Conformational spread as a mechanism for cooperativity in the bacterial flagellar switch

Thermodynamic dissection of the intrinsically disordered N-terminal domain of human glucocorticoid receptor

Intrinsically disordered (ID) sequence segments are abundant in cell signaling proteins and transcription factors. Because ID regions commonly fold as part of their intracellular function, it is crucial to understand the folded states as well as the transitions between the unfolded and folded states. Specifically, it is important to determine 1) whether large ID segments contain different thermodynamically and/or functionally distinct regions, 2) whether any ID regions fold upon activation, 3) the degree of coupling between the different ID regions, and 4) whether the stability of ID domains is a determinant of function. In this study, we thermodynamically characterized the full-length ID N-terminal domain (NTD) of human glucocorticoid receptor (GR) and two of its naturally occurring translational isoforms. The protective osmolyte trimethylamine N-oxide (TMAO) was used to induce folding transitions. Each of the three NTD isoforms was found to undergo a cooperative folding transition that is thermodynamically indistinguishable (based on m-values) from that of a globular protein of similar size. The extrapolated stabilities for the NTD isoforms showed clear correlation with the known activities of their corresponding GR translational isoforms. The data reveal that the full-length NTD can be viewed as having at least two thermodynamically coupled regions, a functional region, which is indispensable for GR transcriptional activity, and a regulatory region, the length of which serves to regulate the stability of NTD and thus the activity of GR. These results suggest a new functional paradigm whereby steroid hormone receptors in particular and ID proteins in general can have multiple functionally distinct ID regions that interact and modulate the stability of important functional sites.

Conformational spread in the flagellar motor switch: a model study

The reliable response to weak biological signals requires that they be amplified with fidelity. In E. coli, the flagellar motors that control swimming can switch direction in response to very small changes in the concentration of the signaling protein CheY-P, but how this works is not well understood. A recently proposed allosteric model based on cooperative conformational spread in a ring of identical protomers seems promising as it is able to qualitatively reproduce switching, locked state behavior and Hill coefficient values measured for the rotary motor. In this paper we undertook a comprehensive simulation study to analyze the behavior of this model in detail and made predictions on three experimentally observable quantities: switch time distribution, locked state interval distribution, Hill coefficient of the switch response. We parameterized the model using experimental measurements, finding excellent agreement with published data on motor behavior. Analysis of the simulated switching dynamics revealed a mechanism for chemotactic ultrasensitivity, in which cooperativity is indispensable for realizing both coherent switching and effective amplification. These results showed how cells can combine elements of analog and digital control to produce switches that are simultaneously sensitive and reliable.

Coupling between switching regulation and torque generation in bacterial flagellar motor

The bacterial flagellar motor plays a crucial role in both bacterial locomotion and chemotaxis. Recent experiments reveal that the switching dynamics of the motor depend on the rotation speed of the motor, and thus the motor torque, nonmonotonically. Here we present a unified mathematical model which treats motor torque generation based on experimental torque-speed curves and the torque-dependent switching based on the conformational spread model. The model successfully reproduces the observed switching rate as a function of the rotation speed, and provides a generic physical explanation independent of most details. A stator affects the switching dynamics through two mechanisms: accelerating the conformational flipping rate of individual rotor-switching units, which contributes most when the stator works at a high torque and thus a low speed; and influencing a larger number of rotor-switching units within unit time, whose contribution is the greatest when the motor rotates at a high speed. Consequently, the switching rate shows a maximum at intermediate speed, where the above two mechanisms find an optimal output. The load-switching relation may serve as a mechanism for sensing the physical environment, similar to the chemotaxis mechanism for sensing the chemical environment. It may also coordinate the switch dynamics of motors within the same cell.

Theoretical insights into bacterial chemotaxis

Research into understanding bacterial chemotactic systems has become a paradigm for Systems Biology. Experimental and theoretical researchers have worked hand-in-hand for over 40 years to understand the intricate behavior driving bacterial species, in particular how such small creatures, usually not more than 5 µm in length, detect and respond to small changes in their extracellular environment. In this review we highlight the importance that theoretical modeling has played in providing new insight and understanding into bacterial chemotaxis. We begin with an overview of the bacterial chemotaxis sensory response, before reviewing the role of theoretical modeling in understanding elements of the system on the single cell scale and features underpinning multiscale extensions to population models.

