Signal transduction in bacteria: phospho-neural network(s) in Escherichia coli?

Living cells and biological mechanisms as prototypes for developing chemical artificial intelligence

Artificial Intelligence (AI) is having a revolutionary impact on our societies. It is helping humans in facing the global challenges of this century. Traditionally, AI is developed in software or through neuromorphic engineering in hardware. More recently, a brand-new strategy has been proposed. It is the so-called Chemical AI (CAI), which exploits molecular, supramolecular, and systems chemistry in wetware to mimic human intelligence. In this work, two promising approaches for boosting CAI are described. One regards designing and implementing neural surrogates that can communicate through optical or chemical signals and give rise to networks for computational purposes and to develop micro/nanorobotics. The other approach concerns "bottom-up synthetic cells" that can be exploited for applications in various scenarios, including future nano-medicine. Both topics are presented at a basic level, mainly to inform the broader audience of non-specialists, and so favour the rise of interest in these frontier subjects.

Cell consciousness: a dissenting opinion : The cellular basis of consciousness theory lacks empirical evidence for its claims that all cells have consciousness

The proponents of CBC claim that all living organisms down to prokaryotes have consciousness. However, their arguments lack empirical evidence or are refuted by established facts.

Chemical Systems for Wetware Artificial Life: Selected Perspectives in Synthetic Cell Research

The recent and important advances in bottom-up synthetic biology (SB), in particular in the field of the so-called "synthetic cells" (SCs) (or "artificial cells", or "protocells"), lead us to consider the role of wetware technologies in the "Sciences of Artificial", where they constitute the third pillar, alongside the more well-known pillars hardware (robotics) and software (Artificial Intelligence, AI). In this article, it will be highlighted how wetware approaches can help to model life and cognition from a unique perspective, complementary to robotics and AI. It is suggested that, through SB, it is possible to explore novel forms of bio-inspired technologies and systems, in particular chemical AI. Furthermore, attention is paid to the concept of semantic information and its quantification, following the strategy recently introduced by Kolchinsky and Wolpert. Semantic information, in turn, is linked to the processes of generation of "meaning", interpreted here through the lens of autonomy and cognition in artificial systems, emphasizing its role in chemical ones.

Explorative Synthetic Biology in AI: Criteria of Relevance and a Taxonomy for Synthetic Models of Living and Cognitive Processes

This article tackles the topic of the special issue "Biology in AI: New Frontiers in Hardware, Software and Wetware Modeling of Cognition" in two ways. It addresses the problem of the relevance of hardware, software, and wetware models for the scientific understanding of biological cognition, and it clarifies the contributions that synthetic biology, construed as the synthetic exploration of cognition, can offer to artificial intelligence (AI). The research work proposed in this article is based on the idea that the relevance of hardware, software, and wetware models of biological and cognitive processes-that is, the concrete contribution that these models can make to the scientific understanding of life and cognition-is still unclear, mainly because of the lack of explicit criteria to assess in what ways synthetic models can support the experimental exploration of biological and cognitive phenomena. Our article draws on elements from cybernetic and autopoietic epistemology to define a framework of reference, for the synthetic study of life and cognition, capable of generating a set of assessment criteria and a classification of forms of relevance, for synthetic models, able to overcome the sterile, traditional polarization of their evaluation between mere imitation and full reproduction of the target processes. On the basis of these tools, we tentatively map the forms of relevance characterizing wetware models of living and cognitive processes that synthetic biology can produce and outline a programmatic direction for the development of "organizationally relevant approaches" applying synthetic biology techniques to the investigative field of (embodied) AI.

A Role for Bottom-Up Synthetic Cells in the Internet of Bio-Nano Things?

The potential role of bottom-up Synthetic Cells (SCs) in the Internet of Bio-Nano Things (IoBNT) is discussed. In particular, this perspective paper focuses on the growing interest in networks of biological and/or artificial objects at the micro- and nanoscale (cells and subcellular parts, microelectrodes, microvessels, etc.), whereby communication takes place in an unconventional manner, i.e., via chemical signaling. The resulting "molecular communication" (MC) scenario paves the way to the development of innovative technologies that have the potential to impact biotechnology, nanomedicine, and related fields. The scenario that relies on the interconnection of natural and artificial entities is briefly introduced, highlighting how Synthetic Biology (SB) plays a central role. SB allows the construction of various types of SCs that can be designed, tailored, and programmed according to specific predefined requirements. In particular, "bottom-up" SCs are briefly described by commenting on the principles of their design and fabrication and their features (in particular, the capacity to exchange chemicals with other SCs or with natural biological cells). Although bottom-up SCs still have low complexity and thus basic functionalities, here, we introduce their potential role in the IoBNT. This perspective paper aims to stimulate interest in and discussion on the presented topics. The article also includes commentaries on MC, semantic information, minimal cognition, wetware neuromorphic engineering, and chemical social robotics, with the specific potential they can bring to the IoBNT.

