Transcriptional Burst Initiation and Polymerase Pause Release Are Key Control Points of Transcriptional Regulation

Clinical significance of spatiotemporal transcriptional bursting and control

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.

Capicua is a fast-acting transcriptional brake

Even though transcriptional repressors are studied with ever-increasing molecular resolution, the temporal aspects of gene repression remain poorly understood. Here, we address the dynamics of transcriptional repression by Capicua (Cic), which is essential for normal development and is commonly mutated in human cancers and neurodegenerative diseases.^1,2 We report the speed limit for Cic-dependent gene repression based on live imaging and optogenetic perturbations in the early Drosophila embryo, where Cic was originally discovered.³ Our measurements of Cic concentration and intranuclear mobility, along with real-time monitoring of the activity of Cic target genes, reveal remarkably fast transcriptional repression within minutes of removing an optogenetic de-repressive signal. In parallel, quantitative analyses of transcriptional bursting of Cic target genes support a repression mechanism providing a fast-acting brake on burst generation. This work sets quantitative constraints on potential mechanisms for gene regulation by Cic.

Quantitative imaging of transcription in living Drosophila embryos reveals the impact of core promoter motifs on promoter state dynamics

Genes are expressed in stochastic transcriptional bursts linked to alternating active and inactive promoter states. A major challenge in transcription is understanding how promoter composition dictates bursting, particularly in multicellular organisms. We investigate two key Drosophila developmental promoter motifs, the TATA box (TATA) and the Initiator (INR). Using live imaging in Drosophila embryos and new computational methods, we demonstrate that bursting occurs on multiple timescales ranging from seconds to minutes. TATA-containing promoters and INR-containing promoters exhibit distinct dynamics, with one or two separate rate-limiting steps respectively. A TATA box is associated with long active states, high rates of polymerase initiation, and short-lived, infrequent inactive states. In contrast, the INR motif leads to two inactive states, one of which relates to promoter-proximal polymerase pausing. Surprisingly, the model suggests pausing is not obligatory, but occurs stochastically for a subset of polymerases. Overall, our results provide a rationale for promoter switching during zygotic genome activation.

TNF stimulation primarily modulates transcriptional burst size of NF-κB-regulated genes

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.

Altering transcription factor binding reveals comprehensive transcriptional kinetics of a basic gene

Transcription is a vital process activated by transcription factor (TF) binding. The active gene releases a burst of transcripts before turning inactive again. While the basic course of transcription is well understood, it is unclear how binding of a TF affects the frequency, duration and size of a transcriptional burst. We systematically varied the residence time and concentration of a synthetic TF and characterized the transcription of a synthetic reporter gene by combining single molecule imaging, single molecule RNA-FISH, live transcript visualisation and analysis with a novel algorithm, Burst Inference from mRNA Distributions (BIRD). For this well-defined system, we found that TF binding solely affected burst frequency and variations in TF residence time had a stronger influence than variations in concentration. This enabled us to device a model of gene transcription, in which TF binding triggers multiple successive steps before the gene transits to the active state and actual mRNA synthesis is decoupled from TF presence. We quantified all transition times of the TF and the gene, including the TF search time and the delay between TF binding and the onset of transcription. Our quantitative measurements and analysis revealed detailed kinetic insight, which may serve as basis for a bottom-up understanding of gene regulation.

Multiplexed single-cell profiling of chromatin states at genomic loci by expansion microscopy

Proper regulation of genome architecture and activity is essential for the development and function of multicellular organisms. Histone modifications, acting in combination, specify these activity states at individual genomic loci. However, the methods used to study these modifications often require either a large number of cells or are limited to targeting one histone mark at a time. Here, we developed a new method called Single Cell Evaluation of Post-TRanslational Epigenetic Encoding (SCEPTRE) that uses Expansion Microscopy (ExM) to visualize and quantify multiple histone modifications at non-repetitive genomic regions in single cells at a spatial resolution of ∼75 nm. Using SCEPTRE, we distinguished multiple histone modifications at a single housekeeping gene, quantified histone modification levels at multiple developmentally-regulated genes in individual cells, and evaluated the relationship between histone modifications and RNA polymerase II loading at individual loci. We find extensive variability in epigenetic states between individual gene loci hidden from current population-averaged measurements. These findings establish SCEPTRE as a new technique for multiplexed detection of combinatorial chromatin states at single genomic loci in single cells.

