Noise generation, amplification and propagation in chemotactic signaling systems of living cells

Insights into the molecular basis of c-di-GMP signalling in Pseudomonas aeruginosa

The opportunistic human pathogen Pseudomonas aeruginosa can cause severe infections in immunocompromized people or cystic fibrosis (CF) patients. Because of its remarkable ability to invade the host and withstand the bacteriocidal effect of most conventional antibiotics, the infection caused by P. aeruginosa has become a major concern for human health. The switch from acute to chronic infection is governed by the second messenger bis-(3'-5')-cyclic dimeric guanosine mono-phosphate (c-di-GMP) in P. aeruginosa, and c-di-GMP is now recognized to regulate many important biological processes in pathogenesis. The c-di-GMP signalling mechanisms in P. aeruginosa have been studied extensively in the past decade, revealing complicated c-di-GMP metabolism and signalling network. In this review, the underlying mechanisms of this signalling network will be discussed, mainly focussing on how environmental cues regulate c-di-GMP signalling, protein-protein interaction mediated functional regulation, heterogeneity of c-di-GMP and cross talk between c-di-GMP signalling and other signalling systems. Understanding the molecular mechanism underlying the complex c-di-GMP signalling network would be beneficial for developing therapeutic approaches and antibacterial agents to combat the threat from P. aeruginosa.

Effects of bidirectional phenotype switching on signal noise in a bacterial community

Cells can sense and process various signals. Noise is inevitable in the cell signaling system. In a bacterial community, the mutual conversion between normal cells and persistent cells forms a bidirectional phenotype switching cascade, in which either one can be used as an upstream signal and the other as a downstream signal. In order to quantitatively describe the relationship between noise and signal amplification of each phenotype, the gain-fluctuation relationship is obtained by using the linear noise approximation of the master equation. Through the simulation of these theoretical formulas, it is found that the bidirectional phenotype switching can directly generate interconversion noise which is usually very small and almost negligible. In particular, the bidirectional phenotype switching can provide a global fluctuating environment, which will not only affect the values of noise and covariance, but also generate additional intrinsic noise. The additional intrinsic noise in each phenotype is the main part of the total noise and can be transmitted to the other phenotype. The transmitted noise is also a powerful supplement to the total noise. Therefore, the indirect impact of bidirectional phenotype switching is far greater than its direct impact, which may be one of the reasons why chronic infections caused by persistent cells are refractory to treat.

A design principle for posttranslational chaotic oscillators

Chaos behavior has been observed in various cellular and molecular processes. Here, we modeled reversible phosphorylation dynamics to elucidate a design principle for autonomous chaos generation that may arise from generic enzymatic reactions. A comprehensive parameter search demonstrated that the reaction system composed of a set of kinases and phosphatases and two substrates with two modification sites exhibits chaos behavior. All reactions are described according to the Michaelis-Menten reaction scheme without exotic functions being applied to enzymes and substrates. Clustering analysis of parameter sets that can generate chaos behavior revealed the existence of motif structures. These chaos motifs allow the two-substrate species to interact via enzyme availability and constrain the two substrates' dynamic changes in phosphorylation status so that they occur at different timescales. This chaos motif structure is found in several enzymatic reactions, suggesting that chaos behavior may underlie cellular autonomy in a variety of biochemical systems.

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Two substrates with reversible two-site phosphorylation can exhibit chaos behavior

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The chaos does not require autocatalysis or allosteric regulation of enzymes

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The chaos is a result of the coupling of two substrates via enzyme availability

Noisy signal propagation and amplification in phenotypic transition cascade of colonic cells

