Coordination logic of the sensing machinery in the transcriptional regulatory network of Escherichia coli

Expression of the ace operon in Escherichia coli is triggered in response to growth rate-dependent flux-signal of ATP

The signal that triggers the expression of the ace operon and, in turn, the transition of central metabolism's architecture from acetogenic to gluconeogenic in Escherichia coli remains elusive despite extensive research both in vivo and in vitro. Here, with the aid of flux analysis together with measurements of the enzymic activity of isocitrate lyase (ICL) and its aceA-messenger ribonucleuc acid (mRNA) transcripts, we provide credible evidence suggesting that the expression of the ace operon in E. coli is triggered in response to growth rate-dependent threshold flux-signal of adenosine triphosphate (ATP). Flux analysis revealed that the shortfall in ATP supply observed as the growth rate ($\mu $) diminishes from µmax to ≤ 0.43h-1 ($ \pm 0.02;n4)\ $is partially redressed by up-regulating flux through succinyl CoA synthetase. Unlike glycerol and glucose, pyruvate cannot feed directly into the two glycolytic ATP-generating reactions catalyzed by phosphoglycerokinase and pyruvate kinase. On the other hand, glycerol, which upon its conversion to D-glyceraldehyde, feeds into the phosphorylation and dephosphorylation parts of glycolysis including the substrate-level phosphorylation-ATP generating reactions, thus preventing ATP flux from dropping to the critical threshold signal required to trigger the acetate-diauxic switch until glycerol is fully consumed. The mRNA transcriptional patterns of key gluconeogenic enzymes, namely, ackA, acetate kinase; pta, phosphotransacetylase; acs, acetyl CoA synthetase and aceA, ICL, suggest that the pyruvate phenotype is better equipped than the glycerol phenotype for the switch from acetogenic to gluconeogenic metabolism.

Few regulatory metabolites coordinate expression of central metabolic genes in Escherichia coli

Transcription networks consist of hundreds of transcription factors with thousands of often overlapping target genes. While we can reliably measure gene expression changes, we still understand relatively little why expression changes the way it does. How does a coordinated response emerge in such complex networks and how many input signals are necessary to achieve it? Here, we unravel the regulatory program of gene expression in Escherichia coli central carbon metabolism with more than 30 known transcription factors. Using a library of fluorescent transcriptional reporters, we comprehensively quantify the activity of central metabolic promoters in 26 environmental conditions. The expression patterns were dominated by growth rate‐dependent global regulation for most central metabolic promoters in concert with highly condition‐specific activation for only few promoters. Using an approximate mathematical description of promoter activity, we dissect the contribution of global and specific transcriptional regulation. About 70% of the total variance in promoter activity across conditions was explained by global transcriptional regulation. Correlating the remaining specific transcriptional regulation of each promoter with the cell's metabolome response across the same conditions identified potential regulatory metabolites. Remarkably, cyclic AMP, fructose‐1,6‐bisphosphate, and fructose‐1‐phosphate alone explained most of the specific transcriptional regulation through their interaction with the two major transcription factors Crp and Cra. Thus, a surprisingly simple regulatory program that relies on global transcriptional regulation and input from few intracellular metabolites appears to be sufficient to coordinate E. coli central metabolism and explain about 90% of the experimentally observed transcription changes in 100 genes.

Metabolic Regulation of a Bacterial Cell System with Emphasis on Escherichia coli Metabolism

It is quite important to understand the overall metabolic regulation mechanism of bacterial cells such as Escherichia coli from both science (such as biochemistry) and engineering (such as metabolic engineering) points of view. Here, an attempt was made to clarify the overall metabolic regulation mechanism by focusing on the roles of global regulators which detect the culture or growth condition and manipulate a set of metabolic pathways by modulating the related gene expressions. For this, it was considered how the cell responds to a variety of culture environments such as carbon (catabolite regulation), nitrogen, and phosphate limitations, as well as the effects of oxygen level, pH (acid shock), temperature (heat shock), and nutrient starvation.