Fast-acting chemical tools to delineate causality in transcriptional control

Multi-dimensional omics profiling continues to illuminate the complexity of cellular processes. Because of difficult mechanistic interpretation of phenotypes induced by slow perturbation, fast experimental setups are increasingly used to dissect causal interactions directly in cells. Here we review a growing body of studies that leverage rapid pharmacological perturbation to delineate causality in gene control. When coupled with kinetically matched readouts, fast chemical genetic tools allow recording of primary phenotypes before confounding secondary effects manifest. The toolbox encompasses directly acting probes, such as active-site inhibitors and proteolysis-targeting chimeras, as well as strategies using genetic engineering to render target proteins chemically tractable, such as analog-sensitive and degron systems. We anticipate that extrapolation of these concepts to single-cell setups will further transform our mechanistic understanding of transcriptional control in the future. Importantly, the concept of leveraging speed to derive causality should be broadly applicable to many aspects of biological regulation.

Cis-regulatory chromatin loops arise before TADs and gene activation, and are independent of cell fate during early Drosophila development

Acquisition of cell fate is thought to rely on the specific interaction of remote cis-regulatory modules (CRMs), for example, enhancers and target promoters. However, the precise interplay between chromatin structure and gene expression is still unclear, particularly within multicellular developing organisms. In the present study, we employ Hi-M, a single-cell spatial genomics approach, to detect CRM-promoter looping interactions within topologically associating domains (TADs) during early Drosophila development. By comparing cis-regulatory loops in alternate cell types, we show that physical proximity does not necessarily instruct transcriptional states. Moreover, multi-way analyses reveal that multiple CRMs spatially coalesce to form hubs. Loops and CRM hubs are established early during development, before the emergence of TADs. Moreover, CRM hubs are formed, in part, via the action of the pioneer transcription factor Zelda and precede transcriptional activation. Our approach provides insight into the role of CRM-promoter interactions in defining transcriptional states, as well as distinct cell types.

Super-Enhancers and CTCF in Early Embryonic Cell Fate Decisions

Cell fate decisions are the backbone of many developmental and disease processes. In early mammalian development, precise gene expression changes underly the rapid division of a single cell that leads to the embryo and are critically dependent on autonomous cell changes in gene expression. To understand how these lineage specifications events are mediated, scientists have had to look past protein coding genes to the cis regulatory elements (CREs), including enhancers and insulators, that modulate gene expression. One class of enhancers, termed super-enhancers, is highly active and cell-type specific, implying their critical role in modulating cell-type specific gene expression. Deletion or mutations within these CREs adversely affect gene expression and development and can cause disease. In this mini-review we discuss recent studies describing the potential roles of two CREs, enhancers and binding sites for CTCF, in early mammalian development.

The transcriptional regulation of normal and malignant blood cell development

Development of multicellular organisms requires the differential usage of our genetic information to change one cell fate into another. This process drives the appearance of different cell types that come together to form specialized tissues sustaining a healthy organism. In the last decade, by moving away from studying single genes toward a global view of gene expression control, a revolution has taken place in our understanding of how genes work together and how cells communicate to translate the information encoded in the genome into a body plan. The development of hematopoietic cells has long served as a paradigm of development in general. In this review, we highlight how transcription factors and chromatin components work together to shape the gene regulatory networks controlling gene expression in the hematopoietic system and to drive blood cell differentiation. In addition, we outline how this process goes astray in blood cancers. We also touch upon emerging concepts that place these processes firmly into their associated subnuclear structures adding another layer of the control of differential gene expression.

