Rate-limiting Steps in Transcription Initiation are Key Regulatory Mechanisms of Escherichia coli Gene Expression Dynamics

In all living organisms, the “blueprints of life” are documented in the genetic material. This material is composed of genes, which are regions of DNA coding for proteins. To produce proteins, cells read the information on the DNA with the help of molecular machines, such as RNAp holoenzymes and a factors. Proteins carry out the cellular functions required for survival and, as such, cells deal with challenging environments by adjusting their gene expression pattern. For this, cells constantly perform decision- making processes of whether or not to actively express a protein, based on intracellular and environmental cues. In Escherichia coli, gene expression is mostly regulated at the stage of transcription initiation. Although most of its regulatory molecules have been identified, the dynamics and regulation of this step remain elusive. Due to a limited number of specific regulatory molecules in the cells, the stochastic fluctuations of these molecular numbers can result in a sizeable temporal change in the numbers of transcription outputs (RNA and proteins) and have consequences on the phenotype of the cells. To understand the dynamics of this process, one should study the activity of the gene by tracking mRNA and protein production events at a detailed level. Recent advancements in single-molecule detection techniques have been used to image and track individually labeled fluorescent macromolecules of living cells. This allows investigating the intermolecular dynamics under any given condition. In this thesis, by using in vivo, single-RNA time-lapse microscopy techniques along with stochastic modelling techniques, we studied the kinetics of multi-rate limiting steps in the transcription process of multiple promoters, in various conditions. Specifically, first, we established a novel method of dissecting transcription in Escherichia coli that combines state-of-the-art microscopy measurements and model fitting techniques to construct detailed models of the rate-limiting steps governing the in vivo transcription initiation of a synthetic Lac-ara-1 promoter. After that, we estimated the duration of the closed and open complex formation, accounting for the rate of reversibility of the first step. From this, we also estimated the duration of periods of promoter inactivity, from which we were able to determine the contribution from each step to the distribution of intervals between consecutive RNA productions in individual cells. Second, using the above method, we studied the a factor selective mechanisms for indirect regulation of promoters whose transcription is primarily initiated by RNAp holoenzymes carrying a70. From the analysis, we concluded that, in E. coli, a promoter’s responsiveness to indirect regulation by a factor competition is determined by its sequence-dependent, dynamically regulated multi-step initiation kinetics. Third, we investigated the effects of extrinsic noise, arising from cell-to-cell variability in cellular components, on the single-cell distribution of RNA numbers, in the context of cell lineages. For this, first, we used stochastic models to predict the variability in the numbers of molecules involved in upstream processes. The models account for the intake of inducers from the environment, which acts as a transient source of variability in RNA production numbers, as well as for the variability in the numbers of molecular species controlling transcription of an active promoter, which acts as a constant source of variability in RNA numbers. From measurement analysis, we demonstrated the existence of lineage-to-lineage variability in gene activation times and mean transcription rates. Finally, we provided evidence that this can be explained by differences in the kinetics of the rate-limiting steps in transcription and of the induction scheme, from which it is possible to conclude that these variabilities differ between promoters and inducers used. Finally, we studied how the multi-rate limiting steps in the transcription initiation are capable of tuning the asymmetry and tailedness of the distribution of time intervals between consecutive RNA production events in individual cells. For this, first, we considered a stochastic model of transcription initiation and predicted that the asymmetry and tailedness in the distribution of intervals between consecutive RNA production events can differ by tuning the rate-limiting steps in transcription. Second, we validated the model with measurements from single-molecule RNA microscopy of transcription kinetics of multiple promoters in multiple conditions. Finally, from our results, we concluded that the skewness and kurtosis in RNA and protein production kinetics are subject to regulation by the kinetics of the steps in transcription initiation and affect the single-cell distributions of RNAs and, thus, proteins. We further showed that this regulation can significantly affect the probability of RNA and protein numbers to cross specific thresholds. Overall, the studies conducted in this thesis are expected to contribute to a better understanding of the dynamic process of bacterial gene expression. The advanced data and image analysis techniques and novel stochastic modeling approaches that we developed during the course of these studies, will allow studying in detail the in vivo regulation of multi-rate limiting steps of transcription initiation of any given promoter. In addition, by tuning the kinetics of the rate-limiting steps in the transcription initiation as executed here should allow engineering new promoters, with predefined RNA and, thus, protein production dynamics in Escherichia coli

Exponential sensitivity of noise-driven switching in genetic networks

Cells are known to utilize biochemical noise to probabilistically switch between distinct gene expression states. We demonstrate that such noise-driven switching is dominated by tails of probability distributions and is therefore exponentially sensitive to changes in physiological parameters such as transcription and translation rates. However, provided mRNA lifetimes are short, switching can still be accurately simulated using protein-only models of gene expression. Exponential sensitivity limits the robustness of noise-driven switching, suggesting cells may use other mechanisms in order to switch reliably

Effects of cell cycle noise on excitable gene circuits

We assess the impact of cell cycle noise on gene circuit dynamics. For bistable genetic switches and excitable circuits, we find that transitions between metastable states most likely occur just after cell division and that this concentration effect intensifies in the presence of transcriptional delay. We explain this concentration effect with a 3-states stochastic model. For genetic oscillators, we quantify the temporal correlations between daughter cells induced by cell division. Temporal correlations must be captured properly in order to accurately quantify noise sources within gene networks.Comment: 15 pages, 8 figure