Extensive cross-talk and global regulators identified from an analysis of the integrated transcriptional and signaling network in Escherichia coli

To understand the regulatory dynamics of transcription factors (TFs) and their interplay with other cellular components we have integrated transcriptional, protein-protein and the allosteric or equivalent interactions which mediate the physiological activity of TFs in Escherichia coli. To study this integrated network we computed a set of network measurements followed by principal component analysis (PCA), investigated the correlations between network structure and dynamics, and carried out a procedure for motif detection. In particular, we show that outliers identified in the integrated network based on their network properties correspond to previously characterized global transcriptional regulators. Furthermore, outliers are highly and widely expressed across conditions, thus supporting their global nature in controlling many genes in the cell. Motifs revealed that TFs not only interact physically with each other but also obtain feedback from signals delivered by signaling proteins supporting the extensive cross-talk between different types of networks. Our analysis can lead to the development of a general framework for detecting and understanding global regulatory factors in regulatory networks and reinforces the importance of integrating multiple types of interactions in underpinning the interrelationships between them.

Large-scale 13C-flux analysis reveals distinct transcriptional control of respiratory and fermentative metabolism in Escherichia coli

The authors analyze the role transcription plays in regulating bacterial metabolic flux. Of 91 transcriptional regulators studied, 2/3 affect absolute fluxes, but only a small number of regulators control the partitioning of flux between different metabolic pathways.

In contrast to the canonical respiro-fermentative glucose metabolism, fully respiratory galactose metabolism depends exclusively on the PEP-glyoxylate cycle.

Of 91 transcription factors, 2/3 affect absolute fluxes, but only one controls the distribution of fluxes on galactose and nine on glucose.

Transcriptional control of hexose flux distributions is confined to the acetyl-CoA branch point.

The PEP-glyoxylate cycle is controlled by cAMP-Crp in a hexose uptake rate-dependent manner.

Focusing on central carbon metabolism of Escherichia coli, we aim here to systematically identify transcriptional regulators that control the distribution of metabolic fluxes during aerobic growth on hexoses. To assess the condition dependence of transcriptional control of flux, we selected glucose and galactose as two substrates that are highly similar, yet lead to distinct growth rates (Soupene et al, 2003), overall metabolic rates (De Anda et al, 2006; Samir El et al, 2009) and levels of catabolite repression (Hogema et al, 1998; Bettenbrock et al, 2007).

Experimentally determined fluxes (Fischer and Sauer, 2003a) during growth on glucose and galactose reveal two distinct metabolic states. On glucose, high metabolic rates lead to high overflow metabolism and respiratory fluxes through the TCA cycle. On galactose, in contrast, metabolism was much slower without overflow metabolism and respiratory fluxes exclusively through the PEP-glyoxylate cycle (Fischer and Sauer, 2003b).

To determine which transcriptional events controlled these two distinct metabolic states, we determined intracellular fluxes in 91 transcription factor mutants. These genetic perturbations primarily affected absolute fluxes but not the distribution of fluxes. The distribution of flux between glycolysis and pentose–phosphate pathway in upper metabolism, e.g., remained constant in all mutants under all conditions. Transcriptional control of the flux distribution was exclusively seen at the acetyl-CoA branch point. On glucose, nine transcription factors controlled the distribution of fluxes at this branch point, five of which (ArcA, IHFA, IHFB, PdhR, Fur) did so presumably directly through their known targets in TCA cycle and/or respiration. Without known targets in the relevant pathways, the remaining four transcription factors (GlpR, QseB, HdfR, GlcC) may act either indirectly or directly through unknown targets. On galactose, transcriptional control focused exclusively on the PEP-glyoxylate cycle. While deletion of six transcription factors (Cra, Crp, IHFA, IHFB, Mlc, NagC) abolished or reduced the PEP-glyoxylate cycle flux, we demonstrate by substrate-limited chemostat experiments, derepression of galactose uptake and show by metabolomics that five of these transcription factors act indirectly through increased cAMP concentrations that allosterically activate Crp, the only direct transcription factors that controls the PEP-glyoxylate cycle (Nanchen et al, 2008).

