Extrinsic Noise Effects Regulation at the Single Gene and Small Gene Network Levels

Recent studies of gene expression in Escherichia coli using novel in vivo measurement techniques revealed that protein and RNA numbers from a gene differ between genetically identical cells. To unravel the causes for this, measurements were conducted and models were developed. These studies revealed that this diversity arises from extrinsic and intrinsic noise. The former is due to cell-to-cell variability in numbers of molecules involved, such as RNA polymerase (RNAp), transcription factors, etc. The latter is due to the stochastic nature of the chemical reactions combined with the fact that the molecules and genes involved exist in small numbers. One aspect that has not been given much attention so far, is the unique nature of the dynamics of transcription of each promoter of the gene regulatory network (GRN). This process has multiple rate-limiting steps whose duration differs between promoters. How this may diversify the variability in RNA and protein numbers between genes is unknown. To address this, we use single-cell empirical data and stochastic models with empirically validated parameter values and study how the kinetics of transcription of a gene affects the influence of extrinsic noise on the kinetics. Interestingly, we find that promoters whose open complex formation is longer lasting tend to suppress the propagation of extrinsic noise that affects only the steps prior to initiation of the open complex formation. In particular, our studies indicate that the cell-to-cell variability in RNA numbers depends on the transcription kinetics. As such, it is sequence-dependent. Further, in a 2-gene toggle switch, we find that its mean switching frequency depends on the transcription kinetics of the promoters but not on the cell-to-cell RNAp variability. On the other hand, the cell-to-cell variability in switching frequency is affected by these two variables. Meanwhile, in a Repressilator network (3 genes where each gene represses the next), we measured the mean and standard deviation of the period of oscillation. From these measurements in silico, we found that both parameters are independent of the RNAP cell-to-cell variability, but are strongly controlled by the transcription kinetics of each of its genes. We conclude that the transcription kinetics of the component genes is a key regulator of small genetic circuits, as it can be used as a tunable filter of extrinsic noise. Overall, the kinetics of the rate-limiting steps in transcription of individual genes act as ‘master regulators’ of the expression of individual genes and the behavior of genetic cir-cuits’, such as switching dynamics, period of oscillation, etc

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

Revealing the vectors of cellular identity with single-cell genomics

Single-cell genomics has now made it possible to create a comprehensive atlas of human cells. At the same time, it has reopened definitions of a cell's identity and of the ways in which identity is regulated by the cell's molecular circuitry. Emerging computational analysis methods, especially in single-cell RNA sequencing (scRNA-seq), have already begun to reveal, in a data-driven way, the diverse simultaneous facets of a cell's identity, from discrete cell types to continuous dynamic transitions and spatial locations. These developments will eventually allow a cell to be represented as a superposition of 'basis vectors', each determining a different (but possibly dependent) aspect of cellular organization and function. However, computational methods must also overcome considerable challenges-from handling technical noise and data scale to forming new abstractions of biology. As the scale of single-cell experiments continues to increase, new computational approaches will be essential for constructing and characterizing a reference map of cell identities.National Institutes of Health (U.S.) (grant P50 HG006193)BRAIN Initiative (grant U01 MH105979)National Institutes of Health (U.S.) (BRAIN grant 1U01MH105960-01)National Cancer Institute (U.S.) (grant 1U24CA180922)National Institute of Allergy and Infectious Diseases (U.S.) (grant 1U24AI118672-01

Regulation of Single-Cell Bacterial Gene Expression at the Stage of Transcription Initiation

