Global network reorganization during dynamic adaptations of Bacillus subtilis metabolism

Reconstruction of temporal activity of microRNAs from gene expression data in breast cancer cell line

Background

MicroRNAs (miRNAs) are small non-coding RNAs that regulate genes at the post-transcriptional level in spatiotemporal manner. Several miRNAs are identified as prognostic and diagnostic markers in many human cancers. Estimation of the temporal activities of the miRNAs is an important step in the way to understand the complex interactions of these important regulatory elements with transcription factors (TFs) and target genes (TGs). However, current research on miRNA activities excludes network dynamics from the studies, disregarding the important element of time in the regulatory network analysis.

Results

In the current study, we combined experimentally verified miRNA-TG interactions with breast cancer microarray TG expression data to identify key miRNAs and compute their temporal activity using network component analysis (NCA). The computed activities showed that miRNAs were regulated in a time dependent manner. Our results allowed constructing a synergistic network of miRNAs using the computed miRNA activities and their shared regulation of TGs. We further extended this network by incorporating miRNA-TG, miRNA-TF, TF-miRNA and TF-TG regulations in the context of breast cancer. Our integrated network identified several miRNAs known to be involved in breast cancer regulation and revealed several novel miRNAs. Our further analysis detected substantial involvement of the miRNAs miR-324, miR-93, miR-615 and miR-1 in breast cancer, which was not known previously. Next, combining our integrated networks with functional annotation of differentially expressed genes resulted in new sub-networks. These sub-networks allowed us to identify the key miRNAs and their interactions with TFs and TGs of several biological processes involved in breast cancer. The identified markers are validated for their potential as prognostic markers for breast cancer through survival analysis.

Conclusions

Our dynamical analysis of the miRNA interactions greatly helps to discover new network based markers, and is highly applicable (but not limited) to cancer research.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-2260-3) contains supplementary material, which is available to authorized users.

FlexFlux: combining metabolic flux and regulatory network analyses

BACKGROUND

Expression of cell phenotypes highly depends on metabolism that supplies matter and energy. To achieve proper utilisation of the different metabolic pathways, metabolism is tightly regulated by a complex regulatory network composed of diverse biological entities (genes, transcripts, proteins, signalling molecules…). The integrated analysis of both regulatory and metabolic networks appears very insightful but is not straightforward because of the distinct characteristics of both networks. The classical method used for metabolic flux analysis is Flux Balance Analysis (FBA), which is constraint-based and relies on the assumption of steady-state metabolite concentrations throughout the network. Regarding regulatory networks, a broad spectrum of methods are dedicated to their analysis although logical modelling remains the major method to take charge of large-scale networks.

RESULTS

We present FlexFlux, an application implementing a new way to combine the analysis of both metabolic and regulatory networks, based on simulations that do not require kinetic parameters and can be applied to genome-scale networks. FlexFlux is based on seeking regulatory network steady-states by performing synchronous updates of multi-state qualitative initial values. FlexFlux is then able to use the calculated steady-state values as constraints for metabolic flux analyses using FBA. As input, FlexFlux uses the standards Systems Biology Markup Language (SBML) and SBML Qualitative Models Package ("qual") extension (SBML-qual) file formats and provides a set of FBA based functions.

CONCLUSIONS

FlexFlux is an open-source java software with executables and full documentation available online at http://lipm-bioinfo.toulouse.inra.fr/flexflux/. It can be defined as a research tool that enables a better understanding of both regulatory and metabolic networks based on steady-state simulations. FlexFlux integrates well in the flux analysis ecosystem thanks to the support of standard file formats and can thus be used as a complementary tool to existing software featuring other types of analyses.

Resource constrained flux balance analysis predicts selective pressure on the global structure of metabolic networks

Background

A universal feature of metabolic networks is their hourglass or bow-tie structure on cellular level. This architecture reflects the conversion of multiple input nutrients into multiple biomass components via a small set of precursor metabolites. However, it is yet unclear to what extent this structural feature is the result of natural selection.