Overall, our absolute flux data demonstrate that control of flux splitting during growth on hexoses was confined to the acetyl-CoA branch point in E. coli. Of the 36 transcription factors known to target genes in pathways that diverge from the acetyl-CoA branch point, only one transcription factor on galactose and five plus potentially four others on glucose showed altered flux splitting. The primary focus of steady state transcriptional control on the acetyl-CoA branch point, and thus the metabolic decision between the energetically efficient respiration and the less efficient but more rapid fermentation, was recently also demonstrated with only relative flux data for Saccharomyces cerevisiae (Fendt et al, 2010).

In contrast to glucose-grown Bacillus subtilis (Fischer et al, 2005), for batch glucose-grown E. coli, none of the investigated transcription factor mutants exhibited improved biomass productivity. However, the mutants Cra, IHF A, IHF B and NagC with increased uptake rates grew much faster at almost unaltered biomass yields. As the removal of the glucose PTS-based repression with a Crr mutant also resulted in increased galactose uptake, we provide evidence that E. coli actively represses its galactose uptake at the expense of otherwise possible rapid growth.

Despite our increasing topological knowledge on regulation networks in model bacteria, it is largely unknown which of the many co-occurring regulatory events actually control metabolic function and the distribution of intracellular fluxes. Here, we unravel condition-dependent transcriptional control of Escherichia coli metabolism by large-scale ¹³C-flux analysis in 91 transcriptional regulator mutants on glucose and galactose. In contrast to the canonical respiro-fermentative glucose metabolism, fully respiratory galactose metabolism depends exclusively on the phosphoenol-pyruvate (PEP)-glyoxylate cycle. While 2/3 of the regulators directly or indirectly affected absolute flux rates, the partitioning between different pathways remained largely stable with transcriptional control focusing primarily on the acetyl-CoA branch point. Flux distribution control was achieved by nine transcription factors on glucose, including ArcA, Fur, PdhR, IHF A and IHF B, but was exclusively mediated by the cAMP-dependent Crp regulation of the PEP-glyoxylate cycle flux on galactose. Five further transcription factors affected this flux only indirectly through cAMP and Crp by increasing the galactose uptake rate. Thus, E. coli actively limits its galactose catabolism at the expense of otherwise possible faster growth.

Bacterial growth rate reflects a bottleneck in resource allocation

BACKGROUND

Growth rate management in fast-growing bacteria is currently an active research area. In spite of the huge progress made in our understanding of the molecular mechanisms controlling the growth rate, fundamental questions concerning its intrinsic limitations are still relevant today. In parallel, systems biology claims that mathematical models could shed light on these questions.

METHODS

This review explores some possible reasons for the limitation of the growth rate in fast-growing bacteria, using a systems biology approach based on constraint-based modeling methods.

RESULTS

Recent experimental results and a new constraint-based modelling method named Resource Balance Analysis (RBA) reveal the existence of constraints on resource allocation between biological processes in bacterial cells. In this context, the distribution of a finite amount of resources between the metabolic network and the ribosomes limits the growth rate, which implies the existence of a bottleneck between these two processes. Any mechanism for saving resources increases the growth rate.

GENERAL SIGNIFICANCE

Consequently, the emergence of genetic regulation of metabolic pathways, e.g. catabolite repression, could then arise as a means to minimise the protein cost, i.e. maximising growth performance while minimising the resource allocation. This article is part of a Special Issue entitled Systems Biology of Microorganisms.

Early Career Research Award Lecture. Structure, evolution and dynamics of transcriptional regulatory networks

The availability of entire genome sequences and the wealth of literature on gene regulation have enabled researchers to model an organism's transcriptional regulation system in the form of a network. In such a network, TFs (transcription factors) and TGs (target genes) are represented as nodes and regulatory interactions between TFs and TGs are represented as directed links. In the present review, I address the following topics pertaining to transcriptional regulatory networks. (i) Structure and organization: first, I introduce the concept of networks and discuss our understanding of the structure and organization of transcriptional networks. (ii) Evolution: I then describe the different mechanisms and forces that influence network evolution and shape network structure. (iii) Dynamics: I discuss studies that have integrated information on dynamics such as mRNA abundance or half-life, with data on transcriptional network in order to elucidate general principles of regulatory network dynamics. In particular, I discuss how cell-to-cell variability in the expression level of TFs could permit differential utilization of the same underlying network by distinct members of a genetically identical cell population. Finally, I conclude by discussing open questions for future research and highlighting the implications for evolution, development, disease and applications such as genetic engineering.

