Systems biology of bacterial chemotaxis

Motile bacteria regulate chemotaxis through a highly conserved chemosensory signal-transduction system. System-wide analyses and mathematical modeling are facilitated by extensive experimental observations regarding bacterial chemotaxis proteins, including biochemical parameters, protein structures and protein-protein interaction maps. Thousands of signaling and regulatory chemotaxis proteins within a bacteria cell form a highly interconnected network through distinct protein-protein interactions. A bacterial cell is able to respond to multiple stimuli through a collection of chemoreceptors with different sensory modalities, which interact to affect the cooperativity and sensitivity of the chemotaxis response. The robustness or insensitivity of the chemotaxis system to perturbations in biochemical parameters is a product of the system's hierarchical network architecture.     

Response regulation in bacterial chemotaxis

The signal transduction system that mediates bacterial chemotaxis allows cells to moduate their swimming behavior in response to fluctuations in chemical stimuli. Receptors at the cell surface receive information from the surroundings. Signals are then passed from the receptors to cytoplasmic chemotaxis components: CheA, CheW, CheZ, CheR, and CheB. These proteins function to regulate the level of phosphorylation of a response regulator designated CheY that interacts with the flagellar motor switch complex to control swimming behavior. The structure of CheY has been determined. Magnesium ion is essential for activity. The active site contains highly conserved Asp residues that are required for divalent metal ion binding and CheY phosphorylation. Another residue-at the active site, Lys109, is important in the phosphorylation-induced conformational change that facilitates communication with the switch complex and another chemotaxis component, CheZ. CheZ facilitates the dephosphorylation of phospho-CheY. Defects in CheY and CheZ can be suppressed by mutations in the flagellar switch complex. CheZ is thought to modulate the switch bias by varying the level of phospho-CheY. © 1993 Wiley-Liss, Inc.     

Lattice-Boltzmann model for bacterial chemotaxis

We present a new numerical approach for modeling bacterial chemotaxis and the fate and transport of a chemoattractant in bulk liquids. This Lattice-Boltzmann method represents the microorganisms and the chemoattractant by quasi-particles that move, collide, and react with each other on a two-dimensional numerical lattice. We use the model to simulate traveling bands of bacteria along self-generated gradients in substrate concentration in bulk liquids. Particularly, we simulate Pseudomonas putida that respond chemotactically to naphthalene dissolved in water. We find that only a fraction of a bacterial slug injected into a domain containing the chemoattractant at constant concentration forms a traveling band as the slug length exceeds a critical value. An expanding bacterial ring forms as one injects a droplet of bacteria into a two-dimensional domain.     

Signal transduction in bacterial chemotaxis

Motile bacteria respond to environmental cues to move to more favorable locations. The components of the chemotaxis signal transduction systems that mediate these responses are highly conserved among prokaryotes including both eubacterial and archael species. The best-studied system is that found in Escherichia coli. Attractant and repellant chemicals are sensed through their interactions with transmembrane chemoreceptor proteins that are localized in multimeric assemblies at one or both cell poles together with a histidine protein kinase, CheA, an SH3-like adaptor protein, CheW, and a phosphoprotein phosphatase, CheZ. These multimeric protein assemblies act to control the level of phosphorylation of a response regulator, CheY, which dictates flagellar motion. Bacterial chemotaxis is one of the most-understood signal transduction systems, and many biochemical and structural details of this system have been elucidated. This is an exciting field of study because the depth of knowledge now allows the detailed molecular mechanisms of transmembrane signaling and signal processing to be investigated.     

Spatial organization in bacterial chemotaxis

Spatial organization of signalling is not an exclusive property of eukaryotic cells. Despite the fact that bacterial signalling pathways are generally simpler than those in eukaryotes, there are several well‐documented examples of higher‐order intracellular signalling structures in bacteria. One of the most prominent and best‐characterized structures is formed by proteins that control bacterial chemotaxis. Signals in chemotaxis are processed by ordered arrays, or clusters, of receptors and associated proteins, which amplify and integrate chemotactic stimuli in a highly cooperative manner. Receptor clusters further serve to scaffold protein interactions, enhancing the efficiency and specificity of the pathway reactions and preventing the formation of signalling gradients through the cell body. Moreover, clustering can also ensure spatial separation of multiple chemotaxis systems in one bacterium. Assembly of receptor clusters appears to be a stochastic process, but bacteria evolved mechanisms to ensure optimal cluster distribution along the cell body for partitioning to daughter cells at division.     