Insights into gene regulation: From regulatory genomic elements to DNA-protein and protein-protein interactions

Transcription is orchestrated by non-coding regulatory elements embedded in chromatin, which exist within the larger context of chromosome topology. Here, we review recent insights into the functions of non-coding regulatory elements and their protein interactors during transcription control. A picture emerges in which the topological environment constraints enhancer-promoter interactions and specific enhancer-bound proteins with distinct promoter-compatibilities refine target promoter choice. Such compatibilities are encoded within the sequences of enhancers and promoters and realized by diverse transcription factors and cofactors with distinct biochemical activities. An emerging property of transcription factors and cofactors is the formation of nuclear microenvironments or membraneless compartments that can have properties of phase-separated liquids. These environments are able to selectively enrich certain proteins and small molecules over others. Further investigation into the interaction of transcriptional regulators with themselves and regulatory DNA elements will help reveal the complexities of gene regulation within the context of the nucleus.

Gene regulation gravitates toward either addition or multiplication when combining the effects of two signals

Two different cell signals often affect transcription of the same gene. In such cases, it is natural to ask how the combined transcriptional response compares to the individual responses. The most commonly used mechanistic models predict additive or multiplicative combined responses, but a systematic genome-wide evaluation of these predictions is not available. Here, we analyzed the transcriptional response of human MCF-7 cells to retinoic acid and TGF-β, applied individually and in combination. The combined transcriptional responses of induced genes exhibited a range of behaviors, but clearly favored both additive and multiplicative outcomes. We performed paired chromatin accessibility measurements and found that increases in accessibility were largely additive. There was some association between super-additivity of accessibility and multiplicative or super-multiplicative combined transcriptional responses, while sub-additivity of accessibility associated with additive transcriptional responses. Our findings suggest that mechanistic models of combined transcriptional regulation must be able to reproduce a range of behaviors.

Recruitment of Xrn1 to stress-induced genes allows efficient transcription by controlling RNA polymerase II backtracking

A new paradigm has emerged proposing that the crosstalk between nuclear transcription and cytoplasmic mRNA stability keeps robust mRNA levels in cells under steady-state conditions. A key piece in this crosstalk is the highly conserved 5'-3' RNA exonuclease Xrn1, which degrades most cytoplasmic mRNAs but also associates with nuclear chromatin to activate transcription by not well-understood mechanisms. Here, we investigated the role of Xrn1 in the transcriptional response of Saccharomyces cerevisiae cells to osmotic stress. We show that a lack of Xrn1 results in much lower transcriptional induction of the upregulated genes but in similar high levels of their transcripts because of parallel mRNA stabilization. Unexpectedly, lower transcription in xrn1 occurs with a higher accumulation of RNA polymerase II (RNAPII) at stress-inducible genes, suggesting that this polymerase remains inactive backtracked. Xrn1 seems to be directly implicated in the formation of a competent elongation complex because Xrn1 is recruited to the osmotic stress-upregulated genes in parallel with the RNAPII complex, and both are dependent on the mitogen-activated protein kinase Hog1. Our findings extend the role of Xrn1 in preventing the accumulation of inactive RNAPII at highly induced genes to other situations of rapid and strong transcriptional upregulation.

Recent advances in understanding RNA polymerase II structure and function

More than 50 years after the identification of RNA polymerase II, the enzyme responsible for the transcription of most eukaryotic genes, studies have continued to reveal fresh aspects of its structure and regulation. New technologies, coupled with years of development of a vast catalog of RNA polymerase II accessory proteins and activities, have led to new revelations about the transcription process. The maturation of cryo-electron microscopy as a tool for unraveling the detailed structure of large molecular machines has provided numerous structures of the enzyme and its accessory factors. Advances in biophysical methods have enabled the observation of a single polymerase’s behavior, distinct from work on aggregate population averages. Other recent work has revealed new properties and activities of the general initiation factors that RNA polymerase II employs to accurately initiate transcription, as well as chromatin proteins that control RNA polymerase II’s firing frequency, and elongation factors that facilitate the enzyme’s departure from the promoter and which control sequential steps and obstacles that must be navigated by elongating RNA polymerase II. There has also been a growing appreciation of the physical properties conferred upon many of these proteins by regions of each polypeptide that are of low primary sequence complexity and that are often intrinsically disordered. This peculiar feature of a surprisingly large number of proteins enables a disordered region of the protein to morph into a stable structure and creates an opportunity for pathway participants to dynamically partition into subcompartments of the nucleus. These subcompartments host designated portions of the chemical reactions that lead to mRNA synthesis. This article highlights a selection of recent findings that reveal some of the resolved workings of RNA polymerase II and its ensemble of supporting factors.

