Transcriptional regulation: effects of promoter proximal pausing on speed, synchrony and reliability

Analytic delay distributions for a family of gene transcription models

Models intended to describe the time evolution of a gene network must somehow include transcription, the DNA-templated synthesis of RNA, and translation, the RNA-templated synthesis of proteins. In eukaryotes, the DNA template for transcription can be very long, often consisting of tens of thousands of nucleotides, and lengthy pauses may punctuate this process. Accordingly, transcription can last for many minutes, in some cases hours. There is a long history of introducing delays in gene expression models to take the transcription and translation times into account. Here we study a family of detailed transcription models that includes initiation, elongation, and termination reactions. We establish a framework for computing the distribution of transcription times, and work out these distributions for some typical cases. For elongation, a fixed delay is a good model provided elongation is fast compared to initiation and termination, and there are no sites where long pauses occur. The initiation and termination phases of the model then generate a nontrivial delay distribution, and elongation shifts this distribution by an amount corresponding to the elongation delay. When initiation and termination are relatively fast, the distribution of elongation times can be approximated by a Gaussian. A convolution of this Gaussian with the initiation and termination time distributions gives another analytic approximation to the transcription time distribution. If there are long pauses during elongation, because of the modularity of the family of models considered, the elongation phase can be partitioned into reactions generating a simple delay (elongation through regions where there are no long pauses), and reactions whose distribution of waiting times must be considered explicitly (initiation, termination, and motion through regions where long pauses are likely). In these cases, the distribution of transcription times again involves a nontrivial part and a shift due to fast elongation processes.

Genome-scale chromatin binding dynamics of RNA Polymerase II general transcription machinery components

A great deal of work has revealed, in structural detail, the components of the preinitiation complex (PIC) machinery required for initiation of mRNA gene transcription by RNA polymerase II (Pol II). However, less-well understood are the in vivo PIC assembly pathways and their kinetics, an understanding of which is vital for determining how rates of in vivo RNA synthesis are established. We used competition ChIP in budding yeast to obtain genome-scale estimates of the residence times for five general transcription factors (GTFs): TBP, TFIIA, TFIIB, TFIIE and TFIIF. While many GTF-chromatin interactions were short-lived ( < 1 min), there were numerous interactions with residence times in the range of several minutes. Sets of genes with a shared function also shared similar patterns of GTF kinetic behavior. TFIIE, a GTF that enters the PIC late in the assembly process, had residence times correlated with RNA synthesis rates. The datasets and results reported here provide kinetic information for most of the Pol II-driven genes in this organism, offering a rich resource for exploring the mechanistic relationships between PIC assembly, gene regulation, and transcription.

Genome-scale chromatin interaction dynamic measurements for key components of the RNA Pol II general transcription machinery

Background

A great deal of work has revealed in structural detail the components of the machinery responsible for mRNA gene transcription initiation. These include the general transcription factors (GTFs), which assemble at promoters along with RNA Polymerase II (Pol II) to form a preinitiation complex (PIC) aided by the activities of cofactors and site-specific transcription factors (TFs). However, less well understood are the in vivo PIC assembly pathways and their kinetics, an understanding of which is vital for determining on a mechanistic level how rates of in vivo RNA synthesis are established and how cofactors and TFs impact them.

Results

We used competition ChIP to obtain genome-scale estimates of the residence times for five GTFs: TBP, TFIIA, TFIIB, TFIIE and TFIIF in budding yeast. While many GTF-chromatin interactions were short-lived (< 1 min), there were numerous interactions with residence times in the several minutes range. Sets of genes with a shared function also shared similar patterns of GTF kinetic behavior. TFIIE, a GTF that enters the PIC late in the assembly process, had residence times correlated with RNA synthesis rates.

Conclusions

The datasets and results reported here provide kinetic information for most of the Pol II-driven genes in this organism and therefore offer a rich resource for exploring the mechanistic relationships between PIC assembly, gene regulation, and transcription. The relationships between gene function and GTF dynamics suggest that shared sets of TFs tune PIC assembly kinetics to ensure appropriate levels of expression.

