Effects of post-transcriptional regulation on phenotypic noise in Escherichia coli

RNase E and HupB dynamics foster mycobacterial cell homeostasis and fitness

RNA turnover is a primary source of gene expression variation, in turn promoting cellular adaptation. Mycobacteria leverage reversible mRNA stabilization to endure hostile conditions. Although RNase E is essential for RNA turnover in several species, its role in mycobacterial single-cell physiology and functional phenotypic diversification remains unexplored. Here, by integrating live-single-cell and quantitative-mass-spectrometry approaches, we show that RNase E forms dynamic foci, which are associated with cellular homeostasis and fate, and we discover a versatile molecular interactome. We show a likely interaction between RNase E and the nucleoid-associated protein HupB, which is particularly pronounced during drug treatment and infection, where phenotypic diversity increases. Disruption of RNase E expression affects HupB levels, impairing Mycobacterium tuberculosis growth homeostasis during treatment, intracellular replication, and host spread. Our work lays the foundation for targeting the RNase E and its partner HupB, aiming to undermine M. tuberculosis cellular balance, diversification capacity, and persistence.

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Single mycobacterial cells exhibit phenotypic variation in RNase E expression

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RNase E is implicated in the maintenance of mycobacterial cell growth homeostasis

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RNase E and HupB show a functional interplay in single mycobacterial cells

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RNase E-HupB disruption impairs Mycobacterium tuberculosis fate under drug and in macrophages

Kinetic modeling reveals additional regulation at co-transcriptional level by post-transcriptional sRNA regulators

Small RNAs (sRNAs) are important gene regulators in bacteria. Many sRNAs act post-transcriptionally by affecting translation and degradation of the target mRNAs upon base-pairing interactions. Here we present a general approach combining imaging and mathematical modeling to determine kinetic parameters at different levels of sRNA-mediated gene regulation that contribute to overall regulation efficacy. Our data reveal that certain sRNAs previously characterized as post-transcriptional regulators can regulate some targets co-transcriptionally, leading to a revised model that sRNA-mediated regulation can occur early in an mRNA’s lifetime, as soon as the sRNA binding site is transcribed. This co-transcriptional regulation is likely mediated by Rho-dependent termination when transcription-coupled translation is reduced upon sRNA binding. Our data also reveal several important kinetic steps that contribute to the differential regulation of mRNA targets by an sRNA. Particularly, binding of sRNA to the target mRNA may dictate the regulation hierarchy observed within an sRNA regulon.

Reyer et al. use fluorescent microscopy and kinetic modeling to find that two sRNAs canonically described as post-transcriptional regulators can regulate their targets co-transcriptionally and determine the in vivo kinetic parameters that dictate differential regulation efficiency.

Post-Transcriptional Noise Control

Recent evidence indicates that transcriptional bursts are intrinsically amplified by messenger RNA cytoplasmic processing to generate large stochastic fluctuations in protein levels. These fluctuations can be exploited by cells to enable probabilistic bet-hedging decisions. But large fluctuations in gene expression can also destabilize cell-fate commitment. Thus, it is unclear if cells temporally switch from high to low noise, and what mechanisms enable this switch. Here, the discovery of a post-transcriptional mechanism that attenuates noise in HIV is reviewed. Early in its life cycle, HIV amplifies transcriptional fluctuations to probabilistically select alternate fates, whereas at late times, HIV utilizes a post-transcriptional feedback mechanism to commit to a specific fate. Reanalyzing various reported post-transcriptional negative feedback architectures reveals that they attenuate noise more efficiently than classic transcriptional autorepression, leading to the derivation of an assay to detect post-transcriptional motifs. It is hypothesized that coupling transcriptional and post-transcriptional autoregulation enables efficient temporal noise control to benefit developmental bet-hedging decisions.