Coordinated regulation of multiple flagellar motors by the Escherichia coli chemotaxis system

Escherichia coli cells swim toward a favorable environment by chemotaxis. The chemotaxis system regulates the swimming behavior of the bacteria by controlling the rotational direction of their flagellar motors. Extracellular stimuli sensed by chemoreceptors are transduced to an intracellular signal molecule, phosphorylated CheY (CheY-P), that switches the rotational direction of the flagellar motors from counterclockwise (CCW) to clockwise (CW) or from CW to CCW. Many studies have focused on identifying the proteins involved in the chemotaxis system, and findings on the structures and intracellular localizations of these proteins have largely elucidated the molecular pathway. On the other hand, quantitative evaluations of the chemotaxis system, including the process of intracellular signaling by the propagation of CheY-P and the rotational switching of flagellar motor by binding of CheY-P molecules, are still uncertain. For instance, scientific consensus has held that the flagellar motors of an E. coli cell switch rotational direction asynchronously. However, recent work shows that the rotational switching of any two different motors on a single E. coli cell is highly coordinated; a sub-second switching delay between motors is clearly correlated with the relative distance of each motor from the chemoreceptor patch located at one pole of the cell. In this review of previous studies and our recent findings, we discuss the regulatory mechanism of the multiple flagellar motors on an individual E. coli cell and the intracellular signaling process that can be inferred from this coordinated switching.

Agonism/antagonism switching in allosteric ensembles

Ligands for several transcription factors can act as agonists under some conditions and antagonists under others. The structural and molecular bases of such effects are unknown. Previously, we demonstrated how the folding of intrinsically disordered (ID) protein sequences, in particular, and population shifts, in general, could be used to mediate allosteric coupling between different functional domains, a model that has subsequently been validated in several systems. Here it is shown that population redistribution within allosteric systems can be used as a mechanism to tune protein ensembles such that a given ligand can act as both an agonist and an antagonist. Importantly, this mechanism can be robustly encoded in the ensemble, and does not require that the interactions between the ligand and the protein differ when it is acting either as an agonist or an antagonist. Instead, the effect is due to the relative probabilities of states prior to the addition of the ligand. The ensemble view of allostery that is illuminated by these studies suggests that rather than being seen as switches with fixed responses to allosteric activation, ensembles can evolve to be "functionally pluripotent," with the capacity to up or down regulate activity in response to a stimulus. This result not only helps to explain the prevalence of intrinsic disorder in transcription factors and other cell signaling proteins, it provides important insights about the energetic ground rules governing site-to-site communication in all allosteric systems.

Experimental conditions can obscure the second high-affinity site in LeuT

Neurotransmitter:Na(+) symporters (NSSs), the targets of antidepressants and psychostimulants, recapture neurotransmitters from the synapse in a Na(+)-dependent symport mechanism. The crystal structure of the NSS homolog LeuT from Aquifex aeolicus revealed one leucine substrate in an occluded, centrally located (S1) binding site next to two Na(+) ions. Computational studies combined with binding and flux experiments identified a second substrate (S2) site and a molecular mechanism of Na(+)-substrate symport that depends upon the allosteric interaction of substrate molecules in the two high-affinity sites. Here we show that the S2 site, which has not yet been identified by crystallographic approaches, can be blocked during preparation of detergent-solubilized LeuT, thereby obscuring its crucial role in Na(+)-coupled symport. This finding points to the need for caution in selecting experimental environments in which the properties and mechanistic features of membrane proteins can be delineated.

Allostery and the Monod-Wyman-Changeux model after 50 years

The Monod-Wyman-Changeux (MWC) model was conceived in 1965 to account for the signal transduction and cooperative properties of bacterial regulatory enzymes and hemoglobin. It was soon extended to pharmacological receptors for neurotransmitters and other macromolecular entities involved in intracellular and intercellular communications. Five decades later, the two main hypotheses of the model are reexamined on the basis of a variety of regulatory proteins with known X-ray structures: (a) Regulatory proteins possess an oligomeric structure with symmetry properties, and (b) the allosteric interactions between topographically distinct sites are mediated by a conformational transition established between a few preestablished states with conservation of symmetry and ligand-directed conformational selection. Several well-documented examples are adequately represented by the MWC model, yet a few possible exceptions are noted. New questions are raised concerning the dynamics of the allosteric transitions and more complex supramolecular ensembles.