Evolution of gene regulatory networks by fluctuating selection and intrinsic constraints

Various characteristics of complex gene regulatory networks (GRNs) have been discovered during the last decade, e.g., redundancy, exponential indegree distributions, scale-free outdegree distributions, mutational robustness, and evolvability. Although progress has been made in this field, it is not well understood whether these characteristics are the direct products of selection or those of other evolutionary forces such as mutational biases and biophysical constraints. To elucidate the causal factors that promoted the evolution of complex GRNs, we examined the effect of fluctuating environmental selection and some intrinsic constraining factors on GRN evolution by using an individual-based model. We found that the evolution of complex GRNs is remarkably promoted by fixation of beneficial gene duplications under unpredictably fluctuating environmental conditions and that some internal factors inherent in organisms, such as mutational bias, gene expression costs, and constraints on expression dynamics, are also important for the evolution of GRNs. The results indicate that various biological properties observed in GRNs could evolve as a result of not only adaptation to unpredictable environmental changes but also non-adaptive processes owing to the properties of the organisms themselves. Our study emphasizes that evolutionary models considering such intrinsic constraining factors should be used as null models to analyze the effect of selection on GRN evolution.

Various organismal traits, including the morphology of multicellular species and metabolism in unicellular species, are determined by the amount and combinations of proteins in the cell. The complex regulatory network plays an important role in controlling the protein profiles in a cell. Recent studies have revealed that gene regulatory networks have many interesting structural and mutational features such as their scale-free structure, mutational robustness, and evolvability. However, why and how these features have emerged from evolution is unknown. In this paper, we constructed an evolutionary model of gene regulatory networks and simulated its evolution under various environmental conditions. The results show that most features of known gene regulatory networks evolve as a result of adaptation to unpredictable environmental fluctuations. In addition, some internal organismal factors, such as mutational bias, gene expression costs, and constraints on expression dynamics, are also important for GRN evolution observed in real organisms. Thus, these GRN features appear to evolve as a result of not only adaptation to unpredictable environmental changes but also non-adaptive processes owing to the properties of the organisms themselves.

Dissecting the expression patterns of transcription factors across conditions using an integrated network-based approach

In prokaryotes, regulation of gene expression is predominantly controlled at the level of transcription. Transcription in turn is mediated by a set of DNA-binding factors called transcription factors (TFs). In this study, we map the complete repertoire of ∼300 TFs of the bacterial model, Escherichia coli, onto gene expression data for a number of nonredundant experimental conditions and show that TFs are generally expressed at a lower level than other gene classes. We also demonstrate that different conditions harbor varying number of active TFs, with an average of about 15% of the total repertoire, with certain stress and drug-induced conditions exhibiting as high as one-third of the collection of TFs. Our results also show that activators are more frequently expressed than repressors, indicating that activation of promoters might be a more common phenomenon than repression in bacteria. Finally, to understand the association of TFs with different conditions and to elucidate their dynamic interplay with other TFs, we develop a network-based framework to identify TFs which act as markers, defined as those which are responsible for condition-specific transcriptional rewiring. This approach allowed us to pinpoint several marker TFs as being central in various specialized conditions such as drug induction or growth condition variations, which we discuss in light of previously reported experimental findings. Further analysis showed that a majority of identified markers effectively control the expression of their regulons and, in general, transcriptional programs of most conditions can be effectively rewired by a very small number of TFs. It was also found that closeness is a key centrality measure which can aid in the successful identification of marker TFs in regulatory networks. Our results suggest the utility of the network-based approaches developed in this study to be applicable for understanding other interactomic data sets.

Bacterial adaptation through distributed sensing of metabolic fluxes

We present a large-scale differential equation model of E. coli's central metabolism and its enzymatic, transcriptional, and posttranslational regulation. This model reproduces E. coli's known physiological behavior.

We found that the interplay of known interactions in E. coli's central metabolism can indirectly recognize the presence of extracellular carbon sources through measuring intracellular metabolic flux patterns.