Like genes and proteins, cells can use biochemical networks to sense and process information. The differentiation of the cell state in colonic crypts forms a typical unidirectional phenotypic transitional cascade, in which stem cells differentiate into the transit-amplifying cells (TACs), and TACs continue to differentiate into fully differentiated cells. In order to quantitatively describe the relationship between the noise of each compartment and the amplification of signals, the gain factor is introduced, and the gain-fluctuation relation is obtained by using the linear noise approximation of the master equation. Through the simulation of these theoretical formulas, the characters of noise propagation and amplification are studied. It is found that the transmitted noise is an important part of the total noise in each downstream cell. Therefore, a small number of downstream cells can only cause its small inherent noise, but the total noise may be very large due to the transmitted noise. The influence of the transmitted noise may be the indirect cause of colon cancer. In addition, the total noise of the downstream cells always has a minimum value. As long as a reasonable value of the gain factor is selected, the number of cells in colonic crypts will be controlled within the normal range. This may be a good method to intervene the uncontrollable growth of tumor cells and effectively control the deterioration of colon cancer.

Differential Production of Psl in Planktonic Cells Leads to Two Distinctive Attachment Phenotypes in Pseudomonas aeruginosa

The attachment of planktonic cells to surfaces is the first and most crucial step in biofilm formation. In this paper, we show that planktonic cells of Pseudomonas aeruginosa differently attach to surfaces. Typically, in the later exponential phase, approximately 80% of the cells can quickly attach to surfaces within 15 min, whereas approximately 20% of the cells slowly attach to surfaces, which greatly affects the initial stage of biofilm formation in the presence of flows. This is because fast-attaching cells are more likely to attach on surfaces to form microcolonies, whereas slow-attaching cells are more likely to remain in the mobile phase. This scenario is different from the previous understanding of biofilm formation in the initial stage, in which planktonic cells were thought to uniformly attach to surfaces. Most notably, the results of this study show that the different attachment manner of planktonic cells to surfaces affects the subsequent stages of biofilm formation. This research highlights that the phenotypic variations in planktonic cells plays significant roles in various stages of biofilm formation.

Exponentially growing bacteria in a well-mixed planktonic culture are generally assumed to be physiologically and phenotypically uniform and distinct from their genetically identical counterparts living in biofilms. Using a combination of high spatiotemporal microscopy and a bacterial tracking algorithm, in this study, we showed that planktonic cells of Pseudomonas aeruginosa differently attached to surfaces even when they remained in the exponential phase. We consistently observed that fast- and slow-attaching phenotypes coexist in planktonic cells, regardless of their growth phase. Furthermore, we found that (i) the distinct attaching phenotypes of planktonic cells resulted from the differential production of Psl and (ii) the RsmYZ/RsmA signaling pathway mainly regulated the differential production of Psl. Our results indicate that the differential production of Psl in P. aeruginosa plays a significant role in biofilm development and formation.

IMPORTANCE The attachment of planktonic cells to surfaces is the first and most crucial step in biofilm formation. In this paper, we show that planktonic cells of Pseudomonas aeruginosa differently attach to surfaces. Typically, in the later exponential phase, approximately 80% of the cells can quickly attach to surfaces within 15 min, whereas approximately 20% of the cells slowly attach to surfaces, which greatly affects the initial stage of biofilm formation in the presence of flows. This is because fast-attaching cells are more likely to attach on surfaces to form microcolonies, whereas slow-attaching cells are more likely to remain in the mobile phase. This scenario is different from the previous understanding of biofilm formation in the initial stage, in which planktonic cells were thought to uniformly attach to surfaces. Most notably, the results of this study show that the different attachment manner of planktonic cells to surfaces affects the subsequent stages of biofilm formation. This research highlights that the phenotypic variations in planktonic cells plays significant roles in various stages of biofilm formation.

