Split Histidine Kinases Enable Ultrasensitivity and Bistability in Two-Component Signaling Networks

Bacteria sense and respond to their environment through signaling cascades generally referred to as two-component signaling networks. These networks comprise histidine kinases and their cognate response regulators. Histidine kinases have a number of biochemical activities: ATP binding, autophosphorylation, the ability to act as a phosphodonor for their response regulators, and in many cases the ability to catalyze the hydrolytic dephosphorylation of their response regulator. Here, we explore the functional role of “split kinases” where the ATP binding and phosphotransfer activities of a conventional histidine kinase are split onto two distinct proteins that form a complex. We find that this unusual configuration can enable ultrasensitivity and bistability in the signal-response relationship of the resulting system. These dynamics are displayed under a wide parameter range but only when specific biochemical requirements are met. We experimentally show that one of these requirements, namely segregation of the phosphatase activity predominantly onto the free form of one of the proteins making up the split kinase, is met in Rhodobacter sphaeroides. These findings indicate split kinases as a bacterial alternative for enabling ultrasensitivity and bistability in signaling networks. Genomic analyses reveal that up 1.7% of all identified histidine kinases have the potential to be split and bifunctional.     

Lipid rafts make for slippery platforms

What''s in a raft? Although cell membranes are certainly not homogeneous mixtures of lipids and proteins, almost all aspects of lipid rafts—how to define them, their size, composition, lifetime, and biological relevance—remain controversial. The answers will shape our views of signaling and of membrane dynamics.In the influential “fluid mosaic” model of Singer and Nicolson, a “mosaic” of integral transmembrane proteins floats about in a “fluid” sea of lipids (Singer and Nicolson, 1972). More recently, researchers have shifted to a view in which membrane lipids are not randomly distributed, but instead show local heterogeneity. One might imagine this as a two-dimensional projection of a lava lamp, with different types of greasy globules in constant motion, endlessly separating and rejoining into distinct but transient domains. These domains are now referred to under the general heading of lipid rafts and domains, a subset of which are the morphologically identifiable “caveolae.”The study of lipid domains has exploded since the debut of the “raft hypothesis” only about fifteen years ago. This torrent of research notwithstanding, there remains heated discussion concerning matters as fundamental as what lipid domains look like—a discussion that peaked but reached little in the way of resolution at a recent conference (Euroconference on Microdomains, Lipid Rafts, and Caveolae; Tomar, Portugal, May 17–22, 2003). Regardless of their actual form, evidence is mounting that lipid rafts are essential participants in signal transduction, membrane and protein sorting, and the pathogenesis of several human diseases.     

Structural basis for two-component system inhibition and pilus sensing by the auxiliary CpxP protein

Bacteria are equipped with two-component systems to cope with environmental changes, and auxiliary proteins provide response to additional stimuli. The Cpx two-component system is the global modulator of cell envelope stress in Gram-negative bacteria that integrates very different signals and consists of the kinase CpxA, the regulator CpxR, and the dual function auxiliary protein CpxP. CpxP both inhibits activation of CpxA and is indispensable for the quality control system of P pili that are crucial for uropathogenic Escherichia coli during kidney colonization. How these two essential biological functions of CpxP are linked is not known. Here, we report the crystal structure of CpxP at 1.45 Å resolution with two monomers being interdigitated like “left hands” forming a cap-shaped dimer. Our combined structural and functional studies suggest that CpxP inhibits the kinase CpxA through direct interaction between its concave polar surface and the negatively charged sensor domain on CpxA. Moreover, an extended hydrophobic cleft on the convex surface suggests a potent substrate recognition site for misfolded pilus subunits. Altogether, the structural details of CpxP provide a first insight how a periplasmic two-component system inhibitor blocks its cognate kinase and is released from it.     