Using a combination of computational and experimental techniques to understand the molecular basis for protein allostery

Allostery is the process by which remote sites of a system are energetically coupled to elicit a functional response. The early models of allostery such as the Monod-Wyman-Changeux model and the Koshland-Némethy-Filmer model explain the allosteric behavior of multimeric proteins. However, these models do not explain how allostery arises from atomic level in detail. Recent developments in computational methods and experimental techniques have led the beginning of a new age in studying allostery. The combination of computational methods and experiments is a powerful research approach to help answering questions regarding allosteric mechanism at atomic resolution. In this review, three case studies are discussed to illustrate how this combined approach helps to increase our understanding of protein allostery.

AH-DB: collecting protein structure pairs before and after binding

This work presents the Apo-Holo DataBase (AH-DB, http://ahdb.ee.ncku.edu.tw/ and http://ahdb.csbb.ntu.edu.tw/), which provides corresponding pairs of protein structures before and after binding. Conformational transitions are commonly observed in various protein interactions that are involved in important biological functions. For example, copper-zinc superoxide dismutase (SOD1), which destroys free superoxide radicals in the body, undergoes a large conformational transition from an 'open' state (apo structure) to a 'closed' state (holo structure). Many studies have utilized collections of apo-holo structure pairs to investigate the conformational transitions and critical residues. However, the collection process is usually complicated, varies from study to study and produces a small-scale data set. AH-DB is designed to provide an easy and unified way to prepare such data, which is generated by identifying/mapping molecules in different Protein Data Bank (PDB) entries. Conformational transitions are identified based on a refined alignment scheme to overcome the challenge that many structures in the PDB database are only protein fragments and not complete proteins. There are 746,314 apo-holo pairs in AH-DB, which is about 30 times those in the second largest collection of similar data. AH-DB provides sophisticated interfaces for searching apo-holo structure pairs and exploring conformational transitions from apo structures to the corresponding holo structures.

Stochastic coordination of multiple actuators reduces latency and improves chemotactic response in bacteria

Individual neuronal, signal transduction, and regulatory pathways often control multiple stochastic downstream actuators, which raises the question of how coordinated response to a single input can be achieved when individual actuators fluctuate independently. In Escherichia coli, the bacterial chemotaxis pathway controls the activity of multiple flagellar motors to generate the run-and-tumble motion of the cell. High-resolution microscopy experiments have identified the key conformational changes adopted by individual flagella during this process. By incorporating these observations into a stochastic model of the flagellar bundle, we demonstrate that the presence of multiple motors imposes a trade-off on chemotactic performance. Multiple motors reduce the latency of the response below the time scale of the stochastic switching of a single motor, which improves performance on steep gradients of attractants. However, the uncoordinated switching of multiple motors interrupts and shortens cell runs, which thereby reduces signal detection and performance on shallow gradients. Remarkably, when slow fluctuations generated by the adaptation mechanism of the chemotaxis system are incorporated in the model at levels measured in experiments, the chemotactic sensitivity and performance in shallow gradients is partially restored with marginal effects for steep gradients. The noise is beneficial because it simultaneously generates long events in the statistics of individual motors and coordinates the motors to generate a long tail in the run length distribution of the cell. Occasional long runs are known to enhance exploration of random walkers. Here we show that they have the additional benefit of enhancing the sensitivity of the bacterium to very shallow gradients.

Responding to chemical gradients: bacterial chemotaxis

Chemotaxis allows bacteria to follow gradients of nutrients and other environmental stimuli. The bacterium Escherichia coli performs chemotaxis via a run-and-tumble strategy in which sensitive temporal comparisons lead to a biased random walk, with longer runs in the preferred gradient direction. The chemotaxis network of E. coli has developed over the years into one of the most thoroughly studied model systems for signal transduction and behavior, yielding general insights into such properties of cellular networks as signal amplification, signal integration, and robustness. Despite its relative simplicity, the operation of the E. coli chemotaxis network is highly refined and evolutionarily optimized at many levels. For example, recent studies revealed that the network adjusts its signaling properties dependent on the extracellular environment, apparently to optimize chemotaxis under particular conditions. The network can even utilize potentially detrimental stochastic fluctuations in protein levels and reaction rates to maximize the chemotactic performance of the population.

