When the going gets tough: survival strategies and environmental signaling networks in Bacillus subtilis.

Regulatory pathways involving two-component histidine kinase/response regulator proteins of Bacillus subtilis are highly interconnected and form a signal transduction network controlling stationary-phase adaptive responses. These include chemotaxis and motility, degradative enzyme synthesis, antibiotic production, natural competence for DNA uptake, and sporulation. Many of these responses are mutually exclusive, with different control levels involving protein-environment, protein-protein and protein-DNA interactions, allowing the bacteria to adapt rapidly to environmental changes.     

Signal transduction in bacterial chemotaxis

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

Learning and evolution in bacterial taxis: an operational amplifier circuit modeling the computational dynamics of the prokaryotic 'two component system' protein network

Adaptive behavior in unicellular organisms (i.e., bacteria) depends on highly organized networks of proteins governing purposefully the myriad of molecular processes occurring within the cellular system. For instance, bacteria are able to explore the environment within which they develop by utilizing the motility of their flagellar system as well as a sophisticated biochemical navigation system that samples the environmental conditions surrounding the cell, searching for nutrients or moving away from toxic substances or dangerous physical conditions. In this paper we discuss how proteins of the intervening signal transduction network could be modeled as artificial neurons, simulating the dynamical aspects of the bacterial taxis. The model is based on the assumption that, in some important aspects, proteins can be considered as processing elements or McCulloch-Pitts artificial neurons that transfer and process information from the bacterium's membrane surface to the flagellar motor. This simulation of bacterial taxis has been carried out on a hardware realization of a McCulloch-Pitts artificial neuron using an operational amplifier. Based on the behavior of the operational amplifier we produce a model of the interaction between CheY and FliM, elements of the prokaryotic two component system controlling chemotaxis, as well as a simulation of learning and evolution processes in bacterial taxis. On the one side, our simulation results indicate that, computationally, these protein 'switches' are similar to McCulloch-Pitts artificial neurons, suggesting a bridge between evolution and learning in dynamical systems at cellular and molecular levels and the evolutive hardware approach. On the other side, important protein 'tactilizing' properties are not tapped by the model, and this suggests further complexity steps to explore in the approach to biological molecular computing.     

Spatial organization in bacterial chemotaxis

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

The Relation of Signal Transduction to the Sensitivity and Dynamic Range of Bacterial Chemotaxis

Complex networks of interacting molecular components of living cells are responsible for many important processes, such as signal processing and transduction. An important challenge is to understand how the individual properties of these molecular interactions and biochemical transformations determine the system-level properties of biological functions. Here, we address the issue of the accuracy of signal transduction performed by a bacterial chemotaxis system. The chemotaxis sensitivity of bacteria to a chemoattractant gradient has been measured experimentally from bacterial aggregation in a chemoattractant-containing capillary. The observed precision of the chemotaxis depended on environmental conditions such as the concentration and molecular makeup of the chemoattractant. In a quantitative model, we derived the chemotactic response function, which is essential to describing the signal transduction process involved in bacterial chemotaxis. In the presence of a gradient, an analytical solution is derived that reveals connections between the chemotaxis sensitivity and the characteristics of the signaling system, such as reaction rates. These biochemical parameters are integrated into two system-level parameters: one characterizes the efficiency of gradient sensing, and the other is related to the dynamic range of chemotaxis. Thus, our approach explains how a particular signal transduction property affects the system-level performance of bacterial chemotaxis. We further show that the two parameters can be derived from published experimental data from a capillary assay, which successfully characterizes the performance of bacterial chemotaxis.     

A protein domain-based interactome network for C. elegans early embryogenesis

Many protein-protein interactions are mediated through independently folding modular domains. Proteome-wide efforts to model protein-protein interaction or "interactome" networks have largely ignored this modular organization of proteins. We developed an experimental strategy to efficiently identify interaction domains and generated a domain-based interactome network for proteins involved in C. elegans early-embryonic cell divisions. Minimal interacting regions were identified for over 200 proteins, providing important information on their domain organization. Furthermore, our approach increased the sensitivity of the two-hybrid system, resulting in a more complete interactome network. This interactome modeling strategy revealed insights into C. elegans centrosome function and is applicable to other biological processes in this and other organisms.     

