively characterize a synthetic receiver device that responds to the 3-oxohexanoyl-homoserine lactone signal, across a range of experimental conditions.

Modeling stochastic noise in gene regulatory systems

The Master equation is considered the gold standard for modeling the stochastic mechanisms of gene regulation in molecular detail, but it is too complex to solve exactly in most cases, so approximation and simulation methods are essential. However, there is still a lack of consensus about the best way to carry these out. To help clarify the situation, we review Master equation models of gene regulation, theoretical approximations based on an expansion method due to N.G. van Kampen and R. Kubo, and simulation algorithms due to D.T. Gillespie and P. Langevin. Expansion of the Master equation shows that for systems with a single stable steady-state, the stochastic model reduces to a deterministic model in a first-order approximation. Additional theory, also due to van Kampen, describes the asymptotic behavior of multistable systems. To support and illustrate the theory and provide further insight into the complex behavior of multistable systems, we perform a detailed simulation study comparing the various approximation and simulation methods applied to synthetic gene regulatory systems with various qualitative characteristics. The simulation studies show that for large stochastic systems with a single steady-state, deterministic models are quite accurate, since the probability distribution of the solution has a single peak tracking the deterministic trajectory whose variance is inversely proportional to the system size. In multistable stochastic systems, large fluctuations can cause individual trajectories to escape from the domain of attraction of one steady-state and be attracted to another, so the system eventually reaches a multimodal probability distribution in which all stable steady-states are represented proportional to their relative stability. However, since the escape time scales exponentially with system size, this process can take a very long time in large systems.

Noise minimisation in gene expression switches

Gene expression is subject to stochastic variation which leads to fluctuations in the rate of protein production. Recently, a study in yeast at a genomic scale showed that, in some cases, gene expression variability alters phenotypes while, in other cases, these remain unchanged despite fluctuations in the expression of other genes. These studies suggested that noise in gene expression is a physiologically relevant trait and, to prevent harmful stochastic variation in the expression levels of some genes, it can be subject to minimisation. However, the mechanisms for noise minimisation are still unclear. In the present work, we analysed how noise expression depends on the architecture of the cis-regulatory system, in particular on the number of regulatory binding sites. Using analytical calculations and stochastic simulations, we found that the fluctuation level in noise expression decreased with the number of regulatory sites when regulatory transcription factors interacted with only one other bound transcription factor. In contrast, we observed that there was an optimal number of binding sites when transcription factors interacted with many bound transcription factors. This finding suggested a new mechanism for preventing large fluctuations in the expression of genes which are sensitive to the concentration of regulators.

Jpx RNA activates Xist by evicting CTCF

In mammals, dosage compensation between XX and XY individuals occurs through X chromosome inactivation (XCI). The noncoding Xist RNA is expressed and initiates XCI only when more than one X chromosome is present. Current models invoke a dependency on the X-to-autosome ratio (X:A), but molecular factors remain poorly defined. Here, we demonstrate that molecular titration between an X-encoded RNA and an autosomally encoded protein dictates Xist induction. In pre-XCI cells, CTCF protein represses Xist transcription. At the onset of XCI, Jpx RNA is upregulated, binds CTCF, and extricates CTCF from one Xist allele. We demonstrate that CTCF is an RNA-binding protein and is titrated away from the Xist promoter by Jpx RNA. Thus, Jpx activates Xist by evicting CTCF. The functional antagonism via molecular titration reveals a role for long noncoding RNA in epigenetic regulation.

Gestational age-dependent changes in gene expression of metabolic enzymes and transporters in pregnant mice

Pregnancy-induced changes in drug pharmacokinetics can be explained by changes in expression of drug-metabolizing enzymes and transporters and/or normal physiology. In this study, we determined gestational age-dependent expression profiles for all metabolic enzyme and transporter genes in the maternal liver, kidney, small intestine, and placenta of pregnant mice by microarray analysis. We specifically examined the expression of genes important for xenobiotic, bile acid, and steroid hormone metabolism and disposition, namely, cytochrome P450s (Cyp), UDP-glucuronosyltranserases (Ugt), sulfotransferases (Sult), and ATP-binding cassette (Abc), solute carrier (Slc), and solute carrier organic anion (Slco) transporters. Few Ugt and Sult genes were affected by pregnancy. Cyp17a1 expression in the maternal liver increased 3- to 10-fold during pregnancy, which was the largest observed change in the maternal tissues. Cyp1a2, most Cyp2 isoforms, Cyp3a11, and Cyp3a13 expression in the liver decreased on gestation days (gd) 15 and 19 compared with nonpregnant controls (gd 0). In contrast, Cyp2d40, Cyp3a16, Cyp3a41a, Cyp3a41b, and Cyp3a44 in the liver were induced throughout pregnancy. In the placenta, Cyp expression on gd 10 and 15 was upregulated compared with gd 19. Notable changes were also observed in Abc and Slc transporters. Abcc3 expression in the liver and Abcb1a, Abcc4, and Slco4c1 expression in the kidney were downregulated on gd 15 and 19. In the placenta, Slc22a3 (Oct3) expression on gd 10 was 90% lower than that on gd 15 and 19. This study demonstrates important gestational age-dependent expression of metabolic enzyme and transporter genes, which may have mechanistic relevance to drug disposition in human pregnancy.