Antoine Coulon

Eukaryotic transcriptional dynamics: from single molecules to cell populations

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

Kinetic competition during the transcription cycle results in stochastic RNA processing

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

On the spontaneous stochastic dynamics of a single gene: complexity of the molecular interplay at the promoter

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.

Single-Molecule Imaging Reveals a Switch between Spurious and Functional ncRNA Transcription

Eukaryotic transcription is pervasive, and many of the resulting RNAs are non-coding. It is unknown whether 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.

Dynamics of chromatin accessibility and long-range interactions in response to glucocorticoid pulsing

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.

Quantifying the contribution of chromatin dynamics to stochastic gene expression reveals long, locus-dependent periods between transcriptional bursts

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.

Membrane microdomains emergence through non-homogeneous diffusion

BackgroundIn the classical view, cell membrane proteins undergo isotropic random motion, that is a 2D Brownian diffusion that should result in an homogeneous distribution of concentration. It is, however, far from the reality: Membrane proteins can assemble into so-called microdomains (sometimes called lipid rafts) which also display a specific lipid composition. We propose a simple mechanism that is able to explain the colocalization of protein and lipid rafts.ResultsUsing very simple mathematical models and particle simulations, we show that a variation of membrane viscosity directly leads to variation of the local concentration of diffusive particles. Since specific lipid phases in the membrane can account for diffusion variation, we show that, in such a situation, the freely diffusing proteins (or any other component) still undergo a Brownian motion but concentrate in areas of lower diffusion. The amount of this so-called overconcentration at equilibrium issimply related to the ratio of diffusion coefficients between zones of high and low diffusion. Expanding the model to include particle interaction, we show that inhomogeneous diffusion can impact particles clusterization as well. The clusters of particles were more numerous and appear for a lower value of interaction strength in the zones of low diffusion compared to zones of high diffusion.ConclusionProvided we assume stable viscosity heterogeneity in the membrane, our model propose a simple mechanism to explain particle concentration heterogeneity. It has also a non-trivial impact on density of particles when interaction is added. This could potentially have an impact on membrane chemical reactions and oligomerization.

High-throughput single-molecule screen for small-molecule perturbation of splicing and transcription kinetics

In eukaryotes, mRNA synthesis is catalyzed by RNA polymerase II and involves several distinct steps, including transcript initiation, elongation, cleavage, and transcript release. Splicing of RNA can occur during (co-transcriptional) or after (post-transcriptional) RNA synthesis. Thus, RNA synthesis and processing occurs through the concerted activity of dozens of enzymes, each of which is potentially susceptible to perturbation by small molecules. However, there are few, if any, high-throughput screening strategies for identifying drugs which perturb a specific step in RNA synthesis and processing. Here we have developed a high-throughput fluorescence microscopy approach in single cells to screen for inhibitors of specific enzymatic steps in RNA synthesis and processing. By utilizing the high affinity interaction between bacteriophage capsid proteins (MS2, PP7) and RNA stem loops, we are able to fluorescently label the intron and exon of a β-globin reporter gene in human cells. This approach allows one to measure the kinetics of transcription, splicing and release in both fixed and living cells using a tractable, genetically encoded assay in a stable cell line. We tested this reagent in a targeted screen of molecules that target chromatin readers and writers and identified three compounds that slow transcription elongation without changing transcription initiation.

Large Multiprotein Structures Modeling and Simulation: The Need for Mesoscopic Models

Recent observational techniques based upon confocal microscopy make it possible to observe cells at a scale that has never been probed before: the mesoscopic scale. In the eukaryotic cell nucleus, many objects demonstrating phenomena occurring at this scale, such as nuclear bodies, are current subjects of investigations. But from a modeling perspective, this scale has not been widely explored, and hence there is a lack of suitable models for such studies. By reviewing higher and lower scale modeling techniques, we analyze their relevance in the context of mesoscale phenomena. We emphasize important characteristics that should be included in a mesoscopic model: an explicit continuous three-dimensional space with discrete simplified molecules that still have the characteristics of steric volume exclusion and realistic distant interaction forces. Then we present 3DSPI, a model dedicated to studies of nuclear bodies based on a simple formalism inspired from molecular dynamics and coarse-grained models: particles interacting through a potential energy function and driven by an overdamped Langevin equation. Finally, we present the features expected to be included in the model, pointing out the difficulties that might arise.

Enhanced stimulus encoding capabilities with spectral selectivity in inhibitory circuits by stdp

The ability to encode and transmit a signal is an essential property that must demonstrate many neuronal circuits in sensory areas in addition to any processing they may provide. It is known that an appropriate level of lateral inhibition, as observed in these areas, can significantly improve the encoding ability of a population of neurons. We show here a homeostatic mechanism by which a spike-timing-dependent plasticity (STDP) rule with a symmetric timing window (swSTDP) spontaneously drives the inhibitory coupling to a level that ensures accurate encoding in response to input signals within a certain frequency range. Interpreting these results mathematically, we find that this coupling level depends on the overlap of spectral information between stimulus and STDP window function. Generalization to arbitrary swSTDP and arbitrary stimuli reveals that the signals for which this improvement of encoding takes place can be finely selected on spectral criteria. We finally show that this spectral overlap principle holds for a variety of neuron types and network characteristics. The highly tunable frequency-power domain of efficiency of this mechanism, together with its ability to operate in very various neuronal contexts, suggest that it may be at work in most sensory areas.