The Effect of STDP Temporal Kernel Structure on the Learning Dynamics of Single Excitatory and Inhibitory Synapses

Spike-Timing Dependent Plasticity (STDP) is characterized by a wide range of temporal kernels. However, much of the theoretical work has focused on a specific kernel – the “temporally asymmetric Hebbian” learning rules. Previous studies linked excitatory STDP to positive feedback that can account for the emergence of response selectivity. Inhibitory plasticity was associated with negative feedback that can balance the excitatory and inhibitory inputs. Here we study the possible computational role of the temporal structure of the STDP. We represent the STDP as a superposition of two processes: potentiation and depression. This allows us to model a wide range of experimentally observed STDP kernels, from Hebbian to anti-Hebbian, by varying a single parameter. We investigate STDP dynamics of a single excitatory or inhibitory synapse in purely feed-forward architecture. We derive a mean-field-Fokker-Planck dynamics for the synaptic weight and analyze the effect of STDP structure on the fixed points of the mean field dynamics. We find a phase transition along the Hebbian to anti-Hebbian parameter from a phase that is characterized by a unimodal distribution of the synaptic weight, in which the STDP dynamics is governed by negative feedback, to a phase with positive feedback characterized by a bimodal distribution. The critical point of this transition depends on general properties of the STDP dynamics and not on the fine details. Namely, the dynamics is affected by the pre-post correlations only via a single number that quantifies its overlap with the STDP kernel. We find that by manipulating the STDP temporal kernel, negative feedback can be induced in excitatory synapses and positive feedback in inhibitory. Moreover, there is an exact symmetry between inhibitory and excitatory plasticity, i.e., for every STDP rule of inhibitory synapse there exists an STDP rule for excitatory synapse, such that their dynamics is identical.     

Virus-host arms race at the joint origin of multicellularity and programmed cell death

Unicellular eukaryotes and most prokaryotes possess distinct mechanisms of programmed cell death (PCD). How an “altruistic” trait, such as PCD, could evolve in unicellular organisms? To address this question, we developed a mathematical model of the virus-host co-evolution that involves interaction between immunity, PCD and cellular aggregation. Analysis of the parameter space of this model shows that under high virus load and imperfect immunity, joint evolution of cell aggregation and PCD is the optimal evolutionary strategy. Given the abundance of viruses in diverse habitats and the wide spread of PCD in most organisms, these findings imply that multiple instances of the emergence of multicellularity and its essential attribute, PCD, could have been driven, at least in part, by the virus-host arms race.     

Population shuffling between ground and high energy excited states

Stochastic processes powered by thermal energy lead to protein motions traversing time-scales from picoseconds to seconds. Fundamental to protein functionality is the utilization of these dynamics for tasks such as catalysis, folding, and allostery. A hierarchy of motion is hypothesized to connect and synergize fast and slow dynamics toward performing these essential activities. Population shuffling predicts a “top-down” temporal hierarchy, where slow time-scale conformational interconversion leads to a shuffling of the free energy landscape for fast time-scale events. Until now, population shuffling was only applied to interconverting ground states. Here, we extend the framework of population shuffling to be applicable for a system interconverting between low energy ground and high energy excited states, such as the SH3 domain mutants G48M and A39V/N53P/V55L from the Fyn tyrosine kinase, providing another tool for accessing the structural dynamics of high energy excited states. Our results indicate that the higher energy gauche− rotameric state for the leucine χ₂ dihedral angle contributes significantly to the distribution of rotameric states in both the major and minor forms of the SH3 domain. These findings are corroborated with unrestrained molecular dynamics (MD) simulations on both the major and minor states of the SH3 domain demonstrating high correlations between experimental and back-calculated leucine χ₂ rotameric populations. Taken together, we demonstrate how fast time-scale rotameric side-chain population distributions can be extracted from slow time-scale conformational exchange data further extending the scope and the applicability of the population shuffling model.     

