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

Novel TCAP mutation c.32C>A causing limb girdle muscular dystrophy 2G

TCAP encoded telethonin is a 19 kDa protein, which plays an important role in anchoring titin in Z disc of the sarcomere and is known to cause LGMD2G, a rare muscle disorder characterised by proximal and distal lower limb weakness, calf hypertrophy and loss of ambulation. A total of 300 individuals with ARLGMD were recruited for this study. Among these we identified 8 clinically well characterised LGMD2G cases from 7 unrelated Dravidian families. Clinical examination revealed predominantly proximo - distal form of weakness, scapular winging, muscle atrophy, calf hypertrophy and foot drop, immunoblot showed either complete absence or severe reduction of telethonin. Genetic analysis revealed a novel nonsense homozygous mutation c.32C>A, p.(Ser11*) in three patients of a consanguineous family and an 8 bp homozygous duplication c.26_33dupAGGTGTCG, p.(Arg12fs31*) in another patient. Both mutations possibly lead to truncated protein or nonsense mediated decay. We could not find any functionally significant TCAP mutation in the remaining 6 samples, except for two other polymorphisms, c.453A>C, p.( = ) and c.-178G>T, which were found in cases and controls. This is the first report from India to demonstrate TCAP association with LGMD2G

Rate-limiting steps in transcription dictate sensitivity to variability in cellular components

Cell-to-cell variability in cellular components generates cell-to-cell diversity in RNA and protein production dynamics. As these components are inherited, this should also cause lineage-to-lineage variability in these dynamics. We conjectured that these effects on transcription are promoter initiation kinetics dependent. To test this, first we used stochastic models to predict that variability in the numbers of molecules involved in upstream processes, such as the intake of inducers from the environment, acts only as a transient source of variability in RNA production numbers, while variability in the numbers of a molecular species controlling transcription of an active promoter acts as a constant source. Next, from single-cell, single-RNA level time-lapse microscopy of independent lineages of Escherichia coli cells, we demonstrate the existence of lineage-to-lineage variability in gene activation times and mean RNA production rates, and that these variabilities differ between promoters and inducers used. Finally, we provide evidence that this can be explained by differences in the kinetics of the rate-limiting steps in transcription between promoters and induction schemes. We conclude that cell-to-cell and consequent lineage-to-lineage variability in RNA and protein numbers are both promoter sequence-dependent and subject to regulation.publishedVersionPeer reviewe

Effects of the Dynamics of the Steps in Transcription Initiation on the Asymmetry of the Distribution of Time Intervals between Consecutive RNA Productions

Asymmetries in the distribution of time intervals between consecutive RNA pro-ductions from a gene can play a critical role in, e.g., allowing/preventing the RNA and, thus, protein numbers to cross thresholds involved in gene network decision making. Here, we use a stochastic, multi-step model of transcription initiation, with all rate constants empirically validated, and explore how the kinetics of its steps affect the temporal asymmetries in RNA production, as measured by the skewness of the distribution of intervals between consecutive RNA productions in individual cells. From the model, first, we show that this skewness differs widely with the mean fraction of time that the RNA polymerase spends in the steps preceding open complex formation, while being independent of the mean transcription rate. Next, we provide empirical validation of these results, using qPCR and live, time-lapse, single-molecule RNA microscopy measurements of the transcription kinetics of multiple promoters. We conclude that the skewness in RNA production kinetics is subject to regulation by the kinetics of the steps in transcription initiation and, thus, evolvable.acceptedVersionPeer reviewe

Regulation of asymmetries in the kinetics and protein numbers of bacterial gene expression