Requirement of ATP in bacterial chemotaxis

Evidence is presented that chemotaxis requires ATP or a closely related metabolite, in addition to its known requirements of ATP for synthesis of S-adenosylmethionine (AdoMet) and maintenance of the proton motive force. Previous studies demonstrated a loss of tumbling and chemotaxis, and depletion of ATP when hisF auxotrophs of Salmonella typhimurium are starved for histidine (Galloway, R. J., and Taylor, B. L. (1980) J. Bacteriol. 144, 1068-1075). In the present study, intracellular [AdoMet], membrane potential, and [ATP] were measured in a hisF mutant of S. typhimurium. Membrane potential, determined from partitioning of [3H]tetraphenylphosphonium ion between the inside and the outside of the cell, was about -150 mV at pH 7.6, and did not decrease in histidine starvation but was slightly increased. The concentration of AdoMet decreased from 0.4 mM to 0.3 mM during starvation but when cycloleucine, an inhibitor of AdoMet synthetase, was used to decrease [AdoMet] by a similar amount in histidine-fed cells there was little change in tumbling frequency. Intracellular [ATP] was reduced from 4.5 mM to less than 0.2 mM by histidine starvation. About 0.2 mM ATP was necessary for spontaneous tumbling. A similar [ATP] was required for tumbling in arsenate-treated cells. Adenine at concentrations as low as 20 nM caused a transient increase in both tumbling frequency and [ATP] in histidine-starved cells. Thus, out of three parameters tested, only the intracellular [ATP] correlated with changes in tumbling frequency in the histidine-starved cells.     

Voltage clamp effects on bacterial chemotaxis

To examine whether or not sensory signaling in bacteria is by way of fluctuations in membrane potential, we studied the effect of clamping the potential on bacterial chemotaxis. The potential was clamped by valinomycin, a K+ -specific ionophore, in the presence of K+. Despite the clamped potential, sensory signaling did occur: both Escherichia coli and Bacillus subtilis cells were still excitable and adaptable under these conditions. It is concluded that signaling in the excitation and adaptation steps of chemotaxis is not by way of fluctuations in the membrane potential.     

A biochemical mechanism for bacterial chemotaxis

Peritrichous bacteria alternately swim and tumble (thrash about with little forward progress). By selective modulation of tumbling frequency, these bacteria carry out chemotaxis, which is migration to higher concentrations of attractant or lower concentrations of repellent. A model for chemotaxis is presented in which tumbling frequency is regulated by concentration of Ca²⁺ ion at the switch that controls tumbling and swimming. Attractants cause decreased levels of free cytoplasmic Ca²⁺ ion due to binding of Ca²⁺ ion by specific proteins. This Ca²⁺ ion is released when these proteins become methylated. An alternative model. involving a cytoplasmic metabolite “compound X”, is discussed.     

Throwing the switch in bacterial chemotaxis

In Escherichia coli chemotaxis, the switch from counterclockwise to clockwise rotation of the flagella occurs as a result of binding of the phosphorylated CheY protein to the base of the flagellum. Analysis of CheY variants has provided a picture of the surface of CheY that undergoes conformational shifts, as a result of phosphorylation, to interact directly with the flagellum. Whether phospho-CheY binding and flagellar switching are sequential steps or can occur in a concerted fashion has yet to be determined.     

Adaptation kinetics in bacterial chemotaxis.