A density-dependent switch drives stochastic clustering and polarization of signaling molecules

Positive feedback plays a key role in the ability of signaling molecules to form highly localized clusters in the membrane or cytosol of cells. Such clustering can occur in the absence of localizing mechanisms such as pre-existing spatial cues, diffusional barriers, or molecular cross-linking. What prevents positive feedback from amplifying inevitable biological noise when an un-clustered “off” state is desired? And, what limits the spread of clusters when an “on” state is desired? Here, we show that a minimal positive feedback circuit provides the general principle for both suppressing and amplifying noise: below a critical density of signaling molecules, clustering switches off; above this threshold, highly localized clusters are recurrently generated. Clustering occurs only in the stochastic regime, suggesting that finite sizes of molecular populations cannot be ignored in signal transduction networks. The emergence of a dominant cluster for finite numbers of molecules is partly a phenomenon of random sampling, analogous to the fixation or loss of neutral mutations in finite populations. We refer to our model as the “neutral drift polarity model.” Regulating the density of signaling molecules provides a simple mechanism for a positive feedback circuit to robustly switch between clustered and un-clustered states. The intrinsic ability of positive feedback both to create and suppress clustering is a general mechanism that could operate within diverse biological networks to create dynamic spatial organization.     

柑橘大实蝇羽化出土及橘园成虫诱集动态研究

【背景】柑橘大实蝇是柑橘类果树上的重要害虫。预测该虫的羽化出土进度、掌握成虫发生动态是指导橘同成虫期防治的重要依据。【方法】本研究通过在25℃恒温、室内常温和室外网室3种条件下饲养柑橘大实蝇的蛹,以逐日观察成虫羽化出土数量;在重庆武隆、四川江油等5个地区共设置240个麦克菲尔（MePhail）诱集器,以糖酒醋液和水解蛋白为诱饵诱集成虫,得到柑橘大实蝇成虫羽化出土的逐日数量和橘园成虫诱集的逐期数量。【结果】用逻辑斯带模型拟合成虫羽化出土和橘园成虫诱集动态,结果表明,成虫的始盛期、高峰期和盛末期在25℃恒温条件下分别为4月25日、28日和30日,盛期的持续时间为6d;在室内常温条件下分别为5月3日、7日和10日,盛期的持续时间为8d;在室外网室条件下分别为5月8日、14日和18日,盛期的持续时间为11d;橘园诱集成虫分别为6月2日、14日和26日,盛期的持续时间为25d。【结论与意义】随着羽化期温度的提高,柑橘大实蝇羽化出土期提前,历期缩短,羽化整齐。虽然网室成虫羽化和橘园成虫诱集都处于室外条件,但后者的始盛期、盛期和盛末期比前者分别迟了36、30和22d。因此,建议采用室外网室饲养蛹的方法监测柑橘大实蝇成虫的发生期,若仅凭橘园诱集成虫的数据,因其滞后性十分明显,对指导柑橘大实蝇成虫防治的意义不大。     

Gustatory imagery reveals functional connectivity from the prefrontal to insular cortices traced with magnetoencephalography

Our experience and prejudice concerning food play an important role in modulating gustatory information processing; gustatory memory stored in the central nervous system influences gustatory information arising from the peripheral nervous system. We have elucidated the mechanism of the “top-down” modulation of taste perception in humans using functional magnetic resonance imaging (fMRI) and demonstrated that gustatory imagery is mediated by the prefrontal (PFC) and insular cortices (IC). However, the temporal order of activation of these brain regions during gustatory imagery is still an open issue. To explore the source of “top-down” signals during gustatory imagery tasks, we analyzed the temporal activation patterns of activated regions in the cerebral cortex using another non-invasive brain imaging technique, magnetoencephalography (MEG). Gustatory imagery tasks were presented by words (Letter G-V) or pictures (Picture G-V) of foods/beverages, and participants were requested to recall their taste. In the Letter G-V session, 7/9 (77.8%) participants showed activation in the IC with a latency of 401.7±34.7 ms (n = 7) from the onset of word exhibition. In 5/7 (71.4%) participants who exhibited IC activation, the PFC was activated prior to the IC at a latency of 315.2±56.5 ms (n = 5), which was significantly shorter than the latency to the IC activation. In the Picture G-V session, the IC was activated in 6/9 (66.7%) participants, and only 1/9 (11.1%) participants showed activation in the PFC. There was no significant dominance between the right and left IC or PFC during gustatory imagery. These results support those from our previous fMRI study in that the Letter G-V session rather than the Picture G-V session effectively activates the PFC and IC and strengthen the hypothesis that the PFC mediates “top-down” control of retrieving gustatory information from the storage of long-term memories and in turn activates the IC.     

