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Transcriptional regulation, orchestrated by the interplay between transcription factors (TFs) and enhancers, governs gene expression dynamics crucial for cellular processes. While gross qualitative fluctuations in transcription factor-dependent gene expression patterning have a long history of characterization, the roles of these factors in the nuclei retaining expression in the presence or absence of these factors are now observable using modern techniques. Our study investigates the impact of Suppressor of Hairless (Su(H)), a broadly expressed transcription factor, on enhancer-driven transcriptional modulation using Drosophila early embryos as a model system. Building upon previous findings, we employ super-resolution microscopy to dissect Su(H)'s influence on sog-Distal (sogD) enhancer activity specifically in nuclei with preserved sogD-driven expression in the absence of Su(H) binding. We demonstrate that Su(H) occupancy perturbations alter expression levels and bursting dynamics. Notably, Su(H) absence during embryonic development exhibits region-specific effects, inhibiting expression dorsally and stabilizing expression ventrally, implying a nuanced role in enhancer regulation. Our findings shed light on the intricate mechanisms that govern transcriptional dynamics and suggest a critical patterning role for Notch/Hairless signaling in sog expression as embryos transition to gastrulation. [ABSTRACT FROM AUTHOR]

Many biological and medical questions can be modeled using time-to-event data in finite-state Markov chains, with the phase-type distribution describing intervals between events. We solve the inverse problem: given a phase-type distribution, can we identify the transition rate parameters of the underlying Markov chain? For a specific class of solvable Markov models, we show this problem has a unique solution up to finite symmetry transformations, and we outline a recursive method for computing symbolic solutions for these models across any number of states. Using the Thomas decomposition technique from computer algebra, we further provide symbolic solutions for any model. Interestingly, different models with the same state count but distinct transition graphs can yield identical phase-type distributions. To distinguish among these, we propose additional properties beyond just the time to the next event. We demonstrate the method’s applicability by inferring transcriptional regulation models from single-cell transcription imaging data. [ABSTRACT FROM AUTHOR]

Genetically identical cell populations growing in the same environment show a large degree of heterogeneity in gene expression profiles, resulting in significant phenotypic consequences. One of the important sources of this heterogeneity is transcriptional bursting. In recent years, single cell transcriptome sequencing (scRNA-seq) data have been widely used to infer the kinetics of transcriptional bursting. Moreover, the increasing evidence showed that genes are jointly regulated by several competitive pathways when activated by external signals, and there is molecular memory in the process of gene state switching. Therefore, a gene expression model considering both gene activation pathway and molecular memory can better reflect the nature of transcriptional bursting. In this paper, we used an approximate Bayesian computation algorithm called ABC-PRC combined with a gene expression model that considered crosstalk and memory, to infer the kinetics of transcriptional bursting. We analyze the internal relationship between transcriptional bursting and gene expression using scRNA-seq data obtained from mouse embryonic stem cells and mouse embryonic fibroblasts. Both model analysis and data simulations demonstrate that the bimodal coefficient increases with the increase of burst size, and the noise intensity decreases with the increase of burst frequency. Additionally, we show that some previously proposed models can be simplified as special cases of the gene expression model and inference algorithm proposed here under certain circumstances. 1. Genome-wide transcriptional bursting kinetics are inferred by approximate Bayesian algorithm ABC-PRC both from scRNA-seq data and gene expression models. 2. This article uses a combination of data-driven and model-driven methods to study the intrinsic relationship between transcriptional bursting and gene expression. 3. The model and inference process used in this article can be simplified and widely applied to the classical telegraph model and the generalized telegraph model. [ABSTRACT FROM AUTHOR]

Recent quantitative measurements have revealed dynamics of transcription factor DNA binding, preinitiation complex assembly, chromatin remodeling, and transcriptional bursting in living cells. Timescale comparison between transcription initiation events (input) and transcriptional burst kinetics (output) reveals mechanisms of transcription regulation. Transcription dynamics are shaped by the complex interplay of transient DNA binding of regulatory factors and the local environment (chromatin, clustering of regulatory factors, and enhancer–promoter interactions). Emerging imaging technologies are beginning to facilitate the dissection of input–output relationships at single genes to understand how regulatory factors affect rate-limiting steps. Recent imaging studies have captured the dynamics of regulatory events of transcription inside living cells. These events include transcription factor (TF) DNA binding, chromatin remodeling and modification, enhancer–promoter (E–P) proximity, cluster formation, and preinitiation complex (PIC) assembly. Together, these molecular events culminate in stochastic bursts of RNA synthesis, but their kinetic relationship remains largely unclear. In this review, we compare the timescales of upstream regulatory steps (input) with the kinetics of transcriptional bursting (output) to generate mechanistic models of transcription dynamics in single cells. We highlight open questions and potential technical advances to guide future endeavors toward a quantitative and kinetic understanding of transcription regulation. [ABSTRACT FROM AUTHOR]

Cells must tightly regulate their gene expression programs and yet rapidly respond to acute biochemical and biophysical cues within their environment. This information is transmitted to the nucleus through various signaling cascades, culminating in the activation or repression of target genes. Transcription factors (TFs) are key mediators of these signals, binding to specific regulatory elements within chromatin. While live-cell imaging has conclusively proven that TF–chromatin interactions are highly dynamic, how such transient interactions can have long-term impacts on developmental trajectories and disease progression is still largely unclear. In this review, we summarize our current understanding of the dynamic nature of TF functions, starting with a historical overview of early live-cell experiments. We highlight key factors that govern TF dynamics and how TF dynamics, in turn, affect downstream transcriptional bursting. Finally, we conclude with open challenges and emerging technologies that will further our understanding of transcriptional regulation. [ABSTRACT FROM AUTHOR]

The transcription of DNA by RNA polymerase occurs as a discontinuous process described as transcriptional bursting. This bursting behavior is observed across species and has been quantified using various stochastic modeling approaches. There is a large body of evidence that suggests the bursts are actively modulated by transcriptional machinery and play a role in regulating developmental processes. Under a commonly used two-state model of transcription, various enhancer-, promoter- and chromatin microenvironment-associated features are found to differentially influence the size and frequency of bursting events - key parameters of the two-state model. Advancement of modeling and analysis tools has revealed that the simple two-state model and associated parameters may not sufficiently characterize the complex relationship between these features. The majority of experimental and modeling findings support the view of bursting as an evolutionarily conserved transcriptional control feature rather than an unintended byproduct of the transcription process. Stochastic transcriptional patterns contribute to enhanced cellular fitness and execution of proper development programs, which posit this mode of transcription as an important feature in developmental gene regulation. In this Review, we present compelling examples of the role of transcriptional bursting in development and explore the question of how stochastic transcription leads to deterministic organism development. [ABSTRACT FROM AUTHOR]