Enhancer-promoter communication: hubs or loops?

There has been a sea change in our view of transcription regulation during the past decade (Fukaya et al., 2016, Lim et al., 2018, Hnisz et al., 2017 [3], Liu et al., 2018 [4], Kato et al., 2012). Classical models of enhancer-promoter interactions and the stepwise assembly of individual RNA Polymerase II (Pol II) complexes have given way to the realization that active transcription foci contain clusters-hubs-of transcriptional activators and Pol II. Here we summarize recent findings pointing to the occurrence of transcription hubs and the implications of such hubs on the regulation of gene activity.

Two classes of active transcription sites and their roles in developmental regulation

The expression of genes encoding powerful developmental regulators is exquisitely controlled, often at multiple levels. Here, we investigate developmental expression of three conserved genes, Caenorhabditis elegans mpk-1, lag-1, and lag-3/sel-8, which encode homologs of ERK/MAPK and core components of the Notch-dependent transcription complex, respectively. We use single-molecule FISH (smFISH) and MATLAB to visualize and quantify nuclear nascent transcripts and cytoplasmic mRNAs as a function of position along the germline developmental axis. Using differentially labeled probes, one spanning an exceptionally long first intron and the other spanning exons, we identify two classes of active transcription sites (ATS). The iATS class, for "incomplete" ATS, harbors only partial nascent transcripts; the cATS class, for "complete" ATS, harbors full-length nascent transcripts. Remarkably, the frequencies of iATS and cATS are patterned along the germline axis. For example, most mpk-1 ATS are iATS in hermaphrodite germline stem cells, but most are cATS in differentiating stem cell daughters. Thus, mpk-1 ATS class frequencies switch in a graded manner as stem cell daughters begin differentiation. Importantly, the patterns of ATS class frequency are gene-, stage-, and sex-specific, and cATS frequency strongly correlates with transcriptional output. Although the molecular mechanism underlying ATS classes is not understood, their primary difference is the extent of transcriptional progression. To generate only partial nascent transcripts in iATS, progression must be slowed, paused, or aborted midway through the gene. We propose that regulation of ATS class can be a critical mode of developmental gene regulation.

Queuing Models of Gene Expression: Analytical Distributions and Beyond

Activation of a gene is a multistep biochemical process, involving recruitments of transcription factors and histone kinases as well as modification of histones. Many of these intermediate reaction steps would have been unspecified by experiments. Therefore, classical two-state models of gene expression established based on the memoryless (or Markovian) assumption would not well describe the reality in gene expression. Recent experimental data have indicated that the inactive phases of gene promoters are differently distributed, showing strong memory. Here, we use a nonexponential waiting-time distribution to model the complex activation process of a gene, and then analyze a queuing model of stochastic transcription. We successfully derive the analytical expression of the stationary mRNA distribution, which provides insight into the effect of molecular memory created by complex activating events on the mRNA expression. We find that the reduction in the waiting-time noise may result in the increase in the mRNA noise, contrary to the previous conclusion. Based on the derived distribution, we also develop a method to infer the waiting-time distribution from a known mRNA distribution. Data analysis on a realistic example verifies the validity of this method.

