Dissecting the functional program of Escherichia coli promoters: the combined mode of action of Lac repressor and AraC activator

Growth phase-dependent ribonucleic acid production dynamics

Transcriptional events play a crucial role in major cellular processes that specify the activity of an individual cells and influences cell population behavior in response to environment. Active (ON) and an inactive (OFF) states controls the transcriptional burst. Yet, the mechanism and kinetics of ON/OFF-state across the different growth phases of Escherichia coli remains elusive. Here, we have used a single mRNA detection method in live-cells to comprehend the ON/OFF mechanism of the first transcriptional (TF) and consecutive events (TC) controlled by lactose promoters, Plac and Plac/ara1. We determined that the duration of TF ON/OFF has different modes, exhibiting a close to inverse behavior to that of TC ON/OFF. Dynamics of ON/OFF states in fast and slow-dividing cells were affected by the promoter region during the initiation of transcription. Period of TF ON-state defines the behavior of TC by altering the number and the frequency of mRNAs formed. Furthermore, we have shown that delayed OFF-time in TF affects the dynamics of TC in both states, which is mainly determined by the upstream promoter region. Furthermore, using elongation arrest experiments, we independently validate that mRNA noise in TC is governed by the delayed OFF-period in TF. We have identified the position of the regulatory regions that plays a crucial role in noise (Fano) modulation. Taken together, our results suggest that the dynamics of the first transcriptional event, TF, pre-defines the diversity of the population.

The transcription factor network of E. coli steers global responses to shifts in RNAP concentration

The robustness and sensitivity of gene networks to environmental changes is critical for cell survival. How gene networks produce specific, chronologically ordered responses to genome-wide perturbations, while robustly maintaining homeostasis, remains an open question. We analysed if short- and mid-term genome-wide responses to shifts in RNA polymerase (RNAP) concentration are influenced by the known topology and logic of the transcription factor network (TFN) of Escherichia coli. We found that, at the gene cohort level, the magnitude of the single-gene, mid-term transcriptional responses to changes in RNAP concentration can be explained by the absolute difference between the gene's numbers of activating and repressing input transcription factors (TFs). Interestingly, this difference is strongly positively correlated with the number of input TFs of the gene. Meanwhile, short-term responses showed only weak influence from the TFN. Our results suggest that the global topological traits of the TFN of E. coli shape which gene cohorts respond to genome-wide stresses.

Analytical kinetic model of native tandem promoters in E. coli

Closely spaced promoters in tandem formation are abundant in bacteria. We investigated the evolutionary conservation, biological functions, and the RNA and single-cell protein expression of genes regulated by tandem promoters in E. coli. We also studied the sequence (distance between transcription start sites ‘d_TSS’, pause sequences, and distances from oriC) and potential influence of the input transcription factors of these promoters. From this, we propose an analytical model of gene expression based on measured expression dynamics, where RNAP-promoter occupancy times and d_TSS are the key regulators of transcription interference due to TSS occlusion by RNAP at one of the promoters (when d_TSS ≤ 35 bp) and RNAP occupancy of the downstream promoter (when d_TSS > 35 bp). Occlusion and downstream promoter occupancy are modeled as linear functions of occupancy time, while the influence of d_TSS is implemented by a continuous step function, fit to in vivo data on mean single-cell protein numbers of 30 natural genes controlled by tandem promoters. The best-fitting step is at 35 bp, matching the length of DNA occupied by RNAP in the open complex formation. This model accurately predicts the squared coefficient of variation and skewness of the natural single-cell protein numbers as a function of d_TSS. Additional predictions suggest that promoters in tandem formation can cover a wide range of transcription dynamics within realistic intervals of parameter values. By accurately capturing the dynamics of these promoters, this model can be helpful to predict the dynamics of new promoters and contribute to the expansion of the repertoire of expression dynamics available to synthetic genetic constructs.

Tandem promoters are common in nature, but investigations on their dynamics have so far largely relied on synthetic constructs. Thus, their regulation and potentially unique dynamics remain unexplored. We first performed a comprehensive exploration of the conservation of genes regulated by these promoters in E. coli and the properties of their input transcription factors. We then measured protein and RNA levels expressed by 30 Escherichia coli tandem promoters, to establish an analytical model of the expression dynamics of genes controlled by such promoters. We show that start site occlusion and downstream RNAP occupancy can be realistically captured by a model with RNAP binding affinity, the time length of open complex formation, and the nucleotide distance between transcription start sites. This study contributes to a better understanding of the unique dynamics tandem promoters can bring to the dynamics of gene networks and will assist in their use in synthetic genetic circuits.

Small binding-site clearance delays are not negligible in gene expression modeling

During the templated biopolymerization processes of transcription and translation, a macromolecular machine, either an RNA polymerase or a ribosome, binds to a specific site on the template. Due to the sizes of these enzymes, there is a waiting time before one clears the binding site and another can bind. These clearance delays are relatively short, and one might think that they could be neglected. However, in the case of transcription, these clearance delays are associated with conservation laws, resulting in surprisingly large effects on the bifurcation diagrams in models of gene expression networks. We study an example of this phenomenon in a model of a gene regulated by a non-coding RNA displaying bistability. Neglecting the binding-site clearance delays in this model can only be compensated for by making ad hoc, unphysical adjustments to the model's kinetic constants.

