Adaptive tuning of cell sensory diversity without changes in gene expression

Potent and Selective Inhibition of Sulfate-Reducing Bacteria by Neutral Red

Sulfate-reducing bacteria (SRB) are anaerobic microorganisms that use sulfate as a terminal electron acceptor for the oxidation of organic compounds or H2. These organisms can cause a serious problem in, for example, the offshore oil industry, due to the production of sulfide. Thus, it is of fundamental and practical importance to identify potent and selective inhibitors of SRB. In this study, neutral red was identified as a previously unrecognized selective inhibitor of SRB, with several orders of magnitude higher potency than most commonly used industrial biocides and inorganic oxyanions. Neutral red remained a potent inhibitor of SRB growth under fermentative conditions and was tolerated by nitrate-reducing bacteria. After 30 days of exposure to 14.2 μM neutral red, the sulfidogenesis activity of SRB-enriched biomass was reduced by 98.3%, and the abundance of SRB populations declined from 25.5% to 0.76%. Transcriptomic analysis revealed that the inhibition of the central sulfate reduction pathway was implicated in the mechanism of neutral red toxicity against SRB growth. Furthermore, downregulation of two electron transport complexes (QmoABC and DsrMKJOP), ATP synthase, as well as cytoplasmic/periplasmic hydrogenase suggested the collapse of the proton gradient. These findings have implications for environmental control of SRB and may enhance economic benefits in industrial operations.

Pseudomonas syringae subpopulations cooperate by coordinating flagellar and type III secretion spatiotemporal dynamics to facilitate plant infection

Isogenic bacterial populations can display probabilistic cell-to-cell variation in response to challenges. This phenotypic heterogeneity can affect virulence in animals, but its impact on plant pathogens is unknown. Previously, we showed that expression of the type III secretion system (T3SS) of the plant pathogen Pseudomonas syringae displays phenotypic variation in planta. Here we use flow cytometry and microscopy to investigate single-cell flagellar expression in relation to T3SS expression, showing that both systems undergo phenotypic heterogeneity in vitro in apoplast-mimicking medium and within apoplastic microcolonies throughout colonization of Phaseolus vulgaris. Stochastic, spatial and time factors shape the dynamics of a phenotypically diverse pathogen population that displays division of labour during colonization: effectors produced by T3SS-expressing bacteria act as 'common goods' to suppress immunity, allowing motile flagella-expressing bacteria to increase and leave infected tissue before necrosis. These results showcase the mechanisms of bacterial specialization during plant colonization in an environmentally and agriculturally relevant system.

Extending visual range of bacteria with upconversion nanoparticles and constructing NIR-responsive bio-microrobots

The motility of bacteria is crucial for navigating competitive environments and is closely linked to physiological activities essential for their survival, such as biofilm development. Precise regulation of bacterial motility enhances our understanding of these complex processes. While optogenetic tools have been used to control and investigate bacterial motility, the excitation light in most existing systems are limited to the visible light spectrum. Here, we introduce a new type of bio-microrobot comprising genetically engineered E. coli cells and orthogonally emissive upconversion nanoparticles that can respond to both 980 nm and 808 nm NIR light. This system allows toggling of bacterial states between tumbling and swimming via simply alternating the NIR light between different wavelengths. It is believed that the use of NIR light with deeper tissue penetration suggests potential applications for these bio-microrobots in areas like targeted drug delivery.

Lattice ultrasensitivity amplifies signals in E. coli without fine-tuning

The E. coli chemosensory lattice, consisting of receptors, kinases, and adaptor proteins, is an important test case for biochemical signal processing. Kinase output is characterized by precise adaptation to a wide range of background ligand levels and large gain in response to small relative changes in concentration. Existing models of this lattice achieve their gain through allosteric interactions between either receptors or core units of receptors and kinases. Here we introduce a model which operates through an entirely different mechanism in which receptors gate inherently far from equilibrium enzymatic reactions between neighboring kinases. Our lattice model achieves gain through a mechanism more closely related to zero-order ultrasensitivity than to allostery. Thus, we call it lattice ultrasensitivity (LU). Unlike other lattice critical models, the LU model can achieve arbitrarily high gain through time-scale separation, rather than through finetuning. The model also captures qualitative experimental results which are difficult to reconcile with existing models. We discuss possible implementations in the lattice's baseplate where long flexible linkers could potentially mediate interactions between neighboring core units.