Cells of Escherichia coli, tethered to glass by a single flagellum, were subjected to constant flow of a medium containing the attractant alpha-methyl-DL-aspartate. The concentration of this chemical was varied with a programmable mixing apparatus over a range spanning the dissociation constant of the chemoreceptor at rates comparable to those experienced by cells swimming in spatial gradients. When an exponentially increasing ramp was turned on (a ramp that increases the chemoreceptor occupancy linearly), the rotational bias of the cells (the fraction of time spent spinning counterclockwise) changed rapidly to a higher stable level, which persisted for the duration of the ramp. The change in bias increased with ramp rate, i.e., with the time rate of change of chemoreceptor occupancy. This behavior can be accounted for by a model for adaptation involving proportional control, in which the flagellar motors respond to an error signal proportional to the difference between the current occupancy and the occupancy averaged over the recent past. Distributions of clockwise and counterclockwise rotation intervals were found to be exponential. This result cannot be explained by a response regular model in which transitions between rotational states are generated by threshold crossings of a regular subject to statistical fluctuation; this mechanism generates distributions with far too many long events. However, the data can be fit by a model in which transitions between rotational states are governed by first-order rate constants. The error signal acts as a bias regulator, controlling the values of these constants.     

Calcium channel blockers inhibit bacterial chemotaxis

The effect of several Ca2+ channel blockers, which inhibit the voltage-dependent Ca2+ uptake in Bacillus subtilis, on chemotactic behaviour of the bacterium was studied. Nitrendipine, verapamil, LaCl3 and omega-conotoxin were tested and these blockers inhibited chemotactic behaviour in the bacterium toward L-alanine. Among these blockers, omega-conotoxin was the most effective inhibitor of chemotaxis. EGTA was also as effective as omega-conotoxin. In contrast, these blockers, did not inhibit the motility and the growth of the bacterium. These results suggest that internal Ca2+ plays an important role in the sensory system of bacterial chemotaxis.     

Effects of receptor interaction in bacterial chemotaxis

Signaling in bacterial chemotaxis is mediated by several types of transmembrane chemoreceptors. The chemoreceptors form tight polar clusters whose functions are of great biological interest. Here, we study the general properties of a chemotaxis model that includes interaction between neighboring chemoreceptors within a receptor cluster and the appropriate receptor methylation and demethylation dynamics to maintain (near) perfect adaptation. We find that, depending on the receptor coupling strength, there are two steady-state phases in the model: a stationary phase and an oscillatory phase. The mechanism for the existence of the two phases is understood analytically. Two important phenomena in transient response, the overshoot in response to a pulse stimulus and the high gain in response to sustained changes in external ligand concentrations, can be explained in our model, and the mechanisms for these two seemingly different phenomena are found to be closely related. The model also naturally accounts for several key in vitro response experiments and the recent in vivo fluorescence resonance energy transfer experiments for various mutant strains. Quantitatively, our study reveals possible choices of parameters for fitting the existing experiments and suggests future experiments to test the model predictions.     

Thermal robustness of signaling in bacterial chemotaxis

Temperature is a global factor that affects the performance of all intracellular networks. Robustness against temperature variations is thus expected to be an essential network property, particularly in organisms without inherent temperature control. Here, we combine experimental analyses with computational modeling to investigate thermal robustness of signaling in chemotaxis of Escherichia coli, a relatively simple and well-established model for systems biology. We show that steady-state and kinetic pathway parameters that are essential for chemotactic performance are indeed temperature-compensated in the entire physiological range. Thermal robustness of steady-state pathway output is ensured at several levels by mutual compensation of temperature effects on activities of individual pathway components. Moreover, the effect of temperature on adaptation kinetics is counterbalanced by preprogrammed temperature dependence of enzyme synthesis and stability to achieve nearly optimal performance at the growth temperature. Similar compensatory mechanisms are expected to ensure thermal robustness in other systems.     

Statistical measures of bacterial motility and chemotaxis

This paper presents a model for the three-dimensional microscopic behavior of motile bacteria, and relates its parameters to five practical measures of bacterial motility and chemotaxis (direction correlation function, diffusion constant, persistence time, average velocity, and up/down ratio). The attractant gradient dependences of persistence time, average velocity, and up/down ratio are related to a single function describing the gradient sensing mechanism.