Chemotherapy, although effective in treating cancer, can induce various cellular responses, including senescence and drug resistance. Here, we investigate the transcriptomic alterations induced by doxorubicin (DOX), a commonly used chemotherapeutic agent, in human colon cancer cells. Using single-cell RNA sequencing, we identified distinct cell populations and their transcriptional profiles following subtoxic DOX treatment, revealing cell clusters characterized by differential expression of genes involved in cell cycle regulation and interferon (IFN) signaling. DOX-persisting proliferating cells exhibited upregulation of genes reported to be linked to the unphosphorylated form of ISGF3 (U‐ISGF3) transcription factor. Furthermore, we found that HSH2D , a poor prognostic marker, was highly upregulated in doxorubicin-surviving proliferative cells, and its expression was correlated with U-ISGF3-related genes. Analysis of transcription kinetics via mathematical modeling revealed that the number of mRNA molecules produced per transcriptional burst was increased for U-ISGF3-related genes. We also observed altered gene co-expression patterns of U-ISGF3-related genes and others upon DOX treatment, which potentially contributes to chemoresistance of DOX-surviving proliferative cells and may influence cancer cell fate after chemotherapy. Our findings highlight U-ISGF3-related genes and the JAK/STAT pathway as potential therapeutic targets for overcoming chemoresistance in colon cancer. [ABSTRACT FROM AUTHOR]

Gene expression in mammalian cells is inherently stochastic and mRNAs are synthesized in discrete bursts. Single-cell transcriptomics provides an unprecedented opportunity to explore the transcriptome-wide kinetics of transcriptional bursting. However, current analysis methods provide limited accuracy in bursting inference due to substantial noise inherent to single-cell transcriptomic data. In this study, we developed BISC, a Bayesian method for inferring bursting parameters from single cell transcriptomic data. Based on a beta-gamma-Poisson model, BISC modeled the mean–variance dependency to achieve accurate estimation of bursting parameters from noisy data. Evaluation based on both simulation and real intron sequential RNA fluorescence in situ hybridization data showed improved accuracy and reliability of BISC over existing methods, especially for genes with low expression values. Further application of BISC found bursting frequency but not bursting size was strongly associated with gene expression regulation. Moreover, our analysis provided new mechanistic insights into the functional role of enhancer and superenhancer by modulating both bursting frequency and size. BISC also formulated a downstream framework to identify differential bursting (in frequency and size separately) genes in samples under different conditions. Applying to multiple datasets (a mouse embryonic cell and fibroblast dataset, a human immune cell dataset and a human pancreatic cell dataset), BISC identified known cell-type signature genes that were missed by differential expression analysis, providing additional insights in understanding the cell-specific stochastic gene transcription. Applying to datasets of human lung and colon cancers, BISC successfully detected tumor signature genes based on alterations in bursting kinetics, which illustrates its value in understanding disease development regarding transcriptional bursting. Collectively, BISC provides a new tool for accurately inferring bursting kinetics and detecting differential bursting genes. This study also produced new insights in the role of transcriptional bursting in regulating gene expression, cell identity and tumor progression. [ABSTRACT FROM AUTHOR]

Regulatory relationships between transcription factors (TFs) and their target genes lie at the heart of cellular identity and function; however, uncovering these relationships is often labor-intensive and requires perturbations. Here, we propose a principled framework to systematically infer gene regulation for all TFs simultaneously in cells at steady state by leveraging the intrinsic variation in the transcriptional abundance across single cells. Through modeling and simulations, we characterize how transcriptional bursts of a TF gene are propagated to its target genes, including the expected ranges of time delay and magnitude of maximum covariation. We distinguish these temporal trends from the time-invariant covariation arising from cell states, and we delineate the experimental and technical requirements for leveraging these small but meaningful cofluctuations in the presence of measurement noise. While current technology does not yet allow adequate power for definitively detecting regulatory relationships for all TFs simultaneously in cells at steady state, we investigate a small-scale dataset to inform future experimental design. This study supports the potential value of mapping regulatory connections through stochastic variation, and it motivates further technological development to achieve its full potential. [ABSTRACT FROM AUTHOR]

Heat Shock (HS) signaling is activated in response to various types of cellular stress. This activation serves to protect cells from immediate threats in the surrounding environment. However, activation of HS signaling occurs in a heterogeneous manner within each cell population and can alter the epigenetic state of the cell, ultimately leading to long-term abnormalities in body function. Here, we summarize recent research findings obtained using molecular and genetic tools to track cells where HS signaling is activated. We then discuss the potential further applications of these tools, their limitations, and the necessary caveats in interpreting data obtained with these tools. [ABSTRACT FROM AUTHOR]

Background: The temporal progression of many fundamental processes in cells and organisms, including homeostasis, differentiation and development, are governed by gene regulatory networks (GRNs). GRNs balance fluctuations in the output of their genes, which trace back to the stochasticity of molecular interactions. Although highly desirable to understand life processes, predicting the temporal progression of gene products within a GRN is challenging when considering stochastic events such as transcription factor–DNA interactions or protein production and degradation. Results: We report a method to simulate and infer GRNs including genes and biochemical reactions at molecular detail. In our approach, we consider each network element to be isolated from other elements during small time intervals, after which we synchronize molecule numbers across all network elements. Thereby, the temporal behaviour of network elements is decoupled and can be treated by local stochastic or deterministic solutions. We demonstrate the working principle of this modular approach with a repressive gene cascade comprising four genes. By considering a deterministic time evolution within each time interval for all elements, our method approaches the solution of the system of deterministic differential equations associated with the GRN. By allowing genes to stochastically switch between on and off states or by considering stochastic production of gene outputs, we are able to include increasing levels of stochastic detail and approximate the solution of a Gillespie simulation. Thereby, CaiNet is able to reproduce noise-induced bi-stability and oscillations in dynamically complex GRNs. Notably, our modular approach further allows for a simple consideration of deterministic delays. We further infer relevant regulatory connections and steady-state parameters of a GRN of up to ten genes from steady-state measurements by identifying each gene of the network with a single perceptron in an artificial neuronal network and using a gradient decent method originally designed to train recurrent neural networks. To facilitate setting up GRNs and using our simulation and inference method, we provide a fast computer-aided interactive network simulation environment, CaiNet. Conclusion: We developed a method to simulate GRNs at molecular detail and to infer the topology and steady-state parameters of GRNs. Our method and associated user-friendly framework CaiNet should prove helpful to analyze or predict the temporal progression of reaction networks or GRNs in cellular and organismic biology. CaiNet is freely available at https://gitlab.com/GebhardtLab/CaiNet. [ABSTRACT FROM AUTHOR]