Interactions of a replication initiator with histone H1-like proteins remodel the condensed mitochondrial genome

Kinetoplast DNA (kDNA), the mitochondrial genome of trypanosomatids, consists of several thousand topologically interlocked DNA circles. Mitochondrial histone H1-like proteins were implicated in the condensation of kDNA into a nucleoid structure in the mitochondrial matrix. However, the mechanism that remodels kDNA, promoting its accessibility to the replication machinery, has not yet been described. Analyses, using yeast two hybrid system, co-immunoprecipitation, and protein-protein cross-linking, revealed specific protein-protein interactions between the kDNA replication initiator protein universal minicircle sequence-binding protein (UMSBP) and two mitochondrial histone H1-like proteins. Fluorescence and electron microscopy, as well as biochemical analyses, demonstrated that these protein-protein interactions result in the decondensation of kDNA. UMSBP-mediated decondensation rendered the kDNA network accessible to topological decatenation by topoisomerase II, yielding free kDNA minicircle monomers. Hence, UMSBP has the potential capacity to function in vivo in the activation of the prereplication release of minicircles from the network, a key step in kDNA replication, which precedes and enables its replication initiation. These observations demonstrate the prereplication remodeling of a condensed mitochondrial DNA, which is mediated via specific interactions of histone-like proteins with a replication initiator, rather than through their posttranslational covalent modifications.     

A new measure of the robustness of biochemical networks

MOTIVATION: The robustness of a biochemical network is defined as the tolerance of variations in kinetic parameters with respect to the maintenance of steady state. Robustness also plays an important role in the fail-safe mechanism in the evolutionary process of biochemical networks. The purposes of this paper are to use the synergism and saturation system (S-system) representation to describe a biochemical network and to develop a robustness measure of a biochemical network subject to variations in kinetic parameters. Since most biochemical networks in nature operate close to the steady state, we consider only the robustness measurement of a biochemical network at the steady state. RESULTS: We show that the upper bound of the tolerated parameter variations is related to the system matrix of a biochemical network at the steady state. Using this upper bound, we can calculate the tolerance (robustness) of a biochemical network without testing many parametric perturbations. We find that a biochemical network with a large tolerance can also better attenuate the effects of variations in rate parameters and environments. Compensatory parameter variations and network redundancy are found to be important mechanisms for the robustness of biochemical networks. Finally, four biochemical networks, such as a cascaded biochemical network, the glycolytic-glycogenolytic pathway in a perfused rat liver, the tricarboxylic acid cycle in Dictyostelium discoideum and the cAMP oscillation network in bacterial chemotaxis, are used to illustrate the usefulness of the proposed robustness measure.     

Comparative structural bioinformatics analysis of <Emphasis Type="Italic">Bacillus amyloliquefaciens</Emphasis> chemotaxis proteins within <Emphasis Type="Italic">Bacillus subtilis</Emphasis> group

Chemotaxis is a process in which bacteria sense their chemical environment and move towards more favorable conditions. Since plant colonization by bacteria is a multifaceted process which requires a response to the complex chemical environment, a finely tuned and sensitive chemotaxis system is needed. Members of the Bacillus subtilis group including Bacillus amyloliquefaciens are industrially important, for example, as bio-pesticides. The group exhibits plant growth-promoting characteristics, with different specificity towards certain host plants. Therefore, we hypothesize that while the principal molecular mechanisms of bacterial chemotaxis may be conserved, the bacterial chemotaxis system may need an evolutionary tweaking to adapt it to specific requirements, particularly in the process of evolution of free-living soil organisms, towards plant colonization behaviour. To date, almost nothing is known about what parts of the chemotaxis proteins are subjected to positive amino acid substitutions, involved in adjusting the chemotaxis system of bacteria during speciation. In this novel study, positively selected and purified sites of chemotaxis proteins were calculated, and these residues were mapped onto homology models that were built for the chemotaxis proteins, in an attempt to understand the spatial evolution of the chemotaxis proteins. Various positively selected amino acids were identified in semi-conserved regions of the proteins away from the known active sites.     