Programming and training rate-independent chemical reaction networks

To program complex behavior in environments incompatible with electronic controllers such as within bioreactors or engineered cells, we turn to chemical information processors. While chemical reactions can perform computation in the stoichiometric exchange of reactants for products (how many molecules of which reactants result in how many molecules of which products), the control of reaction rates is usually thought to allow more complex computation. Motivated by the fact that correct stoichiometry is easier to ensure than reaction rates, we provide a method for programming and training chemical computation by stoichiometry. We show that such computation can be straightforwardly programmed in a manner analogous to sequential programming, and demonstrate the execution of neural networks capable of complex machine learning tasks.

Embedding computation in biochemical environments incompatible with traditional electronics is expected to have a wide-ranging impact in synthetic biology, medicine, nanofabrication, and other fields. Natural biochemical systems are typically modeled by chemical reaction networks (CRNs) which can also be used as a specification language for synthetic chemical computation. In this paper, we identify a syntactically checkable class of CRNs called noncompetitive (NC) whose equilibria are absolutely robust to reaction rates and kinetic rate law, because their behavior is captured solely by their stoichiometric structure. In spite of the inherently parallel nature of chemistry, the robustness property allows for programming as if each reaction applies sequentially. We also present a technique to program NC-CRNs using well-founded deep learning methods, showing a translation procedure from rectified linear unit (ReLU) neural networks to NC-CRNs. In the case of binary weight ReLU networks, our translation procedure is surprisingly tight in the sense that a single bimolecular reaction corresponds to a single ReLU node and vice versa. This compactness argues that neural networks may be a fitting paradigm for programming rate-independent chemical computation. As proof of principle, we demonstrate our scheme with numerical simulations of CRNs translated from neural networks trained on traditional machine learning datasets, as well as tasks better aligned with potential biological applications including virus detection and spatial pattern formation.

Signal transduction schemes in Pseudomonas syringae

To cope with their continually fluctuating surroundings, pathovars of the unicellular phytopathogen Pseudomonas syringae have developed rapid and sophisticated signalling networks to sense extracellular stimuli, which allow them to adjust their cellular composition to survive and cause diseases in host plants. Comparative genomic analyses of P. syringae strains have identified various genes that encode several classes of signalling proteins, although how this bacterium directly perceives these environmental cues remains elusive. Recent work has revealed new mechanisms of a cluster of bacterial signal transduction systems that mainly include two-component systems (such as RhpRS, GacAS, CvsRS and AauRS), extracytoplasmic function sigma factors (such as HrpL and AlgU), nucleotide-based secondary messengers, methyl-accepting chemotaxis sensor proteins and several other intracellular surveillance systems. In this review, we compile a list of the signal transduction mechanisms that P. syringae uses to monitor and respond in a timely manner to intracellular and external conditions. Further understanding of these surveillance processes will provide new perspectives from which to combat P. syringae infections.

Two Component Regulatory Systems and Antibiotic Resistance in Gram-Negative Pathogens

Gram-negative pathogens such as Klebsiella pneumoniae, Acinetobacter baumannii, and Pseudomonas aeruginosa are the leading cause of nosocomial infections throughout the world. One commonality shared among these pathogens is their ubiquitous presence, robust host-colonization and most importantly, resistance to antibiotics. A significant number of two-component systems (TCSs) exist in these pathogens, which are involved in regulation of gene expression in response to environmental signals such as antibiotic exposure. While the development of antimicrobial resistance is a complex phenomenon, it has been shown that TCSs are involved in sensing antibiotics and regulating genes associated with antibiotic resistance. In this review, we aim to interpret current knowledge about the signaling mechanisms of TCSs in these three pathogenic bacteria. We further attempt to answer questions about the role of TCSs in antimicrobial resistance. We will also briefly discuss how specific two-component systems present in K. pneumoniae, A. baumannii, and P. aeruginosa may serve as potential therapeutic targets.