Chemotaxis when bacteria remember: drift versus diffusion

Escherichia coli (E. coli) bacteria govern their trajectories by switching between running and tumbling modes as a function of the nutrient concentration they experienced in the past. At short time one observes a drift of the bacterial population, while at long time one observes accumulation in high-nutrient regions. Recent work has viewed chemotaxis as a compromise between drift toward favorable regions and accumulation in favorable regions. A number of earlier studies assume that a bacterium resets its memory at tumbles – a fact not borne out by experiment – and make use of approximate coarse-grained descriptions. Here, we revisit the problem of chemotaxis without resorting to any memory resets. We find that when bacteria respond to the environment in a non-adaptive manner, chemotaxis is generally dominated by diffusion, whereas when bacteria respond in an adaptive manner, chemotaxis is dominated by a bias in the motion. In the adaptive case, favorable drift occurs together with favorable accumulation. We derive our results from detailed simulations and a variety of analytical arguments. In particular, we introduce a new coarse-grained description of chemotaxis as biased diffusion, and we discuss the way it departs from older coarse-grained descriptions.

The chemotaxis of Escherichia coli is a prototypical model of navigational strategy. The bacterium maneuvers by switching between near-straight motion, termed runs, and tumbles which reorient its direction. To reach regions of high nutrient concentration, the run-durations are modulated according to the nutrient concentration experienced in recent past. This navigational strategy is quite general, in that the mathematical description of these modulations also accounts for the active motility of C. elegans and for thermotaxis in Escherichia coli. Recent studies have pointed to a possible incompatibility between reaching regions of high nutrient concentration quickly and staying there at long times. We use numerical investigations and analytical arguments to reexamine navigational strategy in bacteria. We show that, by accounting properly for the full memory of the bacterium, this paradox is resolved. Our work clarifies the mechanism that underlies chemotaxis and indicates that chemotactic navigation in wild-type bacteria is controlled by drift while in some mutant bacteria it is controlled by a modulation of the diffusion. We also propose a new set of effective, large-scale equations which describe bacterial chemotactic navigation. Our description is significantly different from previous ones, as it results from a conceptually different coarse-graining procedure.

Structural dynamics, intrinsic disorder, and allostery in nuclear receptors as transcription factors

Steroid hormone receptors (SHRs) and nuclear receptors (NRs) in general are flexible, allosterically regulated transcription factors. The classic model is inadequate to explain all their behavior. Keys to function are their regions of intrinsic disorder (ID). Data show the dynamic structure and allosteric interactions of the three classic SHR domains: ligand-binding (LBD), DNA-binding (DBD), and N-terminal (NTD). Each responds to its ligands by stabilizing its structure. The LBD responds to classic steroidal and nonsteroidal small ligands; both may selectively modify SHR activity. The DBD responds differentially to the DNA sequences of its response elements. The NTD, with its high ID content and AF1, interacts allosterically with the LBD and DBD. Each domain binds heterologous proteins, potential allosteric ligands. An ensemble framework improves the classic model, shows how ID regions poise the SHR/NR family for optimal allosteric response, and provides a basis for quantitative evaluation of SHR/NR actions.

Noise underlies switching behavior of the bacterial flagellum

We report the switching behavior of the full bacterial flagellum system that includes the filament and the motor in wild-type Escherichia coli cells. In sorting the motor behavior by the clockwise bias, we find that the distributions of the clockwise (CW) and counterclockwise (CCW) intervals are either exponential or nonexponential with long tails. At low bias, CW intervals are exponentially distributed and CCW intervals exhibit long tails. At intermediate CW bias (0.5) both CW and CCW intervals are mainly exponentially distributed. A simple model suggests that these two distinct switching behaviors are governed by the presence of signaling noise within the chemotaxis network. Low noise yields exponentially distributed intervals, whereas large noise yields nonexponential behavior with long tails. These drastically different motor statistics may play a role in optimizing bacterial behavior for a wide range of environmental conditions.

Dynamics of cooperativity in chemical sensing among cell-surface receptors

Cooperative interactions among sensory receptors provide a general mechanism to increase the sensitivity of signal transduction. In particular, bacterial chemotaxis receptors interact cooperatively to produce an ultrasensitive response to chemoeffector concentrations. However, cooperativity between receptors in large macromolecular complexes is necessarily based on local interactions and consequently is fundamentally connected to slowing of receptor-conformational dynamics, which increases intrinsic noise. Therefore, it is not clear whether or under what conditions cooperativity actually increases the precision of the concentration measurement. We explicitly calculate the signal-to-noise ratio (SNR) for sensing a concentration change using a simple, Ising-type model of receptor-receptor interactions, generalized via scaling arguments, and find that the optimal SNR is always achieved by independent receptors.