Chemotaxing E. coli do not count single molecules

Understanding biological functions requires identifying the physical limits and system-specific constraints that have shaped them. In Escherichia coli chemotaxis, gradient-climbing speed is information-limited, bounded by the sensory information they acquire from real-time measurements of their environment. However, it remains unclear what limits this information. Past work conjectured that E. coli's chemosensing is limited by the physics of molecule arrivals at their sensors. Here, we derive the physical limit on behaviorally-relevant information, and then perform single-cell experiments to quantify how much information E. coli's signaling pathway encodes. We find that E. coli encode two orders of magnitude less information than the physical limit due to their stochastic signal processing. Thus, system-specific constraints, rather than the physical limit, have shaped the evolution of this canonical sensory-motor behavior.

Signal integration and adaptive sensory diversity tuning in Escherichia coli chemotaxis

In uncertain environments, phenotypic diversity can be advantageous for survival. However, as the environmental uncertainty decreases, the relative advantage of having diverse phenotypes decreases. Here, we show how populations of E. coli integrate multiple chemical signals to adjust sensory diversity in response to changes in the prevalence of each ligand in the environment. Measuring kinase activity in single cells, we quantified the sensitivity distribution to various chemoattractants in different mixtures of background stimuli. We found that when ligands bind uncompetitively, the population tunes sensory diversity to each signal independently, decreasing diversity when the signal's ambient concentration increases. However, among competitive ligands, the population can only decrease sensory diversity one ligand at a time. Mathematical modeling suggests that sensory diversity tuning benefits E. coli populations by modulating how many cells are committed to tracking each signal proportionally as their prevalence changes.

E. coli do not count single molecules

Organisms must perform sensory-motor behaviors to survive. What bounds or constraints limit behavioral performance? Previously, we found that the gradient-climbing speed of a chemotaxing Escherichia coli is near a bound set by the limited information they acquire from their chemical environments (1). Here we ask what limits their sensory accuracy. Past theoretical analyses have shown that the stochasticity of single molecule arrivals sets a fundamental limit on the precision of chemical sensing (2). Although it has been argued that bacteria approach this limit, direct evidence is lacking. Here, using information theory and quantitative experiments, we find that E. coli's chemosensing is not limited by the physics of particle counting. First, we derive the physical limit on the behaviorally-relevant information that any sensor can get about a changing chemical concentration, assuming that every molecule arriving at the sensor is recorded. Then, we derive and measure how much information E. coli's signaling pathway encodes during chemotaxis. We find that E. coli encode two orders of magnitude less information than an ideal sensor limited only by shot noise in particle arrivals. These results strongly suggest that constraints other than particle arrival noise limit E. coli's sensory fidelity.

Signal Integration and Adaptive Sensory Diversity Tuning in Escherichia coli Chemotaxis

In uncertain environments, phenotypic diversity can be advantageous for survival. However, as the environmental uncertainty decreases, the relative advantage of having diverse phenotypes decreases. Here, we show how populations of E. coli integrate multiple chemical signals to adjust sensory diversity in response to changes in the prevalence of each ligand in the environment. Measuring kinase activity in single cells, we quantified the sensitivity distribution to various chemoattractants in different mixtures of background stimuli. We found that when ligands bind uncompetitively, the population tunes sensory diversity to each signal independently, decreasing diversity when the signal ambient concentration increases. However, amongst competitive ligands the population can only decrease sensory diversity one ligand at a time. Mathematical modeling suggests that sensory diversity tuning benefits E. coli populations by modulating how many cells are committed to tracking each signal proportionally as their prevalence changes.