Real-time single-cell characterization of the eukaryotic transcription cycle reveals correlations between RNA initiation, elongation, and cleavage

The eukaryotic transcription cycle consists of three main steps: initiation, elongation, and cleavage of the nascent RNA transcript. Although each of these steps can be regulated as well as coupled with each other, their in vivo dissection has remained challenging because available experimental readouts lack sufficient spatiotemporal resolution to separate the contributions from each of these steps. Here, we describe a novel application of Bayesian inference techniques to simultaneously infer the effective parameters of the transcription cycle in real time and at the single-cell level using a two-color MS2/PP7 reporter gene and the developing fruit fly embryo as a case study. Our method enables detailed investigations into cell-to-cell variability in transcription-cycle parameters as well as single-cell correlations between these parameters. These measurements, combined with theoretical modeling, suggest a substantial variability in the elongation rate of individual RNA polymerase molecules. We further illustrate the power of this technique by uncovering a novel mechanistic connection between RNA polymerase density and nascent RNA cleavage efficiency. Thus, our approach makes it possible to shed light on the regulatory mechanisms in play during each step of the transcription cycle in individual, living cells at high spatiotemporal resolution.

TATA and paused promoters active in differentiated tissues have distinct expression characteristics

Core promoter types differ in the extent to which RNA polymerase II (Pol II) pauses after initiation, but how this affects their tissue-specific gene expression characteristics is not well understood. While promoters with Pol II pausing elements are active throughout development, TATA promoters are highly active in differentiated tissues. We therefore used a genomics approach on late-stage Drosophila embryos to analyze the properties of promoter types. Using tissue-specific Pol II ChIP-seq, we found that paused promoters have high levels of paused Pol II throughout the embryo, even in tissues where the gene is not expressed, while TATA promoters only show Pol II occupancy when the gene is active. The promoter types are associated with different chromatin accessibility in ATAC-seq data and have different expression characteristics in single-cell RNA-seq data. The two promoter types may therefore be optimized for different properties: paused promoters show more consistent expression when active, while TATA promoters have lower background expression when inactive. We propose that tissue-specific genes have evolved to use two different strategies for their differential expression across tissues.

Broad domains of histone H3 lysine 4 trimethylation in transcriptional regulation and disease

Histone modifications affect transcription by changing the chromatin structure. In particular, histone H3 lysine 4 trimethylation (H3K4me3) is one of the most recognized epigenetic marks of active transcription. While many studies have provided evidence of the correlation between H3K4me3 and active transcription, details regarding the mechanism involved remain unclear. The first study on the broad H3K4me3 domain was reported in 2014; subsequently, the function of this domain has been studied in various cell types. In this review, we summarized the recent studies on the role of the broad H3K4me3 domain in transcription, development, memory formation, and several diseases, including cancer and autoimmune diseases. The broadest H3K4me3 domains are associated with increased transcriptional precision of cell-type-specific genes related to cell identity and other essential functions. The broad H3K4me3 domain regulates maternal zygotic activation in early mammalian development. In systemic autoimmune diseases, high expression of immune-responsive genes requires the presence of the broad H3K4me3 domain in the promoter-proximal regions. Transcriptional repression of tumor-suppressor genes is associated with the shortening of the broad H3K4me3 domains in cancer cells. Additionally, the broad H3K4me3 domain interacts with the super-enhancer to regulate cancer-associated genes. During memory formation, H3K4me3 breadth is regulated in the hippocampus CA1 neurons. Taken together, these findings indicate that H3K4me3 breadth is essential for the regulation of the transcriptional output across multiple cell types.