A molecular mechanism of direction switching in the flagellar motor of Escherichia coli

The direction of flagellar rotation is regulated by a rotor-mounted protein assembly, termed the "switch complex," formed from multiple copies of the proteins FliG, FliM, and FliN. The structures of major parts of these proteins are known, and the overall organization of proteins in the complex has been elucidated previously using a combination of protein-binding, mutational, and cross-linking approaches. In Escherichia coli, the switch from counterclockwise to clockwise rotation is triggered by the signaling protein phospho-CheY, which binds to the lower part of the switch complex and induces small movements of FliM and FliN subunits relative to each other. Direction switching also must produce movements in the upper part of the complex, particularly in the C-terminal domain of FliG (FliG(C)), which interacts with the stator to generate the torque for flagellar rotation. In the present study, protein movements in the middle and upper parts of the switch complex have been probed by means of targeted cross-linking and mutational analysis. Switching induces a tilting movement of the FliM domains that form the middle part of the switch and a consequent rotation of the affixed FliG(C) domains that reorients the stator interaction sites by about 90°. In a recently proposed hypothesis for the motor mechanism, such a reorientation of FliG(C) would reverse the direction of motor rotation.

Protein dynamics and mechanisms controlling the rotational behaviour of the bacterial flagellar motor

The proteins that make up the bacterial flagellar rotary motor have recently been shown to be more dynamic than previously thought, with some key proteins exchanging with pools of proteins in the membrane/cytoplasm. It has also become clear that in addition to simply switching in response to chemosensory signals, the rotation of the bacterial flagellar motor can be slowed or stopped, using a clutch or a brake, by signals from metabolism and growth state.

Timescale analysis of rule-based biochemical reaction networks

The flow of information within a cell is governed by a series of protein-protein interactions that can be described as a reaction network. Mathematical models of biochemical reaction networks can be constructed by repetitively applying specific rules that define how reactants interact and what new species are formed on reaction. To aid in understanding the underlying biochemistry, timescale analysis is one method developed to prune the size of the reaction network. In this work, we extend the methods associated with timescale analysis to reaction rules instead of the species contained within the network. To illustrate this approach, we applied timescale analysis to a simple receptor-ligand binding model and a rule-based model of interleukin-12 (IL-12) signaling in naïve CD4+ T cells. The IL-12 signaling pathway includes multiple protein-protein interactions that collectively transmit information; however, the level of mechanistic detail sufficient to capture the observed dynamics has not been justified based on the available data. The analysis correctly predicted that reactions associated with Janus Kinase 2 and Tyrosine Kinase 2 binding to their corresponding receptor exist at a pseudo-equilibrium. By contrast, reactions associated with ligand binding and receptor turnover regulate cellular response to IL-12. An empirical Bayesian approach was used to estimate the uncertainty in the timescales. This approach complements existing rank- and flux-based methods that can be used to interrogate complex reaction networks. Ultimately, timescale analysis of rule-based models is a computational tool that can be used to reveal the biochemical steps that regulate signaling dynamics.

Expanding the proteome: disordered and alternatively folded proteins

Proteins provide much of the scaffolding for life, as well as undertaking a variety of essential catalytic reactions. These characteristic functions have led us to presuppose that proteins are in general functional only when well structured and correctly folded. As we begin to explore the repertoire of possible protein sequences inherent in the human and other genomes, two stark facts that belie this supposition become clear: firstly, the number of apparent open reading frames in the human genome is significantly smaller than appears to be necessary to code for all of the diverse proteins in higher organisms, and secondly that a significant proportion of the protein sequences that would be coded by the genome would not be expected to form stable three-dimensional (3D) structures. Clearly the genome must include coding for a multitude of alternative forms of proteins, some of which may be partly or fully disordered or incompletely structured in their functional states. At the same time as this likelihood was recognized, experimental studies also began to uncover examples of important protein molecules and domains that were incompletely structured or completely disordered in solution, yet remained perfectly functional. In the ensuing years, we have seen an explosion of experimental and genome-annotation studies that have mapped the extent of the intrinsic disorder phenomenon and explored the possible biological rationales for its widespread occurrence. Answers to the question 'why would a particular domain need to be unstructured?' are as varied as the systems where such domains are found. This review provides a survey of recent new directions in this field, and includes an evaluation of the role not only of intrinsically disordered proteins but also of partially structured and highly dynamic members of the disorder-order continuum.

