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

Synthesis of mRNA in eukaryotes involves the coordinated action of many enzymatic processes, including initiation, elongation, splicing, and cleavage. Kinetic competition between these processes has been proposed to determine RNA fate, yet such coupling has never been observed in vivo on single transcripts. In this study, we use dual-color single-molecule RNA imaging in living human cells to construct a complete kinetic profile of transcription and splicing of the β-globin gene. We find that kinetic competition results in multiple competing pathways for pre-mRNA splicing. Splicing of the terminal intron occurs stochastically both before and after transcript release, indicating there is not a strict quality control checkpoint. The majority of pre-mRNAs are spliced after release, while diffusing away from the site of transcription. A single missense point mutation (S34F) in the essential splicing factor U2AF1 which occurs in human cancers perturbs this kinetic balance and defers splicing to occur entirely post-release.DOI: http://dx.doi.org/10.7554/eLife.03939.001

Summary Eukaryotic transcription is pervasive, and many of the resulting RNAs are non-coding. It is unknown if ubiquitous transcription is functional or simply reflects stochastic transcriptional noise. By single-molecule visualization of the dynamic interplay between coding and non-coding transcription at the GAL locus in living yeast cells, we show that antisense GAL10 ncRNA transcription can switch between functional and spurious under different conditions. During galactose induction, GAL10 sense transcription occurs in short stochastic bursts which are unaffected by transcription of antisense GAL10 ncRNA, even when both are present simultaneously at the same locus. In contrast, when GAL10 is not induced, ncRNA transcription is critical to prevent transcriptional leakage of GAL1 and GAL10. Suppression of ncRNA transcription by strand-specific CRISPR/dCas9 results in transcriptional leakage of the inducer GAL1, leading to a more sensitive transcription activation threshold, an alteration of metabolic switching, and a fitness defect in competition experiments.

Although physiological steroid levels are often pulsatile (ultradian), the genomic effects of this pulsatility are poorly understood. By utilizing glucocorticoid receptor (GR) signaling as a model system, we uncovered striking spatiotemporal relationships between receptor loading, lifetimes of the DNase I hypersensitivity sites (DHSs), long-range interactions, and gene regulation. We found that hormone-induced DHSs were enriched within ±50 kb of GR-responsive genes and displayed a broad spectrum of lifetimes upon hormone withdrawal. These lifetimes dictate the strength of the DHS interactions with gene targets and contribute to gene regulation from a distance. Our results demonstrate that pulsatile and constant hormone stimulations induce unique, treatment-specific patterns of gene and regulatory element activation. These modes of activation have implications for corticosteroid function in vivo and for steroid therapies in various clinical settings.

A significant part of eukaryotic noncoding DNA is viewed as the passive result of mutational processes, such as the proliferation of mobile elements. However, sequences lacking an immediate utility can nonetheless play a major role in the long-term evolvability of a lineage, for instance by promoting genomic rearrangements. They could thus be subject to an indirect selection. Yet, such a long-term effect is difficult to isolate either in vivo or in vitro. Here, by performing in silico experimental evolution, we demonstrate that, under low mutation rates, the indirect selection of variability promotes the accumulation of noncoding sequences: Even in the absence of self-replicating elements and mutational bias, noncoding sequences constituted an important fraction of the evolved genome because the indirectly selected genomes were those that were variable enough to discover beneficial mutations. On the other hand, high mutation rates lead to compact genomes, much like the viral ones, although no selective cost of genome size was applied: The indirectly selected genomes were those that were small enough for the genetic information to be reliably transmitted. Thus, the spontaneous evolution of the amount of noncoding DNA strongly depends on the mutation rate. Our results suggest the existence of an additional pressure on the amount of noncoding DNA, namely the indirect selection of an appropriate trade-off between the fidelity of the transmission of the genetic information and the exploration of the mutational neighborhood. Interestingly, this trade-off resulted robustly in the accumulation of noncoding DNA so that the best individual leaves one offspring without mutation (or only neutral ones) per generation.

BackgroundGene promoters can be in various epigenetic states and undergo interactions with many molecules in a highly transient, probabilistic and combinatorial way, resulting in a complex global dynamics as observed experimentally. However, models of stochastic gene expression commonly consider promoter activity as a two-state on/off system. We consider here a model of single-gene stochastic expression that can represent arbitrary prokaryotic or eukaryotic promoters, based on the combinatorial interplay between molecules and epigenetic factors, including energy-dependent remodeling and enzymatic activities.ResultsWe show that, considering the mere molecular interplay at the promoter, a single-gene can demonstrate an elaborate spontaneous stochastic activity (eg. multi-periodic multi-relaxation dynamics), similar to what is known to occur at the gene-network level. Characterizing this generic model with indicators of dynamic and steady-state properties (including power spectra and distributions), we reveal the potential activity of any promoter and its influence on gene expression. In particular, we can reproduce, based on biologically relevant mechanisms, the strongly periodic patterns of promoter occupancy by transcription factors (TF) and chromatin remodeling as observed experimentally on eukaryotic promoters. Moreover, we link several of its characteristics to properties of the underlying biochemical system. The model can also be used to identify behaviors of interest (eg. stochasticity induced by high TF concentration) on minimal systems and to test their relevance in larger and more realistic systems. We finally show that TF concentrations can regulate many aspects of the stochastic activity with a considerable flexibility and complexity.ConclusionsThis tight promoter-mediated control of stochasticity may constitute a powerful asset for the cell. Remarkably, a strongly periodic activity that demonstrates a complex TF concentration-dependent control is obtained when molecular interactions have typical characteristics observed on eukaryotic promoters (high mobility, functional redundancy, many alternate states/pathways). We also show that this regime results in a direct and indirect energetic cost. Finally, this model can constitute a framework for unifying various experimental approaches. Collectively, our results show that a gene - the basic building block of complex regulatory networks - can itself demonstrate a significantly complex behavior.

BackgroundA number of studies have established that stochasticity in gene expression may play an important role in many biological phenomena. This therefore calls for further investigations to identify the molecular mechanisms at stake, in order to understand and manipulate cell-to-cell variability. In this work, we explored the role played by chromatin dynamics in the regulation of stochastic gene expression in higher eukaryotic cells.ResultsFor this purpose, we generated isogenic chicken-cell populations expressing a fluorescent reporter integrated in one copy per clone. Although the clones differed only in the genetic locus at which the reporter was inserted, they showed markedly different fluorescence distributions, revealing different levels of stochastic gene expression. Use of chromatin-modifying agents showed that direct manipulation of chromatin dynamics had a marked effect on the extent of stochastic gene expression. To better understand the molecular mechanism involved in these phenomena, we fitted these data to a two-state model describing the opening/closing process of the chromatin. We found that the differences between clones seemed to be due mainly to the duration of the closed state, and that the agents we used mainly seem to act on the opening probability.ConclusionsIn this study, we report biological experiments combined with computational modeling, highlighting the importance of chromatin dynamics in stochastic gene expression. This work sheds a new light on the mechanisms of gene expression in higher eukaryotic cells, and argues in favor of relatively slow dynamics with long (hours to days) periods of quiet state.

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