Characterization of the Direct Interaction between Hybrid Sensor Kinases PA1611 and RetS That Controls Biofilm Formation and the Type III Secretion System in Pseudomonas aeruginosa

One of the leading causes of morbidity and mortality in cystic fibrosis (CF) patients is pulmonary infection with Pseudomonas aeruginosa, and the pathophysiology of pulmonary infection in CF is affected by the lifestyle of this micro-organism. RetS-GacS/A-RsmA is a key regulatory pathway in P. aeruginosa that determines the bacterium's lifestyle choice. Previously, we identified PA1611, a hybrid sensor kinase, as a new player in this pathway that interacts with RetS and influences biofilm formation and type III secretion system. In this study, we explored the structural and mechanistic basis of the interaction between PA1611 and RetS. We identified the amino acid residues critical for PA1611-RetS interactions by molecular modeling. These residues were then targeted for site-directed mutagenesis. Amino acid substitutions were carried out at seven key positions in PA1611 and at six corresponding key positions in RetS. The influence of such substitutions in PA1611 on the interaction was analyzed by bacterial two-hybrid assays. We carried out functional analysis of these mutants in P. aeruginosa for their effect on specific phenotypes. Two residues, F269 and E276, located within the histidine kinase A and histidine kinase-like ATPase domains of PA1611 were found to play crucial roles in the PA1611-RetS interaction and had profound effects on phenotypes. Corresponding mutations in RetS demonstrated similar results. We further confirmed that these mutations in PA1611 function through the GacS/GacA-RsmY/Z signaling pathway. Collectively, our findings provide a noncognate sensor kinase direct interaction model for a signaling pathway, key for lifestyle selection in P. aeruginosa, and targeting such interaction may serve as a novel way of controlling infections with P. aeruginosa.

Cross-talk and specificity in two-component signal transduction pathways

Two-component signaling systems (TCSs) are composed of two proteins, sensor kinases and response regulators, which can cross-talk and integrate information between them by virtue of high-sequence conservation and modular nature, to generate concerted and diversified responses. However, TCSs have been shown to be insulated, to facilitate linear signal transmission and response generation. Here, we discuss various mechanisms that confer specificity or cross-talk among TCSs. The presented models are supported with evidence that indicate the physiological significance of the observed TCS signaling architecture. Overall, we propose that the signaling topology of any TCSs cannot be predicted using obvious sequence or structural rules, as TCS signaling is regulated by multiple factors, including spatial and temporal distribution of the participating proteins.

The cognitive cell: bacterial behavior reconsidered

Research on how bacteria adapt to changing environments underlies the contemporary biological understanding of signal transduction (ST), and ST provides the foundation of the information-processing approach that is the hallmark of the ‘cognitive revolution,’ which began in the mid-20th century. Yet cognitive scientists largely remain oblivious to research into microbial behavior that might provide insights into problems in their own domains, while microbiologists seem equally unaware of the potential importance of their work to understanding cognitive capacities in multicellular organisms, including vertebrates. Evidence in bacteria for capacities encompassed by the concept of cognition is reviewed. Parallels exist not only at the heuristic level of functional analogue, but also at the level of molecular mechanism, evolution and ecology, which is where fruitful cross-fertilization among disciplines might be found.

Macromolecular networks and intelligence in microorganisms

Living organisms persist by virtue of complex interactions among many components organized into dynamic, environment-responsive networks that span multiple scales and dimensions. Biological networks constitute a type of information and communication technology (ICT): they receive information from the outside and inside of cells, integrate and interpret this information, and then activate a response. Biological networks enable molecules within cells, and even cells themselves, to communicate with each other and their environment. We have become accustomed to associating brain activity - particularly activity of the human brain - with a phenomenon we call "intelligence." Yet, four billion years of evolution could have selected networks with topologies and dynamics that confer traits analogous to this intelligence, even though they were outside the intercellular networks of the brain. Here, we explore how macromolecular networks in microbes confer intelligent characteristics, such as memory, anticipation, adaptation and reflection and we review current understanding of how network organization reflects the type of intelligence required for the environments in which they were selected. We propose that, if we were to leave terms such as "human" and "brain" out of the defining features of "intelligence," all forms of life - from microbes to humans - exhibit some or all characteristics consistent with "intelligence." We then review advances in genome-wide data production and analysis, especially in microbes, that provide a lens into microbial intelligence and propose how the insights derived from quantitatively characterizing biomolecular networks may enable synthetic biologists to create intelligent molecular networks for biotechnology, possibly generating new forms of intelligence, first in silico and then in vivo.