Stochastic models coupling gene expression and partitioning in cell division in Escherichia coli

Regulation of future RNA and protein numbers is a key process by which cells continuously best fit the environment. In bacteria, RNA and proteins exist in small numbers and their regulatory processes are stochastic. Consequently, there is cell-to-cell variability in these numbers, even between sister cells. Traditionally, the two most studied sources of this variability are gene expression and RNA and protein degradation, with evidence suggesting that the latter is subject to little regulation, when compared to the former. However, time-lapse microscopy and single molecule fluorescent tagging have produced evidence that cell division can also be a significant source of variability due to asymmetries in the partitioning of RNA and proteins. Relevantly, the impact of this noise differs from noise in production and degradation since, unlike these, it is not continuous. Rather, it occurs at specific time points, at which moment it can introduce major fluctuations. Several models have now been proposed that integrate noise from cell division, in addition to noise in gene expression, to mimic the dynamics of RNA and protein numbers of cell lineages. This is expected to be particularly relevant in genetic circuits, where significant fluctuations in one component protein, at specific time moments, are expected to perturb near-equilibrium states of the circuits, which can have long-lasting consequences. Here we review stochastic models coupling these processes in Escherichia coli, from single genes to small circuits.

Transcription closed and open complex formation coordinate expression of genes with a shared promoter region

Many genes are spaced closely, allowing coordination without explicit control through shared regulatory elements and molecular interactions. We study the dynamics of a stochastic model of a gene-pair in a head-to-head configuration, sharing promoter elements, which accounts for the rate-limiting steps in transcription initiation. We find that only in specific regions of the parameter space of the rate-limiting steps is orderly coexpression exhibited, suggesting that successful cooperation between closely spaced genes requires the coevolution of compatible rate-limiting step configuration. The model predictions are validated using in vivo single-cell, single-RNA measurements of the dynamics of pairs of genes sharing promoter elements. Our results suggest that, in E. coli, the kinetics of the rate-limiting steps in active transcription can play a central role in shaping the dynamics of gene-pairs sharing promoter elements.

Added value of autoregulation and multi-step kinetics of transcription initiation

Bacterial gene expression regulation occurs mostly during transcription, which has two main rate-limiting steps: the close complex formation, when the RNA polymerase binds to an active promoter, and the subsequent open complex formation, after which it follows elongation. Tuning these steps' kinetics by the action of e.g. transcription factors, allows for a wide diversity of dynamics. For example, adding autoregulation generates single-gene circuits able to perform more complex tasks. Using stochastic models of transcription kinetics with empirically validated parameter values, we investigate how autoregulation and the multi-step transcription initiation kinetics of single-gene autoregulated circuits can be combined to fine-tune steady state mean and cell-to-cell variability in protein expression levels, as well as response times. Next, we investigate how they can be jointly tuned to control complex behaviours, namely, time counting, switching dynamics and memory storage. Overall, our finding suggests that, in bacteria, jointly regulating a single-gene circuit's topology and the transcription initiation multi-step dynamics allows enhancing complex task performance.

Temperature-dependence of the single-cell variability in the kinetics of transcription activation in Escherichia coli

From in vivo single-cell, single-RNA measurements of the activation times and subsequent steady-state active transcription kinetics of a single-copy Lac-ara-1 promoter in Escherichia coli, we characterize the intake kinetics of the inducer (IPTG) from the media, following temperature shifts. For this, for temperature shifts of various degrees, we obtain the distributions of transcription activation times as well as the distributions of intervals between consecutive RNA productions following activation in individual cells. We then propose a novel methodology that makes use of deconvolution techniques to extract the mean and the variability of the distribution of intake times. We find that cells, following shifts to low temperatures, have higher intake times, although, counter-intuitively, the cell-to-cell variability of these times is lower. We validate the results using a new methodology for direct estimation of mean intake times from measurements of activation times at various inducer concentrations. The results confirm that E. coli's inducer intake times from the environment are significantly higher following a shift to a sub-optimal temperature. Finally, we provide evidence that this is likely due to the emergence of additional rate-limiting steps in the intake process at low temperatures, explaining the reduced cell-to-cell variability in intake times.

Tuning the dynamic range of bacterial promoters regulated by ligand-inducible transcription factors

One challenge for synthetic biologists is the predictable tuning of genetic circuit regulatory components to elicit desired outputs. Gene expression driven by ligand-inducible transcription factor systems must exhibit the correct ON and OFF characteristics: appropriate activation and leakiness in the presence and absence of inducer, respectively. However, the dynamic range of a promoter (i.e., absolute difference between ON and OFF states) is difficult to control. We report a method that tunes the dynamic range of ligand-inducible promoters to achieve desired ON and OFF characteristics. We build combinatorial sets of AraC-and LasR-regulated promoters containing -10 and -35 sites from synthetic and Escherichia coli promoters. Four sequence combinations with diverse dynamic ranges were chosen to build multi-input transcriptional logic gates regulated by two and three ligand-inducible transcription factors (LacI, TetR, AraC, XylS, RhlR, LasR, and LuxR). This work enables predictable control over the dynamic range of regulatory components.

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.

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

Transcription kinetics is limited by its initiation steps, which differ between promoters and with intra- and extracellular conditions. Regulation of these steps allows tuning both the rate and stochasticity of RNA production. We used time-lapse, single-RNA microscopy measurements in live Escherichia coli to study how the rate-limiting steps in initiation of the P_lac/ara-1 promoter change with temperature and induction scheme. For this, we compared detailed stochastic models fit to the empirical data in maximum likelihood sense using statistical methods. Using this analysis, we found that temperature affects the rate limiting steps unequally, as nonlinear changes in the closed complex formation suffice to explain the differences in transcription dynamics between conditions. Meanwhile, a similar analysis of the P_tetA promoter revealed that it has a different rate limiting step configuration, with temperature regulating different steps. Finally, we used the derived models to explore a possible cause for why the identified steps are preferred as the main cause for behavior modifications with temperature: we find that transcription dynamics is either insensitive or responds reciprocally to changes in the other steps. Our results suggests that different promoters employ different rate limiting step patterns that control not only their rate and variability, but also their sensitivity to environmental changes.

