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

IST Austria Thesis

Expression of genes is a fundamental molecular phenotype that is subject to evolution by different types of mutations. Both the rate and the effect of mutations may depend on the DNA sequence context of a particular gene or a particular promoter sequence. In this thesis I investigate the nature of this dependence using simple genetic systems in Escherichia coli. With these systems I explore the evolution of constitutive gene expression from random starting sequences at different loci on the chromosome and at different locations in sequence space. First, I dissect chromosomal neighborhood effects that underlie locus-dependent differences in the potential of a gene under selection to become more highly expressed. Next, I find that the effects of point mutations in promoter sequences are dependent on sequence context, and that an existing energy matrix model performs poorly in predicting relative expression of unrelated sequences. Finally, I show that a substantial fraction of random sequences contain functional promoters and I present an extended thermodynamic model that predicts promoter strength in full sequence space. Taken together, these results provide new insights and guides on how to integrate information on sequence context to improve our qualitative and quantitative understanding of bacterial gene expression, with implications for rapid evolution of drug resistance, de novo evolution of genes, and horizontal gene transfer

Using systems biology approaches to elucidate gene regulatory networks controlling the plant defence response

Transcriptional regulation controlling pathogen-responsive gene expression in Arabidopsis is believed to underlie the plant defence response, which confers partial immunity of Arabidopsis to infection by Botrytis cinerea. In this thesis networks of transcriptional regulation mediating the defence response are studied in various ways. First transcriptional regulation was predicted for all genes differentially expressed during B. cinerea infection by development of a novel clustering approach, Temporal Clustering by Affinity Propagation (TCAP). This approach finds groups of genes whose expression profile time series have strong time-delayed correlation, a measure that is demonstrated to be more predictive of transcriptional regulation than conventionally used similarity measures. TCAP predicts the known regulation of GI by LHY, and co-clusters ORA59 and some of its downstream targets. Predicted novel regulators of pathogen-responsive gene expression were then studied in a reverse genetics screen, which discovered several novel but weakly altered susceptibility phenotypes. Comparison of predicted targets to known targets was complicated by the sparsity of mutant versus wildtype gene expression experiments performed during B. cinerea infections in the literature. To explore the context-dependence of transcriptional regulation, evidence of transcriptional regulation in different contexts was collected. This was compiled to generate a qualitative model of transcriptional regulation during the defence response. This model was validated and extended by experimental analysis of transcription factor-promoter binding in Yeast and transcriptional activation in planta. Comparative transcriptomics showed that downstream genes of some of these regulators | TGA3, ARF2, ERF1 and ANAC072 | are over-represented in the list of genes differentially expressed during B. cinerea infection, which is consistent with these targets being regulated by them during B. cinerea infection. Finally this qualitative model was used as prior information and was used along with gene expression time series to infer quantitative models of the gene regulatory network mediating the defence response. Some known regulation was predicted, and additionally ANAC055 was predicted to be a central regulator of pathogenresponsive gene expression

Metastability and Dynamics of Stem Cells: From Direct Observations to Inference at the Single Cell Level

Organismal development, homeostasis, and pathology are rooted in inherently probabilistic events. From gene expression to cellular differentiation, rates and likelihoods shape the form and function of biology. Processes ranging from growth to cancer homeostasis to reprogramming of stem cells all require transitions between distinct phenotypic states, and these occur at defined rates. Therefore, measuring the fidelity and dynamics with which such transitions occur is central to understanding natural biological phenomena and is critical for therapeutic interventions. While these processes may produce robust population-level behaviors, decisions are made by individual cells. In certain circumstances, these minuscule computing units effectively roll dice to determine their fate. And while the 'omics' era has provided vast amounts of data on what these populations are doing en masse, the behaviors of the underlying units of these processes get washed out in averages. Therefore, in order to understand the behavior of a sample of cells, it is critical to reveal how its underlying components, or mixture of cells in distinct states, each contribute to the overall phenotype. As such, we must first define what states exist in the population, determine what controls the stability of these states, and measure in high dimensionality the dynamics with which these cells transition between states. To address a specific example of this general problem, we investigate the heterogeneity and dynamics of mouse embryonic stem cells (mESCs). While a number of reports have identified particular genes in ES cells that switch between 'high' and 'low' metastable expression states in culture, it remains unclear how levels of many of these regulators combine to form states in transcriptional space. Using a method called single molecule mRNA fluorescent in situ hybridization (smFISH), we quantitatively measure and fit distributions of core pluripotency regulators in single cells, identifying a wide range of variabilities between genes, but each explained by a simple model of bursty transcription. From this data, we also observed that strongly bimodal genes appear to be co-expressed, effectively limiting the occupancy of transcriptional space to two primary states across genes studied here. However, these states also appear punctuated by the conditional expression of the most highly variable genes, potentially defining smaller substates of pluripotency. Having defined the transcriptional states, we next asked what might control their stability or persistence. Surprisingly, we found that DNA methylation, a mark normally associated with irreversible developmental progression, was itself differentially regulated between these two primary states. Furthermore, both acute or chronic inhibition of DNA methyltransferase activity led to reduced heterogeneity among the population, suggesting that metastability can be modulated by this strong epigenetic mark. Finally, because understanding the dynamics of state transitions is fundamental to a variety of biological problems, we sought to develop a high-throughput method for the identification of cellular trajectories without the need for cell-line engineering. We achieved this by combining cell-lineage information gathered from time-lapse microscopy with endpoint smFISH for measurements of final expression states. Applying a simple mathematical framework to these lineage-tree associated expression states enables the inference of dynamic transitions. We apply our novel approach in order to infer temporal sequences of events, quantitative switching rates, and network topology among a set of ESC states. Taken together, we identify distinct expression states in ES cells, gain fundamental insight into how a strong epigenetic modifier enforces the stability of these states, and develop and apply a new method for the identification of cellular trajectories using scalable in situ readouts of cellular state.</p

Protein kinase C-dependent signaling controls the midgut epithelial barrier to malaria parasite infection in anopheline mosquitoes.