Gene network analysis identifies a central post-transcriptional regulator of cellular stress survival

Cells adapt to shifts in their environment by remodeling transcription. Measuring changes in transcription at the genome scale is now routine, but defining the functional significance of individual genes within large gene expression datasets remains a major challenge. We applied a network-based algorithm to interrogate publicly available gene expression data to predict genes that serve major functional roles in Caulobacter crescentus stress survival. This approach identified GsrN, a conserved small RNA that is directly activated by the general stress sigma factor, σ^T, and functions as a potent post-transcriptional regulator of survival across distinct conditions including osmotic and oxidative stress. Under hydrogen peroxide stress, GsrN protects cells by base pairing with the leader of katG mRNA and activating expression of KatG catalase/peroxidase protein. We conclude that GsrN convenes a post-transcriptional layer of gene expression that serves a central functional role in Caulobacter stress physiology.

Method for Labeling Transcripts in Individual Escherichia coli Cells for Single-molecule Fluorescence In Situ Hybridization Experiments

A method is described for labeling individual messenger RNA (mRNA) transcripts in fixed bacteria for use in single-molecule fluorescence in situ hybridization (smFISH) experiments in E. coli. smFISH allows the measurement of cell-to-cell variability in mRNA copy number of genes of interest, as well as the subcellular location of the transcripts. The main steps involved are fixation of the bacterial cell culture, permeabilization of cell membranes, and hybridization of the target transcripts with sets of commercially available short fluorescently-labeled oligonucleotide probes. smFISH can allow the imaging of the transcripts of multiple genes in the same cell, with limitations imposed by the spectral overlap between different fluorescent markers. Following completion of the protocol illustrated below, cells can be readily imaged using a microscope coupled with a camera suitable for low-intensity fluorescence. These images, together with cell contours obtained from segmentation of phase contrast frames, or from cell membrane staining, allow the calculation of the mRNA copy number distribution of a sample of cells using open-source or custom-written software. The labeling method described here can also be applied to image transcripts with stochastic optical reconstruction microscopy (STORM).

Integration of Bacterial Small RNAs in Regulatory Networks

Small RNAs (sRNAs) are central regulators of gene expression in bacteria, controlling target genes posttranscriptionally by base pairing with their mRNAs. sRNAs are involved in many cellular processes and have unique regulatory characteristics. In this review, we discuss the properties of regulation by sRNAs and how it differs from and combines with transcriptional regulation. We describe the global characteristics of the sRNA-target networks in bacteria using graph-theoretic approaches and review the local integration of sRNAs in mixed regulatory circuits, including feed-forward loops and their combinations, feedback loops, and circuits made of an sRNA and another regulator, both derived from the same transcript. Finally, we discuss the competition effects in posttranscriptional regulatory networks that may arise over shared targets, shared regulators, and shared resources and how they may lead to signal propagation across the network.

Increasing dissolved-oxygen disrupts iron homeostasis in production cultures of Escherichia coli

The damaging effect of high oxygen concentration on growth of Escherichia coli is well established. Over-oxygenation increases the intracellular concentration of reactive oxygen species (ROS), causing the destruction of the [4Fe-4S] cluster of dehydratases and limiting the biosynthesis of both branched-chain amino acids and nicotinamide adenine dinucleotide. A key enzyme that reduces the damaging effect of superoxide is superoxide dismutase (SOD). Its transcriptional regulation is controlled by global transcription regulators that respond to changes in oxygen and iron concentrations and pH. Production of biological compounds from E. coli is currently achieved using cultures grown to high cell densities which require oxygen-enriched air supply. It is, therefore, important to study the effect of over-oxygenation on E. coli metabolism and the bacterial protecting mechanism. The effect of over-oxygenation on the superoxide dismutase regulation system was evaluated in cultures grown in a bioreactor by increasing the oxygen concentration from 30 to 300 % air saturation. Following the change in the dissolved oxygen (DO), the expression of sodC, the periplasmic CuZn-containing SOD, and sodA, the cytosolic Mn-containing SOD, was higher in all the tested strains, while the expression of the sodB, the cytosolic Fe-containing SOD, was lower. The down-regulation of the sodB was found to be related to the activation of the small RNA RyhB. It was revealed that iron homeostasis, in particular ferric iron, was involved in the RyhB activation and in sodB regulation but not in sodA. Supplementation of amino acids to the culture medium reduced the intracellular ROS accumulation and reduced the activation of both SodA and SodC following the increase in the oxygen concentration. The study provides evidence that at conditions of over-oxygenation, sodA and sodC are strongly regulated by the amount of ROS, in particular superoxide; and sodB is regulated by iron availability through the small RNA RyhB. In addition, information on the impact of NADH, presence of amino acids and type of iron on SOD regulation, and consequently, on the ROS concentration is provided.

