A library of reporters of the global regulators of gene expression in Escherichia coli.

The topology of the transcription factor network (TFN) of Escherichia coli is far from uniform, with 22 global regulator (GR) proteins controlling one-third of all genes. So far, their production rates cannot be tracked by comparable fluorescent proteins. We developed a library of fluorescent reporters for 16 GRs for this purpose. Each consists of a single-copy plasmid coding for green fluorescent protein (GFP) fused to the full-length copy of the native promoter. We tracked their activity in exponential and stationary growth, as well as under weak and strong stresses. We show that the reporters have high sensitivity and specificity to all stresses tested and detect single-cell variability in transcription rates. Given the influence of GRs on the TFN, we expect that the new library will contribute to dissecting global transcriptional stress-response programs of E. coli. Moreover, the library can be invaluable in bioindustrial applications that tune those programs to, instead of cell growth, favor productivity while reducing energy consumption.IMPORTANCECells contain thousands of genes. Many genes are involved in the control of cellular activities. Some activities require a few hundred genes to run largely synchronous transcriptional programs. To achieve this, cells have evolved global regulator (GR) proteins that can influence hundreds of genes simultaneously. We have engineered a library of Escherichia coli strains to track the levels over time of these, phenotypically critical, GRs. Each strain has a single-copy plasmid coding for a fast-maturing green fluorescent protein whose transcription is controlled by a copy of the natural GR promoter. By allowing the tracking of GR levels, with sensitivity and specificity, this library should become of wide use in scientific research on bacterial gene expression (from molecular to synthetic biology) and, later, be used in applications in therapeutics and bioindustries.

Label-free deep learning-based species classification of bacteria imaged by phase-contrast microscopy.

Reliable detection and classification of bacteria and other pathogens in the human body, animals, food, and water is crucial for improving and safeguarding public health. For instance, identifying the species and its antibiotic susceptibility is vital for effective bacterial infection treatment. Here we show that phase contrast time-lapse microscopy combined with deep learning is sufficient to classify four species of bacteria relevant to human health. The classification is performed on living bacteria and does not require fixation or staining, meaning that the bacterial species can be determined as the bacteria reproduce in a microfluidic device, enabling parallel determination of susceptibility to antibiotics. We assess the performance of convolutional neural networks and vision transformers, where the best model attained a class-average accuracy exceeding 98%. Our successful proof-of-principle results suggest that the methods should be challenged with data covering more species and clinically relevant isolates for future clinical use.

Rapid antibiotic susceptibility testing and species identification for mixed samples.

Antimicrobial resistance is an increasing problem on a global scale. Rapid antibiotic susceptibility testing (AST) is urgently needed in the clinic to enable personalized prescriptions in high-resistance environments and to limit the use of broad-spectrum drugs. Current rapid phenotypic AST methods do not include species identification (ID), leaving time-consuming plating or culturing as the only available option when ID is needed to make the sensitivity call. Here we describe a method to perform phenotypic AST at the single-cell level in a microfluidic chip that allows subsequent genotyping by in situ FISH. By stratifying the phenotypic AST response on the species of individual cells, it is possible to determine the susceptibility profile for each species in a mixed sample in 2 h. In this proof-of-principle study, we demonstrate the operation with four antibiotics and mixed samples with combinations of seven species.

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.

Sequence-dependent model of genes with dual σ factor preference.

Escherichia coli uses σ factors to quickly control large gene cohorts during stress conditions. While most of its genes respond to a single σ factor, approximately 5% of them have dual σ factor preference. The most common are those responsive to both σ70, which controls housekeeping genes, and σ38, which activates genes during stationary growth and stresses. Using RNA-seq and flow-cytometry measurements, we show that 'σ70+38 genes' are nearly as upregulated in stationary growth as 'σ38 genes'. Moreover, we find a clear quantitative relationship between their promoter sequence and their response strength to changes in σ38 levels. We then propose and validate a sequence dependent model of σ70+38 genes, with dual sensitivity to σ38 and σ70, that is applicable in the exponential and stationary growth phases, as well in the transient period in between. We further propose a general model, applicable to other stresses and σ factor combinations. Given this, promoters controlling σ70+38 genes (and variants) could become important building blocks of synthetic circuits with predictable, sequence-dependent sensitivity to transitions between the exponential and stationary growth phases.

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 'dTSS', 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 dTSS are the key regulators of transcription interference due to TSS occlusion by RNAP at one of the promoters (when dTSS ≤ 35 bp) and RNAP occupancy of the downstream promoter (when dTSS > 35 bp). Occlusion and downstream promoter occupancy are modeled as linear functions of occupancy time, while the influence of dTSS 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 dTSS. 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.

Efficiency and Robustness of Processes Driven by Nucleoid Exclusion in Escherichia coli.