Regulatory variants: from detection to predicting impact

Variants within non-coding genomic regions can greatly affect disease. In recent years, increasing focus has been given to these variants, and how they can alter regulatory elements, such as enhancers, transcription factor binding sites and DNA methylation regions. Such variants can be considered regulatory variants. Concurrently, much effort has been put into establishing international consortia to undertake large projects aimed at discovering regulatory elements in different tissues, cell lines and organisms, and probing the effects of genetic variants on regulation by measuring gene expression. Here, we describe methods and techniques for discovering disease-associated non-coding variants using sequencing technologies. We then explain the computational procedures that can be used for annotating these variants using the information from the aforementioned projects, and prediction of their putative effects, including potential pathogenicity, based on rule-based and machine learning approaches. We provide the details of techniques to validate these predictions, by mapping chromatin-chromatin and chromatin-protein interactions, and introduce Clustered Regularly Interspaced Short Palindromic Repeats-Associated Protein 9 (CRISPR-Cas9) technology, which has already been used in this field and is likely to have a big impact on its future evolution. We also give examples of regulatory variants associated with multiple complex diseases. This review is aimed at bioinformaticians interested in the characterization of regulatory variants, molecular biologists and geneticists interested in understanding more about the nature and potential role of such variants from a functional point of views, and clinicians who may wish to learn about variants in non-coding genomic regions associated with a given disease and find out what to do next to uncover how they impact on the underlying mechanisms.

RNA synthesis is associated with multiple TBP-chromatin binding events

Competition ChIP is an experimental method that allows transcription factor (TF) chromatin turnover dynamics to be measured across a genome. We develop and apply a physical model of TF-chromatin competitive binding using chemical reaction rate theory and are able to derive the physical half-life or residence time for TATA-binding protein (TBP) across the yeast genome from competition ChIP data. Using our physical modeling approach where we explicitly include the induction profile of the competitor in the model, we are able to estimate yeast TBP-chromatin residence times as short as 1.3 minutes, demonstrating that competition ChIP is a relatively high temporal-resolution approach. Strikingly, we find a median value of ~5 TBP-chromatin binding events associated with the synthesis of one RNA molecule across Pol II genes, suggesting multiple rounds of pre-initiation complex assembly and disassembly before productive elongation of Pol II is achieved at most genes in the yeast genome.

Collective Properties of a Transcription Initiation Model Under Varying Environment

The dynamics of gene transcription is tightly regulated in eukaryotes. Recent experiments have revealed various kinds of transcriptional dynamics, such as RNA polymerase II pausing, that involves regulation at the transcription initiation stage, and the choice of different regulation pattern is closely related to the physiological functions of the target gene. Here we consider a simplified model of transcription initiation, a process including the assembly of transcription complex and the pausing and releasing of the RNA polymerase II. Focusing on the collective behaviors of a population level, we explore the potential regulatory functions this model can offer. These functions include fast and synchronized response to environmental change, or long-term memory about the transcriptional status. As a proof of concept we also show that, by selecting different control mechanisms cells can adapt to different environments. These findings may help us better understand the design principles of transcriptional regulation.

RNA polymerase II pausing can be retained or acquired during activation of genes involved in the epithelial to mesenchymal transition

Promoter-proximal RNA polymerase II (Pol II) pausing is implicated in the regulation of gene transcription. However, the mechanisms of pausing including its dynamics during transcriptional responses remain to be fully understood. We performed global analysis of short capped RNAs and Pol II Chromatin Immunoprecipitation sequencing in MCF-7 breast cancer cells to map Pol II pausing across the genome, and used permanganate footprinting to specifically follow pausing during transcriptional activation of several genes involved in the epithelial to mesenchymal transition (EMT). We find that the gene for EMT master regulator Snail (SNAI1), but not Slug (SNAI2), shows evidence of Pol II pausing before activation. Transcriptional activation of the paused SNAI1 gene is accompanied by a further increase in Pol II pausing signal, whereas activation of non-paused SNAI2 gene results in the acquisition of a typical pausing signature. The increase in pausing signal reflects increased transcription initiation without changes in Pol II pausing. Activation of the heat shock HSP70 gene involves pausing release that speeds up Pol II turnover, but does not change pausing location. We suggest that Pol II pausing is retained during transcriptional activation and can further undergo regulated release in a signal-specific manner.