Nitrogen assimilation in Escherichia coli: putting molecular data into a systems perspective

We present a comprehensive overview of the hierarchical network of intracellular processes revolving around central nitrogen metabolism in Escherichia coli. The hierarchy intertwines transport, metabolism, signaling leading to posttranslational modification, and transcription. The protein components of the network include an ammonium transporter (AmtB), a glutamine transporter (GlnHPQ), two ammonium assimilation pathways (glutamine synthetase [GS]-glutamate synthase [glutamine 2-oxoglutarate amidotransferase {GOGAT}] and glutamate dehydrogenase [GDH]), the two bifunctional enzymes adenylyl transferase/adenylyl-removing enzyme (ATase) and uridylyl transferase/uridylyl-removing enzyme (UTase), the two trimeric signal transduction proteins (GlnB and GlnK), the two-component regulatory system composed of the histidine protein kinase nitrogen regulator II (NRII) and the response nitrogen regulator I (NRI), three global transcriptional regulators called nitrogen assimilation control (Nac) protein, leucine-responsive regulatory protein (Lrp), and cyclic AMP (cAMP) receptor protein (Crp), the glutaminases, and the nitrogen-phosphotransferase system. First, the structural and molecular knowledge on these proteins is reviewed. Thereafter, the activities of the components as they engage together in transport, metabolism, signal transduction, and transcription and their regulation are discussed. Next, old and new molecular data and physiological data are put into a common perspective on integral cellular functioning, especially with the aim of resolving counterintuitive or paradoxical processes featured in nitrogen assimilation. Finally, we articulate what still remains to be discovered and what general lessons can be learned from the vast amounts of data that are available now.

Deep evolutionary origins of neurobiology: Turning the essence of 'neural' upside-down

It is generally assumed, both in common-sense argumentations and scientific concepts, that brains and neurons represent late evolutionary achievements which are present only in more advanced animals. Here we overview recently published data clearly revealing that our understanding of bacteria, unicellular eukaryotic organisms, plants, brains and neurons, rooted in the Aristotelian philosophy is flawed. Neural aspects of biological systems are obvious already in bacteria and unicellular biological units such as sexual gametes and diverse unicellular eukaryotic organisms. Altogether, processes and activities thought to represent evolutionary 'recent' specializations of the nervous system emerge rather to represent ancient and fundamental cell survival processes.

Linking the proteins--elucidation of proteome-scale networks using mass spectrometry

Proteomes are intricate. Typically, thousands of proteins interact through physical association and post-translational modifications (PTMs) to give rise to the emergent functions of cells. Understanding these functions requires one to study proteomes as "systems" rather than collections of individual protein molecules. The abstraction of the interacting proteome to "protein networks" has recently gained much attention, as networks are effective representations, that lose specific molecular details, but provide the ability to see the proteome as a whole. Mostly two aspects of the proteome have been represented by network models: proteome-wide physical protein-protein-binding interactions organized into Protein Interaction Networks (PINs), and proteome-wide PTM relations organized into Protein Signaling Networks (PSNs). Mass spectrometry (MS) techniques have been shown to be essential to reveal both of these aspects on a proteome-wide scale. Techniques such as affinity purification followed by MS have been used to elucidate protein-protein interactions, and MS-based quantitative phosphoproteomics is critical to understand the structure and dynamics of signaling through the proteome. We here review the current state-of-the-art MS-based analytical pipelines for the purpose to characterize proteome-scale networks.

Analyzing longitudinal microbial metabolomics data

A longitudinal experimental design in combination with metabolomics and multiway data analysis is a powerful approach in the identification of metabolites whose correlation with bioproduct formation shows a shift in time. In this paper, a strategy is presented for the analysis of longitudinal microbial metabolomics data, which was performed in order to identify metabolites that are likely inducers of phenylalanine production by Escherichia coli. The variation in phenylalanine production as a function of differences in metabolism induced by the different environmental conditions in time was described by a validated multiway statistical model. Notably, most of the metabolites showing the strongest relations with phenylalanine production seemed to hardly change in time. Apparently, potential bottlenecks in phenylalanine seem to hardly change in the course of a batch fermentation. The approach described in this study is not limited to longitudinal microbial studies but can also be applied to other (biological) studies in which similar longitudinal data need to be analyzed.

