An allosteric model for heterogeneous receptor complexes: Understanding bacterial chemotaxis responses to multiple stimuli

Mello, Bernardo A.; Tu, Yuhai

doi:10.1073/pnas.0506961102

Cited by 149 publications

(173 citation statements)

References 27 publications

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“…Because stimuli encountered by the bacterium in its natural habitat may be small, we calculate the response to stimulus using a linear perturbation analysis of the kinetic system. Although we chose to keep our study independent from the actual chemical stimulus present in the environment, the chemotactic response in the real system also depends on the initial amplification of the input stimulus mediated by complex allosteric mechanisms taking place in the receptorkinase complex (17,22,(33)(34)(35)(36)(37). In this model, we consider that a small external perturbation, such as a sudden exposure to attractant, causes an ''instantaneous'' change of the receptor activity, ⌬ A* input .…”

Section: Relationship Between Behavioral Variability In Nonstimulatedmentioning

confidence: 99%

Relationship between cellular response and behavioral variability in bacterial chemotaxis

Emonet

Cluzel

2008

Proc. Natl. Acad. Sci. U.S.A.

105

161

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Over the last decades, bacterial chemotaxis in Escherichia coli has emerged as a canonical system for the study of signal transduction. A remarkable feature of this system is the coexistence of a robust adaptive behavior observed at the population level with a large fluctuating behavior in single cells [Korobkova E, Emonet T, Vilar JMG, Shimizu TS, Cluzel P (2004) Nature 428:574 -578]. Using a unified stochastic model, we demonstrate that this coexistence is not fortuitous but a direct consequence of the architecture of this adaptive system. The methylation and demethylation cycles that regulate the activity of receptor-kinase complexes are ultrasensitive because they operate outside the region of first-order kinetics. As a result, the receptor-kinase that governs cellular behavior exhibits a sigmoidal activation curve. We propose that the steepness of this kinase activation curve simultaneously controls the behavioral variability in nonstimulated individual bacteria and the duration of the adaptive response to small stimuli. We predict that the fluctuating behavior and the chemotactic response of individual cells both peak within the transition region of this sigmoidal curve. Large-scale simulations of digital bacteria suggest that the chemotaxis network is tuned to simultaneously maximize both the random spread of cells in the absence of nutrients and the cellular response to gradients of attractant. This study highlights a fundamental relation from which the behavioral variability of nonstimulated cells is used to infer the timing of the cellular response to small stimuli.agent-based ͉ fluctuation-dissipation ͉ noise ͉ ultrasensitivity M olecular noise (i.e., stochastic fluctuations) has been largely reported as one important source of phenotypic variability in a number of biological systems as diverse as gene expression and signal transduction in prokaryotes and eukaryotes (1-3). A standard method to characterize noise in biological systems is to analyze the distribution of behaviors across a population of cells at steady state. This approach has been powerful in identifying specific molecular mechanisms responsible for controlling phenotypic variability. Alternatively, the temporal evolution of noise within a single cell also contains key information about underlying cellular dynamics, but this approach is seldom used to characterize cellular behavior outside the steady-state regime (4). It is conceivable, however, that biological systems that are sensitive to intracellular spontaneous noise are also sensitive to small extracellular perturbations such as a sudden change of environmental conditions (5). We wish to investigate whether there exists in bacterial chemotaxis a general relationship between the fluctuation of cellular behavior in single cells and the timing of the cellular response to a small external stimulus.Bacterial chemotaxis, a cellular locomotion system, has become a classic model for signal transduction. Although the chemotaxis network consists of just a few molecular species, it can perfor...

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Section: Relationship Between Behavioral Variability In Nonstimulatedmentioning

confidence: 99%

Relationship between cellular response and behavioral variability in bacterial chemotaxis

Emonet

Cluzel

2008

Proc. Natl. Acad. Sci. U.S.A.

105

161

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“…This is currently explained through the cooperativity of the receptors, which react in clusters of dimers to the binding of a ligand [20][21][22][23], and of the molecules in the ring controlling the flagellar motor rotation [24,25], which can cooperatively rearrange to induce a motor switch in response to a single CheY binding event. The response is also subject to adaptation: the MCPs are desensitized by successive methylations (governed by the proteins CheR and CheB) [17], so that the activity of CheA, and hence the tumbling rate, finally adapts to a new background concentration of the chemoattractant.…”

Section: Introductionmentioning

confidence: 99%

Fast, high-throughput measurement of collective behaviour in a bacterial population