Synergism between different germinant receptors in the germination of Bacillus subtilis spores

Rates of commitment to germinate and germination of Bacillus subtilis spores with mixtures of low concentrations of germinants acting on different germinant receptors (GRs) were much higher than the sums of the rates of commitment and germination with individual germinants alone. This synergism with mixtures of nutrient germinants was not seen with spores lacking GRs responsible for recognizing one or several components of the germinant mixtures and was not eliminated by either a gerD mutation or overexpression of one of the GRs involved in this synergism. This synergism was also not seen between the germinant L-valine, which acts via a GR, and the germinant dodecylamine, which does not act via any GR. These results indicate that spores not only integrate but can also amplify signals from multiple germinants and multiple GRs in determining rates of commitment and overall spore germination. This amplification can be explained by a simple mechanism in which a single signal integrator triggers germination above an accumulation threshold. Direct cooperative action between GRs may further add to the synergism seen in germination triggered by multiple GRs. Further experiments and modeling are required to determine the relative contributions of these different mechanisms.

Structural insight into the rotational switching mechanism of the bacterial flagellar motor

Structural analysis of a clockwise-biased rotation mutant of the bacterial flagellar rotor protein FliG provides a new model for the arrangement of FliG subunits in the motor, and novel insights into rotation switching.

The bacterial flagellar motor can rotate either clockwise (CW) or counterclockwise (CCW). Three flagellar proteins, FliG, FliM, and FliN, are required for rapid switching between the CW and CCW directions. Switching is achieved by a conformational change in FliG induced by the binding of a chemotaxis signaling protein, phospho-CheY, to FliM and FliN. FliG consists of three domains, FliG_N, FliG_M, and FliG_C, and forms a ring on the cytoplasmic face of the MS ring of the flagellar basal body. Crystal structures have been reported for the FliG_MC domains of Thermotoga maritima, which consist of the FliG_M and FliG_C domains and a helix E that connects these two domains, and full-length FliG of Aquifex aeolicus. However, the basis for the switching mechanism is based only on previously obtained genetic data and is hence rather indirect. We characterized a CW-biased mutant (fliG(ΔPAA)) of Salmonella enterica by direct observation of rotation of a single motor at high temporal and spatial resolution. We also determined the crystal structure of the FliG_MC domains of an equivalent deletion mutant variant of T. maritima (fliG(ΔPEV)). The FliG(ΔPAA) motor produced torque at wild-type levels under a wide range of external load conditions. The wild-type motors rotated exclusively in the CCW direction under our experimental conditions, whereas the mutant motors rotated only in the CW direction. This result suggests that wild-type FliG is more stable in the CCW state than in the CW state, whereas FliG(ΔPAA) is more stable in the CW state than in the CCW state. The structure of the TM-FliG_MC(ΔPEV) revealed that extremely CW-biased rotation was caused by a conformational change in helix E. Although the arrangement of FliG_C relative to FliG_M in a single molecule was different among the three crystals, a conserved FliG_M-FliG_C unit was observed in all three of them. We suggest that the conserved FliG_M-FliG_C unit is the basic functional element in the rotor ring and that the PAA deletion induces a conformational change in a hinge-loop between FliG_M and helix E to achieve the CW state of the FliG ring. We also propose a novel model for the arrangement of FliG subunits within the motor. The model is in agreement with the previous mutational and cross-linking experiments and explains the cooperative switching mechanism of the flagellar motor.

The bacterial flagellum is a rotating organelle that governs cell motility. At the base of each flagellum is a motor powered by the electrochemical potential difference of specific ions across the cytoplasmic membrane. In response to environmental stimuli, rotation of the motor switches between counterclockwise and clockwise, with a corresponding effect on the swimming direction of the cell. Switching is triggered by the binding of the signaling protein phospho-CheY to FliM and FliN, and achieved by conformational changes in the rotor protein FliG. The actual switching mechanism, however, remains unclear. In this study, we characterized a fliG mutant of Salmonella that shows an extreme clockwise-biased rotation, and determined the structure of a fragment of FliG (FliG_MC) of the equivalent mutant variant of Thermotoga maritima. FliG_MC is composed of two domains and covers the regions essential for torque generation and FliM binding. We showed that the mutant structure has a conformational change in the helix connecting the two domains, leading to a domain orientation distinct from that of the wild-type FliG. On the basis of this structure, we propose a new model for the arrangement of FliG subunits in the rotor that is consistent with the previous mutational studies and explains how cooperative switching occurs in the motor.