Many eukaryotic genes are expressed in randomly initiated bursts that are punctuated by periods of quiescence. Here, we show that the intermittent access of the promoters to transcription factors through relatively impervious chromatin contributes to this "noisy" transcription. We tethered a nuclease-deficient Cas9 fused to a histone acetyl transferase at the promoters of two endogenous genes in HeLa cells. An assay for transposase-accessible chromatin using sequencing showed that the activity of the histone acetyl transferase altered the chromatin architecture locally without introducing global changes in the nucleus and rendered the targeted promoters constitutively accessible. We measured the gene expression variability from the gene loci by performing single-molecule fluorescence in situ hybridization against mature messenger RNAs (mRNAs) and by imaging nascent mRNA molecules present at active gene loci in single cells. Because of the increased accessibility of the promoter to transcription factors, the transcription from two genes became less noisy, even when the average levels of expression did not change. In addition to providing evidence for chromatin accessibility as a determinant of the noise in gene expression, our study offers a mechanism for controlling gene expression noise which is otherwise unavoidable. [ABSTRACT FROM AUTHOR]

The rapid development of technologies provides the potential to perform real‐time visualization of transcriptional bursting patterns, superenhancer formation and sensitivity to perturbation, and interactions between enhancers, promoters, and regulators during the burst. The transcriptional bursting‐induced fluctuation can modify cell capacities, cell–cell communications, cell responses to microenvironmental changes, and forms of cell death. A large number of clinical and translational studies describe the existence of heterogeneity among cells, tissues, and organs but mechanism‐based understanding of how and why the heterogeneity exists and how it is formed. The transcriptional bursting, fluctuation, and control determine the development of heterogeneity and optimize cell functions in the cell development and differentiation, contribute to the initiation of cell dysfunction and tumorigenesis in response to environments, and development/evolvement of hyper/hyposensitivity to drugs. Spatiotemporal monitoring of transcriptional bursting and control provides a new insight and deeper understanding of spatiotemporal molecular medicine by integrating the transcriptional positioning and function with cell phenotypes, cell–cell communication, and clinical phenomes. [ABSTRACT FROM AUTHOR]

Differentiation can be modeled at the single cell level as a stochastic process resulting from the dynamical functioning of an underlying Gene Regulatory Network (GRN), driving stem or progenitor cells to one or many differentiated cell types. Metastability seems inherent to differentiation process as a consequence of the limited number of cell types. Moreover, mRNA is known to be generally produced by bursts, which can give rise to highly variable non-Gaussian behavior, making the estimation of a GRN from transcriptional profiles challenging. In this article, we present CARDAMOM (Cell type Analysis from scRna-seq Data achieved from a Mixture MOdel), a new algorithm for inferring a GRN from timestamped scRNA-seq data, which crucially exploits these notions of metastability and transcriptional bursting. We show that such inference can be seen as the successive resolution of as many regression problem as timepoints, after a preliminary clustering of the whole set of cells with regards to their associated bursts frequency. We demonstrate the ability of CARDAMOM to infer a reliable GRN from in silico expression datasets, with good computational speed. To the best of our knowledge, this is the first description of a method which uses the concept of metastability for performing GRN inference. [ABSTRACT FROM AUTHOR]

Cell‐to‐cell heterogeneity is a feature of the tumor necrosis factor (TNF)‐stimulated inflammatory response mediated by the transcription factor NF‐κB, motivating an exploration of the underlying sources of this noise. Here, we combined single‐transcript measurements with computational models to study transcriptional noise at six NF‐κB‐regulated inflammatory genes. In the basal state, NF‐κB‐target genes displayed an inverse correlation between mean and noise characteristic of transcriptional bursting. By analyzing transcript distributions with a bursting model, we found that TNF primarily activated transcription by increasing burst size while maintaining burst frequency for gene promoters with relatively high basal histone 3 acetylation (AcH3) that marks open chromatin environments. For promoters with lower basal AcH3 or when AcH3 was decreased with a small molecule drug, the contribution of burst frequency to TNF activation increased. Finally, we used a mathematical model to show that TNF positive feedback amplified gene expression noise resulting from burst size–mediated transcription, leading to a subset of cells with high TNF protein expression. Our results reveal potential sources of noise underlying intercellular heterogeneity in the TNF‐mediated inflammatory response. SYNOPSIS: Single‐cell transcript measurements show that TNF stimulation primarily increases transcriptional burst size at NF‐κB target genes. Chromatin measurements and mathematical modeling are used to explore the sources and consequences of TNF‐mediated bursting. NF‐κB‐target genes display an inverse correlation between mean and noise characteristic of transcriptional bursting.TNF primarily activates transcription by increasing burst size while maintaining burst frequency for promoters with relatively high basal histone 3 acetylation.The contribution of burst frequency to TNF activation increases when basal AcH3 at target promoters is reduced.Mathematical modeling shows that TNF positive feedback amplification of gene expression noise can produce a subset of cells with high TNF protein expression. [ABSTRACT FROM AUTHOR]

Enhancers bind transcription factors, chromatin regulators, and non-coding transcripts to modulate the expression of target genes. Here, we report 3D genome structures of single mouse ES cells as they are induced to exit pluripotency and transition through a formative stage prior to undergoing neuroectodermal differentiation. We find that there is a remarkable reorganization of 3D genome structure where inter-chromosomal intermingling increases dramatically in the formative state. This intermingling is associated with the formation of a large number of multiway hubs that bring together enhancers and promoters with similar chromatin states from typically 5–8 distant chromosomal sites that are often separated by many Mb from each other. In the formative state, genes important for pluripotency exit establish contacts with emerging enhancers within these multiway hubs, suggesting that the structural changes we have observed may play an important role in modulating transcription and establishing new cell identities. [Display omitted] • 3D genome structure and H3K27me3 levels are reorganized in formative state pluripotency • Hundreds of multiway chromatin hubs form in each formative state cell • Enhancer-promoter interactions are reconfigured in multiway hubs • The structural reorganization in the formative state is regulated by DNMT3a/b and TET1 Lando, Ma, et al. report 3D genome structures of single mouse ES cells as they begin to differentiate and transition through a formative state. Multiway chromatin hubs form in the formative state, providing a place where genes that are going to be turned on establish long-range contacts with emerging enhancers. [ABSTRACT FROM AUTHOR]