Experimental and theoretical bases for mechanisms of antigen discrimination by T cells

Interaction only within specific molecules is a requisite for accurate operations of a biochemical reaction in a cell where bulk of background molecules exist. While structural specificity is a well-established mechanism for specific interaction, biophysical and biochemical experiments indicate that the mechanism is not sufficient for accounting for the antigen discrimination by T cells. In addition, the antigen discrimination by T cells also accompanies three intriguing properties other than the specificity: sensitivity, speed, and concentration compensation. In this work, we review experimental and theoretical works on the antigen discrimination by focusing on these four properties and show future directions towards understanding of the fundamental principle for molecular discrimination.

c-di-GMP heterogeneity is generated by the chemotaxis machinery to regulate flagellar motility

Individual cell heterogeneity is commonly observed within populations, although its molecular basis is largely unknown. Previously, using FRET-based microscopy, we observed heterogeneity in cellular c-di-GMP levels. In this study, we show that c-di-GMP heterogeneity in Pseudomonas aeruginosa is promoted by a specific phosphodiesterase partitioned after cell division. We found that subcellular localization and reduction of c-di-GMP levels by this phosphodiesterase is dependent on the histidine kinase component of the chemotaxis machinery, CheA, and its phosphorylation state. Therefore, individual cell heterogeneity in c-di-GMP concentrations is regulated by the activity and the asymmetrical inheritance of the chemotaxis organelle after cell division. c-di-GMP heterogeneity results in a diversity of motility behaviors. The generation of diverse intracellular concentrations of c-di-GMP by asymmetric partitioning is likely important to the success and survival of bacterial populations within the environment by allowing a variety of motility behaviors.

DOI:http://dx.doi.org/10.7554/eLife.01402.001

Bacterial populations have traditionally been assumed to be made up of identical cells. However, while the bacteria within a population may be genetically identical, individual cells have different growth rates, metabolisms and motilities, among other things. This ‘phenotypic heterogeneity’ has been observed in many different species of bacteria, and in some cases it can be attributed to changes in the concentration of molecules called second messengers that help to relay signals from the external environment to targets within the cell.

It can be challenging to monitor changes in the concentration of specific molecules inside cells, but researchers recently developed a form of microscopy based on FRET (short for Forster resonance energy transfer) that can measure the levels of a second messenger molecule called cyclic di-guanylate (c-di-GMP) inside individual cells. This technique was used to study P. aeruginosa, a bacterium that has a single corkscrew-shaped propeller that enables it to swim through liquid. P. aeruginosa divides to form two daughter cells—one with a propeller and one without.

Although the daughter cell that does not have a propeller quickly grows one, FRET-based microscopy revealed that the daughter cell with a propeller had less c-di-GMP than the daughter without a propeller, but the reasons underlying this difference and its effects on bacterial behavior were not clear.

Now Kulasekara et al. show that the cell that inherits the propeller contains an enzyme that degrades c-di-GMP, and that the low levels of this second messenger molecule—caused by the enzyme being concentrated near the base of the propeller, and the presence of a protein (CheA) that enables the bacteria to swim towards sources of nutrients—result in faster swimming speeds and increased responsiveness to nutrients. In other words, although the two daughter cells are genetically identical, they behave quite differently because of the different levels of this second messenger molecule.

The existence of heterogeneity within a bacterial population likely leads to increased success and survival within changing diverse environments, and this work sets the stage for similar investigations into what establishes heterogeneity in other bacterial populations.

DOI:http://dx.doi.org/10.7554/eLife.01402.002

Atomic water channel controlling remarkable properties of a single brain microtubule: correlating single protein to its supramolecular assembly

Microtubule nanotubes are found in every living eukaryotic cells; these are formed by reversible polymerization of the tubulin protein, and their hollow fibers are filled with uniquely arranged water molecules. Here we measure single tubulin molecule and single brain-neuron extracted microtubule nanowire with and without water channel inside to unravel their unique electronic and optical properties for the first time. We demonstrate that the energy levels of a single tubulin protein and single microtubule made of 40,000 tubulin dimers are identical unlike conventional materials. Moreover, the transmitted ac power and the transient fluorescence decay (single photon count) are independent of the microtubule length. Even more remarkable is the fact that the microtubule nanowire is more conducting than a single protein molecule that constitutes the nanowire. Microtubule's vibrational peaks condense to a single mode that controls the emergence of size independent electronic/optical properties, and automated noise alleviation, which disappear when the atomic water core is released from the inner cylinder. We have carried out several tricky state-of-the-art experiments and identified the electromagnetic resonance peaks of single microtubule reliably. The resonant vibrations established that the condensation of energy levels and periodic oscillation of unique energy fringes on the microtubule surface, emerge as the atomic water core resonantly integrates all proteins around it such that the nanotube irrespective of its size functions like a single protein molecule. Thus, a monomolecular water channel residing inside the protein-cylinder displays an unprecedented control in governing the tantalizing electronic and optical properties of microtubule.