Signaling Mechanism of Transcriptional Bursting: A Technical Resolution-Independent Study

Simple Summary

Following changing cellular signals, various genes adjust their activities and initiate transcripts with the right rates. The precision of such a transcriptional response has a fundamental role in the survival and development of lives. Quite unexpectedly, gene transcription has been uncovered to occur in sporadic bursts, rather than in a continuous manner. This has raised a provoking issue of how the bursting transmits regulatory signals, and it remains controversial whether the burst size, frequency, or both, take the role of signal transmission. Here, this study showed that only the burst frequency was subject to modulation by activators that carry the regulatory signals. A higher activator concentration led to a larger frequency, whereas the size remains unchanged. When very high, the burst cluster emerged, which may be mistaken as a large burst. This work thus supports the conclusion that transcription regulation is in a “digital” way.

Abstract

Gene transcription has been uncovered to occur in sporadic bursts. However, due to technical difficulties in differentiating individual transcription initiation events, it remains debated as to whether the burst size, frequency, or both are subject to modulation by transcriptional activators. Here, to bypass technical constraints, we addressed this issue by introducing two independent theoretical methods including analytical research based on the classic two-model and information entropy research based on the architecture of transcription apparatus. Both methods connect the signaling mechanism of transcriptional bursting to the characteristics of transcriptional uncertainty (i.e., the differences in transcriptional levels of the same genes that are equally activated). By comparing the theoretical predictions with abundant experimental data collected from published papers, the results exclusively support frequency modulation. To further validate this conclusion, we showed that the data that appeared to support size modulation essentially supported frequency modulation taking into account the existence of burst clusters. This work provides a unified scheme that reconciles the debate on burst signaling.

Modulation of the Promoter Activation Rate Dictates the Transcriptional Response to Graded BMP Signaling Levels in the Drosophila Embryo

Morphogen gradients specify cell fates during development, with a classic example being the bone morphogenetic protein (BMP) gradient's conserved role in embryonic dorsal-ventral axis patterning. Here, we elucidate how the BMP gradient is interpreted in the Drosophila embryo by combining live imaging with computational modeling to infer transcriptional burst parameters at single-cell resolution. By comparing burst kinetics in cells receiving different levels of BMP signaling, we show that BMP signaling controls burst frequency by regulating the promoter activation rate. We provide evidence that the promoter activation rate is influenced by both enhancer and promoter sequences, whereas Pol II loading rate is primarily modulated by the enhancer. Consistent with BMP-dependent regulation of burst frequency, the numbers of BMP target gene transcripts per cell are graded across their expression domains. We suggest that graded mRNA output is a general feature of morphogen gradient interpretation and discuss how this can impact on cell-fate decisions.

Urn models for stochastic gene expression yield intuitive insights into the probability distributions of single-cell mRNA and protein counts

Fitting the probability mass functions from analytical solutions of stochastic models of gene expression to the single-cell count distributions of mRNA and protein molecules can yield valuable insights into mechanisms underlying gene expression. Solutions of chemical master equations are available for various kinetic schemes but, even for the basic ON-OFF genetic switch, they take complex forms with generating functions given as hypergeometric functions. Interpretation of gene expression dynamics in terms of bursts is not consistent with the complete range of parameters for these functions. Physical insights into the probability mass functions are essential to ensure proper interpretations but are lacking for models considering genetic switches. To fill this gap, we develop urn models for stochastic gene expression. We sample RNA polymerases or ribosomes from a master urn, which represents the cytosol, and assign them to recipient urns of two or more colors, which represent time intervals in which no switching occurs. Colors of the recipient urns represent sub-systems of the promoter states, and the assignments to urns of a specific color represent gene expression. We use elementary principles of discrete probability theory to solve a range of kinetic models without feedback, including the Peccoud-Ycart model, the Shahrezaei-Swain model, and models with an arbitrary number of promoter states. In the last case, we obtain a novel result for the protein distribution. For activated genes, we show that transcriptional lapses, which are events of gene inactivation for short time intervals separated by long active intervals, quantify the transcriptional dynamics better than bursts. We show that the intuition gained from our urn models may also be useful in understanding existing solutions for models with feedback. We contrast our models with urn models for related distributions, discuss a generalization of the Delaporte distribution for single-cell data analysis, and highlight the limitations of our models.