We found that E. coli's system-level adaptations between glycolytic and gluconeogenic carbon sources are realized on the molecular level by global feedback architectures that overarch the enzymatic and transcriptional regulatory layers.

We found that the capability for closed-loop self-regulation can emerge within metabolism itself and therefore, metabolic operation may adapt itself autonomously to changing carbon sources (not requiring upstream sensing and signaling).

Adaptations to fluctuating carbon source availability are of particular importance for bacteria. To understand these adaptations, it needs to be understood how a system's behavior emerges from the interactions between the characterized molecules (Kitano, 2002b). To attain such a system understanding of bacterial metabolic adaptations to carbon source availability, the coupling between the recognition and adjustment aspects and between the enzymatic and genetic regulatory layers must be understood. For many carbon sources, neither transmembrane sensors nor regulatory proteins with sensing function have been identified. Also, it remains unclear how multiple local regulations work together to accomplish a coherent adjustment on the systems level. In this paper, we show that (1) the interplay of the known interactions in E. coli's central metabolism is capable of recognizing carbon sources indirectly, and that (2) these molecular interactions can adjust E. coli's metabolic operation between growth on glycolytic and gluconeogenic carbon sources, and that (3) this adaptation is governed by general principles.

We hypothesized that the system-level adaptations between growth on glycolytic and gluconeogenic carbon sources are accomplished by a system-wide regulation architecture that emerges when the known enzymatic and transcriptional regulations become coupled through five transcription factor (TF)–metabolite interactions. To (1) assess whether such coupled molecular interactions can indeed work together to adapt metabolic operation, and if yes, (2) to understand this system-level adaptation in molecular-level detail, we constructed a large-scale differential equation model. The model topology comprises the Embden–Meyerhoff pathway, the tricarboxylic acid (TCA) cycle, the glyoxylate (GLX) shunt, the anaplerotic reactions, the diversion of carbon flux to the GLX shunt, the uptake of glucose, the uptake and excretion of acetate, enzymatic regulation, transcriptional regulation by four TFs, and the regulation of these TFs' activities through TF–metabolite interactions. We translated the topology into differential equations by assigning the most appropriate rate law to each interaction. The kinetic model comprises 47 ordinary differential equations and 193 parameters. Parameter values were estimated through application of the ‘divide-and-conquer approach' (Kotte and Heinemann, 2009) on published experimental steady state-omics data sets.

Model simulations reproduce E. coli's known physiological behavior in an environment with fluctuating carbon source availability. But how does the in silico cell recognize acetate without a transmembrane sensor for extracellular acetate or a TF binding to intracellular acetate? Similarly, it is unclear whether the glucose sensing function of the phosphotransferase system is the exclusive mechanism to recognize glucose, or whether this sensing function is integrated into a larger sensing architecture. The model suggests that the recognition is performed indirectly through a mechanism we termed distributed sensing of intracellular metabolic fluxes. This mechanism uses two distinct motifs, which we termed pathway usage and flux direction, to establish defined correlations between metabolic fluxes and the levels of certain, here termed flux-signaling metabolites. The binding of these metabolites to TFs propagates the flux information to the transcriptional regulatory layer. A molecular sensor for intracellular metabolic flux is thus defined as a system of regulations and enzyme kinetics, comprising (1) either of the two motifs pathway usage or flux direction and (2) the binding of the thus established flux-signaling metabolites to TF(s).

As the in silico cell establishes and uses sensors for several intracellular metabolic fluxes, the overall sensing architecture infers the present carbon sources from a pattern of metabolic fluxes and is as such of a distributed nature. The core of this sensing architecture is formed not by transmembrane sensors but by four flux sensors, which establish flux-signaling metabolites according to the two proposed general motifs. These flux sensors use intracellular metabolic flux as a means to correlate the presence of extracellular carbon sources with the levels of intracellular metabolites. The recognition of glucose through the PTS transmembrane complex is embedded as one flux sensor in this distributed sensing architecture; the other three flux sensors function without the help of transmembrane complexes.