Kinetic buffering of cross talk between bacterial two-component sensors

Two-component systems are a class of sensors that enable bacteria to respond to environmental and cell-state signals. The canonical system consists of a membrane-bound sensor histidine kinase that autophosphorylates in response to a signal and transfers the phosphate to an intracellular response regulator. Bacteria typically have dozens of two-component systems. The key questions are whether these systems are linear and, if they are, how cross talk between systems is buffered. In this work, we studied the EnvZ/OmpR and CpxA/CpxR systems from Escherichia coli, which have been shown previously to exhibit slow cross talk in vitro. Using in vitro radiolabeling and a rapid quenched-flow apparatus, we experimentally measured 10 biochemical parameters capturing the cognate and non-cognate phosphotransfer reactions between the systems. These data were used to parameterize a mathematical model that was used to predict how cross talk is affected as different genes are knocked out. It was predicted that significant cross talk between EnvZ and CpxR only occurs for the triple mutant DeltaompR DeltacpxA DeltaactA-pta. All seven combinations of these knockouts were made to test this prediction and only the triple mutant demonstrated significant cross talk, where the cpxP promoter was induced 280-fold upon the activation of EnvZ. Furthermore, the behavior of the other knockouts agrees with the model predictions. These results support a kinetic model of buffering where both the cognate bifunctional phosphatase activity and the competition between regulator proteins for phosphate prevent cross talk in vivo.

Cross-talk suppression between the CpxA-CpxR and EnvZ-OmpR two-component systems in E. coli

Many bacteria possess large numbers of two-component signalling systems, which are composed of histidine kinase-response regulator pairs. The high level of sequence similarity between some systems raises the possibility of undesired cross-talk between a histidine kinase and a non-cognate response regulator. Although molecular specificity ensures that phospho-transfer occurs primarily between correct partners, even a low level of inappropriate cross-talk could lead to unacceptable levels of noise or interference in signal transduction. To explore mechanisms that provide insulation against such interference, we have examined cross-talk between the histidine kinase CpxA and non-cognate response regulator OmpR in Escherichia coli. Our results show that there are two mechanisms that suppress cross-talk between these two proteins, which depend on the corresponding cognate partners CpxR and EnvZ and on the bifunctional nature of the histidine kinases CpxA and EnvZ. When cross-talk is detectable, we find it is independent of CpxA stimulus. We also show that cross-talk suppression leads to mutational robustness, i.e. it masks the effects of mutations that would otherwise lead to increased cross-talk. The mechanisms that provide insulation against interference described here may be applicable to many other two-component systems.

Novel gene members in the Pho regulon of Escherichia coli

The transcriptome profiles of the wild-type and the phoB mutant strains were compared at the time point showing the highest expression levels of the phoB and phoR genes under a P-limiting condition. Among the 18 new putative genes that were found to be under the control of the PhoB transcriptional regulator, five genes that contain the consensus Pho box were identified by sequence analysis. A reporter gene assay was carried out by fusing the upstream regions of these genes to the promoterless enhanced green fluorescent protein gene, followed by expression. It was found that the expressions of the amn (AMP nucleosidase), yibD (metal ion stress response gene) and ytfK (hypothetical protein) genes were activated by PhoB. These results indicate the additional roles of PhoB as a global regulator.

The role of two-component regulation systems in the physiology of the bacterial cell

Two-component regulation systems (TCRSs) are the dominant type of signal transduction system in prokaryotes that are used to inform the cellular trancriptional machinery (and additional targets for regulation, like the motility apparatus) about actual changes in the extracellular physicochemical conditions. We now review their molecular structure and enzymatic characteristics, their mutual interactions and its implications, and their role in cellular physiology. Specific emphasis is placed on the ArcB/A system, a representative of the phosphorelay type of TCRS, and a key player in the adjustment of the cellular make-up of enterobacteria in response to alterations in the oxygen availability. Also some applied aspects of the TCRSs are discussed, i.e. their role as a target to develop new anti-bacterials and their application in biotechnology (or: 'synthetic biology').

Green plants as intelligent organisms

Intelligent behaviour, even in humans, is an aspect of complex adaptive behaviour that provides a capacity for problem solving. This article assesses whether plants have a capacity to solve problems and, therefore, could be classified as intelligent organisms. The complex molecular network that is found in every plant cell and underpins plant behaviour is described. The problems that many plants face and that need solution are briefly outlined, and some of the kinds of behaviour used to solve these problems are discussed. A simple way of comparing plant intelligence between two genotypes is illustrated and some of the objections raised against the idea of plant intelligence are considered but discarded. It is concluded that plants exhibit the simple forms of behaviour that neuroscientists describe as basic intelligence.

Bacterial observations: a rudimentary form of intelligence?