A Stochastic Model of Gene Expression with Polymerase Recruitment and Pause Release

Transcriptional bursting is a major source of noise in gene expression. The telegraph model of gene expression, whereby transcription switches between on and off states, is the dominant model for bursting. Recently, it was shown that the telegraph model cannot explain a number of experimental observations from perturbation data. Here, we study an alternative model that is consistent with the data and which explicitly describes RNA polymerase recruitment and polymerase pause release, two steps necessary for messenger RNA (mRNA) production. We derive the exact steady-state distribution of mRNA numbers and an approximate steady-state distribution of protein numbers, which are given by generalized hypergeometric functions. The theory is used to calculate the relative sensitivity of the coefficient of variation of mRNA fluctuations for thousands of genes in mouse fibroblasts. This indicates that the size of fluctuations is mostly sensitive to the rate of burst initiation and the mRNA degradation rate. Furthermore, we show that 1) the time-dependent distribution of mRNA numbers is accurately approximated by a modified telegraph model with a Michaelis-Menten like dependence of the effective transcription rate on RNA polymerase abundance, and 2) the model predicts that if the polymerase recruitment rate is comparable or less than the pause release rate, then upon gene replication, the mean number of RNA per cell remains approximately constant. This gene dosage compensation property has been experimentally observed and cannot be explained by the telegraph model with constant rates.

Transcription in Living Cells: Molecular Mechanisms of Bursting

Transcription in several organisms from certain bacteria to humans has been observed to be stochastic in nature: toggling between active and inactive states. Periods of active nascent RNA synthesis known as bursts represent individual gene activation events in which multiple polymerases are initiated. Therefore, bursting is the single locus illustration of both gene activation and repression. Although transcriptional bursting was originally observed decades ago, only recently have technological advances enabled the field to begin elucidating gene regulation at the single-locus level. In this review, we focus on how biochemical, genomic, and single-cell data describe the regulatory steps of transcriptional bursts.

The yeast exoribonuclease Xrn1 and associated factors modulate RNA polymerase II processivity in 5' and 3' gene regions

mRNA levels are determined by the balance between mRNA synthesis and decay. Protein factors that mediate both processes, including the 5'-3' exonuclease Xrn1, are responsible for a cross-talk between the two processes that buffers steady-state mRNA levels. However, the roles of these proteins in transcription remain elusive and controversial. Applying native elongating transcript sequencing (NET-seq) to yeast cells, we show that Xrn1 functions mainly as a transcriptional activator and that its disruption manifests as a reduction of RNA polymerase II (Pol II) occupancy downstream of transcription start sites. By combining our sequencing data and mathematical modeling of transcription, we found that Xrn1 modulates transcription initiation and elongation of its target genes. Furthermore, Pol II occupancy markedly increased near cleavage and polyadenylation sites in xrn1Δ cells, whereas its activity decreased, a characteristic feature of backtracked Pol II. We also provide indirect evidence that Xrn1 is involved in transcription termination downstream of polyadenylation sites. We noted that two additional decay factors, Dhh1 and Lsm1, seem to function similarly to Xrn1 in transcription, perhaps as a complex, and that the decay factors Ccr4 and Rpb4 also perturb transcription in other ways. Interestingly, the decay factors could differentiate between SAGA- and TFIID-dominated promoters. These two classes of genes responded differently to XRN1 deletion in mRNA synthesis and were differentially regulated by mRNA decay pathways, raising the possibility that one distinction between these two gene classes lies in the mechanisms that balance mRNA synthesis with mRNA decay.