Opportunities in optical and electrical single-cell technologies to study microbial ecosystems

New techniques are revolutionizing single-cell research, allowing us to study microbes at unprecedented scales and in unparalleled depth. This review highlights the state-of-the-art technologies in single-cell analysis in microbial ecology applications, with particular attention to both optical tools, i.e., specialized use of flow cytometry and Raman spectroscopy and emerging electrical techniques. The objectives of this review include showcasing the diversity of single-cell optical approaches for studying microbiological phenomena, highlighting successful applications in understanding microbial systems, discussing emerging techniques, and encouraging the combination of established and novel approaches to address research questions. The review aims to answer key questions such as how single-cell approaches have advanced our understanding of individual and interacting cells, how they have been used to study uncultured microbes, which new analysis tools will become widespread, and how they contribute to our knowledge of ecological interactions.

Phenotypic heterogeneity in human genetic diseases: ultrasensitivity-mediated threshold effects as a unifying molecular mechanism

Phenotypic heterogeneity is very common in genetic systems and in human diseases and has important consequences for disease diagnosis and treatment. In addition to the many genetic and non-genetic (e.g., epigenetic, environmental) factors reported to account for part of the heterogeneity, we stress the importance of stochastic fluctuation and regulatory network topology in contributing to phenotypic heterogeneity. We argue that a threshold effect is a unifying principle to explain the phenomenon; that ultrasensitivity is the molecular mechanism for this threshold effect; and discuss the three conditions for phenotypic heterogeneity to occur. We suggest that threshold effects occur not only at the cellular level, but also at the organ level. We stress the importance of context-dependence and its relationship to pleiotropy and edgetic mutations. Based on this model, we provide practical strategies to study human genetic diseases. By understanding the network mechanism for ultrasensitivity and identifying the critical factor, we may manipulate the weak spot to gently nudge the system from an ultrasensitive state to a stable non-disease state. Our analysis provides a new insight into the prevention and treatment of genetic diseases.

Optimal inference of molecular interaction dynamics in FRET microscopy

Intensity-based time-lapse fluorescence resonance energy transfer (FRET) microscopy has been a major tool for investigating cellular processes, converting otherwise unobservable molecular interactions into fluorescence time series. However, inferring the molecular interaction dynamics from the observables remains a challenging inverse problem, particularly when measurement noise and photobleaching are nonnegligible-a common situation in single-cell analysis. The conventional approach is to process the time-series data algebraically, but such methods inevitably accumulate the measurement noise and reduce the signal-to-noise ratio (SNR), limiting the scope of FRET microscopy. Here, we introduce an alternative probabilistic approach, B-FRET, generally applicable to standard 3-cube FRET-imaging data. Based on Bayesian filtering theory, B-FRET implements a statistically optimal way to infer molecular interactions and thus drastically improves the SNR. We validate B-FRET using simulated data and then apply it to real data, including the notoriously noisy in vivo FRET time series from individual bacterial cells to reveal signaling dynamics otherwise hidden in the noise.

Real-time detection of response regulator phosphorylation dynamics in live bacteria

Bacteria utilize two-component system (TCS) signal transduction pathways to sense and adapt to changing environments. In a typical TCS, a stimulus induces a sensor histidine kinase (SHK) to phosphorylate a response regulator (RR), which then dimerizes and activates a transcriptional response. Here, we demonstrate that oligomerization-dependent depolarization of excitation light by fused mNeonGreen fluorescent protein probes enables real-time monitoring of RR dimerization dynamics in live bacteria. Using inducible promoters to independently express SHKs and RRs, we detect RR dimerization within seconds of stimulus addition in several model pathways. We go on to combine experiments with mathematical modeling to reveal that TCS phosphosignaling accelerates with SHK expression but decelerates with RR expression and SHK phosphatase activity. We further observe pulsatile activation of the SHK NarX in response to addition and depletion of the extracellular electron acceptor nitrate when the corresponding TCS is expressed from both inducible systems and the native chromosomal operon. Finally, we combine our method with polarized light microscopy to enable single-cell measurements of RR dimerization under changing stimulus conditions. Direct in vivo characterization of RR oligomerization dynamics should enable insights into the regulation of bacterial physiology.