Genome sequencing has revealed that signal transduction in bacteria makes use of a limited number of different devices, such as two-component systems, LuxI-LuxR quorum-sensing systems, phosphodiesterases, Ser-Thr (serine-threonine) kinases, OmpR-type regulators, and sigma factor-anti-sigma factor pathways. These systems use modular proteins with a large variety of input and output domains, yet strikingly conserved transmission domains. This conservation might lead to redundancy of output function, for example, via crosstalk (i.e. phosphoryl transfer from a non-cognate sensory kinase). The number of similar devices in a single cell, particularly of the two-component type, might amount to several dozen, and most of these operate in parallel. This could bestow bacteria with cellular intelligence if the network of two-component systems in a single cell fulfils the requirements of a neural network. Testing these ideas poses a great challenge for prokaryotic systems biology.

Synergistic activation of signalling to extracellular signal-regulated kinases 1 and 2 by epidermal growth factor and 4β-phorbol 12-myristate 13-acetate

Signal transduction pathways are often embedded in complex networks, which result from interactions between pathways and feedback circuitry. In order to understand such networks, qualitative information on which interactions take place and quantitative data on their strength become essential. Here, we have investigated how the multiple interactions between the mitogen-activated protein kinase cascade and protein kinase C (PKC) affect the time profile of extracellular signal-regulated kinase (ERK) phosphorylation upon epidermal growth factor (EGF) stimulation in normal rat kidney fibroblasts. This profile is a major determinant for the cellular response that is evoked. We found that EGF stimulation leads to a biphasic ERK-PP pattern, consisting of an initial peak and a relaxation to a low quasi-steady state-phase. Costimulation with the EGF and PKC activator, 4 beta-phorbol 12-myristate 13-acetate (PMA) resulted in a similar pattern, but the ERK-PP concentration in the quasi-steady state-phase was synergistically higher than after stimulation with either EGF or PMA only. This resulted in prolonged signalling to ERK. PMA increased the EGF concentration sufficient to obtain half-maximum ERK phosphorylation. These data suggest that PKC amplifies EGF-induced signalling to ERK, without increasing its sensitivity to low EGF concentrations. Furthermore, PKC inhibition did not affect the ERK-PP time profile upon EGF stimulation and a cellular phospholipase A2 (cPLA(2)) inhibitor did not decrease the synergistic effect of EGF and PMA. This indicates that the positive feedback loop from ERK to Raf via cPLA(2) and PKC does not contribute significantly to signalling from EGF to ERK in normal rat kidney cells. Taken together, we provide a quantitative description of which reported interactions in this network affect the time profile of ERK phosphorylation.

Think like a bacterium. Conference on bacterial neural networks

Conference on Bacterial Neural Networks

Selectivity in overlapping MAP kinase cascades

Some protein kinases operate in more than one mitogen-activated protein-kinase (MAPK) cascade. We here address the question whether specificity of the cascades necessitates physical sequestration of these "promiscuous" kinases (e.g. by binding to scaffolds). A model is constructed, in which two MAPK cascades depend on a single MAP-kinase kinase that is not sequestered in two subpopulations. We show that in this model selective signal transduction is possible provided that there is an additional interaction at the MAP-kinase level, there is no simultaneous activation of more than one response by either signal. We discuss a number of additional interactions that can generate the selectivity, as well as some kinetic features by which this mechanism may be recognized experimentally.

Autoamplification of a two-component regulatory system results in "learning" behavior

We have tested the hypothesis that the autoamplification of two-component regulatory systems results in "learning" behavior, i.e., that bacteria respond faster or more extensively to a signal when a similar signal has been perceived in the past. Indeed, the induction of alkaline phosphatase activity upon phosphate limitation was faster if the cultures had been limited for phosphate previously, and this faster response correlated with the autoamplification of the cognate two-component system.

Understanding glucose transport by the bacterial phosphoenolpyruvate:glycose phosphotransferase system on the basis of kinetic measurements in vitro