The circadian clock is an endogenous and self‐sustained oscillator that anticipates daily environmental cycles. While rhythmic gene expression of circadian genes is well‐described in populations of cells, the single‐cell mRNA dynamics of multiple core clock genes remain largely unknown. Here we use single‐molecule fluorescence in situ hybridisation (smFISH) at multiple time points to measure pairs of core clock transcripts, Rev‐erbα (Nr1d1), Cry1 and Bmal1, in mouse fibroblasts. The mean mRNA level oscillates over 24 h for all three genes, but mRNA numbers show considerable spread between cells. We develop a probabilistic model for multivariate mRNA counts using mixtures of negative binomials, which accounts for transcriptional bursting, circadian time and cell‐to‐cell heterogeneity, notably in cell size. Decomposing the mRNA variability into distinct noise sources shows that clock time contributes a small fraction of the total variability in mRNA number between cells. Thus, our results highlight the intrinsic biological challenges in estimating circadian phase from single‐cell mRNA counts and suggest that circadian phase in single cells is encoded post‐transcriptionally. SYNOPSIS: Single‐molecule imaging of transcripts is combined with mathematical modelling to investigate how the mRNA distributions of core‐clock circadian genes evolve over the circadian cycle while considering multiple sources of intrinsic and extrinsic variability. Single molecule fluorescence in‐situ hybridization (smFISH) of core‐clock genes at multiple time points over the circadian cycle shows that the mean mRNA level oscillates but there is significant variability in mRNA counts between cells.Bayesian model selection identifies a mixture model of negative binomials as the preferred model of the mRNA counts.Noise decomposition of the preferred model shows that a small percentage of measured variation in core clock transcript number is attributable to circadian time. [ABSTRACT FROM AUTHOR]

Eukaryotic transcription generally occurs in bursts of activity lasting minutes to hours; however, state-of-the-art measurements have revealed that many of the molecular processes that underlie bursting, such as transcription factor binding to DNA, unfold on timescales of seconds. This temporal disconnect lies at the heart of a broader challenge in physical biology of predicting transcriptional outcomes and cellular decision-making from the dynamics of underlying molecular processes. Here, we review how new dynamical information about the processes underlying transcriptional control can be combined with theoretical models that predict not only averaged transcriptional dynamics, but also their variability, to formulate testable hypotheses about the molecular mechanisms underlying transcriptional bursting and control. [ABSTRACT FROM AUTHOR]

Single-cell RNA sequencing data have complex features such as dropout events, overdispersion, and high-magnitude outliers, resulting in complicated probability distributions of mRNA abundances that are statistically characterized in terms of a zero-inated negative binomial (ZINB) model. Here we provide a mesoscopic kinetic foundation for the widely used ZINB model based on the biochemical reaction kinetics underlying transcription. Using multiscale modeling and simplification techniques, we show that the ZINB distribution of mRNA abundance and the related phenomenon of transcriptional bursting naturally emerge from a three-state stochastic transcription model. We further reveal a nontrivial quantitative relationship between dropout events and transcriptional bursting, which provides novel insights into how the burst size and burst frequency affect the dropout rate. Two different biophysical origins of overdispersion are also clarified at the single-cell level. [ABSTRACT FROM AUTHOR]

The idea that gene activity can be discontinuous will not surprise many biologists – many genes are restricted in when and where they can be expressed. Yet during the past 15 years, a collection of observations compiled under the umbrella term 'transcriptional bursting' has received considerable interest. Direct visualization of the dynamics of discontinuous transcription has expanded our understanding of basic transcriptional mechanisms and their regulation and provides a real-time readout of gene activity during the life of a cell. In this review, we try to reconcile the different views of the transcriptional process emerging from studies of bursting, and how this work contextualizes the relative importance of different regulatory inputs to normal dynamic ranges of gene activity. Demystification of the term 'transcriptional bursting.' Models with one or two gene states are unable to accurately describe dynamic transcription for many genes. Many alternative multistate models have been proposed, but these may be highly context specific. Understanding the contributions of numerous different cellular features and processes to bursting is required to build more accurate and general models of transcription dynamics. Emerging imaging technologies are beginning to facilitate the monitoring of these diverse sources of regulation. [ABSTRACT FROM AUTHOR]

Gene expression variability in mammalian systems plays an important role in physiological and pathophysiological conditions. This variability can come from differential regulation related to cell state (extrinsic) and allele‐specific transcriptional bursting (intrinsic). Yet, the relative contribution of these two distinct sources is unknown. Here, we exploit the qualitative difference in the patterns of covariance between these two sources to quantify their relative contributions to expression variance in mammalian cells. Using multiplexed error robust RNA fluorescent in situ hybridization (MERFISH), we measured the multivariate gene expression distribution of 150 genes related to Ca2+ signaling coupled with the dynamic Ca2+ response of live cells to ATP. We show that after controlling for cellular phenotypic states such as size, cell cycle stage, and Ca2+ response to ATP, the remaining variability is effectively at the Poisson limit for most genes. These findings demonstrate that the majority of expression variability results from cell state differences and that the contribution of transcriptional bursting is relatively minimal. Synopsis: Single‐cell coupled measurements of Ca2+ signaling dynamics, cell size, cell cycle, and expression of Ca2+ signaling‐related genes show that most of the gene expression variability is not gene‐specific. The remaining gene‐specific variability approaches Poisson limit for most genes. Cell state information, i.e. cell size, cell cycle stage, and Ca2+ signaling dynamic response to ligand was measured alongside single‐cell measurements of gene expression for 150 genes related to Ca2+ signaling using MERFISH.The majority of observed gene expression variance can be explained by 13 features related to cell state.The remaining unexplained variance approaches the Poisson limit for most genes. [ABSTRACT FROM AUTHOR]