Global self-organization of the cellular metabolic structure

Background

Over many years, it has been assumed that enzymes work either in an isolated way, or organized in small catalytic groups. Several studies performed using “metabolic networks models” are helping to understand the degree of functional complexity that characterizes enzymatic dynamic systems. In a previous work, we used “dissipative metabolic networks” (DMNs) to show that enzymes can present a self-organized global functional structure, in which several sets of enzymes are always in an active state, whereas the rest of molecular catalytic sets exhibit dynamics of on-off changing states. We suggested that this kind of global metabolic dynamics might be a genuine and universal functional configuration of the cellular metabolic structure, common to all living cells. Later, a different group has shown experimentally that this kind of functional structure does, indeed, exist in several microorganisms.

Methodology/Principal Findings

Here we have analyzed around 2.500.000 different DMNs in order to investigate the underlying mechanism of this dynamic global configuration. The numerical analyses that we have performed show that this global configuration is an emergent property inherent to the cellular metabolic dynamics. Concretely, we have found that the existence of a high number of enzymatic subsystems belonging to the DMNs is the fundamental element for the spontaneous emergence of a functional reactive structure characterized by a metabolic core formed by several sets of enzymes always in an active state.

Conclusions/Significance

This self-organized dynamic structure seems to be an intrinsic characteristic of metabolism, common to all living cellular organisms. To better understand cellular functionality, it will be crucial to structurally characterize these enzymatic self-organized global structures.     

Simplification traps

When experiments are analyzed with simple functions, one gets simple results. A trap springs when experiments show deviations from the expected simplicity. When kinetic experiments do not follow exponential curves, they simply are not of the first or pseudofirst order. They can and have to be calculated on the base of plausible reaction schemes. When dose–response curves are analyzed with logistic functions (“4-parameter fit”) and give Hill coefficients different from one, this is an experimental result stating that more than one molecule is involved in eliciting the response. If one ignores that result, one usually finds forgiving referees, but one will loose real money when one tries to develop such an unspecific compound into a drug.

Electronic supplementary material

The online version of this article (doi:10.1007/s12154-011-0069-3) contains supplementary material, which is available to authorized users.     

Embedding Responses in Spontaneous Neural Activity Shaped through Sequential Learning

Recent experimental measurements have demonstrated that spontaneous neural activity in the absence of explicit external stimuli has remarkable spatiotemporal structure. This spontaneous activity has also been shown to play a key role in the response to external stimuli. To better understand this role, we proposed a viewpoint, “memories-as-bifurcations,” that differs from the traditional “memories-as-attractors” viewpoint. Memory recall from the memories-as-bifurcations viewpoint occurs when the spontaneous neural activity is changed to an appropriate output activity upon application of an input, known as a bifurcation in dynamical systems theory, wherein the input modifies the flow structure of the neural dynamics. Learning, then, is a process that helps create neural dynamical systems such that a target output pattern is generated as an attractor upon a given input. Based on this novel viewpoint, we introduce in this paper an associative memory model with a sequential learning process. Using a simple Hebbian-type learning, the model is able to memorize a large number of input/output mappings. The neural dynamics shaped through the learning exhibit different bifurcations to make the requested targets stable upon an increase in the input, and the neural activity in the absence of input shows chaotic dynamics with occasional approaches to the memorized target patterns. These results suggest that these dynamics facilitate the bifurcations to each target attractor upon application of the corresponding input, which thus increases the capacity for learning. This theoretical finding about the behavior of the spontaneous neural activity is consistent with recent experimental observations in which the neural activity without stimuli wanders among patterns evoked by previously applied signals. In addition, the neural networks shaped by learning properly reflect the correlations of input and target-output patterns in a similar manner to those designed in our previous study.     