Temperature affects the behavior of cells, such as their growth rate. However, it is not well understood how these changes result from the changes at the single molecule level. We observed the production of individual RNA molecules in live cells under a wide range of temperatures. This allowed us to determine not only how fast they are produced, but also how much variability there is in this process. Next, we fit a stochastic model to the data to identify which rate-limiting steps during RNA production are responsible for the observed differences between conditions. We found that genes differ in how their RNA production is limited by different steps and in how these are affected by the temperature, which explains why different genes respond differently to temperature fluctuations.

Dissecting the stochastic transcription initiation process in live Escherichia coli

We investigate the hypothesis that, in Escherichia coli, while the concentration of RNA polymerases differs in different growth conditions, the fraction of RNA polymerases free for transcription remains approximately constant within a certain range of these conditions. After establishing this, we apply a standard model-fitting procedure to fully characterize the in vivo kinetics of the rate-limiting steps in transcription initiation of the Plac/ara-1 promoter from distributions of intervals between transcription events in cells with different RNA polymerase concentrations. We find that, under full induction, the closed complex lasts ∼788 s while subsequent steps last ∼193 s, on average. We then establish that the closed complex formation usually occurs multiple times prior to each successful initiation event. Furthermore, the promoter intermittently switches to an inactive state that, on average, lasts ∼87 s. This is shown to arise from the intermittent repression of the promoter by LacI. The methods employed here should be of use to resolve the rate-limiting steps governing the in vivo dynamics of initiation of prokaryotic promoters, similar to established steady-state assays to resolve the in vitro dynamics.

Characterizing rate limiting steps in transcription from RNA production times in live cells

MOTIVATION

Single-molecule measurements of live Escherichia coli transcription dynamics suggest that this process ranges from sub- to super-Poissonian, depending on the conditions and on the promoter. For its accurate quantification, we propose a model that accommodates all these settings, and statistical methods to estimate the model parameters and to select the relevant components.

RESULTS

The new methodology has improved accuracy and avoids overestimating the transcription rate due to finite measurement time, by exploiting unobserved data and by accounting for the effects of discrete sampling. First, we use Monte Carlo simulations of models based on measurements to show that the methods are reliable and offer substantial improvements over previous methods. Next, we apply the methods on measurements of transcription intervals of different promoters in live E. coli, and show that they produce significantly different results, both in low- and high-noise settings, and that, in the latter case, they even lead to qualitatively different results. Finally, we demonstrate that the methods can be generalized for other similar purposes, such as for estimating gene activation kinetics. In this case, the new methods allow quantifying the inducer uptake dynamics as opposed to just comparing them between cases, which was not previously possible. We expect this new methodology to be a valuable tool for functional analysis of cellular processes using single-molecule or single-event microscopy measurements in live cells.

AVAILABILITY AND IMPLEMENTATION

Source code is available under Mozilla Public License at http://www.cs.tut.fi/%7Ehakkin22/censored/

CONTACT

andre.ribeiro@tut.fi or andre.sanchesribeiro@tut.fi

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Estimation of kinetic parameters of transcription from temporal single-RNA measurements

Gene expression dynamics in prokaryotes is largely controlled by the multi-step process of transcription initiation whose kinetics is subject to regulation. Since the number and duration of these steps cannot be currently measured in vivo, we propose a novel method for estimating them from time series of RNA numbers in individual cells. We demonstrate the method's applicability on measurements of fluorescence-tagged RNA molecules in Escherichia coli cells, and compare with a previous method. We show that the results of the two methods agree for equal data. We also show that, when incorporating additional data, the new method produces significantly different estimates, which are in closer agreement with qPCR measurements. Unlike the previous method, the new method requires no preprocessing of the RNA numbers, using maximal information from the RNA time series. In addition, it can use data outside of the observed RNA productions. Overall, the new method characterizes the transcription initiation process with enhanced detail.

An Escherichia coli system for evolving improved light-controlled DNA-binding proteins

Light-switchable proteins offer numerous opportunities as tools for manipulating biological systems with exceptional degrees of spatiotemporal control. Most designed light-switchable proteins currently in use have not been optimised using the randomisation and selection/screening approaches that are widely used in other areas of protein engineering. Here we report an approach for screening light-switchable DNA-binding proteins that relies on light-dependent repression of the transcription of a fluorescent reporter. We demonstrate that the method can be used to recover a known light-switchable DNA-binding protein from a random library.

Kinetics of the cellular intake of a gene expression inducer at high concentrations

From in vivo single-event measurements of the transient and steady-state transcription activity of a single-copy lac-ara-1 promoter in Escherichia coli, we characterize the intake kinetics of its inducer (IPTG) from the media. We show that the empirical data are well-fit by a model of intake assuming a bilayer membrane, with the passage through the second layer being rate-limiting, coupled to a stochastic, sub-Poissonian, multi-step transcription process. Using this model, we show that for a wide range of extracellular inducer levels (up to 1.25 mM) the intake process is diffusive-like, suggesting unsaturated membrane permeability. Inducer molecules travel from the periplasm to the cytoplasm in, on average, 31.7 minutes, strongly affecting cells' response time. The novel methodology followed here should aid the study of cellular intake mechanisms at the single-event level.