The kinetic parameters in vitro of the components of the phosphoenolpyruvate:glycose phosphotransferase system (PTS) in enteric bacteria were collected. To address the issue of whether the behavior in vivo of the PTS can be understood in terms of these enzyme kinetics, a detailed kinetic model was constructed. Each overall phosphotransfer reaction was separated into two elementary reactions, the first entailing association of the phosphoryl donor and acceptor into a complex and the second entailing dissociation of the complex into dephosphorylated donor and phosphorylated acceptor. Literature data on the K(m) values and association constants of PTS proteins for their substrates, as well as equilibrium and rate constants for the overall phosphotransfer reactions, were related to the rate constants of the elementary steps in a set of equations; the rate constants could be calculated by solving these equations simultaneously. No kinetic parameters were fitted. As calculated by the model, the kinetic parameter values in vitro could describe experimental results in vivo when varying each of the PTS protein concentrations individually while keeping the other protein concentrations constant. Using the same kinetic constants, but adjusting the protein concentrations in the model to those present in cell-free extracts, the model could reproduce experiments in vitro analyzing the dependence of the flux on the total PTS protein concentration. For modeling conditions in vivo it was crucial that the PTS protein concentrations be implemented at their high in vivo values. The model suggests a new interpretation of results hitherto not understood; in vivo, the major fraction of the PTS proteins may exist as complexes with other PTS proteins or boundary metabolites, whereas in vitro, the fraction of complexed proteins is much smaller.

Metabolic networks: a signal-oriented approach to cellular models

Complete genomes, far advanced proteomes, and even 'metabolomes' are available for at least a few organisms, e.g., Escherichia coli. Systematic functional analyses of such complete data sets will produce a wealth of information and promise an understanding of the dynamics of complex biological networks and perhaps even of entire living organisms. Such complete and holistic descriptions of biological systems, however, will increasingly require a quantitative analysis and the help of mathematical models for simulating whole systems. In particular, new procedures are required that allow a meaningful reduction of the information derived from complex systems that will consequently be used in the modeling process. In this review the biological elements of such a modeling procedure will be described. In a first step, complex living systems must be structured into well-defined and clearly delimited functional units, the elements of which have a common physiological goal, belong to a single genetic unit, and respond to the signals of a signal transduction system that senses changes in physiological states of the organism. These functional units occur at each level of complexity and more complex units originate by grouping several lower level elements into a single, more complex unit. To each complexity level corresponds a global regulator that is epistatic over lower level regulators. After its structuring into modules (functional units), a biological system is converted in a second step into mathematical submodels that by progressive combination can also be assembled into more aggregated model structures. Such a simplification of a cell (an organism) reduces its complexity to a level amenable to present modeling capacities. The universal biochemistry, however, promises a set of rules valid for modeling biological systems, from unicellular microorganisms and cells, to multicellular organisms and to populations.

Macromolecular intelligence in microorganisms

Biochemistry and molecular biology have been focusing on the structural, catalytic, and regulatory properties of individual macromolecules from the perspective of clarifying the mechanisms of metabolism and gene expression. Complete genomes of 'primitive' living organisms seem to be substantially larger than necessary for metabolism and gene expression alone. This is in line with the findings of silent phenotypes for supposedly important genes, apparent redundancy of functions, and variegated networks of signal transduction and transcription factors. Here we propose that evolutionary optimization has been much more intensive than to lead to the bare minima necessary for autonomous life. Much more complex organisms prevail. Much of this complexity arises in the nonlinear interactions between cellular macromolecules and in subtle differences between paralogs (isoenzymes). The complexity can only be understood when analyzed quantitatively, for which quantitative experimentation is needed in living systems that are as simple and manipulatable as possible, yet complex in the above sense. We illustrate this for the glutamine synthetase cascade in Escherichia coli. By reviewing recent molecular findings, we show that this cascade is much more complex than necessary for simple regulation of ammonia assimilation. Simulations suggest that the function of this complexity may lie in quasi-intelligent behavior, including conditioning and learning.

On the vital role of fluid movement in organisms and cells: a brief historical account from Harvey to Coulson, extending the hypothesis of circulation

The importance of the orchestrated movement of substrates over multicatalytic enzymes, topographically arranged for maximum efficacy for metabolic function, means that the organization of the body right down to the subcellular level is based upon the principle of perfusion for most normal living processes. The history of the development of this idea, which is contrary to the received wisdom arising and still persisting since the early days of biochemistry, will be followed from the time when the overall need for a circulation at the gross level was fully appreciated, extending the argument through a historical perspective to include the control of flow within the individual cell. Life, seen from this new perspective, seems to be inherently dependent upon this principle to such an extent that it leads to a clearer distinction between the living and the dead, and provides a framework for more critical experiments concerning control mechanisms in the cell.