Predicting how interactions between transcription factors and regulatory DNA sequence dictate rates of transcription and, ultimately, drive developmental outcomes remains an open challenge in physical biology. Using stripe 2 of the even-skipped gene in Drosophila embryos as a case study, we dissect the regulatory forces underpinning a key step along the developmental decision-making cascade: the generation of cytoplasmic mRNA patterns via the control of transcription in individual cells. Using live imaging and computational approaches, we found that the transcriptional burst frequency is modulated across the stripe to control the mRNA production rate. However, we discovered that bursting alone cannot quantitatively recapitulate the formation of the stripe and that control of the window of time over which each nucleus transcribes even-skipped plays a critical role in stripe formation. Theoretical modeling revealed that these regulatory strategies (bursting and the time window) respond in different ways to input transcription factor concentrations, suggesting that the stripe is shaped by the interplay of 2 distinct underlying molecular processes. [ABSTRACT FROM AUTHOR]

Single-molecule imaging inside living cells has revealed that transcription factors (TFs) bind to DNA transiently, but a long-standing question is how this transient binding is related to transcription activation. Here, we devised a microscopy method to simultaneously measure transient TF binding at a single locus and the effect of these binding events on transcription. We show that DNA binding of the yeast TF Gal4 activates transcription of a target gene within a few seconds, with at least ∼20% efficiency and with a high initiation rate of ∼1 RNA/s. Gal4 DNA dissociation decreases transcription rapidly. Moreover, at a gene with multiple binding sites, individual Gal4 molecules only rarely stay bound throughout the entire burst but instead frequently exchange during a burst to increase the transcriptional burst duration. Our results suggest a mechanism for enhancer regulation in more complex eukaryotes, where TF cooperativity and exchange enable robust and responsive transcription regulation. [Display omitted] • In vivo imaging of transcription factor Gal4 DNA binding and GAL10 transcription • Gal4 association and dissociation activates and deactivates GAL10 transcription rapidly • Gal4 binds DNA surprisingly long, but rarely throughout the entire burst • Gal4 molecules frequently exchange to increase the transcriptional burst duration Pomp et al. use simultaneous imaging of transcription factor DNA binding and transcriptional activity of a single target gene in living cells to understand their kinetic relationship. Transcription factor Gal4 binds DNA transiently but exchanges frequently to prolong transcriptionally active periods. Transcription factor cooperativity and exchange enable robust transcription regulation. [ABSTRACT FROM AUTHOR]

Transcriptional enhancers are regulatory DNAs that instruct when and where genes should be transcribed in response to a variety of intrinsic and external signals. They contain a cluster of binding sites for sequence‐specific transcription factors and co‐activators to determine the spatiotemporal specificity of gene activities during development. Enhancers are often positioned in distal locations from their target promoters. In some cases, they work over a million base pairs or more. In the traditional view, enhancers have been thought to stably interact with promoters in a targeted manner. However, quantitative imaging studies provide a far more dynamic picture of enhancer action. Moreover, recent Hi‐C methods suggest that regulatory interactions are dynamically regulated by the higher‐order chromosome topology. In this review, we summarize the emerging findings in the field and propose that assembly of "transcription hubs" in the context of 3D genome structure plays an important role in transcriptional regulation. [ABSTRACT FROM AUTHOR]

During the evolution of gene families, functional diversification of proteins often follows gene duplication. However, many gene families expand while preserving protein sequence. Why do cells maintain multiple copies of the same gene? Here we have addressed this question for an actin family with 17 genes encoding an identical protein. The genes have divergent flanking regions and are scattered throughout the genome. Surprisingly, almost the entire family showed similar developmental expression profiles, with their expression also strongly coupled in single cells. Using live cell imaging, we show that differences in gene expression were apparent over shorter timescales, with family members displaying different transcriptional bursting dynamics. Strong "bursty" behaviors contrasted steady, more continuous activity, indicating different regulatory inputs to individual actin genes. To determine the sources of these different dynamic behaviors, we reciprocally exchanged the upstream regulatory regions of gene family members. This revealed that dynamic transcriptional behavior is directly instructed by upstream sequence, rather than features specific to genomic context. A residual minor contribution of genomic context modulates the gene OFF rate. Our data suggest promoter diversification following gene duplication could expand the range of stimuli that regulate the expression of essential genes. These observations contextualize the significance of transcriptional bursting. [ABSTRACT FROM AUTHOR]

Many mammalian genes are transcribed during short bursts of variable frequencies and sizes that substantially contribute to cell-to-cell variability. However, which molecular mechanisms determine bursting properties remains unclear. To probe putative mechanisms, we combined temporal analysis of transcription along the circadian cycle with multiple genomic reporter integrations, using both shortlived luciferase live microscopy and single-molecule RNA-FISH. Using the Bmall circadian promoter as our model, we observed that rhythmic transcription resulted predominantly from variations in burst frequency, while the genomic position changed the burst size. Thus, burst frequency and size independently modulated Bmall transcription. We then found that promoter histone-acetylation level covaried with burst frequency, being greatest at peak expression and lowest at trough expression, while remaining unaffected by the genomic location. In addition, specific deletions of ROR-responsive elements led to constitutively elevated histone acetylation and burst frequency. We then investigated the suggested link between histone acetylation and burst frequency by dCas9p300-targeted modulation of histone acetylation, revealing that acetylation levels influence burst frequency more than burst size. The correlation between acet- ylation levels at the promoter and burst frequency was also observed in endogenous circadian genes and in embryonic stem cell fate genes. Thus, our data suggest that histone acetylation-mediated control of transcription burst frequency is a common mechanism to control mammalian gene expression. [ABSTRACT FROM AUTHOR]

Gene expression is inherently stochastic. Advanced single-cell microscopy techniques together with mathematical models for single gene expression led to important insights in elucidating the sources of intrinsic noise in prokaryotic and eukaryotic cells. In addition to the finite size effects due to low copy numbers, translational bursting is a dominant source of stochasticity in cell scenarios involving few short lived mRNA transcripts with high translational efficiency (as is typically the case for prokaryotes), causing protein synthesis to occur in random bursts. In the context of gene regulation cascades, the Chemical Master Equation (CME) governing gene expression has in general no closed form solution, and the accurate stochastic simulation of the dynamics of complex gene regulatory networks is a major computational challenge. The CME associated to a single gene self regulatory motif has been previously approximated by a one dimensional time dependent partial integral differential equation (PIDE). However, to the best of our knowledge, multidimensional versions for such PIDE have not been developed yet. Here we propose a multidimensional PIDE model for regulatory networks involving multiple genes with self and cross regulations (in which genes can be regulated by different transcription factors) derived as the continuous counterpart of a CME with jump process. The model offers a reliable description of systems with translational bursting. In order to provide an efficient numerical solution, we develop a semilagrangian method to discretize the differential part of the PIDE, combined with a composed trapezoidal quadrature formula to approximate the integral term. We apply the model and numerical method to study sustained stochastic oscillations and the development of competence, a particular case of transient differentiation attained by certain bacterial cells under stress conditions. We found that the resulting probability distributions are distinguishable from those characteristic of other transient differentiation processes. In this way, they can be employed as markers or signatures that identify such phenomena from bacterial population experimental data, for instance. The computational efficiency of the semilagrangian method makes it suitable for purposes like model identification and parameter estimation from experimental data or, in combination with optimization routines, the design of gene regulatory networks under molecular noise. [ABSTRACT FROM AUTHOR]