Non-genetic individuality in Escherichia coli motor switching

By analyzing 30 min, high-resolution recordings of single Escherichia coli flagellar motors in the physiological regime, we show that two main properties of motor switching-the mean clockwise and mean counter-clockwise interval durations-vary significantly. When we represent these quantities on a two-dimensional plot for several cells, the data do not fall on a one-dimensional curve, as expected with a single control parameter, but instead spread in two dimensions, pointing to motor individuality. The largest variations are in the mean counter-clockwise interval, and are attributable to variations in the concentration of the internal signaling molecule CheY-P. In contrast, variations in the mean clockwise interval are interpreted in terms of motor individuality. We argue that the sensitivity of the mean counter-clockwise interval to fluctuations in CheY-P is consistent with an optimal strategy of run and tumble. The concomittent variability in mean run length may allow populations of cells to better survive in rapidly changing environments by 'hedging their bets'.

Sensing and adhesion are adaptive functions in the plant pathogenic xanthomonads

Background

Bacterial plant pathogens belonging to the Xanthomonas genus are tightly adapted to their host plants and are not known to colonise other environments. The host range of each strain is usually restricted to a few host plant species. Bacterial strains responsible for the same type of symptoms on the same host range cluster in a pathovar. The phyllosphere is a highly stressful environment, but it provides a selective habitat and a source of substrates for these bacteria. Xanthomonads colonise host phylloplane before entering leaf tissues and engaging in an invasive pathogenic phase. Hence, these bacteria are likely to have evolved strategies to adapt to life in this environment. We hypothesised that determinants responsible for bacterial host adaptation are expressed starting from the establishment of chemotactic attraction and adhesion on host tissue.

Results

We established the distribution of 70 genes coding sensors and adhesins in a large collection of xanthomonad strains. These 173 strains belong to different pathovars of Xanthomonas spp and display different host ranges. Candidate genes are involved in chemotactic attraction (25 genes), chemical environment sensing (35 genes), and adhesion (10 genes). Our study revealed that candidate gene repertoires comprised core and variable gene suites that likely have distinct roles in host adaptation. Most pathovars were characterized by unique repertoires of candidate genes, highlighting a correspondence between pathovar clustering and repertoires of sensors and adhesins. To further challenge our hypothesis, we tested for molecular signatures of selection on candidate genes extracted from sequenced genomes of strains belonging to different pathovars. We found strong evidence of adaptive divergence acting on most candidate genes.

Conclusions

These data provide insight into the potential role played by sensors and adhesins in the adaptation of xanthomonads to their host plants. The correspondence between repertoires of sensor and adhesin genes and pathovars and the rapid evolution of sensors and adhesins shows that, for plant pathogenic xanthomonads, events leading to host specificity may occur as early as chemotactic attraction by host and adhesion to tissues.

Receptors, signaling networks, and disease

Over the past years, a holistic approach has been applied to the study of the field of receptor signaling, permitting the analysis of how the interaction between receptors and their cellular environment determines receptor function and the study of the role of these receptors, under both normal and pathophysiological conditions, in whole organisms. This has been facilitated by the development of high-resolution microscopy techniques, which allow single-molecule or spatiotemporal resolution, or both, of signaling processes at the cellular and organismal levels. Concurrently, the role of these signaling pathways can be tested in increasingly sophisticated murine disease models. Finally, computational approaches aid in predicting and understanding receptor behavior. The program of the Madrid meeting reflected this integrated approach, highlighting signaling by and dynamics and regulation of immune cell receptors, the T cell receptor and B cell receptor, and signaling by and regulation of G protein-coupled receptors.

PCDB: a database of protein conformational diversity

PCDB (http://www.pcdb.unq.edu.ar) is a database of protein conformational diversity. For each protein, the database contains the redundant compilation of all the corresponding crystallographic structures obtained under different conditions. These structures could be considered as different instances of protein dynamism. As a measure of the conformational diversity we use the maximum RMSD obtained comparing the structures deposited for each domain. The redundant structures were extracted following CATH structural classification and cross linked with additional information. In this way it is possible to relate a given amount of conformational diversity with different levels of information, such as protein function, presence of ligands and mutations, structural classification, active site information and organism taxonomy among others. Currently the database contains 7989 domains with a total of 36581 structures from 4171 different proteins. The maximum RMSD registered is 26.7 Å and the average of different structures per domain is 4.5.