Studies of dynamic protein-protein interactions in bacteria using renilla luciferase complementation are undermined by nonspecific enzyme inhibition

The luciferase protein fragment complementation assay is a powerful tool for studying protein-protein interactions. Two inactive fragments of luciferase are genetically fused to interacting proteins, and when these two proteins interact, the luciferase fragments can reversibly associate and reconstitute enzyme activity. Though this technology has been used extensively in live eukaryotic cells, split luciferase complementation has not yet been applied to studies of dynamic protein-protein interactions in live bacteria. As proof of concept and to develop a new tool for studies of bacterial chemotaxis, fragments of Renilla luciferase (Rluc) were fused to the chemotaxis-associated response regulator CheY3 and its phosphatase CheZ in the enteric pathogen Vibrio cholerae. Luciferase activity was dependent on the presence of both CheY3 and CheZ fusion proteins, demonstrating the specificity of the assay. Furthermore, enzyme activity was markedly reduced in V. cholerae chemotaxis mutants, suggesting that this approach can measure defects in chemotactic signaling. However, attempts to measure changes in dynamic CheY3-CheZ interactions in response to various chemoeffectors were undermined by nonspecific inhibition of the full-length luciferase. These observations reveal an unexpected limitation of split Rluc complementation that may have implications for existing data and highlight the need for great caution when evaluating small molecule effects on dynamic protein-protein interactions using the split luciferase technology.     

Protein phosphorylation in the bacterial chemotaxis system

Bacterial chemotaxis involves the detection of changes in concentration of specific chemicals in the environment of the cell as a function of time. This process is mediated by a series of cell surface receptors that interact with and activate intracellular protein phosphorylation. Five cytoplasmic proteins essential for chemotaxis have been shown to be involved in a coupled system of protein phosphorylation. Ligand binding to cell surface receptors affects the rate of autophosphorylation of the CheA protein. In the absence of an attractant bound to receptor and in the presence of the CheW protein, the rate of CheA autophosphorylation is markedly increased. Phosphorylated CheA can transfer phosphate to the CheY or CheB proteins; phosphorylation of these "effector" proteins may increase their activity. The CheY protein is thought to regulate flagellar rotation and thus control swimming behavior. The CheB protein modifies the cell surface receptor and thus regulates receptor function. Finally, another chemotaxis protein, CheZ, acts to specifically dephosphorylate CheY-phosphate. This system shows marked similarity to the 2-component sensor-regulator systems found to control specific gene expression in a variety of bacteria.     

Foreword: The structural biology of membrane proteins

Membrane protein-protein interactions are important for regulation, targeting, and activity of proteins in membranes but are difficult to detect and analyse. This review covers current approaches to studying membrane protein interactions. In addition to standard biochemical and genetic techniques, the classic yeast nuclear two-hybrid system has been highly successful in identification and characterization of soluble protein-protein interactions. However, classic yeast two-hybrid assays do not work for membrane proteins because such yeast-based interactions must occur in the nucleus. Here, we highlight recent advances in yeast systems for the detection and characterization of eukaryote membrane protein-protein interactions. We discuss these implications for drug screening and discovery.     

Simulating the evolution of signal transduction pathways

We use a generic model of a network of proteins that can activate or deactivate each other to explore the emergence and evolution of signal transduction networks and to gain a basic understanding of their general properties. Starting with a set of non-interacting proteins, we evolve a signal transduction network by random mutation and selection to fulfill a complex biological task. In order to validate this approach we base selection on a fitness function that captures the essential features of chemotactic behavior as seen in bacteria. We find that a system of as few as three proteins can evolve into a network mediating chemotaxis-like behavior by acting as a "derivative sensor". Furthermore, we find that the dynamics and topology of such networks show many similarities to the natural chemotaxis pathway, that the response magnitude can increase with increasing network size and that network behavior shows robustness towards variations in some of the internal parameters. We conclude that simulating the evolution of signal transduction networks to mediate a certain behavior may be a promising approach for understanding the general properties of the natural pathway for that behavior.     

On the path to uncover the bacterial type II secretion system

Gram-negative bacteria have evolved several secretory pathways to release enzymes or toxins into the surrounding environment or into the target cells. The type II secretion system (T2SS) is conserved in Gram-negative bacteria and involves a set of 12 to 16 different proteins. Components of the T2SS are located in both the inner and outer membranes where they assemble into a supramolecular complex spanning the bacterial envelope, also called the secreton. The T2SS substrates transiently go through the periplasm before they are translocated across the outer membrane and exposed to the extracellular milieu. The T2SS is unique in its ability to promote secretion of large and sometimes multimeric proteins that are folded in the periplasm. The present review describes recently identified protein-protein interactions together with structural and functional advances in the field that have contributed to improve our understanding on how the type II secretion apparatus assembles and on the role played by individual proteins of this highly sophisticated system.     