The internal spatial organization of prokaryotic organisms, including Escherichia coli, is essential for the proper functioning of processes such as cell division. One source of this organization in E. coli is the nucleoid, which causes the exclusion of macromolecules - e.g. protein aggregates and the chemotaxis network - from midcell. Similarly, following DNA replication, the nucleoid(s) assist in placing the Z-ring at midcell. These processes need to be efficient in optimal conditions and robust to suboptimal conditions. After reviewing recent findings on these topics, we make use of past data to study the efficiency of the spatial constraining of Z-rings, chemotaxis networks, and protein aggregates, as a function of the nucleoid(s) morphology. Also, we compare the robustness of these processes to nonoptimal temperatures. We show that Z-rings, Tsr clusters, and protein aggregates have temperature-dependent spatial distributions along the major cell axis that are consistent with the nucleoid(s) morphology and the volume-exclusion phenomenon. Surprisingly, the consequences of the changes in nucleoid size with temperature are most visible in the kurtosis of these spatial distributions, in that it has a statistically significant linear correlation with the mean nucleoid length and, in the case of Z-rings, with the distance between nucleoids prior to cell division. Interestingly, we also find a negative, statistically significant linear correlation between the efficiency of these processes at the optimal condition and their robustness to suboptimal conditions, suggesting a trade-off between these traits.

Dissecting the in vivo dynamics of transcription locking due to positive supercoiling buildup.

Positive supercoiling buildup (PSB) is a pervasive phenomenon in the transcriptional programs of Escherichia coli. After finding a range of Gyrase concentrations where the inverse of the transcription rate of a chromosome-integrated gene changes linearly with the inverse of Gyrase concentration, we apply a LineWeaver-Burk plot to dissect the expected in vivo transcription rate in absence of PSB. We validate the estimation by time-lapse microscopy of single-RNA production kinetics of the same gene when single-copy plasmid-borne, shown to be impervious to Gyrase inhibition. Next, we estimate the fraction of time in locked states and number of transcription events prior to locking, which we validate by measurements under Gyrase inhibition. Replacing the gene of interest by one with slower transcription rate decreases the fraction of time in locked states due to PSB. Finally, we combine data from both constructs to infer a range of possible transcription initiation locking kinetics in a chromosomal location, obtainable by tuning the transcription rate. We validate with measurements of transcription activity at different induction levels. This strategy for dissecting transcription initiation locking kinetics due to PSB can contribute to resolve the transcriptional programs of E. coli and in the engineering of synthetic genetic circuits.

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.

Chromosome and plasmid-borne PLacO3O1 promoters differ in sensitivity to critically low temperatures.

Temperature shifts trigger genome-wide changes in Escherichia coli's gene expression. We studied if chromosome integration impacts on a gene's sensitivity to these shifts, by comparing the single-RNA production kinetics of a PLacO3O1 promoter, when chromosomally-integrated and when single-copy plasmid-borne. At suboptimal temperatures their induction range, fold change, and response to decreasing temperatures are similar. At critically low temperatures, the chromosome-integrated promoter becomes weaker and noisier. Dissection of its initiation kinetics reveals longer lasting states preceding open complex formation, suggesting enhanced supercoiling buildup. Measurements with Gyrase and Topoisomerase I inhibitors suggest hindrance to escape supercoiling buildup at low temperatures. Consistently, similar phenomena occur in energy-depleted cells by DNP at 30 °C. Transient, critically-low temperatures have no long-term consequences, as raising temperature quickly restores transcription rates. We conclude that the chromosomally-integrated PLacO3O1 has higher sensitivity to low temperatures, due to longer-lasting super-coiled states. A lesser active, chromosome-integrated native lac is shown to be insensitive to Gyrase overexpression, even at critically low temperatures, indicating that the rate of escaping positive supercoiling buildup is temperature and transcription rate dependent. A genome-wide analysis supports this, since cold-shock genes exhibit atypical supercoiling-sensitivities. This phenomenon might partially explain the temperature-sensitivity of some transcriptional programs of E. coli.

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.

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 Plac/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 PtetA 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.

Effects of σ factor competition are promoter initiation kinetics dependent.

In Escherichia coli, the expression of a σ factor is expected to indirectly down-regulate the expression of genes recognized by another σ factor, due to σ factor competition for a limited pool of RNA polymerase core enzymes. Evidence suggests that the sensitivity of genes to indirect down-regulation differs widely. We studied the variability in this sensitivity in promoters primarily recognized by RNAP holoenzymes carrying σ(70). From qPCR and live single-cell, single-RNA measurements of the transcription kinetics of several σ(70)-dependent promoters in various conditions and from the analysis of σ factors population-dependent models of transcription initiation, we find that, the smaller is the time-scale of the closed complex formation relative to the open complex formation, the weaker is a promoter's responsiveness to changes in σ(38) numbers. We conclude that, in E. coli, a promoter's responsiveness to indirect regulation by σ factor competition is determined by the sequence-dependent kinetics of the rate limiting steps of transcription initiation.

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.