DNA supercoiling has emerged as a major contributor to gene regulation in bacteria, but how DNA supercoiling impacts transcription dynamics in eukaryotes is unclear. Here, using single-molecule dual-color nascent transcription imaging in budding yeast, we show that transcriptional bursting of divergent and tandem GAL genes is coupled. Temporal coupling of neighboring genes requires rapid release of DNA supercoils by topoisomerases. When DNA supercoils accumulate, transcription of one gene inhibits transcription at its adjacent genes. Transcription inhibition of the GAL genes results from destabilized binding of the transcription factor Gal4. Moreover, wild-type yeast minimizes supercoiling-mediated inhibition by maintaining sufficient levels of topoisomerases. Overall, we discover fundamental differences in transcriptional control by DNA supercoiling between bacteria and yeast and show that rapid supercoiling release in eukaryotes ensures proper gene expression of neighboring genes. [Display omitted] • Transcription of neighboring GAL genes is coupled, and supercoils impede coupling • Buildup of supercoils from transcription inhibits transcription at adjacent genes • Inhibition occurs through destabilized DNA binding of the transcription factor • Wild-type yeast contains sufficient topoisomerase levels to prevent inhibition Patel et al. use single-molecule visualization of transcription of neighboring genes at the GAL locus in budding yeast to elucidate how DNA supercoils regulate transcription dynamics in eukaryotes. Accumulation of DNA supercoils from transcription impedes transcription at adjacent genes, but wild-type yeast prevents inhibition by sufficient supercoiling release. [ABSTRACT FROM AUTHOR]

Transcriptional activation of eukaryotic genes is accompanied, in general, by a change in the sensitivity of promoter chromatin to endonucleases. The structural basis of this alteration has remained elusive for decades; but the change has been viewed as a transformation of one structure into another, from "closed" to "open" chromatin. In contradistinction to this static and deterministic view of the problem, a dynamical and probabilistic theory of promoter chromatin has emerged as its solution. This theory, which we review here, explains observed variation in promoter chromatin structure at the level of single gene molecules and provides a molecular basis for random bursting in transcription-the conjecture that promoters stochastically transition between transcriptionally conducive and inconducive states. The mechanism of transcriptional regulation may be understood only in probabilistic terms. [ABSTRACT FROM AUTHOR]

Mammalian transcription occurs stochastically in short bursts interspersed by silent intervals showing a refractory period. However, the underlying processes and consequences on fluctuations in gene products are poorly understood. Here, we use single allele time-lapse recordings in mouse cells to identify minimal models of promoter cycles, which inform on the number and durations of rate-limiting steps responsible for refractory periods. The structure of promoter cycles is gene specific and independent of genomic location. Typically, five rate-limiting steps underlie the silent periods of endogenous promoters, while minimal synthetic promoters exhibit only one. Strikingly, endogenous or synthetic promoters with TATA boxes show simplified two-state promoter cycles. Since transcriptional bursting constrains intrinsic noise depending on the number of promoter steps, this explains why TATA box genes display increased intrinsic noise genome-wide in mammals, as revealed by single-cell RNA-seq. These findings have implications for basic transcription biology and shed light on interpreting single-cell RNA-counting experiments. [ABSTRACT FROM AUTHOR]

DNA copy number alterations (CNAs) are known to be related to genetic diseases, including cancer. The unlimited transcription (UT) model, in which transcription occurs permissively with a simple activation probability, has been proposed to investigate long-term effects of CNAs on gene expression values. Queueing theory was applied, and the copy-number–gene-expression relationship has been shown to be generally nonlinear in the UT model. However, the dynamic effects of CNAs on transcription and the underlying disorders related to diseases remain greatly unknown. Since most genes in a single cell are permissively transcribed in short periods of time interspersed by long periods of limited transcription, an alternative model for transcription in the restrictive state is needed for unraveling the effects of CNAs on gene expression levels with time. To address these issues, herein a single transcription (ST) model is proposed, in which bound TFs are assumed to be unloaded immediately after stimulating a transcription. Using the Laplace–Stieltjes transform and numerical analysis, the relationship between DNA copy number and gene expression level is evaluated. Dynamic modeling reveals that CNAs would potentially alter, or even reverse, the burst-like gene expression modifications while shifting from the ST model to the UT model. Moreover, functional disorders in transcriptional oscillation due to CNAs are shown via simulation. This paper demonstrates how mathematical theories could be helpful to interpret statistical findings from real data and achieve a better understanding of cancer biology. [ABSTRACT FROM PUBLISHER]

Abstract: Recent studies have shown that the in vivo dynamics of RNA numbers in bacteria is regulated, to a great extent, by the kinetics of rate limiting steps in transcription. Strong evidence suggests that the kinetics of these steps is sequence dependent. We investigate the selective advantages of rate limiting steps of differing kinetics. For that, we model the kinetics of expression of a gene responsible for promoting cell division at the expense of resources in the environment in individual cells of a population. We model mutations that affect the kinetics of the rate limiting steps and selective pressure in various environmental conditions. Depletion of resources leads to cell death. We find that small changes in the evolutionary constraints can favor widely different noise levels in RNA and protein numbers. Increasing the cost in nutrients for division favors noisier expression. The results provide a better understanding of why different genes differ in the kinetics of production of RNA and proteins. [Copyright &y& Elsevier]

Abstract: We study how long pause-prone sites, commonly sequence-dependent, affect transcription and RNA temporal levels in a delayed stochastic model of transcription at the single nucleotide level. We vary pause propensity, duration and the probability of premature termination of elongation at the pause site. We also study the effects of multiple pause sites. We show that pause sites can be used to fine-tune noise strength and burst size distribution of RNA levels. Varying pause rate and duration alone affects bursting but noise is not significantly affected. Noise strength can be changed by varying both parameters and, even more pronouncedly, by varying the probability of premature termination. Adding multiple pause sites amplifies the increase in noise and bursting. This regulatory mechanism of noise and bursting, being evolvable, may partially explain how different genes exhibit a wide spectrum of different behaviors. The results might assist the engineering of genes with a desired degree of noise. [ABSTRACT FROM AUTHOR]