Cis-Antisense Transcription Gives Rise to Tunable Genetic Switch Behavior: A Mathematical Modeling Approach

Antisense transcription has been extensively recognized as a regulatory mechanism for gene expression across all kingdoms of life. Despite the broad importance and extensive experimental determination of cis-antisense transcription, relatively little is known about its role in controlling cellular switching responses. Growing evidence suggests the presence of non-coding cis-antisense RNAs that regulate gene expression via antisense interaction. Recent studies also indicate the role of transcriptional interference in regulating expression of neighboring genes due to traffic of RNA polymerases from adjacent promoter regions. Previous models investigate these mechanisms independently, however, little is understood about how cells utilize coupling of these mechanisms in advantageous ways that could also be used to design novel synthetic genetic devices. Here, we present a mathematical modeling framework for antisense transcription that combines the effects of both transcriptional interference and cis-antisense regulation. We demonstrate the tunability of transcriptional interference through various parameters, and that coupling of transcriptional interference with cis-antisense RNA interaction gives rise to hypersensitive switches in expression of both antisense genes. When implementing additional positive and negative feed-back loops from proteins encoded by these genes, the system response acquires a bistable behavior. Our model shows that combining these multiple-levels of regulation allows fine-tuning of system parameters to give rise to a highly tunable output, ranging from a simple-first order response to biologically complex higher-order response such as tunable bistable switch. We identify important parameters affecting the cellular switch response in order to provide the design principles for tunable gene expression using antisense transcription. This presents an important insight into functional role of antisense transcription and its importance towards design of synthetic biological switches.

Transcription closed and open complex dynamics studies reveal balance between genetic determinants and co-factors

In E. coli, promoter closed and open complexes are key steps in transcription initiation, where magnesium-dependent RNA polymerase catalyzes RNA synthesis. However, the exact mechanism of initiation remains to be fully elucidated. Here, using single mRNA detection and dual reporter studies, we show that increased intracellular magnesium concentration affects Plac initiation complex formation resulting in a highly dynamic process over the cell growth phases. Mg2+ regulates transcription transition, which modulates bimodality of mRNA distribution in the exponential phase. We reveal that Mg2+ regulates the size and frequency of the mRNA burst by changing the open complex duration. Moreover, increasing magnesium concentration leads to higher intrinsic and extrinsic noise in the exponential phase. RNAP-Mg2+ interaction simulation reveals critical movements creating a shorter contact distance between aspartic acid residues and Nucleotide Triphosphate residues and increasing electrostatic charges in the active site. Our findings provide unique biophysical insights into the balanced mechanism of genetic determinants and magnesium ion in transcription initiation regulation during cell growth.

Origins of transcriptional transition: balance between upstream and downstream regulatory gene sequences

By measuring individual mRNA production at the single-cell level, we investigated the lac promoter’s transcriptional transition during cell growth phases. In exponential phase, variation in transition rates generates two mixed phenotypes, low and high numbers of mRNAs, by modulating their burst frequency and sizes. Independent activation of the regulatory-gene sequence does not produce bimodal populations at the mRNA level, but bimodal populations are produced when the regulatory gene is activated coordinately with the upstream and downstream region promoter sequence (URS and DRS, respectively). Time-lapse microscopy of mRNAs for lac and a variant lac promoter confirm this observation. Activation of the URS/DRS elements of the promoter reveals a counterplay behavior during cell phases. The promoter transition rate coupled with cell phases determines the mRNA and transcriptional noise. We further show that bias in partitioning of RNA does not lead to phenotypic switching. Our results demonstrate that the balance between the URS and the DRS in transcriptional regulation determines population diversity.

By measuring individual mRNA production at the single-cell level, we investigated the lac promoter transcriptional transition during cell growth phases. In exponential phase, variation in transition rate generates two mixed phenotypes producing low and high numbers of mRNAs by modulating the burst frequency and size. Independent activation of the regulatory gene sequence does not produce bimodal populations at the mRNA level, while it does when activated together through the coordination of upstream/downstream promoter sequences (URS/DRS). Time-lapse microscopy of mRNAs for lac and a lac variant promoter confirm this observation. Activation of the URS/DRS elements of the promoter reveals a counterplay behavior during cell phases. The promoter transition rate coupled with cell phases determines the mRNA and transcriptional noise. We further show that bias in partitioning of RNA does not lead to phenotypic switching. Our results demonstrate that the balance between URS and DRS in transcription regulation is determining the population diversity.

In vivo transcription kinetics of a synthetic gene uninvolved in stress-response pathways in stressed Escherichia coli cells

The fast adaptation of Escherichia coli to stressful environments includes the regulation of gene expression rates, mainly of transcription, by specific and global stress-response mechanisms. To study the effects of mechanisms acting on a global level, we observed with single molecule sensitivity the effects of mild acidic shift and oxidative stress on the in vivo transcription dynamics of a probe gene encoding an RNA target for MS2d-GFP, under the control of a synthetic promoter. After showing that this promoter is uninvolved in fast stress-response pathways, we compared its kinetics of transcript production under stress and in optimal conditions. We find that, following the application of either stress, the mean rates of transcription activation and of subsequent RNA production of the probe gene are reduced, particularly under oxidative stress. Meanwhile, the noise in RNA production decreases under oxidative stress, but not under acidic shift. From distributions of intervals between consecutive RNA productions, we infer that the number and duration of the rate-limiting steps in transcription initiation change, following the application of stress. These changes differ in the two stress conditions and are consistent with the changes in noise in RNA production. Overall, our measurements of the transcription initiation kinetics of the probe gene indicate that, following sub-lethal stresses, there are stress-specific changes in the dynamics of transcription initiation of the probe gene that affect its mean rate and noise of transcript production. Given the non-involvement of the probe gene in stress-response pathways, we suggest that these changes are caused by global response mechanisms of E. coli to stress.

Dual role of MsRbpA: transcription activation and rescue of transcription from the inhibitory effect of rifampicin

MsRbpA is an RNA polymerase (RNAP) binding protein from Mycobacterium smegmatis. According to previous studies, MsRbpA rescues rifampicin-induced transcription inhibition upon binding to the RNAP. Others have shown that RbpA from Mycobacterium tuberculosis (MtbRbpA) is a transcription activator. In this study, we report that both MsRbpA and MtbRbpA activate transcription as well as rescue rifampicin-induced transcription inhibition. Transcription activation is achieved through the increased formation of closed RNAP-promoter complex as well as enhanced rate of conversion of this complex to a stable transcriptionally competent RNAP-promoter complex. When a 16 aa peptide fragment (Asp 58 to Lys 73) was deleted from MsRbpA, the resulting protein showed 1000-fold reduced binding with core RNAP. The deletion results in abolition of transcription activation and rescue of transcription from the inhibitory effect of rifampicin. Through alanine scanning of this essential region of MsRbpA, Gly 67, Val 69, Pro 70 and Pro 72 residues are identified to be important for MsRbpA function. Furthermore, we report here that the protein is indispensable for M. smegmatis, and it appears to help the organism grow in the presence of the antibiotic rifampicin.