The in silico cell achieves the coupling between recognition and adjustment through its TFs, whose activities respond to the available carbon sources and at the same time regulate the expression of target genes. This combined recognition and adjustment, centered on the four TFs, closes four global feedback loops that overarch the metabolic and genetic layers as illustrated in Figure 6. The adaptation of the in silico cell arises from the global feedback loop-embedded, flux sensor-adjusted transcriptional regulation of the four TFs, with each TF performing one part of the overall adaptation. This adaptation incorporates both the influence of the metabolic on the genetic layer, achieved through TF–metabolite interactions, and of the genetic on the metabolic layer, achieved through the impact of adjusted enzyme levels on metabolic fluxes.

The existence of the global feedback architectures challenges the conventional view that top-level regulatory proteins recognize environmental conditions and adjust downstream metabolic operation. It suggests that the capability for closed-loop self-regulation can emerge within metabolism itself and therefore, metabolic operation may adapt itself autonomously (not requiring upstream sensing and regulation) to changing carbon sources.

To conclude, the presented differential equation model of E. coli's central metabolism offers a consistent explanation of how a multitude of known molecular interactions fit into a coherent systems picture; the interactions work together like gear wheels that mesh with one another to adapt central metabolism between growth on the glycolytic substrate glucose and the gluconeogenic substrate acetate. The deduced general functional principles provide the missing link to understand system-level adaptations to carbon sources in molecular-level detail. The proposed principles fall under the umbrella of distributed flux sensing. The flux sensing mechanism entails the binding of TFs to flux-signaling metabolites, which are established through the motifs signaling of pathway usage and signaling of flux direction, and are embedded in global feedback loop architectures. These principles allow an autonomous adaptation of metabolic operation to growth in fluctuating environments.

The recognition of carbon sources and the regulatory adjustments to recognized changes are of particular importance for bacterial survival in fluctuating environments. Despite a thorough knowledge base of Escherichia coli's central metabolism and its regulation, fundamental aspects of the employed sensing and regulatory adjustment mechanisms remain unclear. In this paper, using a differential equation model that couples enzymatic and transcriptional regulation of E. coli's central metabolism, we show that the interplay of known interactions explains in molecular-level detail the system-wide adjustments of metabolic operation between glycolytic and gluconeogenic carbon sources. We show that these adaptations are enabled by an indirect recognition of carbon sources through a mechanism we termed distributed sensing of intracellular metabolic fluxes. This mechanism uses two general motifs to establish flux-signaling metabolites, whose bindings to transcription factors form flux sensors. As these sensors are embedded in global feedback loop architectures, closed-loop self-regulation can emerge within metabolism itself and therefore, metabolic operation may adapt itself autonomously (not requiring upstream sensing and signaling) to fluctuating carbon sources.

Regulation by transcription factors in bacteria: beyond description

Transcription is an essential step in gene expression and its understanding has been one of the major interests in molecular and cellular biology. By precisely tuning gene expression, transcriptional regulation determines the molecular machinery for developmental plasticity, homeostasis and adaptation. In this review, we transmit the main ideas or concepts behind regulation by transcription factors and give just enough examples to sustain these main ideas, thus avoiding a classical ennumeration of facts. We review recent concepts and developments: cis elements and trans regulatory factors, chromosome organization and structure, transcriptional regulatory networks (TRNs) and transcriptomics. We also summarize new important discoveries that will probably affect the direction of research in gene regulation: epigenetics and stochasticity in transcriptional regulation, synthetic circuits and plasticity and evolution of TRNs. Many of the new discoveries in gene regulation are not extensively tested with wetlab approaches. Consequently, we review this broad area in Inference of TRNs and Dynamical Models of TRNs. Finally, we have stepped backwards to trace the origins of these modern concepts, synthesizing their history in a timeline schema.

Functional models for large-scale gene regulation networks: realism and fiction

High-throughput experiments are shedding light on the topology of large regulatory networks and at the same time their functional states, namely the states of activation of the nodes (for example transcript or protein levels) in different conditions, times, environments. We now possess a certain amount of information about these two levels of description, stored in libraries, databases and ontologies. A current challenge is to bridge the gap between topology and function, i.e. developing quantitative models aimed at characterizing the expression patterns of large sets of genes. However, approaches that work well for small networks become impossible to master at large scales, mainly because parameters proliferate. In this review we discuss the state of the art of large-scale functional network models, addressing the issue of what can be considered as "realistic" and what the main limitations may be. We also show some directions for future work, trying to set the goals that future models should try to achieve. Finally, we will emphasize the possible benefits in the understanding of biological mechanisms underlying complex multifactorial diseases, and in the development of novel strategies for the description and the treatment of such pathologies.