One of the qualities that allow bacterial cells to survive in diverse, fluctuating environments is phenotypic plasticity, which is the ability to exhibit different phenotypes depending on the environmental conditions. Phenotypic plasticity arises via coordinated work of small genetic circuits that provide the cell with the means for decision-making. The behavior of these circuits depends, among other factors, on the ability of protein numbers to cross certain thresholds for a sufficient amount of time. In bacteria, RNA numbers largely define protein numbers and thus can be used to study the decision-making processes. Previous research outlined the effects of mean and variance in RNA or protein numbers on the behavior of small genetic circuits. However, noise in gene expression is often highly asymmetric. This could impact the threshold-crossing abilities of molecular numbers in a way that is not detectable by considering only their mean and variance. The focus of this thesis is to study the regulation of multi-step kinetics of bacterial gene expression in live bacteria and its effects on the shape of the distribution of RNA or protein levels. In particular, the thesis investigates how the rate-limiting steps in bacterial transcription, such as closed and open complex formation, intermittent inactive states, and promoter escape contribute to the dynamics of RNA numbers, and how this dynamics propagates to the distribution of protein levels in a cell population. This study made use of already existing techniques such as measurements at the single-RNA level and dynamically accurate stochastic modeling, complemented by the novel methodology developed in this work. First, the thesis introduced a new method for estimating the numbers of fluorescently tagged molecules present in a cell from time series data obtained by microscopy. This method allows improving the accuracy of the estimation when fluorescently tagged molecules are absent from the cell image for time intervals comparable with cell lifetime. Second, the new methodology for dissecting in vivo kinetics of rate-limiting steps in transcription initiation was proposed. Applying this methodology to study initiation kinetics at lac/ara-1 promoter provided insights on the amount, duration, and reversibility of the rate-limiting steps in this process. Further, the thesis investigated the kinetics of transcription activation of lac/ara-1 promoter at various temperatures. The results indicate that additional rate-limiting steps emerge in inducer intake kinetics as temperature decreases from optimal (37 °C). Finally, the focus was shifted specifically to quantifying the asymmetry and tailedness in RNA and protein level distributions, since these features are relevant for determining threshold crossing propensities. Here, these features were found to depend both on promoter sequence and on regulatory molecules, thus being evolvable and adaptable. Overall, the work conducted in this thesis suggests that asymmetries in RNA and protein numbers may be crucial for decision-making in bacteria, since they can be regulated by promoter sequence, regulatory molecules levels, and temperature shifts. The thesis also contributes to the pool of existing methodology for studying in vivo bacterial gene expression using single-cell biology approach. These findings should be of use both for better understanding of natural systems and for fine-tuning behavior of synthetic gene circuits

Regulatory Mechanisms of Gene Transcription in Escherichia Coli

Bacteria have always been exposed to a wide variety of environments, many of which are fluctuating. In order to survive in these environments, they have had to develop the ability to adapt to changing conditions, particularly hostile ones. The adaptiveness of these microorganisms primarily depends upon their gene regulatory mechanisms. Some of these are ‘local’, affecting only a few genes. For example, when a specific nutrient appears in the media, it activates a few genes. Other regulatory mechanisms are more complex, involving a large number of genes that need to be activated and/or repressed at specific time moments. The survival of bacterial species, in some cases, depends on the existence of diversity in their genes’ expression across the cell population, particularly since it is not always possible to predict the best action to take next. Understanding the mechanisms of bacteria that regulate the diversity in gene expression would help the bioindustries to benefit from them. Moreover, it would help finding ways to mitigate the harm caused by some species. Bacterial genes are primarily regulated by their promoter strength in recruiting RNAP and specificity to a σ factor and, in some cases, by one or more global regulators. In addition, many genes are also regulated by specific transcription factors that can act as activators or as repressors, when present. Aside from these, other influential factors are the supercoiling in the DNA region occupied by the gene, whether there are other promoters closely spaced to the promoter of interest and, if so, their orientation, etc (Dash, et al., 2021). This thesis focused on the study of some of the mechanisms that can affect genes’ transcription kinetics. We focused on three mechanisms: i) Building up of positive supercoils, ii) Transcription interference between closely spaced promoters in tandem formation, and iii) Global regulation by input transcription factors. First, we studied how the intrinsic and extrinsic sources of noise in gene expression could be regulated by tuning the relative duration of transcription initiation. The study was done using stochastic models. It was found that the diversity in transcription kinetics across a cell population increases with the increase in the relative duration of the closed complex formation. Second, a method was proposed to dissect the kinetics of transcription locking due to the effects of positive supercoiling buildup. Using RNA fluorescent protein tagging and microscopy, RNA transcripts were quantified in individual cells. It was found that increasing intracellular gyrase concentration decreases how often a promoter goes into the locked state, which in turn increases the gene’s transcription rate. Using that information, it is possible to infer how long the promoter is locked. Third, a method was proposed to quantify the RNA numbers in individual cells using information from flow cytometry. This method allows the quantification of RNA numbers in thousands of cells, and thus the mean and variability in those cells, with much less manual labour and in much lesser time than when using microscopy and image analysis. Fourth, a method was proposed to dissect the rate-liming steps of gene transcription regulated by promoters in tandem orientation. Using protein fusion library and flow cytometry, the protein abundance was quantified. It was found that the gene’s expression could be regulated by tuning the transcriptional interference by varying the promoters’ strength and the distance between the transcription start site of the promoters. Overall, the four studies above allow for, first, better extracting raw data from microscopy and flow cytometry, and from there, to either dissect the kinetics of rate- limiting steps during transcription initiation or, inversely, how they can be tuned to regulate the single-cell RNA and protein numbers. Having studied two core mechanisms regulating transcription, the fifth and final study focus on a third mechanism, which is transcription factor (TF) regulation. For this, we used RNA-seq to study how RNAP and TFs affect the kinetics of gene cohorts from measurements after RNAP shifts. We found that the magnitude of genes’ response is proportional to the asymmetry in the number of activators and repressors regulating them. Overall, the works conducted in this thesis show that the gene expression and its products diversity in cell populations can be regulated by varying the rate-limiting steps in transcription. These rate-limiting steps can be tuned by various mechanisms, such as the tuning of the accumulation of positive coils, tuning transcriptional interference of closely spaced promoters in tandem orientation and, tuning which and how many transcription factors act on each gene