Cellular differentiation is marked by temporally and spatially regulated gene expression. The ocular lens is one of the most powerful mammalian model system since it is composed from only two cell subtypes, called lens epithelial and fiber cells. Lens epithelial cells differentiate into fiber cells through a series of spatially and temporally orchestrated processes, including massive production of crystallins, cellular elongation and the coordinated degradation of nuclei and other organelles. Studies of transcriptional and posttranscriptional gene regulatory mechanisms in lens provide a wide range of opportunities to understand global molecular mechanisms of gene expression as steady-state levels of crystallin mRNAs reach very high levels comparable to globin genes in erythrocytes. Importantly, dysregulation of crystallin gene expression results in lens structural abnormalities and cataracts. The mRNA life cycle is comprised of multiple stages, including transcription, splicing, nuclear export into cytoplasm, stabilization, localization, translation and ultimate decay. In recent years, development of modern mRNA detection methods with single molecule and single cell resolution enabled transformative studies to visualize the mRNA life cycle to generate novel insights into the sequential regulatory mechanisms of gene expression during embryogenesis. This review is focused on recent major advancements in studies of transcriptional bursting in differentiating lens fiber cells, analysis of nascent mRNA expression from bi-directional promoters, transient nuclear accumulation of specific mRNAs, condensation of chromatin prior lens fiber cell denucleation, and outlines future studies to probe the interactions of individual mRNAs with specific RNA-binding proteins (RBPs) in the cytoplasm and regulation of translation and mRNA decay. • First review on transcriptional bursts in the eye and lens. • First review on bi-directional gene expression in the lens. • Up-to-date summary of the lens fiber cell denucleation process with respect of active transcription. • First introduction of zip code concept for subcellular localization of mRNAs in the eye. • Identification of major research gaps in the field of mRNA life cycle in the lens. [ABSTRACT FROM AUTHOR]

Expression of the gap and pair-rule genes plays an essential role in body segmentation during Drosophila embryogenesis. 1–5 However, it remains unclear how precise expression patterns of these key developmental genes arise from stochastic transcriptional activation at the single-cell level. Here, I employed genome-editing and live-imaging approaches to comprehensively visualize regulation of the gap and pair-rule genes at the endogenous loci. Quantitative image analysis revealed that the total duration of active transcription (transcription period) is a major determinant of spatial patterning of gene expression in early embryos. The length of the transcription period is determined by the continuity of bursting activities in individual nuclei, with the core expression domain producing more bursts than boundary regions. Each gene exhibits a distinct rate of nascent RNA production during transcriptional bursting, which contributes to gene-to-gene variability in the total output. I also provide evidence for "enhancer interference," wherein a distal weak enhancer interferes with transcriptional activation by a strong proximal enhancer to downregulate the length of the transcription period without changing the transcription rate. Analysis of the endogenous hunchback (hb) locus revealed that the removal of the distal shadow enhancer induces strong ectopic transcriptional activation, which suppresses refinement of the initial broad expression domain into narrower stripe patterns at the anterior part of embryos. This study provides key insights into the link between transcriptional bursting, enhancer-promoter interaction, and spatiotemporal patterning of gene expression during animal development. • Length of the transcription period determines the spatial patterns of gene expression • The continuity of transcriptional bursts controls length of the transcription period • The shadow enhancer of hb interferes with its activation by the proximal enhancer • "Enhancer interference" mediates the dynamic refinement of hb expression Using quantitative live-imaging and genome-editing methods, Fukaya shows that length of the transcription period is a major determinant of the spatial patterning of gap and pair-rule gene expression in early Drosophila embryos. The shadow enhancer mediates dynamic refinement of hunchback expression through "enhancer interference". [ABSTRACT FROM AUTHOR]

The coactivator p300/CREB-binding protein (CBP) regulates genes by facilitating the assembly of transcriptional machinery and by acetylating histones and other factors. However, it remains mostly unclear how both functions of p300 are dynamically coordinated during gene control. Here, we showed that p300 can orchestrate two functions through the formation of dynamic clusters with certain transcription factors (TFs), which is mediated by the interactions between a TF's transactivation domain (TAD) and the intrinsically disordered regions of p300. Co-condensation can enable spatially defined, all-or-none activation of p300's catalytic activity, priming the recruitment of coactivators, including Brd4. We showed that co-condensation can modulate transcriptional initiation rate and burst duration of target genes, underlying nonlinear gene regulatory functions. Such modulation is consistent with how p300 might shape gene bursting kinetics globally. Altogether, these results suggest an intriguing gene regulation mechanism, in which TF and p300 co-condensation contributes to transcriptional bursting regulation and cooperative gene control. [Display omitted] • Some transcription factors can recruit p300 by co-condensation • Interactions between disordered regions of TF and p300 can drive co-condensation • Co-condensation can promote p300 transactivation and stabilize TF-p300 assembly • Transcriptional bursting and dose-response curve are modulated by co-condensation Ma et al. demonstrate that transcription factor and coactivator p300 can cocondense. They systematically dissect the mechanism and function of co-condensation. The findings offer quantitative insights into how p300 catalytic activity, gene transcriptional bursting, and gene dose-response curve can be regulated by co-condensation. [ABSTRACT FROM AUTHOR]

RNA polymerase II (RNA Pol II) contains a disordered C-terminal domain (CTD) whose length enigmatically correlates with genome size. The CTD is crucial to eukaryotic transcription, yet the functional and evolutionary relevance of this variation remains unclear. Here, we investigate how CTD length and disorder influence transcription. We find that length modulates the size and frequency of transcriptional bursting. Disorder is highly conserved and facilitates CTD-CTD interactions, an ability we show is separable from protein sequence and necessary for efficient transcription. We build a data-driven quantitative model, simulations of which recapitulate experiments and support that CTD length promotes initial polymerase recruitment to the promoter and slows down its release from it and that CTD-CTD interactions enable recruitment of multiple polymerases. Our results reveal how these parameters provide access to a range of transcriptional activity, offering a new perspective for the mechanistic significance of CTD length and disorder in transcription across eukaryotes. • RNA Pol II's C-terminal domain (CTD) length inversely correlates with gene density • CTD length modulates transcriptional bursting and strength of self-interactions • CTD promotes RNA Pol II recruitment via direct and indirect (self-)interactions • Self-interactions link transcription, phase separation, and scaling across genome sizes Quintero-Cadena et al. investigate RNA Pol II's CTD in the context of transcription activation. The authors propose a model to explain the role of CTD-length variation, providing a link between the canonical and phase-separation perspectives of transcription and a potential mechanism for scaling transcription activation across genome sizes. [ABSTRACT FROM AUTHOR]