Colin¹,

Zhang

Wilson³

2014

J. R. Soc. Interface.

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Swimming bacteria explore their environment by performing a random walk, which is biased in response to, for example, chemical stimuli, resulting in a collective drift of bacterial populations towards 'a better life'. This phenomenon, called chemotaxis, is one of the best known forms of collective behaviour in bacteria, crucial for bacterial survival and virulence. Both single-cell and macroscopic assays have investigated bacterial behaviours. However, theories that relate the two scales have previously been difficult to test directly. We present an image analysis method, inspired by light scattering, which measures the average collective motion of thousands of bacteria simultaneously. Using this method, a time-varying collective drift as small as 50 nm s 21 can be measured. The method, validated using simulations, was applied to chemotactic Escherichia coli bacteria in linear gradients of the attractant a-methylaspartate. This enabled us to test a coarse-grained minimal model of chemotaxis. Our results clearly map the onset of receptor methylation, and the transition from linear to logarithmic sensing in the bacterial response to an external chemoeffector. Our method is broadly applicable to problems involving the measurement of collective drift with high time resolution, such as cell migration and fluid flows measurements, and enables fast screening of tactic behaviours.

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“…Both in vivo (7) and in vitro (14) Monod-Wyman-Changeux (MWC) models have been used to explain a variety of experimental dose-response curves (7,(23)(24)(25)(26)(27)(28). In these MWC models, two-state receptors form signaling teams, which are either active or inactive as a whole.…”

mentioning

confidence: 99%

A dynamic-signaling-team model for chemotaxis receptors in Escherichia coli

Hansen

Sourjik

Wingreen

2010

Proc. Natl. Acad. Sci. U.S.A.

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The chemotaxis system of Escherichia coli is sensitive to small relative changes in ambient chemoattractant concentrations over a broad range. Interactions among receptors are crucial to this sensitivity, as is precise adaptation, the return of chemoreceptor activity to prestimulus levels in a constant chemoeffector environment through methylation and demethylation of receptors. Signal integration and cooperativity have been attributed to strongly coupled, mixed teams of receptors, but receptors become individually methylated according to their ligand occupancy states. Here, we present a model of dynamic signaling teams that reconciles strong coupling among receptors with receptor-specific methylation. Receptor trimers of dimers couple to form a honeycomb lattice, consistent with cryo-electron microscopy (cryoEM) tomography, within which the boundaries of signaling teams change rapidly. Our model helps explain the inferred increase in signaling team size with receptor modification, and indicates that active trimers couple more strongly than inactive trimers.adaptation | modeling | receptor clustering A daptation to persistent stimuli is important in maintaining a broad range of response-sensitivity in numerous sensory systems, including visual (1), olfactory (2), and auditory (3). The best understood example of such adaptation is the Escherichia coli chemotaxis system (Fig. 1A), in which receptor signaling adapts precisely to persistent chemoeffector concentrations. Adaptation is implemented by the methylation and demethylation of transmembrane receptors through an integral-feedback control mechanism (4, 5). Different receptor types bind specific ligands, but signal cooperatively in mixed teams (6, 7). One feature of this system that has remained mysterious is that, at short times, adaptation leads to similar modification of all receptors, but at longer times, modification is receptor-type specific, affecting primarily the receptors stimulated by ligand (8). Here, we present a model of dynamic-signaling teams that reconciles receptor cooperativity with receptor-specific methylation.Receptors direct cell movement by biasing the switching between tumbling and straight-swimming states, producing a biased random walk up gradients of chemoattractants or down gradients of chemorepellents. Of the five receptor types in E. coli, the highest abundance are Tar (aspartate binding) and Tsr (serine binding). Receptors form homodimers that can each bind one molecule of ligand. Homodimers, which we will refer to as receptors, in turn form mixed trimers of dimers, and associate with the linker protein CheW and the histidine kinase CheA (9). Receptor signaling activates CheA autophosphorylation, and the phosphoryl group is transferred to the response regulator CheY (or to the methylesterase CheB). Phosphorylated CheY diffuses and binds to the flagellar motors, causing tumbling. CheY is dephosphorylated by the phosphatase CheZ. Receptor trimers of dimers form the basic signaling units, with single receptors unable to activate Che...

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An allosteric model for heterogeneous receptor complexes: Understanding bacterial chemotaxis responses to multiple stimuli

Cited by 149 publications

References 27 publications

Relationship between cellular response and behavioral variability in bacterial chemotaxis

Relationship between cellular response and behavioral variability in bacterial chemotaxis

Fast, high-throughput measurement of collective behaviour in a bacterial population

A dynamic-signaling-team model for chemotaxis receptors in Escherichia coli

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