Anopheline mosquitoes are the primary vectors of parasites in the genus Plasmodium, the causative agents of malaria. Malaria parasites undergo a series of complex transformations upon ingestion by the mosquito host. During this process, the physical barrier of the midgut epithelium, along with innate immune defenses, functionally restrict parasite development. Although these defenses have been studied for some time, the regulatory factors that control them are poorly understood. The protein kinase C (PKC) gene family consists of serine/threonine kinases that serve as central signaling molecules and regulators of a broad spectrum of cellular processes including epithelial barrier function and immunity. Indeed, PKCs are highly conserved, ranging from 7 isoforms in Drosophila to 16 isoforms in mammals, yet none have been identified in mosquitoes. Despite conservation of the PKC gene family and their potential as targets for transmission-blocking strategies for malaria, no direct connections between PKCs, the mosquito immune response or epithelial barrier integrity are known. Here, we identify and characterize six PKC gene family members--PKCδ, PKCε, PKCζ, PKD, PKN, and an indeterminate conventional PKC--in Anopheles gambiae and Anopheles stephensi. Sequence and phylogenetic analyses of the anopheline PKCs support most subfamily assignments. All six PKCs are expressed in the midgut epithelia of A. gambiae and A. stephensi post-blood feeding, indicating availability for signaling in a tissue that is critical for malaria parasite development. Although inhibition of PKC enzymatic activity decreased NF-κB-regulated anti-microbial peptide expression in mosquito cells in vitro, PKC inhibition had no effect on expression of a panel of immune genes in the midgut epithelium in vivo. PKC inhibition did, however, significantly increase midgut barrier integrity and decrease development of P. falciparum oocysts in A. stephensi, suggesting that PKC-dependent signaling is a negative regulator of epithelial barrier function and a potential new target for transmission-blocking strategies

Quantitative dissection of the simple repression input–output function

We present a quantitative case study of transcriptional regulation in which we carry out a systematic dialogue between theory and measurement for an important and ubiquitous regulatory motif in bacteria, namely, that of simple repression. This architecture is realized by a single repressor binding site overlapping the promoter. From the theory point of view, this motif is described by a single gene regulation function based upon only a few parameters that are convenient theoretically and accessible experimentally. The usual approach is turned on its side by using the mathematical description of these regulatory motifs as a predictive tool to determine the number of repressors in a collection of strains with a large variation in repressor copy number. The predictions and corresponding measurements are carried out over a large dynamic range in both expression fold change (spanning nearly four orders of magnitude) and repressor copy number (spanning about two orders of magnitude). The predictions are tested by measuring the resulting level of gene expression and are then validated by using quantitative immunoblots. The key outcomes of this study include a systematic quantitative analysis of the limits and validity of the input–output relation for simple repression, a precise determination of the in vivo binding energies for DNA–repressor interactions for several distinct repressor binding sites, and a repressor census for Lac repressor in Escherichia coli

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

First-principles prediction of the information processing capacity of a simple genetic circuit

Given the stochastic nature of gene expression, genetically identical cells exposed to the same environmental inputs will produce different outputs. This heterogeneity has been hypothesized to have consequences for how cells are able to survive in changing environments. Recent work has explored the use of information theory as a framework to understand the accuracy with which cells can ascertain the state of their surroundings. Yet the predictive power of these approaches is limited and has not been rigorously tested using precision measurements. To that end, we generate a minimal model for a simple genetic circuit in which all parameter values for the model come from independently published data sets. We then predict the information processing capacity of the genetic circuit for a suite of biophysical parameters such as protein copy number and protein-DNA affinity. We compare these parameter-free predictions with an experimental determination of protein expression distributions and the resulting information processing capacity of E. coli cells. We find that our minimal model captures the scaling of the cell-to-cell variability in the data and the inferred information processing capacity of our simple genetic circuit up to a systematic deviation

The performance of bacterial phytosensing transgenic tobacco under field conditions

Currently the platforms for wide-area detection of environmental contamination are limited. Therefore, there is interest in developing new platforms, especially for use in crop plants to detect and report the presence of biotic and abiotic stress agents. A biosensor uses a biological organism or substrate to detect the presence of an elicitor (i.e., heavy metal, TNT, or bacteria). The foundational groundwork to create biosensors in transgenic plants exists. The creation of bacterial phytosensing transgenic tobacco containing an orange fluorescent protein (OFP) reporter driven by synthetic pathogen-inducible promoters provides a fluorescent signal when infected with phytopathogens for earlier detection in the field. This thesis research performed time-course analysis of field grown transgenic phytosensing tobacco plants infected with Pseudomonas phytopathogens. Some of the phytosensors responded in predictable ways to a suite of treatments, with more than 2-fold of expression of the OFP reporter driven by two different salicylic acid inducible motifs, SARE and PR1. Specifically, transgenic lines containing synthetic promoters with salicylic acid inducible cis-acting regulatory elements showed earlier OFP fluorescence induction by phytopathogen treatments (within 48 hours) than transgenic lines harboring other synthetic promoters; such as the synthetic promoters containing ethylene inducible cis-acting regulatory elements (ERE) which induced OFP fluorescence after phytopathogen treatment only at 72 hours post inoculation. Transgenic lines harboring the OFP reporter driven by synthetic promoters containing defense-related cis-acting regulatory elements were indicative of plant defenses during phytopathogen interactions. Results reported here indicate the functionality of phytosensors in the field that could play a role in presision agriculture in the future