Results

We extend flux balance analysis to account for limited cellular resources. Using this model, optimal structure of metabolic networks can be calculated for different environmental conditions. We observe a significant structural reshaping of metabolic networks for a toy-network and E. coli core metabolism if we increase the share of invested resources for switching between different nutrient conditions. Here, hub nodes emerge and the optimal network structure becomes bow-tie-like as a consequence of limited cellular resource constraint. We confirm this theoretical finding by comparing the reconstructed metabolic networks of bacterial species with respect to their lifestyle.

Conclusions

We show that bow-tie structure can give a system-level fitness advantage to organisms that live in highly competitive and fluctuating environments. Here, limitation of cellular resources can lead to an efficiency-flexibility tradeoff where it pays off for the organism to shorten catabolic pathways if they are frequently activated and deactivated. As a consequence, generalists that shuttle between diverse environmental conditions should have a more predominant bow-tie structure than specialists that visit just a few isomorphic habitats during their life cycle.

Electronic supplementary material

The online version of this article (doi:10.1186/s12918-015-0232-5) contains supplementary material, which is available to authorized users.

An experimentally supported model of the Bacillus subtilis global transcriptional regulatory network

Organisms from all domains of life use gene regulation networks to control cell growth, identity, function, and responses to environmental challenges. Although accurate global regulatory models would provide critical evolutionary and functional insights, they remain incomplete, even for the best studied organisms. Efforts to build comprehensive networks are confounded by challenges including network scale, degree of connectivity, complexity of organism–environment interactions, and difficulty of estimating the activity of regulatory factors. Taking advantage of the large number of known regulatory interactions in Bacillus subtilis and two transcriptomics datasets (including one with 38 separate experiments collected specifically for this study), we use a new combination of network component analysis and model selection to simultaneously estimate transcription factor activities and learn a substantially expanded transcriptional regulatory network for this bacterium. In total, we predict 2,258 novel regulatory interactions and recall 74% of the previously known interactions. We obtained experimental support for 391 (out of 635 evaluated) novel regulatory edges (62% accuracy), thus significantly increasing our understanding of various cell processes, such as spore formation.

Quantitative prediction of genome-wide resource allocation in bacteria

Predicting resource allocation between cell processes is the primary step towards decoding the evolutionary constraints governing bacterial growth under various conditions. Quantitative prediction at genome-scale remains a computational challenge as current methods are limited by the tractability of the problem or by simplifying hypotheses. Here, we show that the constraint-based modeling method Resource Balance Analysis (RBA), calibrated using genome-wide absolute protein quantification data, accurately predicts resource allocation in the model bacterium Bacillus subtilis for a wide range of growth conditions. The regulation of most cellular processes is consistent with the objective of growth rate maximization except for a few suboptimal processes which likely integrate more complex objectives such as coping with stressful conditions and survival. As a proof of principle by using simulations, we illustrated how calibrated RBA could aid rational design of strains for maximizing protein production, offering new opportunities to investigate design principles in prokaryotes and to exploit them for biotechnological applications.

Real-time metabolome profiling of the metabolic switch between starvation and growth

Metabolic systems are often the first networks to respond to environmental changes, and the ability to monitor metabolite dynamics is key for understanding these cellular responses. Because monitoring metabolome changes is experimentally tedious and demanding, dynamic data on time scales from seconds to hours are scarce. Here we describe real-time metabolome profiling by direct injection of living bacteria, yeast or mammalian cells into a high-resolution mass spectrometer, which enables automated monitoring of about 300 compounds in 15-30-s cycles over several hours. We observed accumulation of energetically costly biomass metabolites in Escherichia coli in carbon starvation-induced stationary phase, as well as the rapid use of these metabolites upon growth resumption. By combining real-time metabolome profiling with modeling and inhibitor experiments, we obtained evidence for switch-like feedback inhibition in amino acid biosynthesis and for control of substrate availability through the preferential use of the metabolically cheaper one-step salvaging pathway over costly ten-step de novo purine biosynthesis during growth resumption.