Modular reconstruction and optimization of the trans-4-hydroxy-L-proline synthesis pathway in Escherichia coli

Background

In recent years, there has been a growing demand for microbial production of trans-4-hydroxy-L-proline (t4Hyp), which is a value-added amino acid and has been widely used in the fields of medicine, food, and cosmetics. In this study, a multivariate modular metabolic engineering approach was used to remove the bottleneck in the synthesis pathway of t4Hyp.

Results

Escherichia coli t4Hyp synthesis was performed using two modules: a α-ketoglutarate (α-KG) synthesis module (K module) and L-proline synthesis with hydroxylation module (H module). First, α-KG attrition was reduced, and then, L-proline consumption was inhibited. Subsequently, to improve the contribution to proline synthesis with hydroxylation, optimization of gene overexpression, promotor, copy number, and the fusion system was performed. Finally, optimization of the H and K modules was performed in combination to balance metabolic flow. Using the final module H1K4 in a shaking flask culture, 8.80 g/L t4Hyp was produced, which was threefold higher than that produced by the W0 strain.

Conclusions

These strategies demonstrate that a microbial cell factory can be systematically optimized by modular engineering for efficient production of t4Hyp.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12934-022-01884-4.

The ecological roles of bacterial chemotaxis

How bacterial chemotaxis is performed is much better understood than why. Traditionally, chemotaxis has been understood as a foraging strategy by which bacteria enhance their uptake of nutrients and energy, yet it has remained puzzling why certain less nutritious compounds are strong chemoattractants and vice versa. Recently, we have gained increased understanding of alternative ecological roles of chemotaxis, such as navigational guidance in colony expansion, localization of hosts or symbiotic partners and contribution to microbial diversity by the generation of spatial segregation in bacterial communities. Although bacterial chemotaxis has been observed in a wide range of environmental settings, insights into the phenomenon are mostly based on laboratory studies of model organisms. In this Review, we highlight how observing individual and collective migratory behaviour of bacteria in different settings informs the quantification of trade-offs, including between chemotaxis and growth. We argue that systematically mapping when and where bacteria are motile, in particular by transgenerational bacterial tracking in dynamic environments and in situ approaches from guts to oceans, will open the door to understanding the rich interplay between metabolism and growth and the contribution of chemotaxis to microbial life.

Clostridioides difficile Phosphoproteomics Shows an Expansion of Phosphorylated Proteins in Stationary Growth Phase

Phosphorylation is a posttranslational modification that can affect both housekeeping functions and virulence characteristics in bacterial pathogens. In the Gram-positive enteropathogen Clostridioides difficile, the extent and nature of phosphorylation events are poorly characterized, though a protein kinase mutant strain demonstrates pleiotropic phenotypes. Here, we used an immobilized metal affinity chromatography strategy to characterize serine, threonine, and tyrosine phosphorylation in C. difficile. We find limited protein phosphorylation in the exponential growth phase but a sharp increase in the number of phosphopeptides after the onset of the stationary growth phase. Our approach identifies expected targets and phosphorylation sites among the more than 1,500 phosphosites, including the protein kinase PrkC, the anti-sigma-F factor antagonist (SpoIIAA), the anti-sigma-B factor antagonist (RsbV), and HPr kinase/phosphorylase (HprK). Analysis of high-confidence phosphosites shows that phosphorylation on serine residues is most common, followed by threonine and tyrosine phosphorylation. This work forms the basis for a further investigation into the contributions of individual kinases to the overall phosphoproteome of C. difficile and the role of phosphorylation in C. difficile physiology and pathogenesis.

IMPORTANCE In this paper, we present a comprehensive analysis of protein phosphorylation in the Gram-positive enteropathogen Clostridioides difficile. To date, only limited evidence on the role of phosphorylation in the regulation of this organism has been published; the current study is expected to form the basis for research on this posttranslational modification in C. difficile.  