Genetic circuits change the status quo of cellular processes when their protein numbers cross thresholds. We investigate the regulation of RNA and protein threshold crossing propensities in Escherichia coli. From in vivo single RNA time-lapse microscopy data from multiple promoters, mutants, induction schemes and media, we study the asymmetry and tailedness (quantified by the skewness and kurtosis, respectively) of the distributions of time intervals between transcription events. We find that higher thresholds can be reached by increasing the skewness and kurtosis, which is shown to be achievable without affecting mean and coefficient of variation, by regulating the rate-limiting steps in transcription initiation. Also, they propagate to the skewness and kurtosis of the distributions of protein expression levels in cell populations. The results suggest that the asymmetry and tailedness of RNA and protein numbers in cell populations, by controlling the propensity for threshold crossing, and due to being sequence dependent and subject to regulation, may be key regulatory variables of decision-making processes in E. coli.publishedVersionPeer reviewe

Rapid antibiotic susceptibility testing and species identification for mixed samples

General description This item contains all the data, code and analysis objects used in the paper: "Rapid antibiotic susceptibility testing and species identification for mixed samples  Vinodh Kandavalli*, Praneeth Karempudi*, Jimmy Larsson & Johan Elf. Dept. Cell and Molecular Biology, Uppsala University, Sweden" *Equal contribution Experimental data description All the experiments done in this paper are timelapse microscopy experiments. Each experiment contains two directories, one containing phase contrast images arranged by positions images on microfluidic device described in the paper. The other contains fluorescence images, also arranged by positions. Phase contrast images are used for phenotyping bacteria growing in fluidic channels and fluorescence images are used for determining genotype of the cells. Unless stated specifically the lower number of positions (100 series) are cells not treated with antibiotic and the upper series of positions (200 series) are for positions treated with antibiotics. Each series corresponds to one side of the fluidic device. Experiments can be broadly divided into 4 categories.  1. Single fluorescence channel per Species In this kind of experiments, each species is colored/stained by one fluorescence probe that is specific to it's ribosomal RNA. There are 4 experiments of this kind in this dataset, all of which are used in making Fig3 of the main manuscript. All these experiments had 4 speices E.coli, E.faecalis, K.pneumoniae, P.aeruginosa. They are in the following zips.  a) data_EXP-21-BV6157.zip (Ciprofloxacin 1ug/ml) b) data_EXP-21-BV6160.zip (Gentamycin 2ug/ml) c) data_EXP-21-BV6171.zip (Nitrofurantion 32ug/ml) d) data_EXP-21-BV6174.zip (Vancomycin 4ug/ml) 2. Single or dual fluorescence channel per Species In this kind of experiments, each species can be colore/stained by either one or two probes at the same time. There are 8 experiments of this kind in this dataset, in which we treat the cells on one side of the chip with antibiotic. There are 4 other experiments of this kind, which are control experiments to show the specificity of the FISH probes. Each of the following experiments has 2 species loaded on to the fluidic device. For these series of experiments we use some of the following 7 species E.coli, E.faecalis, K.pneumoniae, S.aureus, P.aureginosa, P.mirabilis, A.baumannii. a) data_EXP-22-BV6194.zip (K.pneumoniae, S.aureus, Ciprofloxacin 1ug/ml) b) data_EXP-22-BV6196.zip (P.aureginosa, A.baumannii, Gentamycin 2ug/ml) c) data_EXP-22-BV6197.zip (E.coli, P.mirabilis, Nitrofurantoin 32ug/ml) d) data_EXP-22-BZ0300.zip (E.coli, E.faecalis, Vancomycin 4ug/ml) e) data_EXP-22-BZ0312.zip (K.pneumoniae, S.aureus, Ciprofloxacin 1ug/ml) f) data_EXP-22-BZ0306.zip (P.aureginosa, A.baumannii, Gentamycin 2ug/ml) g) data_EXP-22-BZ0313.zip (E.coli, P.mirabilis, Nitrofurantoin 32ug/ml) h) data_EXP-22-BZ0307.zip (E.coli, E.faecalis, Vancomycin 4ug/ml) i) data_EXP-22-BV6188.zip (E.coli, K.pneumoinae, Control expt) j) data_EXP-22-BV6189.zip (P.aureginosa, E.faecalis, Control expt) k) data_EXP-22-BV6190.zip (P.mirabilis, A.baumannii, Control expt) l) data_EXP-22-BV6191.zip (S.aureus, Control expt) m) data_EXP-22-BZ0308.zip (E.coli, E.faecalis, Loading ratio control expt) n) data_EXP-22-BZ0314.zip (E.coli, E.faecalis, Loading ratio control expt) o) data_EXP-22-BV6195.zip (All seven species) 3. Analysis experiment and model development. The data and code used developing segmentation, tracking and FISH classification models are in the following experiment. This experiment as mulitple directories containing data (data directory), dbscan-python (cloned code for paralled-dbscan implementation, compile for your own architectures), narsil2 (our pipeline code for all the analysis done in the paper, you will have to install this package in an environment using anaconda from .yml files provided), notebooks (notebooks for running various tasks and some sample notebooks), saved_models (directory containing all the models applied in the paper). These are all bundled in the following experiment. a) data_EXP-22-BP0394.zip 4. Extra experiments from which supplementary videos were made a) data_EXP-21-BV6169.zip S3 video b) data_EXP-21-BV6175.zip S1 video Analyis Objects All processed data for each of the experiments described are bundle together into on zip. Inside this file there is a zip file for each of the experiments describe in 1, 2 sections above. Each of this zip file contains all the analysis object corresponding to a single experiment. All the AST figures in the paper are done using these analysis objects. They are in the file named a) analysis_zips.zip Code Code used for paper is seperated in this smaller directory without the raw data used for model development. This code zip has all the code, saved_models, notebooks used to replicate all the analysis done in the paper and all the raw figures generated by the notebooks. narsil2 directory has the python package that needs to be installed in a new environment. saved_models has all the models used in the paper. notebooks directory has jupyter notebooks for analysis of all the experiments in expt_notebooks directory. paper_figures directory has all the code organized according to figure numbers and has all the instructions written to replicate the figures/table in the paper. README.txt file in code_paper.zip also provides description on how to replicate figures shown in the paper.</p