Metabolic Adaptation Processes That Converge to Optimal Biomass Flux Distributions

In simple organisms like E.coli, the metabolic response to an external perturbation passes through a transient phase in which the activation of a number of latent pathways can guarantee survival at the expenses of growth. Growth is gradually recovered as the organism adapts to the new condition. This adaptation can be modeled as a process of repeated metabolic adjustments obtained through the resilencings of the non-essential metabolic reactions, using growth rate as selection probability for the phenotypes obtained. The resulting metabolic adaptation process tends naturally to steer the metabolic fluxes towards high growth phenotypes. Quite remarkably, when applied to the central carbon metabolism of E.coli, it follows that nearly all flux distributions converge to the flux vector representing optimal growth, i.e., the solution of the biomass optimization problem turns out to be the dominant attractor of the metabolic adaptation process.

In modeling metabolic networks, concepts like biomass optimization are often used to determine flux distributions of simple organisms such as E.coli. Although they often give good results in practice, they normally rely on heuristic considerations like “evolution has tuned metabolic fluxes to optimize growth, hence optimizing growth gives reasonable fluxes”. The main result of this paper is to show that metabolic adaptation naturally leads to optimal growth, in the sense that the flux distribution associated to optimal growth is the dominant attractor of the fitness landscape of the metabolic adaptation process.

The phosphoenolpyruvate:sugar phosphotransferase system is involved in sensitivity to the glucosylated bacteriocin sublancin

The mode of action of a group of glycosylated antimicrobial peptides known as glycocins remains to be elucidated. In the current study of one glycocin, sublancin, we identified the phosphoenolpyruvate:sugar phosphotransferase system (PTS) of Bacillus species as a key player in bacterial sensitivity. Sublancin kills several Gram-positive bacteria, such as Bacillus species and Staphylococcus aureus, including methicillin-resistant S. aureus (MRSA). Unlike other classes of bacteriocins for which the PTS is involved in their mechanism of action, we show that the addition of PTS-requiring sugars leads to increased resistance rather than increased sensitivity, suggesting that sublancin has a distinct mechanism of action. Collectively, our present mutagenesis and genomic studies demonstrate that the histidine-containing phosphocarrier protein (HPr) and domain A of enzyme II (PtsG) in particular are critical determinants for bacterial sensitivity to sublancin.

A roadmap for interpreting (13)C metabolite labeling patterns from cells

Measuring intracellular metabolism has increasingly led to important insights in biomedical research. (13)C tracer analysis, although less information-rich than quantitative (13)C flux analysis that requires computational data integration, has been established as a time-efficient method to unravel relative pathway activities, qualitative changes in pathway contributions, and nutrient contributions. Here, we review selected key issues in interpreting (13)C metabolite labeling patterns, with the goal of drawing accurate conclusions from steady state and dynamic stable isotopic tracer experiments.

Development of a markerless gene deletion system for Bacillus subtilis based on the mannose phosphoenolpyruvate-dependent phosphotransferase system

To optimize Bacillus subtilis as a production strain for proteins and low molecular substances by genome engineering, we developed a markerless gene deletion system. We took advantage of a general property of the phosphoenolpyruvate-dependent phosphotransferase system (PTS), in particular the mannose PTS. Mannose is phosphorylated during uptake by its specific transporter (ManP) to mannose 6-phosphate, which is further converted to fructose 6-phosphate by the mannose-6-phosphate isomerase (ManA). When ManA is missing, accumulation of the phosphorylated mannose inhibits cell growth. This system was constructed by deletion of manP and manA in B. subtilis Δ6, a 168 derivative strain with six large deletions of prophages and antibiotic biosynthesis genes. The manP gene was inserted into an Escherichia coli plasmid together with a spectinomycin resistance gene for selection in B. subtilis. To delete a specific region, its up- and downstream flanking sites (each of approximately 700 bp) were inserted into the vector. After transformation, integration of the plasmid into the chromosome of B. subtilis by single cross-over was selected by spectinomycin. In the second step, excision of the plasmid was selected by growth on mannose. Finally, excision and concomitant deletion of the target region were verified by colony PCR. In this way, all nine prophages, seven antibiotic biosynthesis gene clusters and two sigma factors for sporulation were deleted and the B. subtilis genome was reduced from 4215 to 3640 kb. Despite these extensive deletions, growth rate and cell morphology remained similar to the B. subtilis 168 parental strain.