Analytic approaches to stochastic gene expression in multicellular systems

Deterministic thermodynamic models of the complex systems, which control gene expression in metazoa, are helping researchers identify fundamental themes in the regulation of transcription. However, quantitative single cell studies are increasingly identifying regulatory mechanisms that control variability in expression. Such behaviors cannot be captured by deterministic models and are poorly suited to contemporary stochastic approaches that rely on continuum approximations, such as Langevin methods. Fortunately, theoretical advances in the modeling of transcription have assembled some general results that can be readily applied to systems being explored only through a deterministic approach. Here, I review some of the recent experimental evidence for the importance of genetically regulating stochastic effects during embryonic development and discuss key results from Markov theory that can be used to model this regulation. I then discuss several pairs of regulatory mechanisms recently investigated through a Markov approach. In each case, a deterministic treatment predicts no difference between the mechanisms, but the statistical treatment reveals the potential for substantially different distributions of transcriptional activity. In this light, features of gene regulation that seemed needlessly complex evolutionary baggage may be appreciated for their key contributions to reliability and precision of gene expression.

H3K4me3 breadth is linked to cell identity and transcriptional consistency

Trimethylation of histone H3 at lysine 4 (H3K4me3) is a chromatin modification known to mark the transcription start sites of active genes. Here, we show that H3K4me3 domains that spread more broadly over genes in a given cell type preferentially mark genes that are essential for the identity and function of that cell type. Using the broadest H3K4me3 domains as a discovery tool in neural progenitor cells, we identify novel regulators of these cells. Machine learning models reveal that the broadest H3K4me3 domains represent a distinct entity, characterized by increased marks of elongation. The broadest H3K4me3 domains also have more paused polymerase at their promoters, suggesting a unique transcriptional output. Indeed, genes marked by the broadest H3K4me3 domains exhibit enhanced transcriptional consistency and [corrected] increased transcriptional levels, and perturbation of H3K4me3 breadth leads to changes in transcriptional consistency. Thus, H3K4me3 breadth contains information that could ensure transcriptional precision at key cell identity/function genes.

Systems biophysics of gene expression

Gene expression is a process central to any form of life. It involves multiple temporal and functional scales that extend from specific protein-DNA interactions to the coordinated regulation of multiple genes in response to intracellular and extracellular changes. This diversity in scales poses fundamental challenges to the use of traditional approaches to fully understand even the simplest gene expression systems. Recent advances in computational systems biophysics have provided promising avenues to reliably integrate the molecular detail of biophysical process into the system behavior. Here, we review recent advances in the description of gene regulation as a system of biophysical processes that extend from specific protein-DNA interactions to the combinatorial assembly of nucleoprotein complexes. There is now basic mechanistic understanding on how promoters controlled by multiple, local and distal, DNA binding sites for transcription factors can actively control transcriptional noise, cell-to-cell variability, and other properties of gene regulation, including precision and flexibility of the transcriptional responses.

Single-cell analysis reveals early manifestation of cancerous phenotype in pre-malignant esophageal cells

Cellular heterogeneity plays a pivotal role in a variety of functional processes in vivo including carcinogenesis. However, our knowledge about cell-to-cell diversity and how differences in individual cells manifest in alterations at the population level remains very limited mainly due to the lack of appropriate tools enabling studies at the single-cell level. We present a study on changes in cellular heterogeneity in the context of pre-malignant progression in response to hypoxic stress. Utilizing pre-malignant progression of Barrett's esophagus (BE) as a disease model system we studied molecular mechanisms underlying the progression from metaplastic to dysplastic (pre-cancerous) stage. We used newly developed methods enabling measurements of cell-to-cell differences in copy numbers of mitochondrial DNA, expression levels of a set of mitochondrial and nuclear genes involved in hypoxia response pathways, and mitochondrial membrane potential. In contrast to bulk cell studies reported earlier, our study shows significant differences between metaplastic and dysplastic BE cells in both average values and single-cell parameter distributions of mtDNA copy numbers, mitochondrial function, and mRNA expression levels of studied genes. Based on single-cell data analysis, we propose that mitochondria may be one of the key factors in pre-malignant progression in BE.