Effects of multimerization on the temporal variability of protein complex abundance

We explore whether the process of multimerization can be used as a means to regulate noise in the abundance of functional protein complexes. Additionally, we analyze how this process affects the mean level of these functional units, response time of a gene, and temporal correlation between the numbers of expressed proteins and of the functional multimers. We show that, although multimerization increases noise by reducing the mean number of functional complexes it can reduce noise in comparison with a monomer, when abundance of the functional proteins are comparable. Alternatively, reduction in noise occurs if both monomeric and multimeric forms of the protein are functional. Moreover, we find that multimerization either increases the response time to external signals or decreases the correlation between number of functional complexes and protein production kinetics. Finally, we show that the results are in agreement with recent genome-wide assessments of cell-to-cell variability in protein numbers and of multimerization in essential and non-essential genes in Escherichia coli, and that the effects of multimerization are tangible at the level of genetic circuits.

Cell segmentation by multi-resolution analysis and maximum likelihood estimation (MAMLE)

BACKGROUND

Cell imaging is becoming an indispensable tool for cell and molecular biology research. However, most processes studied are stochastic in nature, and require the observation of many cells and events. Ideally, extraction of information from these images ought to rely on automatic methods. Here, we propose a novel segmentation method, MAMLE, for detecting cells within dense clusters.

METHODS

MAMLE executes cell segmentation in two stages. The first relies on state of the art filtering technique, edge detection in multi-resolution with morphological operator and threshold decomposition for adaptive thresholding. From this result, a correction procedure is applied that exploits maximum likelihood estimate as an objective function. Also, it acquires morphological features from the initial segmentation for constructing the likelihood parameter, after which the final segmentation is obtained.

CONCLUSIONS

We performed an empirical evaluation that includes sample images from different imaging modalities and diverse cell types. The new method attained very high (above 90%) cell segmentation accuracy in all cases. Finally, its accuracy was compared to several existing methods, and in all tests, MAMLE outperformed them in segmentation accuracy.

Effects of rate-limiting steps in transcription initiation on genetic filter motifs

The behavior of genetic motifs is determined not only by the gene-gene interactions, but also by the expression patterns of the constituent genes. Live single-molecule measurements have provided evidence that transcription initiation is a sequential process, whose kinetics plays a key role in the dynamics of mRNA and protein numbers. The extent to which it affects the behavior of cellular motifs is unknown. Here, we examine how the kinetics of transcription initiation affects the behavior of motifs performing filtering in amplitude and frequency domain. We find that the performance of each filter is degraded as transcript levels are lowered. This effect can be reduced by having a transcription process with more steps. In addition, we show that the kinetics of the stepwise transcription initiation process affects features such as filter cutoffs. These results constitute an assessment of the range of behaviors of genetic motifs as a function of the kinetics of transcription initiation, and thus will aid in tuning of synthetic motifs to attain specific characteristics without affecting their protein products.

Kinetics of gene expression in bacteria — From models to measurements, and back again

Dennis Salahub has contributed to several scientific areas. Likely, one of the lesser known contributions is the one on the study of the kinetics of gene expression in prokaryotes, which resulted in a delayed stochastic model of transcription and translation dynamics in bacteria. The model has become the basis for modeling stochastic genetic circuits and has provided a framework for interpreting recent measurements of transcripts production dynamics in live cells, one event at a time. Here, we review the contributions by Salahub and colleagues to the modeling strategies of gene expression as a multi-delayed stochastic process. Next, we describe recent findings, which build upon this work and provide experimental validation of the models. Finally, we discuss potential future developments in this field of research.

Global regulation of promoter melting in naive lymphocytes

Lymphocyte activation is initiated by a global increase in messenger RNA synthesis. However, the mechanisms driving transcriptome amplification during the immune response are unknown. By monitoring single-stranded DNA genome wide, we show that the genome of naive cells is poised for rapid activation. In G0, ∼90% of promoters from genes to be expressed in cycling lymphocytes are polymerase loaded but unmelted and support only basal transcription. Furthermore, the transition from abortive to productive elongation is kinetically limiting, causing polymerases to accumulate nearer to transcription start sites. Resting lymphocytes also limit the expression of the transcription factor IIH complex, including XPB and XPD helicases involved in promoter melting and open complex extension. To date, two rate-limiting steps have been shown to control global gene expression in eukaryotes: preinitiation complex assembly and polymerase pausing. Our studies identify promoter melting as a third key regulatory step and propose that this mechanism ensures a prompt lymphocyte response to invading pathogens.

In vivo single-molecule kinetics of activation and subsequent activity of the arabinose promoter

Using a single-RNA detection technique in live Escherichia coli cells, we measure, for each cell, the waiting time for the production of the first RNA under the control of P_BAD promoter after induction by arabinose, and subsequent intervals between transcription events. We find that the kinetics of the arabinose intake system affect mean and diversity in RNA numbers, long after induction. We observed the same effect on P_lac/ara-1 promoter, which is inducible by arabinose or by IPTG. Importantly, the distribution of waiting times of P_lac/ara-1 is indistinguishable from that of P_BAD, if and only if induced by arabinose alone. Finally, RNA production under the control of P_BAD is found to be a sub-Poissonian process. We conclude that inducer-dependent waiting times affect mean and cell-to-cell diversity in RNA numbers long after induction, suggesting that intake mechanisms have non-negligible effects on the phenotypic diversity of cell populations in natural, fluctuating environments.