Exploring and exploiting allostery: Models, evolution, and drug targeting

The concept of allostery was elaborated almost 50years ago by Monod and coworkers to provide a framework for interpreting experimental studies on the regulation of protein function. In essence, binding of a ligand at an allosteric site affects the function at a distant site exploiting protein flexibility and reshaping protein energy landscape. Both monomeric and oligomeric proteins can be allosteric. In the past decades, the behavior of allosteric systems has been analyzed in many investigations while general theoretical models and variations thereof have been steadily proposed to interpret the experimental data. Allostery has been established as a fundamental mechanism of regulation in all organisms, governing a variety of processes that range from metabolic control to receptor function and from ligand transport to cell motility. A number of studies have shed light on how evolutionary pressures have favored and molded the development of allosteric features in specific macromolecular systems. The widespread occurrence of allostery has been recently exploited for the development and design of allosteric drugs that bind to either physiological or non-physiological allosteric sites leading to gain of function or loss of function. This article is part of a Special Issue entitled: Protein Dynamics: Experimental and Computational Approaches.

Allosteric conformational spread: exact results using a simple transfer matrix method

A transfer matrix method is described for the conformational spread (CS) model of allosteric cooperativity within a one-dimensional arrangement of four-state binding sites. Each such binding site can realize one of two possible conformational states. Each of these states can either bind ligand or not bind ligand. Thus, analytical expressions that are exact within the context of the CS model are derived for the grand partition function, for the mean fraction of binding sites occupied by ligand versus ligand concentration, and for the mean fraction of binding sites in a given allosteric state versus ligand concentration. The utility of our analytical results is demonstrated by least-mean-square fitting of prior experimental results obtained on the bacterial flagellar motor for the fraction of FliM/FliG/FliN complexes with CheY-P bound [V. Sourjik and H. C. Berg, Proc. Natl. Acad. Sci. U.S.A. 99, 12669 (2002)] and for the cw bias [P. Cluzel, Science 287, 1652 (2000)], which plausibly may be identified as the fraction of protomers realizing state 2. Finally, the relationships between our analytical results and the classical Monod-Wyman-Changeaux, Koshland-Nemethy-Filmer, and McGhee-Von Hippel treatments of allosteric cooperativity are elucidated, as is the connection to an earlier approximate analytical treatment of the CS model.

Structure of the torque ring of the flagellar motor and the molecular basis for rotational switching

The flagellar motor drives the rotation of flagellar filaments at hundreds of revolutions per second, efficiently propelling bacteria through viscous media. The motor uses the potential energy from an electrochemical gradient of cations across the cytoplasmic membrane to generate torque. A rapid switch from anticlockwise to clockwise rotation determines whether a bacterium runs smoothly forward or tumbles to change its trajectory. A protein called FliG forms a ring in the rotor of the flagellar motor that is involved in the generation of torque through an interaction with the cation-channel-forming stator subunit MotA. FliG has been suggested to adopt distinct conformations that induce switching but these structural changes and the molecular mechanism of switching are unknown. Here we report the molecular structure of the full-length FliG protein, identify conformational changes that are involved in rotational switching and uncover the structural basis for the formation of the FliG torque ring. This allows us to propose a model of the complete ring and switching mechanism in which conformational changes in FliG reverse the electrostatic charges involved in torque generation.

Coupling metabolism and chemotaxis-dependent behaviours by energy taxis receptors

Bacteria have evolved the ability to monitor changes in various physico-chemical parameters and to adapt their physiology and metabolism by implementing appropriate cellular responses to these changes. Energy taxis is a metabolism-dependent form of taxis and is the directed movement of motile bacteria in gradients of physico-chemical parameters that affect metabolism. Energy taxis has been described in diverse bacterial species and several dedicated energy sensors have been identified. The molecular mechanism of energy taxis has not been studied in as much detail as chemotaxis, but experimental evidence indicates that this behaviour differs from metabolism-independent taxis only by the presence of dedicated energy taxis receptors. Energy taxis receptors perceive changes in energy-related parameters, including signals related to the redox and/or intracellular energy status of the cell. The best-characterized energy taxis receptors are those that sense the redox state of the electron transport chain via non-covalently bound FAD cofactors. Other receptors shown to mediate energy taxis lack any recognizable redox cofactor or conserved energy-sensing motif, and some have been suggested to monitor changes in the proton motive force. The exact energy-sensing mechanism(s) involved are yet to be elucidated for most of these energy sensors. By monitoring changes in energy-related parameters, energy taxis receptors allow cells to couple motility behaviour with metabolism under diverse environmental conditions. Energy taxis receptors thus provide fruitful models to decipher how cells integrate sensory behaviours with metabolic activities.

Biochemistry. An ensemble view of allostery

Proteins existing as ensembles of conformations may be key to understanding signal transduction processes.