Interplay between pleiotropy and secondary selection determines rise and fall of mutators in stress response

Dramatic rise of mutators has been found to accompany adaptation of bacteria in response to many kinds of stress. Two views on the evolutionary origin of this phenomenon emerged: the pleiotropic hypothesis positing that it is a byproduct of environmental stress or other specific stress response mechanisms and the second order selection which states that mutators hitchhike to fixation with unrelated beneficial alleles. Conventional population genetics models could not fully resolve this controversy because they are based on certain assumptions about fitness landscape. Here we address this problem using a microscopic multiscale model, which couples physically realistic molecular descriptions of proteins and their interactions with population genetics of carrier organisms without assuming any a priori fitness landscape. We found that both pleiotropy and second order selection play a crucial role at different stages of adaptation: the supply of mutators is provided through destabilization of error correction complexes or fluctuations of production levels of prototypic mismatch repair proteins (pleiotropic effects), while rise and fixation of mutators occur when there is a sufficient supply of beneficial mutations in replication-controlling genes. This general mechanism assures a robust and reliable adaptation of organisms to unforeseen challenges. This study highlights physical principles underlying physical biological mechanisms of stress response and adaptation

Connecting protein and mRNA burst distributions for stochastic models of gene expression

The intrinsic stochasticity of gene expression can lead to large variability in protein levels for genetically identical cells. Such variability in protein levels can arise from infrequent synthesis of mRNAs which in turn give rise to bursts of protein expression. Protein expression occurring in bursts has indeed been observed experimentally and recent studies have also found evidence for transcriptional bursting, i.e. production of mRNAs in bursts. Given that there are distinct experimental techniques for quantifying the noise at different stages of gene expression, it is of interest to derive analytical results connecting experimental observations at different levels. In this work, we consider stochastic models of gene expression for which mRNA and protein production occurs in independent bursts. For such models, we derive analytical expressions connecting protein and mRNA burst distributions which show how the functional form of the mRNA burst distribution can be inferred from the protein burst distribution. Additionally, if gene expression is repressed such that observed protein bursts arise only from single mRNAs, we show how observations of protein burst distributions (repressed and unrepressed) can be used to completely determine the mRNA burst distribution. Assuming independent contributions from individual bursts, we derive analytical expressions connecting means and variances for burst and steady-state protein distributions. Finally, we validate our general analytical results by considering a specific reaction scheme involving regulation of protein bursts by small RNAs. For a range of parameters, we derive analytical expressions for regulated protein distributions that are validated using stochastic simulations. The analytical results obtained in this work can thus serve as useful inputs for a broad range of studies focusing on stochasticity in gene expression

Stochastic Gene Expression in Cells: A Point Process Approach

This paper investigates the stochastic fluctuations of the number of copies of a given protein in a cell. This problem has already been addressed in the past and closed-form expressions of the mean and variance have been obtained for a simplified stochastic model of the gene expression. These results have been obtained under the assumption that the duration of all the protein production steps are exponentially distributed. In such a case, a Markovian approach (via Fokker-Planck equations) is used to derive analytic formulas of the mean and the variance of the number of proteins at equilibrium. This assumption is however not totally satisfactory from a modeling point of view since the distribution of the duration of some steps is more likely to be Gaussian, if not almost deterministic. In such a setting, Markovian methods can no longer be used. A finer characterization of the fluctuations of the number of proteins is therefore of primary interest to understand the general economy of the cell. In this paper, we propose a new approach, based on marked Poisson point processes, which allows to remove the exponential assumption. This is applied in the framework of the classical three stages models of the literature: transcription, translation and degradation. The interest of the method is shown by recovering the classical results under the assumptions that all the durations are exponentially distributed but also by deriving new analytic formulas when some of the distributions are not anymore exponential. Our results show in particular that the exponential assumption may, surprisingly, underestimate significantly the variance of the number of proteins when some steps are in fact not exponentially distributed. This counter-intuitive result stresses the importance of the statistical assumptions in the protein production process

Multiple binding sites for transcriptional repressors can produce regular bursting and enhance noise suppression

Cells may control fluctuations in protein levels by means of negative autoregulation, where transcription factors bind DNA sites to repress their own production. Theoretical studies have assumed a single binding site for the repressor, while in most species it is found that multiple binding sites are arranged in clusters. We study a stochastic description of negative autoregulation with multiple binding sites for the repressor. We find that increasing the number of binding sites induces regular bursting of gene products. By tuning the threshold for repression, we show that multiple binding sites can also suppress fluctuations. Our results highlight possible roles for the presence of multiple binding sites of negative autoregulators

Synthetic Gene Circuits: Design with Directed Evolution

Synthetic circuits offer great promise for generating insights into nature's underlying design principles or forward engineering novel biotechnology applications. However, construction of these circuits is not straightforward. Synthetic circuits generally consist of components optimized to function in their natural context, not in the context of the synthetic circuit. Combining mathematical modeling with directed evolution offers one promising means for addressing this problem. Modeling identifies mutational targets and limits the evolutionary search space for directed evolution, which alters circuit performance without the need for detailed biophysical information. This review examines strategies for integrating modeling and directed evolution and discusses the utility and limitations of available methods