Transcript degradation and noise of small RNA-controlled genes in a switch activated network in Escherichia coli

Post-transcriptional regulatory processes may change transcript levels and affect cell-to-cell variability or noise. We study small-RNA downregulation to elucidate its effects on noise in the iron homeostasis network of Escherichia coli. In this network, the small-RNA RyhB undergoes stoichiometric degradation with the transcripts of target genes in response to iron stress. Using single-molecule fluorescence in situ hybridization, we measured transcript numbers of the RyhB-regulated genes sodB and fumA in individual cells as a function of iron deprivation. We observed a monotonic increase of noise with iron stress but no evidence of theoretically predicted, enhanced stoichiometric fluctuations in transcript numbers, nor of bistable behavior in transcript distributions. Direct detection of RyhB in individual cells shows that its noise is much smaller than that of these two targets, when RyhB production is significant. A generalized two-state model of bursty transcription that neglects RyhB fluctuations describes quantitatively the dependence of noise and transcript distributions on iron deprivation, enabling extraction of in vivo RyhB-mediated transcript degradation rates. The transcripts’ threshold-linear behavior indicates that the effective in vivo interaction strength between RyhB and its two target transcripts is comparable. Strikingly, the bacterial cell response exhibits Fur-dependent, switch-like activation instead of a graded response to iron deprivation.

Noise and low-level dynamics can coordinate multicomponent bet hedging mechanisms

To counter future uncertainty, cells can stochastically express stress response mechanisms to diversify their population and hedge against stress. This approach allows a small subset of the population to survive without the prohibitive cost of constantly expressing resistance machinery at the population level. However, expression of multiple genes in concert is often needed to ensure survival, requiring coordination of infrequent events across many downstream targets. This raises the question of how cells orchestrate the timing of multiple rare events without adding cost. To investigate this, we used a stochastic model to study regulation of downstream target genes by a transcription factor. We compared several upstream regulator profiles, including constant expression, pulsatile dynamics, and noisy expression. We found that pulsatile dynamics and noise are sufficient to coordinate expression of multiple downstream genes. Notably, this is true even when fluctuations in the upstream regulator are far below the dissociation constants of the regulated genes, as with infrequently activated genes. As an example, we simulated the dynamics of the multiple antibiotic resistance activator (MarA) and 40 diverse downstream genes it regulates, determining that low-level dynamics in MarA are sufficient to coordinate expression of resistance mechanisms. We also demonstrated that noise can play a similar coordinating role. Importantly, we found that these benefits are present without a corresponding increase in the population-level cost. Therefore, our model suggests that low-level dynamics or noise in a transcription factor can coordinate expression of multiple stress response mechanisms by engaging them simultaneously without adding to the overall cost.

Fluorescence-based tools for single-cell approaches in food microbiology

The better understanding of the functioning of microbial communities is a challenging and crucial issue in the field of food microbiology, as it constitutes a prerequisite to the optimization of positive and technological microbial population functioning, as well as for the better control of pathogen contamination of food. Heterogeneity appears now as an intrinsic and multi-origin feature of microbial populations and is a major determinant of their beneficial or detrimental functional properties. The understanding of the molecular and cellular mechanisms behind the behavior of bacteria in microbial communities requires therefore observations at the single-cell level in order to overcome "averaging" effects inherent to traditional global approaches. Recent advances in the development of fluorescence-based approaches dedicated to single-cell analysis provide the opportunity to study microbial communities with an unprecedented level of resolution and to obtain detailed insights on the cell structure, metabolism activity, multicellular behavior and bacterial interactions in complex communities. These methods are now increasingly applied in the field of food microbiology in different areas ranging from research laboratories to industry. In this perspective, we reviewed the main fluorescence-based tools used for single-cell approaches and their concrete applications with specific focus on food microbiology.