Changes in the Acetylome and Succinylome of Bacillus subtilis in Response to Carbon Source

Lysine residues can be post-translationally modified by various acyl modifications in bacteria and eukarya. Here, we showed that two major acyl modifications, acetylation and succinylation, were changed in response to the carbon source in the Gram-positive model bacterium Bacillus subtilis. Acetylation was more common when the cells were grown on glucose, glycerol, or pyruvate, whereas succinylation was upregulated when the cells were grown on citrate, reflecting the metabolic states that preferentially produce acetyl-CoA and succinyl-CoA, respectively. To identify and quantify changes in acetylation and succinylation in response to the carbon source, we performed a stable isotope labeling by amino acids in cell culture (SILAC)-based quantitative proteomic analysis of cells grown on glucose or citrate. We identified 629 acetylated proteins with 1355 unique acetylation sites and 204 succinylated proteins with 327 unique succinylation sites. Acetylation targeted different metabolic pathways under the two growth conditions: branched-chain amino acid biosynthesis and purine metabolism in glucose and the citrate cycle in citrate. Succinylation preferentially targeted the citrate cycle in citrate. Acetylation and succinylation mostly targeted different lysine residues and showed a preference for different residues surrounding the modification sites, suggesting that the two modifications may depend on different factors such as characteristics of acyl-group donors, molecular environment of the lysine substrate, and/or the modifying enzymes. Changes in acetylation and succinylation were observed in proteins involved in central carbon metabolism and in components of the transcription and translation machineries, such as RNA polymerase and the ribosome. Mutations that modulate protein acylation affected B. subtilis growth. A mutation in acetate kinase (ackA) increased the global acetylation level, suggesting that acetyl phosphate-dependent acetylation is common in B. subtilis, just as it is in Escherichia coli. Our results suggest that acyl modifications play a role in the physiological adaptations to changes in carbon nutrient availability of B. subtilis.

Posttranslational regulation of microbial metabolism

Fluxes in microbial metabolism are controlled by various regulatory layers that alter abundance or activity of metabolic enzymes. Recent studies suggest a division of labor between these layers: transcriptional regulation mostly controls the allocation of protein resources, passive flux regulation by enzyme saturation and thermodynamics allows rapid responses at the expense of higher protein cost, and posttranslational regulation is utilized by cells to directly take control of metabolic decisions. We present recent advances in elucidating the role of these regulatory layers, focusing on posttranslational modifications and allosteric interactions. As the systematic mapping of posttranslational regulatory events has now become possible, the next challenge is to identify those regulatory events that are functionally relevant under a given condition.

Interactions between genotype and environment drive the metabolic phenotype within Escherichia coli isolates

To gain insights into the adaptation of the Escherichia coli species to different environments, we monitored protein abundances using quantitative proteomics and measurements of enzymatic activities of central metabolism in a set of five representative strains grown in four contrasted culture media including human urine. Two hundred and thirty seven proteins representative of the genome-scale metabolic network were identified and classified into pathway categories. We found that nutrient resources shape the general orientation of metabolism through coordinated changes in the average abundances of proteins and in enzymatic activities that all belong to the same pathway category. For example, each culture medium induces a specific oxidative response whatever the strain. On the contrary, differences between strains concern isolated proteins and enzymes within pathway categories in single environments. Our study confirms the predominance of genotype by environment interactions at the proteomic and enzyme activity levels. The buffering of genetic variation when considering life-history traits suggests a multiplicity of evolutionary strategies. For instance, the uropathogenic isolate CFT073 shows a deregulation of iron demand and increased oxidative stress response.