Tuning promoter strength through RNA polymerase binding site design in Escherichia coli

One of the paramount goals of synthetic biology is to have the ability to tune transcriptional networks to targeted levels of expression at will. As a step in that direction, we have constructed a set of 18 unique binding sites for E. coli RNA Polymerase (RNAP) δ⁷⁰ holoenzyme, designed using a model of sequence-dependent binding energy combined with a thermodynamic model of transcription to produce a targeted level of gene expression. This promoter set allows us to determine the correspondence between the absolute numbers of mRNA molecules or protein products and the predicted promoter binding energies measured in k(B)T energy units. These binding sites adhere on average to the predicted level of gene expression over 3 orders of magnitude in constitutive gene expression, to within a factor of 3 in both protein and mRNA copy number. With these promoters in hand, we then place them under the regulatory control of a bacterial repressor and show that again there is a strict correspondence between the measured and predicted levels of expression, demonstrating the transferability of the promoters to an alternate regulatory context. In particular, our thermodynamic model predicts the expression from our promoters under a range of repressor concentrations between several per cell up to over 100 per cell. After correcting the predicted polymerase binding strength using the data from the unregulated promoter, the thermodynamic model accurately predicts the expression for the simple repression strains to within 30%. Demonstration of modular promoter design, where parts of the circuit (such as RNAP/TF binding strength and transcription factor copy number) can be independently chosen from a stock list and combined to give a predictable result, has important implications as an engineering tool for use in synthetic biology.

Dependence of transcription-coupled DNA supercoiling on promoter strength in Escherichia coli topoisomerase I deficient strains

Transcription by RNA polymerase can induce the formation of hypernegatively supercoiled DNA in vitro and in vivo. This phenomenon has been nicely explained by a "twin-supercoiled-domain" model of transcription where a positively supercoiled domain is generated ahead of the RNA polymerase and a negatively supercoiled domain behind it. In Escherichia coli topA strains, DNA gyrase selectively converts the positively supercoiled domain into negative supercoils to produce hypernegatively supercoiled DNA. In this article, in order to examine whether promoter strength affects transcription-coupled DNA supercoiling (TCDS), we developed a two-plasmid system in which a linear, non-supercoiled plasmid was used to express lac repressor constitutively while a circular plasmid was used to gage TCDS in E. coli cells. Using this two-plasmid system, we found that TCDS in topA strains is dependent on promoter strength. We also demonstrated that transcription-coupled hypernegative supercoiling of plasmid DNA did not need the expression of a membrane-insertion protein for strong promoters; however, it might require co-transcriptional synthesis of a polypeptide. Furthermore, we found that for weak promoters the expression of a membrane-insertion tet gene was not sufficient for the production of hypernegatively supercoiled DNA. Our results can be explained by the "twin-supercoiled-domain" model of transcription where the friction force applied to E. coli RNA polymerase plays a critical role in the generation of hypernegatively supercoiled DNA.

Effects of gene length on the dynamics of gene expression

In Escherichia coli, the nucleotide length of a gene is bound to affect its expression dynamics. From simulations of a stochastic model of gene expression at the nucleotide and codon levels we show that, within realistic parameter values, the nucleotide length affects RNA and protein mean levels, as well as the expected transient time for RNA and protein numbers to change, following a signal. Fluctuations in RNA and protein numbers are found to be minimized for a small range of lengths, which matches the means of the distributions of lengths found in E. coli of both essential and non-essential genes. The variance of the length distribution for essential genes is found to be smaller than for non-essential genes, implying that these distributions are far from random. Finally, gene lengths are shown to affect the kinetics of a genetic switch, namely, the correlation between temporal proteins numbers, the stability of the two noisy attractors of the switch, and how biased is the choice of noisy attractor. The stability increases with gene length due to increased 'memory' about the previous states of the switch. We argue that, by affecting the dynamics of gene expression and of genetic circuits, gene lengths are subject to selection.

Regulation of mean and noise of the in vivo kinetics of transcription under the control of the lac/ara-1 promoter

The kinetics of transcription initiation in Escherichia coli depend on the duration of two rate-limiting steps, the closed and the open complex formation. In a lac promoter variant, P(lac/ara-1), the kinetics of these steps is controlled by IPTG and arabinose. From in vivo single-RNA measurements, we find that induction affects the mean and normalized variance of the intervals between consecutive RNA productions. Transcript production is sub-Poissonian in all conditions tested. The kinetics of each step is independently controlled by a different inducer. We conclude that the regulatory mechanism of P(lac/ara-1) allows the stochasticity of gene expression to be environment-dependent.

Detecting sequence dependent transcriptional pauses from RNA and protein number time series

BACKGROUND

Evidence suggests that in prokaryotes sequence-dependent transcriptional pauses affect the dynamics of transcription and translation, as well as of small genetic circuits. So far, a few pause-prone sequences have been identified from in vitro measurements of transcription elongation kinetics.

RESULTS

Using a stochastic model of gene expression at the nucleotide and codon levels with realistic parameter values, we investigate three different but related questions and present statistical methods for their analysis. First, we show that information from in vivo RNA and protein temporal numbers is sufficient to discriminate between models with and without a pause site in their coding sequence. Second, we demonstrate that it is possible to separate a large variety of models from each other with pauses of various durations and locations in the template by means of a hierarchical clustering and a random forest classifier. Third, we introduce an approximate likelihood function that allows to estimate the location of a pause site.

CONCLUSIONS

This method can aid in detecting unknown pause-prone sequences from temporal measurements of RNA and protein numbers at a genome-wide scale and thus elucidate possible roles that these sequences play in the dynamics of genetic networks and phenotype.

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

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.