Spatial fluctuations in expression of the heterocyst differentiation regulatory gene hetR in Anabaena filaments

Under nitrogen deprivation, filaments of the cyanobacterium Anabaena undergo a process of development, resulting in a one-dimensional pattern of nitrogen-fixing heterocysts separated by about ten photosynthetic vegetative cells. Many aspects of gene expression before nitrogen deprivation and during the developmental process remain to be elucidated. Furthermore, the coupling of gene expression fluctuations between cells along a multicellular filament is unknown. We studied the statistics of fluctuations of gene expression of HetR, a transcription factor essential for heterocyst differentiation, both under steady-state growth in nitrogen-rich conditions and at different times following nitrogen deprivation, using a chromosomally-encoded translational hetR-gfp fusion. Statistical analysis of fluorescence at the individual cell level in wild-type and mutant filaments demonstrates that expression fluctuations of hetR in nearby cells are coupled, with a characteristic spatial range of circa two to three cells, setting the scale for cellular interactions along a filament. Correlations between cells predominantly arise from intercellular molecular transfer and less from cell division. Fluctuations after nitrogen step-down can build up on those under nitrogen-replete conditions. We found that under nitrogen-rich conditions, basal, steady-state expression of the HetR inhibitor PatS, cell-cell communication influenced by the septal protein SepJ and positive HetR auto-regulation are essential determinants of fluctuations in hetR expression and its distribution along filaments. A comparison between the expression of hetR-gfp under nitrogen-rich and nitrogen-poor conditions highlights the differences between the two HetR inhibitors PatS and HetN, as well as the differences in specificity between the septal proteins SepJ and FraC/FraD. Activation, inhibition and cell-cell communication lie at the heart of developmental processes. Our results show that proteins involved in these basic ingredients combine together in the presence of inevitable stochasticity in gene expression, to control the coupled fluctuations of gene expression that give rise to a one-dimensional developmental pattern in this organism.

Under prolonged nitrogen deprivation, one-dimensional filaments of the multicellular cyanobacterium Anabaena undergo a process of development, forming a pattern consisting of cells specialized for nitrogen fixation-heterocysts-, separated by a chain of about ten photosynthetic vegetative cells. The developmental program uses activation, inhibition, and transport to create spatial and temporal patterns of gene expression, in the presence of unavoidable stochastic fluctuations in gene expression among cells. Using a chromosomally-encoded fluorescent marker, we followed the expression of the important regulator HetR in individual cells along filaments, both under abundant nitrogen conditions as well as at different times after nitrogen deprivation. The results of our statistical analysis of these fluctuations illuminate the fundamental role that positive feedback, lateral inhibition and cell-cell communication play in the developmental program, not only after exposure to the external cue that triggers differentiation but also under non-inducing conditions. Furthermore our results establish the spatial extent to which gene expression is correlated along filaments.

Small regulatory RNA-induced growth rate heterogeneity of Bacillus subtilis

Isogenic bacterial populations can consist of cells displaying heterogeneous physiological traits. Small regulatory RNAs (sRNAs) could affect this heterogeneity since they act by fine-tuning mRNA or protein levels to coordinate the appropriate cellular behavior. Here we show that the sRNA RnaC/S1022 from the Gram-positive bacterium Bacillus subtilis can suppress exponential growth by modulation of the transcriptional regulator AbrB. Specifically, the post-transcriptional abrB-RnaC/S1022 interaction allows B. subtilis to increase the cell-to-cell variation in AbrB protein levels, despite strong negative autoregulation of the abrB promoter. This behavior is consistent with existing mathematical models of sRNA action, thus suggesting that induction of protein expression noise could be a new general aspect of sRNA regulation. Importantly, we show that the sRNA-induced diversity in AbrB levels generates heterogeneity in growth rates during the exponential growth phase. Based on these findings, we hypothesize that the resulting subpopulations of fast- and slow-growing B. subtilis cells reflect a bet-hedging strategy for enhanced survival of unfavorable conditions.