Transcriptional enhancers are major genomic elements that control gene activity in eukaryotes. Recent studies provided deeper insight into the temporal and spatial organization of transcription in the nucleus, the role of non-coding RNAs in the process, and the epigenetic control of gene expression. Thus, multiple molecular details of enhancer functioning were revealed. Here, we describe the recent data and models of molecular organization of enhancer-driven transcription. [ABSTRACT FROM AUTHOR]

Formation of self-associating loop domains is a fundamental organizational feature of metazoan genomes. Here, we employed quantitative live-imaging methods to visualize impacts of higher-order chromosome topology on enhancer-promoter communication in developing Drosophila embryos. Evidence is provided that distal enhancers effectively produce transcriptional bursting from target promoters over distances when they are flanked with boundary elements. Importantly, neither inversion nor deletion of a boundary element abrogates this "enhancer-assisting activity," suggesting that they can facilitate intra-domain enhancer-promoter interaction and production of transcriptional bursting independently of topologically associating domain (TAD) formation. In contrast, domain-skipping activity of distal enhancers was lost after disruption of topological domains. This observation raises a possibility that intra-domain and inter-domain enhancer-promoter interactions are differentially regulated by chromosome topology. • Intra-domain enhancer-promoter interaction occurs in the absence of TADs • Topological boundaries increase transcriptional output independently of TAD formation • TAD formation is essential for domain-skipping activity of distal enhancers Yokoshi et al. employ quantitative live-imaging methods to visualize impacts of TAD formation on enhancer-promoter communication. They show that intra-domain enhancer-promoter interaction occurs independently of TAD formation. In contrast, domain-skipping activity of distal enhancers is lost upon TAD disruption, suggesting that intra-domain and inter-domain enhancer-promoter interactions are differentially regulated by chromosome topology. [ABSTRACT FROM AUTHOR]

Information from developmental signaling pathways must be accurately decoded to generate transcriptional outcomes. In the case of Notch, the intracellular domain (NICD) transduces the signal directly to the nucleus. How enhancers decipher NICD in the real time of developmental decisions is not known. Using the MS2-MCP system to visualize nascent transcripts in single cells in Drosophila embryos, we reveal how two target enhancers read Notch activity to produce synchronized and sustained profiles of transcription. By manipulating the levels of NICD and altering specific motifs within the enhancers, we uncover two key principles. First, increased NICD levels alter transcription by increasing duration rather than frequency of transcriptional bursts. Second, priming of enhancers by tissue-specific transcription factors is required for NICD to confer synchronized and sustained activity; in their absence, transcription is stochastic and bursty. The dynamic response of an individual enhancer to NICD thus differs depending on the cellular context. • Notch-responsive enhancers are activated synchronously in Drosophila embryos • Notch levels modulate transcription by changing burst size • Notch elicits stochastic versus deterministic activation depending on cell context • Priming by Twist and Dorsal promotes fast and sustained response to Notch Falo-Sanjuan et al. use live imaging to investigate how enhancers detect and respond to Notch signaling in real time in vivo. They discover that responses are synchronized and sustained in the normal context, due to local transcription factor priming, and that elevated Notch activity increases transcription burst size rather than frequency. [ABSTRACT FROM AUTHOR]

Transcription is well known to be inherently stochastic and episodic, but the regulation of transcriptional dynamics is not well understood. Here, we analyze how Notch signaling modulates transcriptional bursting during animal development. Our focus is Notch regulation of transcription in germline stem cells of the nematode C. elegans. Using the MS2 system to visualize nascent transcripts and live imaging to record dynamics, we analyze bursting as a function of position within the intact animal. We find that Notch-dependent transcriptional activation is indeed "bursty"; that wild-type Notch modulates burst duration (ON-time) rather than duration of pauses between bursts (OFF-time) or mean burst intensity; and that a mutant Notch receptor, which is compromised for assembly into the Notch transcription factor complex, primarily modifies burst size (duration × intensity). These analyses thus visualize the effect of a canonical signaling pathway on metazoan transcriptional bursting in its native context. • Notch-dependent transcription assayed in its native context is "bursty" • Notch-dependent transcriptional bursts are graded across the stem cell pool • Burst duration is the key determinant of Notch-dependent transcriptional probability • Notch NICD strength affects both burst duration and intensity Lee et al. assay Notch regulation of transcription in C. elegans germline stem cells within the intact animal. They find that Notch signaling modulates the duration of transcriptional burst and not the intensity of signaling or the time between bursts. [ABSTRACT FROM AUTHOR]

Summary Transcriptional regulation occurs via changes to rates of different biochemical steps of transcription, but it remains unclear which rates are subject to change upon biological perturbation. Biochemical studies have suggested that stimuli predominantly affect the rates of RNA polymerase II (Pol II) recruitment and polymerase release from promoter-proximal pausing. Single-cell studies revealed that transcription occurs in discontinuous bursts, suggesting that features of such bursts like frequency and intensity could also be regulated. We combined Pol II chromatin immunoprecipitation sequencing (ChIP-seq) and single-cell transcriptional measurements to show that an independently regulated burst initiation step is required before polymerase recruitment can occur. Using a number of global and targeted transcriptional regulatory perturbations, we showed that biological perturbations regulated both burst initiation and polymerase pause release rates but seemed not to regulate polymerase recruitment rate. Our results suggest that transcriptional regulation primarily acts by changing the rates of burst initiation and polymerase pause release. Graphical Abstract Highlights • Burst initiation is required before polymerase recruitment can occur • Biological stimuli changed only burst initiation and polymerase pause release rates • No biological stimuli tested altered polymerase recruitment rate Mammalian genes are transcribed in discontinuous bursts. Using experimental data and computational modeling, Bartman et al. show that the key control points of transcriptional regulation are burst initiation and the release of RNA polymerase II from a paused state, but, unexpectedly, not polymerase recruitment rate. [ABSTRACT FROM AUTHOR]