Dynamics of transcription driven by the tetA promoter, one event at a time, in live Escherichia coli cells

Abstract

In Escherichia coli , tetracycline prevents translation. When subject to tetracycline, E. coli express TetA to pump it out by a mechanism that is sensitive, while fairly independent of cellular metabolism. We constructed a target gene, P tetA -mRFP1-96BS, with a 96 MS2-GFP binding site array in a single-copy BAC vector, whose expression is controlled by the tetA promoter. We measured the in vivo kinetics of production of individual RNA molecules of the target gene as a function of inducer concentration and temperature. From the distributions of intervals between transcription events, we find that RNA production by P tetA is a sub-Poissonian process. Next, we infer the number and duration of the prominent sequential steps in transcription initiation by maximum likelihood estimation. Under full induction and at optimal temperature, we observe three major steps. We find that the kinetics of RNA production under the control of P tetA , including number and duration of the steps, varies with induction strength and temperature. The results are supported by a set of logical pairwise Kolmogorov-Smirnov tests. We conclude that the expression of TetA is controlled by a sequential mechanism that is robust, whereas sensitive to external signals.

Temperature-Dependent Model of Multi-step Transcription Initiation in Escherichia coli Based on Live Single-Cell Measurements

“…Which and how many steps most contribute to the observed transcription dynamics appears to depend on the promoter and intra- and extracellular conditions [10, 11, 13, 22, 23]. In what concerns modeling this process, for example, if the promoter’s visit to the off-state or a sequential step are fast, these can be eliminated from the model, as they do not contribute significantly to the transcription dynamics.…”

“…Once formed, the RNA-96-MS2-GFP complex remains fluorescent for much longer than the cell lifetime [38]. The constructs used here were engineered previously [7, 11]. An example microscopy image is shown in Fig 1.…”

“…Live-cell measurements report that different promoters and intra- and extracellular conditions result in wide differences in transcription dynamics, both in rate and stochasticity [10]. Sub-Poissonian [11], Poissonian [12], and super-Poissonian [10, 13] dynamics (featuring less, equal to, and more variability than a corresponding Poisson process, respectively) have been reported, each resulting from a different combination of mechanistic properties that shape RNA production dynamics.…”

Effects of multimerization on the temporal variability of protein complex abundance

“…The RNA production rate of a gene is mainly controlled during the process of transcription initiation, at the promoter region (see [1] for a review). Recent in vivo measurements of the intervals between the production of individual transcripts [13,14] suggest that, under normal growth conditions, there are two to three significant rate-limiting steps at the initiation stage that, aside from determining the mean rate of production, also determine the degree of noise in the process of RNA production. In prokaryotes, these observations relate directly to protein copy numbers, which tend to follow closely those of RNA [15].…”

“…To account for the stochasticity and the rate limiting steps of the underlying steps in the process of gene expression, we use the delayed stochastic modeling strategy [16] to drive the dynamics of the models, as it allows the use of non-Markovian dynamics to model the non-instantaneous processes underlying transcription and translation [17]. The parameters used in the models are extracted from live, single-cell, single-molecule measurements [6,13,14]. …”