Regulatory mechanisms are revealed by the distribution of transcription initiation times in single microbial cells (original) (raw)
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Distribution of Initiation Times Reveals Mechanisms of Transcriptional Regulation in Single Cells
Biophysical journal, 2018
Transcription is the dominant point of control of gene expression. Biochemical studies have revealed key molecular components of transcription and their interactions, but the dynamics of transcription initiation in cells is still poorly understood. This state of affairs is being remedied with experiments that observe transcriptional dynamics in single cells using fluorescent reporters. Quantitative information about transcription initiation dynamics can also be extracted from experiments that use electron micrographs of RNA polymerases caught in the act of transcribing a gene (Miller spreads). Inspired by these data, we analyze a general stochastic model of transcription initiation and elongation and compute the distribution of transcription initiation times. We show that different mechanisms of initiation leave distinct signatures in the distribution of initiation times that can be compared to experiments. We analyze published data from micrographs of RNA polymerases transcribing r...
Stochastic Model of Transcription Initiation of Closely Spaced Promoters In Escherichia Coli
The regulatory mechanisms of transcription allow organisms to quickly adapt to changes in their environment and often act during transcription initiation. Here, a stochastic model of transcription initiation at the nucleotide level is proposed to study the dynamics of RNA production in closely spaced promoters and their regulatory mechanisms. We study how different arrangements (convergent and divergent), distance between Transcription Start Sites, and kinetic parameters affect the dynamics of RNA production. From the results, we observe that the rate limiting steps have strong influence in the kinetics of RNA production. We observe that small changes in the distance between Transcription Start Sites can lead to abrupt transitions in the dynamics of RNA production, particularly when this change changes the geometry from overlapped to non-overlapped promoters. Further, we analyze how the kinetics of various steps in transcription initiation can be regulated by varying the location of repressor binding sites. The study of these models can help to understand how genetic circuits have evolved and assist in designing artificial genetic circuits with desired dynamics.
Proceedings of the National Academy of Sciences, 2009
The transition from initiation to elongation of the RNA polymerase (RNAP) is an important stage of transcription that often limits the production of the full-length RNA. Little is known about the RNAP transition kinetics and the steps that dictate the transition rate, because of the challenge in monitoring subpopulations of the transient and heterogeneous transcribing complexes in rapid and real time. Here, we have dissected the complete transcription initiation pathway of T7 RNAP by using kinetic modeling of RNA synthesis and by determining the initiation (IC) to elongation (EC) transition kinetics at each RNA polymerization step using single-molecule and stopped-flow FRET methods. We show that the conversion of IC to EC in T7 RNAP consensus promoter occurs only after 8- to 12-nt synthesis, and the 12-nt synthesis represents a critical juncture in the transcriptional initiation pathway when EC formation is most efficient. We show that the slow steps of transcription initiation, inc...
PLoS computational biology, 2016
Transcription kinetics is limited by its initiation steps, which differ between promoters and with intra- and extracellular conditions. Regulation of these steps allows tuning both the rate and stochasticity of RNA production. We used time-lapse, single-RNA microscopy measurements in live Escherichia coli to study how the rate-limiting steps in initiation of the Plac/ara-1 promoter change with temperature and induction scheme. For this, we compared detailed stochastic models fit to the empirical data in maximum likelihood sense using statistical methods. Using this analysis, we found that temperature affects the rate limiting steps unequally, as nonlinear changes in the closed complex formation suffice to explain the differences in transcription dynamics between conditions. Meanwhile, a similar analysis of the PtetA promoter revealed that it has a different rate limiting step configuration, with temperature regulating different steps. Finally, we used the derived models to explore a...
Probing mechanisms of transcription elongation through cell-to-cell variability of RNA polymerase
The process of transcription initiation and elongation are primary points of control in the regulation of gene expression. While biochemical studies have uncovered the mechanisms involved in controlling transcription at each step, how these mechanisms manifest in vivo at the level of individual genes is still unclear. Recent experimental advances have enabled single-cell measurements of RNAP molecules engaged in the process of transcribing a gene of interest. In this manuscript, we use Gillespie simulations to show that measurements of cell-to-cell variability of RNAP numbers and inter-polymerase distances can reveal the prevailing mode of regulation of a given gene. Mechanisms of regulation at each step, from initiation to elongation dynamics, produce qualitatively distinct signatures which can further be used to discern between them. Intriguingly, depending on the initiation kinetics, stochastic elongation can either enhance or suppress cell-to-cell variability at the RNAP level. ...
Stochastic models of transcription: From single molecules to single cells
Methods, 2013
Genes in prokaryotic and eukaryotic cells are typically regulated by complex promoters containing multiple binding sites for a variety of transcription factors leading to a specific functional dependence between regulatory inputs and transcriptional outputs. With increasing regularity, the transcriptional outputs from different promoters are being measured in quantitative detail in single-cell experiments thus providing the impetus for the development of quantitative models of transcription. We describe recent progress in developing models of transcriptional regulation that incorporate, to different degrees, the complexity of multi-state promoter dynamics, and its effect on the transcriptional outputs of single cells. The goal of these models is to predict the statistical properties of transcriptional outputs and characterize their variability in time and across a population of cells, as a function of the input concentrations of transcription factors. The interplay between mathematical models of different regulatory mechanisms and quantitative biophysical experiments holds the promise of elucidating the molecular-scale mechanisms of transcriptional regulation in cells, from bacteria to higher eukaryotes.
In metazoans, both transcription initiation and the escape of RNA polymerase (RNAP) from promoter-proximal pausing are key rate-limiting steps in gene expression. These processes play out at physically proximal sites on the DNA template and appear to influence one another through steric interactions, leading to a complex dynamic equilibrium in RNAP occupancy of the ~100 bp immediately downstream of the transcription start site. In this article, we examine the dynamics of these processes using a combination of statistical modeling, simulation, and analysis of real nascent RNA sequencing data. We develop a simple probabilistic model that jointly describes the kinetics of transcription initiation, pause-escape, and elongation, and the generation of nascent RNA sequencing read counts under steady-state conditions. We then extend this initial model to allow for variability across cells in promoter-proximal pause site locations and steric hindrance of transcription initiation from paused ...
2022
Gene transcription is a stochastic process that involves thousands of reactions. The first set of these reactions, which happen near a gene promoter, are considered to be the most important in the context of stochastic noise. The most common models of transcription are primarily concerned with the effect of activators/repressors on the overall transcription rate and approximate the basal transcription processes as a one step event. According to such effective models, the Fano factor of mRNA copy distributions is always greater than (super-Poissonian) or equal to 1 (Poissonian), and the only way to go below this limit (sub-Poissonian) is via a negative feedback. It is partly due to this limit that the first stage of transcription is held responsible for most of the stochastic noise in mRNA copy numbers. However, by considering all major reactions that build and drive the basal transcription machinery, from the first protein that binds a promoter to the entrance of the transcription c...
Random dynamics of gene transcription activation in single cells
Journal of Differential Equations, 2009
MSC: primary 37H10 secondary 34F05, 60J10, 92C40, 92D99 Keywords: Master equation Master operator Mollification property P-type functions The recent measurements of gene transcription activity at single cell resolution revealed that genes are often transcribed randomly and discontinuously. In order to elucidate how the environmental signals contribute to the stochasticity of gene transcription, a random transition model was recently proposed [M. Tang, The mean and noise of stochastic gene transcription, J. Theor. Biol. 253 (2008) 271-280; M. Tang, The mean frequency of transcriptional bursting and its variation in single cells, 7, in press; published online: March 10, 2009