The head module of Mediator directs activation of preloaded RNAPII in vivo

The successful synthesis of a transcript by RNA polymerase II (RNAPII) is a multistage process with distinct rate-limiting steps that can vary depending on the particular gene. A growing number of genes in a variety of organisms are regulated at steps after the recruitment of RNAPII. The best-characterized Saccharomyces cerevisiae gene regulated in this manner is CYC1. This gene has high occupancy of RNAPII under non-inducing conditions, defining it as a poised gene. Here, we find that subunits of the head module of Mediator, Med18 and Med20, and Med19 are required for activation of transcription at the CYC1 promoter in response to environmental cues. These subunits of Mediator are required at the preloaded promoter for normal levels of recruitment and activity of the general transcription factor TFIIH. Strikingly, these Mediator components are dispensable for activation by the same activator at a different gene, which lacks a preloaded polymerase in the promoter region. Based on these results and other studies, we speculate that Mediator plays an essential role in triggering an inactive polymerase at CYC1 into a productively elongating form.

Eukaryotic transcriptional dynamics: from single molecules to cell populations

Transcriptional regulation is achieved through combinatorial interactions between regulatory elements in the human genome and a vast range of factors that modulate the recruitment and activity of RNA polymerase. Experimental approaches for studying transcription in vivo now extend from single-molecule techniques to genome-wide measurements. Parallel to these developments is the need for testable quantitative and predictive models for understanding gene regulation. These conceptual models must also provide insight into the dynamics of transcription and the variability that is observed at the single-cell level. In this Review, we discuss recent results on transcriptional regulation and also the models those results engender. We show how a non-equilibrium description informs our view of transcription by explicitly considering time- and energy-dependence at the molecular level.

Rapid transcription fosters coordinate snail expression in the Drosophila embryo

Transcription is commonly held to be a highly stochastic process, resulting in considerable heterogeneity of gene expression among the different cells in a population. Here, we employ quantitative in situ hybridization methods coupled with high-resolution imaging assays to measure the expression of snail, a developmental patterning gene necessary for coordinating the invagination of the mesoderm during gastrulation of the Drosophila embryo. Our measurements of steady-state mRNAs suggest that there is very little variation in snail expression across the different cells that make up the mesoderm and that synthesis approaches the kinetic limits of Pol II processivity. We propose that rapid transcription kinetics and negative autoregulation are responsible for the remarkable homogeneity of snail expression and the coordination of mesoderm invagination.

Mechanisms of transcriptional precision in animal development

We review recently identified mechanisms of transcriptional control that ensure reliable and reproducible patterns of gene expression in natural populations of developing embryos, despite inherent fluctuations in gene regulatory processes, variations in genetic backgrounds and exposure to diverse environmental conditions. These mechanisms are not responsible for switching genes on and off. Instead, they control the fine-tuning of gene expression and ensure regulatory precision. Several such mechanisms are discussed, including redundant binding sites within transcriptional enhancers, shadow enhancers, and 'poised' enhancers and promoters, as well as the role of 'redundant' gene interactions within regulatory networks. We propose that such regulatory mechanisms provide population fitness and 'fine-tune' the spatial and temporal control of gene expression.

Genes at work in random bouts: stochastically discontinuous gene activity makes cell cycle duration and cell fate decisions variable, thus providing for stem cells plasticity

Cell interdivision periods (IDP) in homogenous cell populations vary stochastically. Another aspect of probabilistic cell behavior is randomness in cell differentiation. These features are suggested to result from competing stochastic events of assembly/disassembly of the transcription pre-initiation complex (PIC) at gene promoters. The time needed to assemble a proper PIC from different proteins, which must be numerous enough to make their combination gene specific, may be comparable to the IDP. Nascent mRNA visualization at defined genes and inferences from protein level fluctuations in single cells suggest that some genes do operate in this way. The onset of mRNA production by such genes may miss the time windows provided by the cell cycle, resulting in cells differentiating into those in which the respective mRNAs are either present or absent. This creates a way to generate cell phenotype diversity in multicellular organisms.

Genomic insights into the different layers of gene regulation in yeast

The model organism Saccharomyces cerevisiae has allowed the development of new functional genomics techniques devoted to the study of transcription in all its stages. With these techniques, it has been possible to find interesting new mechanisms to control gene expression that act at different levels and for different gene sets apart from the known cis-trans regulation in the transcription initiation step. Here we discuss a method developed in our laboratory, Genomic Run-On, and other new methods that have recently appeared with distinct technical features. A comparison between the datasets generated by them provides interesting genomic insights into the different layers of gene regulation in eukaryotes.