Direct comparison of small RNA and transcription factor signaling

Small RNAs (sRNAs) and proteins acting as transcription factors (TFs) are the principal components of gene networks. These two classes of signaling molecules have distinct mechanisms of action; sRNAs control mRNA translation, whereas TFs control mRNA transcription. Here, we directly compare the properties of sRNA and TF signaling using mathematical models and synthetic gene circuits in Escherichia coli. We show the abilities of sRNAs to act on existing target mRNAs (as opposed to TFs, which alter the production of future target mRNAs) and, without needing to be first translated, have surprisingly little impact on the dynamics. Instead, the dynamics are primarily determined by the clearance rates, steady-state concentrations and response curves of the sRNAs and TFs; these factors determine the time delay before a target gene’s expression can maximally respond to changes in sRNA and TF transcription. The findings are broadly applicable to the analysis of signaling in gene networks, and we demonstrate that they can be used to rationally reprogram the dynamics of synthetic circuits.

Dynamics of transcription of closely spaced promoters in Escherichia coli, one event at a time

Many pairs of genes in Escherichia coli are driven by closely spaced promoters. We study the dynamics of expression of such pairs of genes driven by a model at the molecule and nucleotide level with delayed stochastic dynamics as a function of the binding affinity of the RNA polymerase to the promoter region, of the geometry of the promoter, of the distance between transcription start sites (TSSs) and of the repression mechanism. We find that the rate limiting steps of transcription at the TSS, the closed and open complex formations, strongly affect the kinetics of RNA production for all promoter configurations. Beyond a certain rate of transcription initiation events, we find that the interference between polymerases correlates the dynamics of production of the two RNA molecules from the two TSS and affects the distribution of intervals between consecutive productions of RNA molecules. The degree of correlation depends on the geometry, the distance between TSSs and repressors. Small changes in the distance between TSSs can cause abrupt changes in behavior patterns, suggesting that the sequence between adjacent promoters may be subject to strong selective pressure. The results provide better understanding on the sequence level mechanisms of transcription regulation in bacteria and may aid in the genetic engineering of artificial circuits based on closely spaced promoters.

Synthetic biology: an emerging engineering discipline

Over the past decade, synthetic biology has emerged as an engineering discipline for biological systems. Compared with other substrates, biology poses a unique set of engineering challenges resulting from an incomplete understanding of natural biological systems and tools for manipulating them. To address these challenges, synthetic biology is advancing from developing proof-of-concept designs to focusing on core platforms for rational and high-throughput biological engineering. These platforms span the entire biological design cycle, including DNA construction, parts libraries, computational design tools, and interfaces for manipulating and probing synthetic circuits. The development of these enabling technologies requires an engineering mindset to be applied to biology, with an emphasis on generalizable techniques in addition to application-specific designs. This review aims to discuss the progress and challenges in synthetic biology and to illustrate areas where synthetic biology may impact biomedical engineering and human health.

In vivo kinetics of transcription initiation of the lar promoter in Escherichia coli. Evidence for a sequential mechanism with two rate-limiting steps

BACKGROUND

In Escherichia coli the mean and cell-to-cell diversity in RNA numbers of different genes vary widely. This is likely due to different kinetics of transcription initiation, a complex process with multiple rate-limiting steps that affect RNA production.

RESULTS

We measured the in vivo kinetics of production of individual RNA molecules under the control of the lar promoter in E. coli. From the analysis of the distributions of intervals between transcription events in the regimes of weak and medium induction, we find that the process of transcription initiation of this promoter involves a sequential mechanism with two main rate-limiting steps, each lasting hundreds of seconds. Both steps become faster with increasing induction by IPTG and Arabinose.

CONCLUSIONS

The two rate-limiting steps in initiation are found to be important regulators of the dynamics of RNA production under the control of the lar promoter in the regimes of weak and medium induction. Variability in the intervals between consecutive RNA productions is much lower than if there was only one rate-limiting step with a duration following an exponential distribution. The methodology proposed here to analyze the in vivo dynamics of transcription may be applicable at a genome-wide scale and provide valuable insight into the dynamics of prokaryotic genetic networks.

Cell-to-cell diversity in protein levels of a gene driven by a tetracycline inducible promoter

BACKGROUND

Gene expression in Escherichia coli is regulated by several mechanisms. We measured in single cells the expression level of a single copy gene coding for green fluorescent protein (GFP), integrated into the genome and driven by a tetracycline inducible promoter, for varying induction strengths. Also, we measured the transcriptional activity of a tetracycline inducible promoter controlling the transcription of a RNA with 96 binding sites for MS2-GFP.

RESULTS

The distribution of GFP levels in single cells is found to change significantly as induction reaches high levels, causing the Fano factor of the cells' protein levels to increase with mean level, beyond what would be expected from a Poisson-like process of RNA transcription. In agreement, the Fano factor of the cells' number of RNA molecules target for MS2-GFP follows a similar trend. The results provide evidence that the dynamics of the promoter complex formation, namely, the variability in its duration from one transcription event to the next, explains the change in the distribution of expression levels in the cell population with induction strength.

CONCLUSIONS

The results suggest that the open complex formation of the tetracycline inducible promoter, in the regime of strong induction, affects significantly the dynamics of RNA production due to the variability of its duration from one event to the next.

Stochastic sequence-level model of coupled transcription and translation in prokaryotes

Background

In prokaryotes, transcription and translation are dynamically coupled, as the latter starts before the former is complete. Also, from one transcript, several translation events occur in parallel. To study how events in transcription elongation affect translation elongation and fluctuations in protein levels, we propose a delayed stochastic model of prokaryotic transcription and translation at the nucleotide and codon level that includes the promoter open complex formation and alternative pathways to elongation, namely pausing, arrests, editing, pyrophosphorolysis, RNA polymerase traffic, and premature termination. Stepwise translation can start after the ribosome binding site is formed and accounts for variable codon translation rates, ribosome traffic, back-translocation, drop-off, and trans-translation.