Coding "what" and "when" in the Archer fish retina

Traditionally, the information content of the neural response is quantified using statistics of the responses relative to stimulus onset time with the assumption that the brain uses onset time to infer stimulus identity. However, stimulus onset time must also be estimated by the brain, making the utility of such an approach questionable. How can stimulus onset be estimated from the neural responses with sufficient accuracy to ensure reliable stimulus identification? We address this question using the framework of colour coding by the archer fish retinal ganglion cell. We found that stimulus identity, “what”, can be estimated from the responses of best single cells with an accuracy comparable to that of the animal''s psychophysical estimation. However, to extract this information, an accurate estimation of stimulus onset is essential. We show that stimulus onset time, “when”, can be estimated using a linear-nonlinear readout mechanism that requires the response of a population of 100 cells. Thus, stimulus onset time can be estimated using a relatively simple readout. However, large nerve cell populations are required to achieve sufficient accuracy.

Authors Summary

In our interaction with the environment we are flooded with a stream of numerous objects and events. Our brain needs to understand the nature of these complex and rich stimuli in order to react. Research has shown ways in which a ‘what’ stimulus was presented can be encoded by the neural responses. However, to understand ‘what was the nature of the stimulus’ the brain needs to know ‘when’ the stimulus was presented. Here, we investigated how the onset of visual stimulus can be signalled by the retina to higher brain regions. We used archer fish as a framework to test the notion that the answer to the question of ‘when’ something has been presented lies within the larger cell population, whereas the answer to the question of ‘what’ has been presented may be found at the single-neuron level. The utility of the archer fish as model animal stems from its remarkable ability to shoot down insects settling on the foliage above the water level, and its ability to distinguish between artificial targets. Thus, the archer fish can provide the fish equivalent of a monkey or a human that can report psychophysical decisions.     

Dynamics of a Cytokine Storm

Six volunteers experienced severe inflammatory response during the Phase I clinical trial of a monoclonal antibody that was designed to stimulate a regulatory T cell response. Soon after the trial began, each volunteer experienced a “cytokine storm”, a dramatic increase in cytokine concentrations. The monoclonal antibody, TGN1412, raised serum concentrations of both pro- and anti-inflammatory cytokines το very hiγh values during the first day, while lymphocyte and monocyte concentrations plummeted. Because the subjects were healthy and had no prior indications of immune deficiency, this event provided an unusual opportunity to study the dynamic interactions of cytokines and other measured parameters. Here, the response histories of nine cytokines have been modeled by a set of linear ordinary differential equations. A general search procedure identifies parameters of the model, whose response fits the data well during the five-day measurement period. The eighteenth-order model reveals plausible cause-and-effect relationships among the cytokines, showing how each cytokine induces or inhibits other cytokines. It suggests that perturbations in IL2, IL8, and IL10 have the most significant inductive effect, while IFN-γ and IL12 have the greatest inhibiting effect on other cytokine concentrations. Although TNF-α is a major pro-inflammatory factor, IFN-γ and three other cytokines have faster initial and median response to TGN1412 infusion. Principal-component analysis of the data reveals three clusters of similar cytokine responses: [TNF-α, IL1, IL10], [IFN-γ, IL2, IL4, IL8, and IL12], and [IL6]. IL1, IL6, IL10, and TNF-α have the highest degree of variability in response to uncertain initial conditions, exogenous effects, and parameter estimates. This study illuminates details of a cytokine storm event, and it demonstrates the value of linear modeling for interpreting complex, coupled biological system dynamics from empirical data.     

Stochastic physics,complex systems and biology

In complex systems, the interplay between nonlinear and stochastic dynamics, e.g., J. Monod’s necessity and chance, gives rise to an evolutionary process in Darwinian sense, in terms of discrete jumps among attractors, with punctuated equilibria, spontaneous random “mutations” and “adaptations”. On an evolutionary time scale it produces sustainable diversity among individuals in a homogeneous population rather than convergence as usually predicted by a deterministic dynamics. The emergent discrete states in such a system, i.e., attractors, have natural robustness against both internal and external perturbations. Phenotypic states of a biological cell, a mesoscopic nonlinear stochastic open biochemical system, could be understood through such a perspective.     