Temperature Dependence of the Transcription Dynamics of Synthetic Genes in Escherichia coli

One of the major current goals in synthetic biology is the design of genetic components with more predictable functions. This predictability, however, does not depend solely on these components, but also on the environment where they will be inserted in.Escherichia coli is one of the most studied microorganisms in Microbiology, and it is commonly used in Synthetic Biology as a host strain to test the functioning of the components of genetic systems. These components are typically well characterized under controlled laboratory conditions. However, it is unclear how unfavorable environmental conditions, such as temperature fluctuations, can affect their functionality and robustness.In this thesis, we investigated how temperature affects the kinetics of transcription activation and subsequent dynamics of RNA production of synthetic genes in E. coli. For this, we made use of state-of-the-art in vivo single RNA-detection techniques and image analysis tools, to dissect, at the single-cell and single-RNA level, the kinetics of the rate-limiting steps in transcription, as well as the intake kinetics of inducer molecules. In addition, we analyzed how the temperature dependency is affected by the promoter structure.Specifically, first, we characterized the intake kinetics of inducer molecules, from the media to the cell periplasm and then cytoplasm, at optimal and suboptimal temperatures. We found that, for a wide range of extracellular inducer concentrations, and in the absence of a transporter protein, the intake process is diffusive-like. The results also show that, the mean intake time increases nonlinearly with decreasing temperature, likely due to the emergence of additional rate-limiting steps at low temperatures. Finally, our results indicate that the dynamics of this intake process affects significantly the expected RNA numbers in individual cells for a significant amount of time following induction and, thus, the overall distribution of RNA numbers of the cell population.Next, we studied the temperature dependence of the dynamics of transcription initiation of a synthetic gene, engineered from a viral promoter. This dependency is shown to occur at the level of the underlying kinetics of the rate limiting steps in initiation. From the analysis of the empirical data, we found that, first, similarly to E. coli promoters, the T7 phage Phi 10 promoter exhibits more than one rate-limiting step during initiation. Also, the mean time-length of these steps is temperature dependent. However, contrary to E. coli promoters, the noise in RNA production increases with increasing temperature within the range of temperatures tested.Finally, we investigated a key mechanism of transcription, namely, the robustness of a transcription repression mechanism by analyzing the rate of ‘leaky’ transcription events, i.e., RNA production events when under full repression. Using the LacO3O1 as a model promoter, from the analysis of the empirical data on single RNA production kinetics, we found that this promoter exhibits a leakiness rate that is higher at low temperatures, suggesting that its repression mechanism is less efficient under these conditions.We believe that the studies presented here contribute to a better understanding of how temperature affects the transcription dynamics of synthetic genes in environments where temperature fluctuations occur. Since the acquired knowledge is of use to better understand the behavior of synthetic promoters, we expect our main contribution to be in the area of Synthetic Biology, namely, to be of value in predicting the robustness of future synthetic genetic circuits to temperature shifts. In particular, our results show that, in the genes studied, the repression mechanism is the most affected by temperature. This strong temperature dependence translates into the hindering of the promoter responsiveness to induction at sub-optimal temperature conditions. Additionally, our results suggest that this temperature-dependence of the robustness and responsiveness can be tuned, which indicates that it is possible to engineer synthetic promoters of higher response accuracy for a wider range of environmental conditions than those studied here. This knowledge can be used in the construction of synthetic genetic circuits with a more predictable, robust behavior

On the Origin of Phenotypic Variation: Novel Technologies to Dissect Molecular Determinants of Phenotype

This thesis describes the conception, design, and development of novel computational tools, theoretical models, and experimental techniques applied to the dissection of molecular factors underlying phenotypic variation. The first part of my work is focused on finding rare genetic variants in pooled DNA samples, leading to the development of a novel set of algorithms, SNPseeker and SPLINTER, applied to next-generation sequencing data. The second part of my work describes the creation of a reporter system for DNA methylation for the purpose of dissecting the genetic contribution of tissue-specific patterns of DNA methylation across the genome. Finally the last part of my work is focused on understanding the basis of stochastic variation in gene expression with a focus on modeling and dissecting the relationship between single-cell protein variance and mean at a genome-wide scale