Network-based approaches for linking metabolism with environment

Genome-wide metabolic maps allow the development of network-based computational approaches for linking an organism with its biochemical habitat.

Progress in the reconstruction of genome-wide metabolic maps has led to the development of network-based computational approaches for linking an organism with its biochemical habitat.

What determines the assembly of transcriptional network motifs in Escherichia coli?

Transcriptional networks are constituted by a collection of building blocks known as network motifs. Why do motifs appear? An adaptive model of motif emergence was recently questioned in favor of neutralist scenarios. Here, we provide a new picture of motif assembly in Escherichia coli which partially clarifies these contrasting explanations. This is based on characterizing the linkage between motifs and sensing or response specificity of their constituent transcriptional factors (TFs). We find that sensing specificity influences the distribution of autoregulation, while the tendency of a TF to establish feed-forward loops (FFLs) depends on response specificity, i.e., regulon size. Analysis of the latter pattern reveals that coregulation between large regulon-size TFs is common under a network neutral model, leading to the assembly of a great number of FFLs and bifans. In addition, neutral exclusive regulation also leads to a collection of single input modules -the fourth basic motif. On the whole, and even under the conservative neutralist scenario considered, a substantial group of regulatory structures revealed adaptive. These structures visibly function as fully-fledged working units.

Functional organisation of Escherichia coli transcriptional regulatory network

Taking advantage of available functional data associated with 115 transcription and 7 sigma factors, we have performed a structural analysis of the regulatory network of Escherichia coli. While the mode of regulatory interaction between transcription factors (TFs) is predominantly positive, TFs are frequently negatively autoregulated. Furthermore, feedback loops, regulatory motifs and regulatory pathways are unevenly distributed in this network. Short pathways, multiple feed-forward loops and negative autoregulatory interactions are particularly predominant in the subnetwork controlling metabolic functions such as the use of alternative carbon sources. In contrast, long hierarchical cascades and positive autoregulatory loops are overrepresented in the subnetworks controlling developmental processes for biofilm and chemotaxis. We propose that these long transcriptional cascades coupled with regulatory switches (positive loops) for external sensing enable the coexistence of multiple bacterial phenotypes. In contrast, short regulatory pathways and negative autoregulatory loops enable an efficient homeostatic control of crucial metabolites despite external variations. TFs at the core of the network coordinate the most basic endogenous processes by passing information onto multi-element circuits. Transcriptional expression data support broader and higher transcription of global TFs compared to specific ones. Global regulators are also more broadly conserved than specific regulators in bacteria, pointing to varying functional constraints.

Reconstruction and analysis of the genetic and metabolic regulatory networks of the central metabolism of Bacillus subtilis

Background

Few genome-scale models of organisms focus on the regulatory networks and none of them integrates all known levels of regulation. In particular, the regulations involving metabolite pools are often neglected. However, metabolite pools link the metabolic to the genetic network through genetic regulations, including those involving effectors of transcription factors or riboswitches. Consequently, they play pivotal roles in the global organization of the genetic and metabolic regulatory networks.

Results

We report the manually curated reconstruction of the genetic and metabolic regulatory networks of the central metabolism of Bacillus subtilis (transcriptional, translational and post-translational regulations and modulation of enzymatic activities). We provide a systematic graphic representation of regulations of each metabolic pathway based on the central role of metabolites in regulation. We show that the complex regulatory network of B. subtilis can be decomposed as sets of locally regulated modules, which are coordinated by global regulators.

Conclusion

This work reveals the strong involvement of metabolite pools in the general regulation of the metabolic network. Breaking the metabolic network down into modules based on the control of metabolite pools reveals the functional organization of the genetic and metabolic regulatory networks of B. subtilis.