Histone methyltransferase SETD2: a potential tumor suppressor in solid cancers

Epigenetic regulation plays an important role in the occurrence, development and treatment of malignant tumors; and a great deal of attention has been paid to the histone methylation level in recent years. As a 230-kD epigenetic regulator, the histone H3 lysine 36 histone (H3K36) methyltransferase SETD2 is a key enzyme of the nuclear receptor SET domain-containing (NSD) family, which is associated with a specific hyperphosphorylated domain, a large subunit of RNA polymerase II (RNAPII), named RNAPII subunit B1 (RPB1), and SETD2 which methylates the ly-36 position of dimethylated histone H3 (H3K36me2) to generate trimethylated H3K36 (H3K36me3). SETD2 is involved in various cellular processes, including transcriptional regulation, DNA damage repair, non-histone protein-related functions and some other processes. Great efforts of high-throughput sequencing have revealed that SETD2 is mutated or its function is lost in a range of solid cancers, including renal cancer, gastrointestinal cancer, lung cancer, pancreatic cancer, osteosarcoma, and so on. Mutation, or functional loss, of the SETD2 gene produces dysfunction in corresponding tumor tissue proteins, leading to tumorigenesis, progression, chemotherapy resistance, and unfavorable prognosis, suggesting that SETD2 possibly acts as a tumor suppressor. However, its underlying mechanism remains largely unexplored. In the present study, we summarized the latest advances of effects of SETD2 expression at the mRNA and protein levels in solid cancers, and its potential molecular and cellular functions as well as clinical applications were also reviewed.

Quantitative genetic analysis deciphers the impact of cis and trans regulation on cell-to-cell variability in protein expression levels

Identifying the factors that shape protein expression variability in complex multi-cellular organisms has primarily focused on promoter architecture and regulation of single-cell expression in cis. However, this targeted approach has to date been unable to identify major regulators of cell-to-cell gene expression variability in humans. To address this, we have combined single-cell protein expression measurements in the human immune system using flow cytometry with a quantitative genetics analysis. For the majority of proteins whose variability in expression has a heritable component, we find that genetic variants act in trans, with notably fewer variants acting in cis. Furthermore, we highlight using Mendelian Randomization that these variability-Quantitative Trait Loci might be driven by the cis regulation of upstream genes. This indicates that natural selection may balance the impact of gene regulation in cis with downstream impacts on expression variability in trans.

Ordered patterning of the sensory system is susceptible to stochastic features of gene expression

Sensory neuron numbers and positions are precisely organized to accurately map environmental signals in the brain. This precision emerges from biochemical processes within and between cells that are inherently stochastic. We investigated impact of stochastic gene expression on pattern formation, focusing on senseless (sens), a key determinant of sensory fate in Drosophila. Perturbing microRNA regulation or genomic location of sens produced distinct noise signatures. Noise was greatly enhanced when both sens alleles were present in homologous loci such that each allele was regulated in trans by the other allele. This led to disordered patterning. In contrast, loss of microRNA repression of sens increased protein abundance but not sensory pattern disorder. This suggests that gene expression stochasticity is a critical feature that must be constrained during development to allow rapid yet accurate cell fate resolution.

Analytical distributions for detailed models of stochastic gene expression in eukaryotic cells

The stochasticity of gene expression presents significant challenges to the modeling of genetic networks. A two-state model describing promoter switching, transcription, and messenger RNA (mRNA) decay is the standard model of stochastic mRNA dynamics in eukaryotic cells. Here, we extend this model to include mRNA maturation, cell division, gene replication, dosage compensation, and growth-dependent transcription. We derive expressions for the time-dependent distributions of nascent mRNA and mature mRNA numbers, provided two assumptions hold: 1) nascent mRNA dynamics are much faster than those of mature mRNA; and 2) gene-inactivation events occur far more frequently than gene-activation events. We confirm that thousands of eukaryotic genes satisfy these assumptions by using data from yeast, mouse, and human cells. We use the expressions to perform a sensitivity analysis of the coefficient of variation of mRNA fluctuations averaged over the cell cycle, for a large number of genes in mouse embryonic stem cells, identifying degradation and gene-activation rates as the most sensitive parameters. Furthermore, it is shown that, despite the model's complexity, the time-dependent distributions predicted by our model are generally well approximated by the negative binomial distribution. Finally, we extend our model to include translation, protein decay, and auto-regulatory feedback, and derive expressions for the approximate time-dependent protein-number distributions, assuming slow protein decay. Our expressions enable us to study how complex biological processes contribute to the fluctuations of gene products in eukaryotic cells, as well as allowing a detailed quantitative comparison with experimental data via maximum-likelihood methods.