Bacterial cells that share the same genetic information can display very different phenotypes, even if they grow under identical conditions. Despite the relevance of this population heterogeneity for processes like drug resistance and development, the molecular players that induce heterogenic phenotypes are often not known. Here we report that in the Gram-positive model bacterium Bacillus subtilis a small regulatory RNA (sRNA) can induce heterogeneity in growth rates by increasing cell-to-cell variation in the levels of the transcriptional regulator AbrB, which is important for rapid growth. Remarkably, the observed variation in AbrB levels is induced post-transcriptionally because of AbrB’s negative autoregulation, and is not observed at the abrB promoter level. We show that our observations are consistent with mathematical models of sRNA action, thus suggesting that induction of protein expression noise could be a new general aspect of sRNA regulation. Since a low growth rate can be beneficial for cellular survival, we propose that the observed subpopulations of fast- and slow-growing B. subtilis cells reflect a bet-hedging strategy for enhanced survival of unfavorable conditions.

Stochastic holin expression can account for lysis time variation in the bacteriophage λ

The inherent stochastic nature of biochemical processes can drive differences in gene expression between otherwise identical cells. While cell-to-cell variability in gene expression has received much attention, randomness in timing of events has been less studied. We investigate event timing at the single-cell level in a simple system, the lytic pathway of the bacterial virus phage λ. In individual cells, lysis occurs on average at 65 min, with an s.d. of 3.5 min. Interestingly, mutations in the lysis protein, holin, alter both the lysis time (LT) mean and variance. In our analysis, LT is formulated as the first-passage time (FPT) for cellular holin levels to cross a critical threshold. Exact analytical formulae for the FPT moments are derived for stochastic gene expression models. These formulae reveal how holin transcription and translation efficiencies independently modulate the LT mean and variation. Analytical expressions for the LT moments are used to evaluate previously published single-cell LT data for λ phages with mutations in the holin sequence or its promoter. Our results show that stochastic holin expression is sufficient to account for the intercellular LT differences in both wild-type phages, and phage variants where holin transcription and the threshold for lysis have been experimentally altered. Finally, our analysis reveals regulatory motifs that enhance the robustness of lysis timing to cellular noise.

A mixed incoherent feed-forward loop allows conditional regulation of response dynamics

Expression of the SodA superoxide dismutase (MnSOD) in Escherichia coli is regulated by superoxide concentration through the SoxRS system and also by Fur (Ferric uptake regulator) through a mixed incoherent feed forward loop (FFL) containing the RyhB small regulatory RNA. In this work I theoretically analyze the function of this feed forward loop as part of the network controlling expression of the two cytoplasmic superoxide dismutases, SodA and SodB. I find that feed forward regulation allows faster response to superoxide stress at low intracellular iron levels compared to iron rich conditions. That is, it can conditionally modulate the response time of a superimposed transcriptional control mechanism.

Phenotypic noise: effects of post-transcriptional regulatory processes affecting mRNA

The inherently stochastic nature of biomolecular processes is one of the main sources giving rise to cell-to-cell variations in protein and mRNA abundance, termed noise. Noise in isogenic populations can enhance survival under adverse conditions and stress, and has therefore played a fundamental role in evolution. On the other hand, noise may have detrimental effects and therefore cells must also display robustness to fluctuations and possess mechanisms of control in order to function properly. Noise can be introduced at every step in the cascade of intermediate events resulting in the production of functional proteins. While initial studies of noise focused on stochasticity introduced at the transcriptional level, recent years have witnessed a gradual shift of emphasis into the effects that post-transcriptional processes have on phenotypic noise. Here, we survey the insights that have been gained on the effects of processes that modify RNA transcript populations on phenotypic noise, including regulation by noncoding RNAs in prokaryotes and eukaryotes, alternative splicing and transcriptional interference.