Signaling in unicellular eukaryotes

Aspects of intercellular and intracellular signaling systems in cell survival, proliferation, differentiation, chemosensory behavior, and programmed cell death in free-living unicellular eukaryotes have been reviewed. Comparisons have been made with both bacteria and metazoa. The central organisms were flagellates (Trypanosoma, Leishmania, and Crithidia), slime molds (Dictyostelium), yeast cells (Saccharomyces cerevisiae), and ciliates (Paramecium, Euplotes, and Tetrahymena). There are two novel aspects in this review. First, cellular responses are viewed in an evolutionary perspective, rather than from the more prevailing one, in which the unicellular eukaryotes are seen by the mammalian organisms. Second, results obtained with cell cultures in minimal, chemically defined nutrient media at low cell densities where intercellular signaling is strongly reduced are discussed. These results shed light on control mechanisms and their cooperation inside the living cell. Intracellular systems have many common features in unicellular and multicellular organisms.

Glucose transporter of Escherichia coli: NMR characterization of the phosphocysteine form of the IIB(Glc) domain and its binding interface with the IIA(Glc) subunit

The transmembrane subunit of the glucose transporter, IICB(Glc), mediates vectorial transport with concomitant phosphorylation of glucose. Glucose phosphorylation proceeds through a cystein phosphate intermediate of the cytosolic IIB domain of IIC(Glc), which is phosphorylated by the IIA(Glc) subunit of the glucose transporter. Two- and three-dimensional NMR experiments were used to characterize the phosphorylation of the 10 kDa subclonal IIB domain and the complementary binding interfaces of [15N]IIB and [15N]IIA(Glc). The largest chemical shift perturbations and the only NOE differences accompanying IIB phosphorylation are confined to the active site residue Cys35, as well as Ile36, Thr37, Arg38, Leu39, and Arg40, which are all located in the turn between strands beta1 and beta2 and on beta2 itself. The significant increase of the amide cross-peak intensities of Ile36, Thr37, and Arg38 upon phosphorylation suggests that the conformational freedom of these groups becomes restrained, possibly due to hydrogen bonding to the oxygens of the bound phosphate and to interactions between the guanidinium group of Arg38 and the phosphoryl group. The residues of IIB which experience chemical shift perturbations upon binding of IIA are located on a protruding surface formed by residues of strands beta1, beta2, and beta4, the beta4/alpha3 loop, and residues from the first two turns of alpha3. The corresponding binding surface of the IIA(Glc) domain is comprised of residues on five adjacent beta-strands and two short helices surrounding the active site His90. The binding surface of IIA(Glc) for IIB coincides with the binding surface for HPr, the phosphoryl carrier protein by which IIA(Glc) is phosphorylated [Chen, Y., Reizer, J., Saier, M. H., Fairbrother, W. J., & Wright, P. E. (1993) Biochemistry 32, 32-37].

Bacterial metabolism in dental biofilms

Dental biofilms could have a structure which, in sections, looks like tissue. The internal structure of the dental biofilm could be the result of interbacterial adhesion mechanisms in combination with nutritional conditions characterized by multiple nutrient starvation. The preservation of the structure of the biofilm over time may also involve the ability of the bacteria to withstand environmental stresses such as starvation, reactive oxygen products, and acid. The present review will describe, first, the regulation of the metabolic defense against environmental stresses and then focus mainly on the energy metabolism of dental biofilms.

Regulation of UDP-3-O-[R-3-hydroxymyristoyl]-N-acetylglucosamine deacetylase in Escherichia coli. The second enzymatic step of lipid a biosynthesis

The first enzyme of lipid A assembly in Escherichia coli is an acyltransferase that attaches an R-3-hydroxymyristoyl moiety to UDP-GlcNAc at the GlcNAc 3-OH. This reaction is reversible and thermodynamically unfavorable. The subsequent deacetylation of the product, UDP-3-O-[R-3-hydroxymyristoyl]-GlcNAc, is therefore the first committed step of lipid A biosynthesis. We now demonstrate that inhibition of either the acyltransferase or the deacetylase in living cells results in a 5-10-fold increase in the specific activity of the deacetylase in extracts prepared from such cells. Five other enzymes of the lipid A pathway are not affected. The elevated specific activity of deacetylase observed in extracts of lipid A-depleted cells is not accompanied by a significant change in the Km for the substrate, but is mainly an effect on Vmax. Western blots demonstrate that more deacetylase protein is indeed made. However, deacetylase messenger RNA levels are not significantly altered. Inhibition of lipid A biosynthesis must either stimulate the translation of available mRNA or slow the turnover of pre-existing deacetylase. In contrast, inhibition of 3-deoxy-D-manno-octulosonic acid (Kdo) biosynthesis has no effect on deacetylase specific activity. The underacylated lipid A-like disaccharide precursors that accumulate during inhibition of Kdo formation may be sufficient to exert normal feedback control.