Evolutionary Branching in a Finite Population: Deterministic Branching vs. Stochastic Branching

Adaptive dynamics formalism demonstrates that, in a constant environment, a continuous trait may first converge to a singular point followed by spontaneous transition from a unimodal trait distribution into a bimodal one, which is called “evolutionary branching.” Most previous analyses of evolutionary branching have been conducted in an infinitely large population. Here, we study the effect of stochasticity caused by the finiteness of the population size on evolutionary branching. By analyzing the dynamics of trait variance, we obtain the condition for evolutionary branching as the one under which trait variance explodes. Genetic drift reduces the trait variance and causes stochastic fluctuation. In a very small population, evolutionary branching does not occur. In larger populations, evolutionary branching may occur, but it occurs in two different manners: in deterministic branching, branching occurs quickly when the population reaches the singular point, while in stochastic branching, the population stays at singularity for a period before branching out. The conditions for these cases and the mean branching-out times are calculated in terms of population size, mutational effects, and selection intensity and are confirmed by direct computer simulations of the individual-based model.     

刺槐木质部栓塞脆弱性检测的方法比较

在全球变暖的背景下, 植物木质部栓塞脆弱性是林木死亡率升高的重要生理学因素。然而不同方法在长导管树种上建立的栓塞脆弱性曲线存在较大差异。该研究以长导管树种刺槐(Robinia pseudoacacia)为研究对象, 利用自然干燥法、Cochard Cavitron离心机法以及Sperry离心机法建立了栓塞脆弱性曲线, 旨在探讨不同检测方法的合理性。在Sperry离心法中, 使用了两种规格的转子, 从而对“开口导管假象”学说进行了检验。研究结果表明: 自然干燥法建立的栓塞脆弱性曲线为“s”形, 而Cochard Cavitron离心机法和Sperry离心机法建立的栓塞脆弱性曲线为“r”形; 自然干燥法与离心机法建立的曲线存在显著性差异, 且两种离心机法建立的曲线也具有显著性差异。尽管刺槐枝条的导管长度分布表明14.4 cm长的刺槐枝条具有更高比例的开放导管, 但用Sperry离心机法在27.4 cm和14.4 cm长茎段上建立的栓塞脆弱性曲线相似, 表明Sperry离心机法检测刺槐脆弱性曲线时未产生“开口导管假象”, 具有更为可靠的检测结果。     

Comparison of methods for detecting vulnerability of xylem embolism in Robinia pseudoacacia

在全球变暖的背景下, 植物木质部栓塞脆弱性是林木死亡率升高的重要生理学因素。然而不同方法在长导管树种上建立的栓塞脆弱性曲线存在较大差异。该研究以长导管树种刺槐(Robinia pseudoacacia)为研究对象, 利用自然干燥法、Cochard Cavitron离心机法以及Sperry离心机法建立了栓塞脆弱性曲线, 旨在探讨不同检测方法的合理性。在Sperry离心法中, 使用了两种规格的转子, 从而对“开口导管假象”学说进行了检验。研究结果表明: 自然干燥法建立的栓塞脆弱性曲线为“s”形, 而Cochard Cavitron离心机法和Sperry离心机法建立的栓塞脆弱性曲线为“r”形; 自然干燥法与离心机法建立的曲线存在显著性差异, 且两种离心机法建立的曲线也具有显著性差异。尽管刺槐枝条的导管长度分布表明14.4 cm长的刺槐枝条具有更高比例的开放导管, 但用Sperry离心机法在27.4 cm和14.4 cm长茎段上建立的栓塞脆弱性曲线相似, 表明Sperry离心机法检测刺槐脆弱性曲线时未产生“开口导管假象”, 具有更为可靠的检测结果。