Amoeba-based computing for traveling salesman problem: long-term correlations between spatially separated individual cells of Physarum polycephalum

A single-celled, multi-nucleated amoeboid organism, a plasmodium of the true slime mold Physarum polycephalum, can perform sophisticated computing by exhibiting complex spatiotemporal oscillatory dynamics while deforming its amorphous body. We previously devised an "amoeba-based computer (ABC)" to quantitatively evaluate the optimization capability of the amoeboid organism in searching for a solution to the traveling salesman problem (TSP) under optical feedback control. In ABC, the organism changes its shape to find a high quality solution (a relatively shorter TSP route) by alternately expanding and contracting its pseudopod-like branches that exhibit local photoavoidance behavior. The quality of the solution serves as a measure of the optimality of which the organism maximizes its global body area (nutrient absorption) while minimizing the risk of being illuminated (exposure to aversive stimuli). ABC found a high quality solution for the 8-city TSP with a high probability. However, it remains unclear whether intracellular communication among the branches of the organism is essential for computing. In this study, we conducted a series of control experiments using two individual cells (two single-celled organisms) to perform parallel searches in the absence of intercellular communication. We found that ABC drastically lost its ability to find a solution when it used two independent individuals. However, interestingly, when two individuals were prepared by dividing one individual, they found a solution for a few tens of minutes. That is, the two divided individuals remained correlated even though they were spatially separated. These results suggest the presence of a long-term memory in the intrinsic dynamics of this organism and its significance in performing sophisticated computing.

Theoretical aspects of cellular decision-making and information-processing

Microscopic biological processes have extraordinary complexity and variety at the sub-cellular, intra-cellular, and multi-cellular levels. In dealing with such complex phenomena, conceptual and theoretical frameworks are crucial, which enable us to understand seemingly different intra- and inter-cellular phenomena from unified viewpoints. Decision-making is one such concept that has attracted much attention recently. Since a number of cellular behavior can be regarded as processes to make specific actions in response to external stimuli, decision-making can cover and has been used to explain a broad range of different cellular phenomena [Balázsi et al. (Cell 144(6):910, 2011), Zeng et al. (Cell 141(4):682, 2010)]. Decision-making is also closely related to cellular information-processing because appropriate decisions cannot be made without exploiting the information that the external stimuli contain. Efficiency of information transduction and processing by intra-cellular networks determines the amount of information obtained, which in turn limits the efficiency of subsequent decision-making. Furthermore, information-processing itself can serve as another concept that is crucial for understanding of other biological processes than decision-making. In this work, we review recent theoretical developments on cellular decision-making and information-processing by focusing on the relation between these two concepts.

Hierarchical organization of noise generates spontaneous signal in Paramecium cell

In many cellular processes, spontaneous activities are often the basis for their functioning. Paramecium cells change their swimming direction under a homogeneous environment, which is induced by a spontaneous signal generation in the membrane electric potential. For such a spontaneous activity, a theoretical model has been proposed by Oosawa (2007) [Biosystems 88, 191-201.], in which intracellular noise is hierarchically organized from thermal fluctuations to spike-like large fluctuations, which induces a signal to change spontaneously the swimming direction. Our analysis of the model shows that the system is a kind of excitable media, in which a spike is induced by a stochastic fluctuation. We show conditions of channels properties to have a spike train.