Escherichia coli chemotaxis is information limited

Organisms acquire and use information from their environment to guide their behaviour. However, it is unclear whether this information quantitatively limits their behavioural performance. Here, we relate information to the ability of Escherichia coli to navigate up chemical gradients, the behaviour known as chemotaxis. First, we derive a theoretical limit on the speed with which cells climb gradients, given the rate at which they acquire information. Next, we measure cells' gradient-climbing speeds and the rate of information acquisition by their chemotaxis signaling pathway. We find that E. coli make behavioural decisions with much less than the one bit required to determine whether they are swimming up-gradient. Some of this information is irrelevant to gradient climbing, and some is lost in communication to behaviour. Despite these limitations, E. coli climb gradients at speeds within a factor of two of the theoretical bound. Thus, information can limit the performance of an organism, and sensory-motor pathways may have evolved to efficiently use information acquired from the environment.

Multiple functions of flagellar motility and chemotaxis in bacterial physiology

Most swimming bacteria are capable of following gradients of nutrients, signaling molecules and other environmental factors that affect bacterial physiology. This tactic behavior became one of the most-studied model systems for signal transduction and quantitative biology, and underlying molecular mechanisms are well characterized in Escherichia coli and several other model bacteria. In this review, we focus primarily on less understood aspect of bacterial chemotaxis, namely its physiological relevance for individual bacterial cells and for bacterial populations. As evident from multiple recent studies, even for the same bacterial species flagellar motility and chemotaxis might serve multiple roles, depending on the physiological and environmental conditions. Among these, finding sources of nutrients and more generally locating niches that are optimal for growth appear to be one of the major functions of bacterial chemotaxis, which could explain many chemoeffector preferences as well as flagellar gene regulation. Chemotaxis might also generally enhance efficiency of environmental colonization by motile bacteria, which involves intricate interplay between individual and collective behaviors and trade-offs between growth and motility. Finally, motility and chemotaxis play multiple roles in collective behaviors of bacteria including swarming, biofilm formation and autoaggregation, as well as in their interactions with animal and plant hosts.

Temporal fluctuations in chemotaxis gain implement a simulated-tempering strategy for efficient navigation in complex environments

Bacterial chemotaxis is a major testing ground for systems biology, including the role of fluctuations and individual variation. Individual bacteria vary in their tumbling frequency and adaptation time. Recently, large cell-cell variation was also discovered in chemotaxis gain, which determines the sensitivity of the tumbling rate to attractant gradients. Variation in gain is puzzling, because low gain impairs chemotactic velocity. Here, we provide a functional explanation for gain variation by establishing a formal analogy between chemotaxis and algorithms for sampling probability distributions. We show that temporal fluctuations in gain implement simulated tempering, which allows sampling of attractant distributions with many local peaks. Periods of high gain allow bacteria to detect and climb gradients quickly, and periods of low gain allow them to move to new peaks. Gain fluctuations thus allow bacteria to thrive in complex environments, and more generally they may play an important functional role for organism navigation.

Non-Genetic Diversity in Chemosensing and Chemotactic Behavior

Non-genetic phenotypic diversity plays a significant role in the chemotactic behavior of bacteria, influencing how populations sense and respond to chemical stimuli. First, we review the molecular mechanisms that generate phenotypic diversity in bacterial chemotaxis. Next, we discuss the functional consequences of phenotypic diversity for the chemosensing and chemotactic performance of single cells and populations. Finally, we discuss mechanisms that modulate the amount of phenotypic diversity in chemosensory parameters in response to changes in the environment.

Connection between the Bacterial Chemotactic Network and Optimal Filtering

The chemotactic network of Escherichia coli has been studied extensively both biophysically and information theoretically. Nevertheless, connection between these two aspects is still elusive. In this work, we report such a connection. We derive an optimal filtering dynamics under the assumption that E. coli's sensory system optimally infers the binary information whether it is swimming up or down along an exponential ligand gradient from noisy sensory signals. Then we show that a standard biochemical model of the chemotactic network is mathematically equivalent to this information-theoretically optimal dynamics. Moreover, we demonstrate that an experimentally observed nonlinear response relation can be reproduced from the optimal dynamics. These results suggest that the biochemical network of E. coli chemotaxis is designed to optimally extract the binary information along an exponential gradient in a noisy condition.