Results

First, we show that the model accurately matches measurements of sequence-dependent translation elongation dynamics. Next, we characterize the degree of coupling between fluctuations in RNA and protein levels, and its dependence on the rates of transcription and translation initiation. Finally, modeling sequence-specific transcriptional pauses, we find that these affect protein noise levels.

Conclusions

For parameter values within realistic intervals, transcription and translation are found to be tightly coupled in Escherichia coli, as the noise in protein levels is mostly determined by the underlying noise in RNA levels. Sequence-dependent events in transcription elongation, e.g. pauses, are found to cause tangible effects in the degree of fluctuations in protein levels.

Dynamical effects of transcriptional pause-prone sites

We study how long pause-prone sites, commonly sequence-dependent, affect transcription and RNA temporal levels in a delayed stochastic model of transcription at the single nucleotide level. We vary pause propensity, duration and the probability of premature termination of elongation at the pause site. We also study the effects of multiple pause sites. We show that pause sites can be used to fine-tune noise strength and burst size distribution of RNA levels. Varying pause rate and duration alone affects bursting but noise is not significantly affected. Noise strength can be changed by varying both parameters and, even more pronouncedly, by varying the probability of premature termination. Adding multiple pause sites amplifies the increase in noise and bursting. This regulatory mechanism of noise and bursting, being evolvable, may partially explain how different genes exhibit a wide spectrum of different behaviors. The results might assist the engineering of genes with a desired degree of noise.

AraC protein, regulation of the l-arabinose operon in Escherichia coli, and the light switch mechanism of AraC action

This review covers the physiological aspects of regulation of the arabinose operon in Escherichia coli and the physical and regulatory properties of the operon's controlling gene, araC. It also describes the light switch mechanism as an explanation for many of the protein's properties. Although many thousands of homologs of AraC exist and regulate many diverse operons in response to many different inducers or physiological states, homologs that regulate arabinose-catabolizing genes in response to arabinose were identified. The sequence similarities among them are discussed in light of the known structure of the dimerization and DNA-binding domains of AraC.

Comparison of promoter-specific events during transcription initiation in mycobacteria

DNA-protein interactions that occur during transcription initiation play an important role in regulating gene expression. To initiate transcription, RNA polymerase (RNAP) binds to promoters in a sequence-specific fashion. This is followed by a series of steps governed by the equilibrium binding and kinetic rate constants, which in turn determine the overall efficiency of the transcription process. We present here the first detailed kinetic analysis of promoter-RNAP interactions during transcription initiation in the sigma(A)-dependent promoters P(rrnAPCL1), P(rrnB) and P(gyr) of Mycobacterium smegmatis. The promoters show comparable equilibrium binding affinity but differ significantly in open complex formation, kinetics of isomerization and promoter clearance. Furthermore, the two rrn promoters exhibit varied kinetic properties during transcription initiation and appear to be subjected to different modes of regulation. In addition to distinct kinetic patterns, each one of the housekeeping promoters studied has its own rate-limiting step in the initiation pathway, indicating the differences in their regulation.

Effects of transcriptional pausing on gene expression dynamics

Stochasticity in gene expression affects many cellular processes and is a source of phenotypic diversity between genetically identical individuals. Events in elongation, particularly RNA polymerase pausing, are a source of this noise. Since the rate and duration of pausing are sequence-dependent, this regulatory mechanism of transcriptional dynamics is evolvable. The dependency of pause propensity on regulatory molecules makes pausing a response mechanism to external stress. Using a delayed stochastic model of bacterial transcription at the single nucleotide level that includes the promoter open complex formation, pausing, arrest, misincorporation and editing, pyrophosphorolysis, and premature termination, we investigate how RNA polymerase pausing affects a gene's transcriptional dynamics and gene networks. We show that pauses' duration and rate of occurrence affect the bursting in RNA production, transcriptional and translational noise, and the transient to reach mean RNA and protein levels. In a genetic repressilator, increasing the pausing rate and the duration of pausing events increases the period length but does not affect the robustness of the periodicity. We conclude that RNA polymerase pausing might be an important evolvable feature of genetic networks.

Effects of the promoter open complex formation on gene expression dynamics

Little is known about the biological mechanisms that shape the distribution of intervals between the completion of RNA molecules (T(p)RNA) , and thus transcriptional noise. We characterize numerically and analytically how the promoter open complex delay (tau(P)) and the transcription initiation rate (k(t)) shape T(p)RNA. From this, we assess the noise and mean of transcript levels and show that these can be tuned both independently and simultaneously by tau(P) and k(t). Finally, we characterize how tau(P) affects bursting in RNA production and show that the tau(P) measured for a lac promoter best fits independent measurements of the burst distribution of the same promoter. Since tau(P) affects noise in gene expression, and given that it is sequence dependent, it is likely to be evolvable.

Tuning cell differentiation patterns and single cell dynamics by regulating proteins' functionalities in a toggle switch

We investigate how the regulation of protein multi-functionalities affect the dynamics of a stochastic model of a toggle switch and the differentiation pattern of cell population regulated by the switch. We study the effects of loss of functionality in DNA-binding and repression and the involvement in differentiation pathway choice. First is shown how the patterns of cell differentiation differ, when each of these functionalities is fully non-functional. Next, tuning the fraction of non-functional proteins regarding the ability to bind DNA is shown to allow fine tuning of the switch and cell differentiation pattern dynamics. Finally, biasing the probability of functionality of the two proteins biases the dynamics of the switch and cell differentiation patterns, especially when transcription factors retain the ability to bind DNA but have lost the ability to repress gene expression. Our results suggest that, besides transcriptional and translational levels of regulation, activation of functionalities in multi-functional proteins are an important regulator of gene networks.