Extracellular noise-induced stochastic synchronization in heterogeneous quorum sensing network

Quorum sensing is a bacterial mechanism used to synchronize the coordinated response of a microbial population. Because quorum sensing in Gram-negative bacteria depends on release and detection of a diffusible signaling molecule (autoinducer) among a multicellular group, it is considered a simple form of cell-cell communication for the purposes of mathematical analysis. Stochastic equation systems have provided a common approach to model biochemical or biophysical processes. Recently, the effect of noise to synchronize a specific homogeneous quorum sensing network was successfully modeled using a stochastic equation system with fixed parameters. The question remains of how to model quorum sensing networks in a general setting. To address this question, we first set a stochastic equation system as a general model for a heterogeneous quorum sensing network. Then, using two relevant biophysical characteristics of Gram-negative bacteria (the permeability of the cell membrane to the autoinducer and the symmetry of autoinducer diffusion) we construct the solution of the stochastic equation system at an abstract level. The solution indicates that stable synchronization of a quorum sensing network is robustly induced by an environment with a heterogenous distribution of extracellular and intracellular noise. The synchronization is independent of the initial state of the system and is solely the result of the connectivity of the cell network established through the effects of extracellular noise.     

Gastric cancer biomarkers; A systems biology approach

Gastric cancer is one of the most fatal cancers in the world. Many efforts in recent years have attempted to find effective proteins in gastric cancer. By using a comprehensive list of proteins involved in gastric cancer, scientists were able to retrieve interaction information. The study of protein-protein interaction networks through systems biology based analysis provides appropriate strategies to discover candidate proteins and key biological pathways.In this study, we investigated dominant functional themes and centrality parameters including betweenness as well as the degree of each topological clusters and expressionally active sub-networks in the resulted network. The results of functional analysis on gene sets showed that neurotrophin signaling pathway, cell cycle and nucleotide excision possess the strongest enrichment signals. According to the computed centrality parameters, HNF4A, TAF1 and TP53 manifested as the most significant nodes in the interaction network of the engaged proteins in gastric cancer. This study also demonstrates pathways and proteins that are applicable as diagnostic markers and therapeutic targets for future attempts to overcome gastric cancer.     

Global protein-protein interaction network in the human pathogen Mycobacterium tuberculosis H37Rv

Analysis of the protein-protein interaction network of a pathogen is a powerful approach for dissecting gene function, potential signal transduction, and virulence pathways. This study looks at the construction of a global protein-protein interaction (PPI) network for the human pathogen Mycobacterium tuberculosis H37Rv, based on a high-throughput bacterial two-hybrid method. Almost the entire ORFeome was cloned, and more than 8000 novel interactions were identified. The overall quality of the PPI network was validated through two independent methods, and a high success rate of more than 60% was obtained. The parameters of PPI networks were calculated. The average shortest path length was 4.31. The topological coefficient of the M. tuberculosis B2H network perfectly followed a power law distribution (correlation = 0.999; R-squared = 0.999) and represented the best fit in all currently available PPI networks. A cross-species PPI network comparison revealed 94 conserved subnetworks between M. tuberculosis and several prokaryotic organism PPI networks. The global network was linked to the protein secretion pathway. Two WhiB-like regulators were found to be highly connected proteins in the global network. This is the first systematic noncomputational PPI data for the human pathogen, and it provides a useful resource for studies of infection mechanisms, new signaling pathways, and novel antituberculosis drug development.     

Global Network Analysis of Lipid-Raft-Related Proteins Reveals Their Centrality in the Network and Their Roles in Multiple Biological Processes

Lipid rafts are specialized cholesterol-enriched microdomains in the cell membrane. They have been known as a platform for protein-protein interactions and to take part in multiple biological processes. Nevertheless, how lipid rafts influence protein properties at the proteomic level is still an open question for researchers using traditional biochemical approaches. Here, by annotating the lipid raft localization of proteins in human protein-protein interaction networks, we performed a systematic analysis of the function of proteins related to lipid rafts. Our results demonstrated that lipid raft proteins and their interactions were critical for the structure and stability of the whole network, and that the interactions between them were significantly enriched. Furthermore, for each protein in the network, we calculated its “lipid raft dependency (LRD),” which indicates how close it is topologically associated with lipid rafts, and we then uncovered the connection between LRD and protein functions. Proteins with high LRD tended to be essential for mammalian development, and malfunction of these proteins was inclined to cause human diseases. Coordinated with their neighbors, high-LRD proteins participated in multiple biological processes and targeted many pathways in diseases pathogenesis. High-LRD proteins were also found to have tissue specificity of expression. In summary, our network-based analysis denotes that lipid raft proteins have higher centrality in the network, and that lipid-raft-related proteins have multiple functions and are probably concerned with many biological processes in disease development.