What Is a Transcriptional Burst?

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.

Stochastic transcription in the p53-mediated response to DNA damage is modulated by burst frequency

Discontinuous transcription has been described for different mammalian cell lines and numerous promoters. However, our knowledge of how the activity of individual promoters is adjusted by dynamic signaling inputs from transcription factors is limited. To address this question, we characterized the activity of selected target genes that are regulated by pulsatile accumulation of the tumor suppressor p53 in response to ionizing radiation. We performed time-resolved measurements of gene expression at the single-cell level by smFISH and used the resulting data to inform a mathematical model of promoter activity. We found that p53 target promoters are regulated by frequency modulation of stochastic bursting and can be grouped along three archetypes of gene expression. The occurrence of these archetypes cannot solely be explained by nuclear p53 abundance or promoter binding of total p53. Instead, we provide evidence that the time-varying acetylation state of p53's C-terminal lysine residues is critical for gene-specific regulation of stochastic bursting.

Nascent RNA analyses: tracking transcription and its regulation

The programmes that direct an organism's development and maintenance are encoded in its genome. Decoding of this information begins with regulated transcription of genomic DNA into RNA. Although transcription and its control can be tracked indirectly by measuring stable RNAs, it is only by directly measuring nascent RNAs that the immediate regulatory changes in response to developmental, environmental, disease and metabolic signals are revealed. Multiple complementary methods have been developed to quantitatively track nascent transcription genome-wide at nucleotide resolution, all of which have contributed novel insights into the mechanisms of gene regulation and transcription-coupled RNA processing. Here we critically evaluate the array of strategies used for investigating nascent transcription and discuss the recent conceptual advances they have provided.

X-chromosome upregulation is driven by increased burst frequency

Ohno's hypothesis postulates that upregulation of X-linked genes rectifies their dosage imbalance relative to autosomal genes, which are present in two active copies per cell. Here we have dissected X-chromosome upregulation into the kinetics of transcription, inferred from allele-specific single-cell RNA sequencing data from somatic and embryonic mouse cells. We confirmed increased X-chromosome expression levels in female and male cells and found that the X chromosome achieved upregulation by elevated burst frequencies. By monitoring transcriptional kinetics in differentiating female mouse embryonic stem cells, we found that increased burst frequency was established on the active X chromosome when X inactivation took place on the other allele. Thus, our study provides mechanistic insights into X-chromosome upregulation.

Mechanisms of Interplay between Transcription Factors and the 3D Genome

Transcription factors (TFs) bind DNA in a sequence-specific manner and thereby serve as the protein anchors and determinants of 3D genome organization. Conversely, chromatin conformation shapes TF activity, for example, by looping TF-bound enhancers to distally located target genes. Despite considerable effort, our understanding of the mechanistic relation between TFs and 3D genome organization remains limited, in large part due to this interdependency. In this review, we summarize the evidence for the diverse mechanisms by which TFs and their activity shape the 3D genome and vice versa. We further highlight outstanding questions and potential approaches for untangling the complex relation between TF activity and the 3D genome.

Challenges in measuring and understanding biological noise

Biochemical reactions are intrinsically stochastic, leading to variation in the production of mRNAs and proteins within cells. In the scientific literature, this source of variation is typically referred to as 'noise'. The observed variability in molecular phenotypes arises from a combination of processes that amplify and attenuate noise. Our ability to quantify cell-to-cell variability in numerous biological contexts has been revolutionized by recent advances in single-cell technology, from imaging approaches through to 'omics' strategies. However, defining, accurately measuring and disentangling the stochastic and deterministic components of cell-to-cell variability is challenging. In this Review, we discuss the sources, impact and function of molecular phenotypic variability and highlight future directions to understand its role.