Modeling and Engineering Promoters with Pre-defined RNA Production Dynamics in Escherichia Coli

Recent developments in live-cell time-lapse microscopy and signal processing methods for single-cell, single-RNA detection now allow characterizing the in vivo dynamics of RNA production of Escherichia coli promoters at the single event level. This dynamics is mostly controlled at the promoter region, which can be engineered with single nucleotide precision. Based on these developments, we propose a new strategy to engineer genes with predefined transcription dynamics (mean and standard deviation of the distribution of RNA numbers of a cell population). For this, we use stochastic modelling followed by genetic engineering, to design synthetic promoters whose rate-limiting steps kinetics allow achieving a desired RNA production kinetics. We present an example where, from a pre-defined kinetics, a stochastic model is first designed, from which a promoter is selected based on its rate-limiting steps kinetics. Next, we engineer mutant promoters and select the one that best fits the intended distribution of RNA numbers in a cell population. As the modelling strategies and databases of models, genetic constructs, and information on these constructs kinetics improve, we expect our strategy to be able to accommodate a wide variety of pre-defined RNA production kinetics.acceptedVersionPeer reviewe

Estimating RNA numbers in single cells by RNA fluorescent tagging and flow cytometry

Estimating the statistics of single-cell RNA numbers has become a key source of information on gene expression dynamics. One of the most informative methods of in vivo single-RNA detection is MS2d-GFP tagging. So far, it requires microscopy and laborious semi-manual image analysis, which hampers the amount of collectable data. To overcome this limitation, we present a new methodology for quantifying the mean, standard deviation, and skewness of single-cell distributions of RNA numbers, from flow cytometry data on cells expressing RNA tagged with MS2d-GFP. The quantification method, based on scaling flow-cytometry data from microscopy single-cell data on integer-valued RNA numbers, is shown to readily produce precise, big data on in vivo single-cell distributions of RNA numbers and, thus, can assist in studies of transcription dynamics.publishedVersionPeer reviewe