Dynamics of intracellular information decoding

A variety of cellular functions are robust even to substantial intrinsic and extrinsic noise in intracellular reactions and the environment that could be strong enough to impair or limit them. In particular, of substantial importance is cellular decision-making in which a cell chooses a fate or behavior on the basis of information conveyed in noisy external signals. For robust decoding, the crucial step is filtering out the noise inevitably added during information transmission. As a minimal and optimal implementation of such an information decoding process, the autocatalytic phosphorylation and autocatalytic dephosphorylation (aPadP) cycle was recently proposed. Here, we analyze the dynamical properties of the aPadP cycle in detail. We describe the dynamical roles of the stationary and short-term responses in determining the efficiency of information decoding and clarify the optimality of the threshold value of the stationary response and its information-theoretical meaning. Furthermore, we investigate the robustness of the aPadP cycle against the receptor inactivation time and intrinsic noise. Finally, we discuss the relationship among information decoding with information-dependent actions, bet-hedging and network modularity.

Stochastic approaches in systems biology

The discrete and random occurrence of chemical reactions far from thermodynamic equilibrium, and low copy numbers of chemical species, in systems biology necessitate stochastic approaches. This review is an effort to give the reader a flavor of the most important stochastic approaches relevant to systems biology. Notions of biochemical reaction systems and the relevant concepts of probability theory are introduced side by side. This leads to an intuitive and easy-to-follow presentation of a stochastic framework for modeling subcellular biochemical systems. In particular, we make an effort to show how the notion of propensity, the chemical master equation (CME), and the stochastic simulation algorithm arise as consequences of the Markov property. Most stochastic modeling reviews focus on stochastic simulation approaches--the exact stochastic simulation algorithm and its various improvements and approximations. We complement this with an outline of an analytical approximation. The most common formulation of stochastic models for biochemical networks is the CME. Although stochastic simulations are a practical way to realize the CME, analytical approximations offer more insight into the influence of randomness on system's behavior. Toward that end, we cover the chemical Langevin equation and the related Fokker-Planck equation and the two-moment approximation (2MA). Throughout the text, two pedagogical examples are used to key illustrate ideas. With extensive references to the literature, our goal is to clarify key concepts and thereby prepare the reader for more advanced texts.

Computational limits to binary genes

We analyse the trade-off between the speed with which a gene can propagate information, the noise of its output and its metabolic cost. Our main finding is that for any given level of metabolic cost there is an optimal trade-off between noise and processing speed. Any system with a non-vanishing leak expression rate is suboptimal, i.e. it will exhibit higher noise and/or slower speed than leak-free systems with the same metabolic cost. We also show that there is an optimal Hill coefficient h which minimizes noise and metabolic cost at fixed speeds, and an optimal threshold K which minimizes noise.

Investigating the two-moment characterisation of subcellular biochemical networks

While ordinary differential equations (ODEs) form the conceptual framework for modelling many cellular processes, specific situations demand stochastic models to capture the influence of noise. The most common formulation of stochastic models for biochemical networks is the chemical master equation (CME). While stochastic simulations are a practical way to realise the CME, analytical approximations offer more insight into the influence of noise. Towards that end, the two-moment approximation (2MA) is a promising addition to the established analytical approaches including the chemical Langevin equation (CLE) and the related linear noise approximation (LNA). The 2MA approach directly tracks the mean and (co)variance which are coupled in general. This coupling is not obvious in CME and CLE and ignored by LNA and conventional ODE models. We extend previous derivations of 2MA by allowing (a) non-elementary reactions and (b) relative concentrations. Often, several elementary reactions are approximated by a single step. Furthermore, practical situations often require the use of relative concentrations. We investigate the applicability of the 2MA approach to the well-established fission yeast cell cycle model. Our analytical model reproduces the clustering of cycle times observed in experiments. This is explained through multiple resettings of M-phase promoting factor (MPF), caused by the coupling between mean and (co)variance, near the G2/M transition.