Elements of the cellular metabolic structure

A large number of studies have demonstrated the existence of metabolic covalent modifications in different molecular structures, which are able to store biochemical information that is not encoded by DNA. Some of these covalent mark patterns can be transmitted across generations (epigenetic changes). Recently, the emergence of Hopfield-like attractor dynamics has been observed in self-organized enzymatic networks, which have the capacity to store functional catalytic patterns that can be correctly recovered by specific input stimuli. Hopfield-like metabolic dynamics are stable and can be maintained as a long-term biochemical memory. In addition, specific molecular information can be transferred from the functional dynamics of the metabolic networks to the enzymatic activity involved in covalent post-translational modulation, so that determined functional memory can be embedded in multiple stable molecular marks. The metabolic dynamics governed by Hopfield-type attractors (functional processes), as well as the enzymatic covalent modifications of specific molecules (structural dynamic processes) seem to represent the two stages of the dynamical memory of cellular metabolism (metabolic memory). Epigenetic processes appear to be the structural manifestation of this cellular metabolic memory. Here, a new framework for molecular information storage in the cell is presented, which is characterized by two functionally and molecularly interrelated systems: a dynamic, flexible and adaptive system (metabolic memory) and an essentially conservative system (genetic memory). The molecular information of both systems seems to coordinate the physiological development of the whole cell.

Spatio-temporal remodeling of functional membrane microdomains organizes the signaling networks of a bacterium

Lipid rafts are membrane microdomains specialized in the regulation of numerous cellular processes related to membrane organization, as diverse as signal transduction, protein sorting, membrane trafficking or pathogen invasion. It has been proposed that this functional diversity would require a heterogeneous population of raft domains with varying compositions. However, a mechanism for such diversification is not known. We recently discovered that bacterial membranes organize their signal transduction pathways in functional membrane microdomains (FMMs) that are structurally and functionally similar to the eukaryotic lipid rafts. In this report, we took advantage of the tractability of the prokaryotic model Bacillus subtilis to provide evidence for the coexistence of two distinct families of FMMs in bacterial membranes, displaying a distinctive distribution of proteins specialized in different biological processes. One family of microdomains harbors the scaffolding flotillin protein FloA that selectively tethers proteins specialized in regulating cell envelope turnover and primary metabolism. A second population of microdomains containing the two scaffolding flotillins, FloA and FloT, arises exclusively at later stages of cell growth and specializes in adaptation of cells to stationary phase. Importantly, the diversification of membrane microdomains does not occur arbitrarily. We discovered that bacterial cells control the spatio-temporal remodeling of microdomains by restricting the activation of FloT expression to stationary phase. This regulation ensures a sequential assembly of functionally specialized membrane microdomains to strategically organize signaling networks at the right time during the lifespan of a bacterium.

Cellular membranes organize proteins related to signal transduction, protein sorting and membrane trafficking into the so-called lipid rafts. It has been proposed that the functional diversity of lipid rafts would require a heterogeneous population of raft domains with varying compositions. However, a mechanism for such diversification is not known due in part to the complexity that entails the manipulation of eukaryotic cells. The recent discovery that bacteria organize many cellular processes in membrane microdomains (FMMs), functionally similar to the eukaryotic lipid rafts, prompted us to explore FMMs diversity in the bacterial model Bacillus subtilis. We show that diversification of FMMs occurs in cells and gives rise to functionally distinct microdomains, which compartmentalize distinct signal transduction pathways and regulate the expression of different genetic programs. We discovered that FMMs diversification does not occur randomly. Cells sequentially regulate the specialization of the FMMs during cell growth to ensure an effective and diverse activation of signaling processes.

Evolutionary Conservation of Bacterial Essential Metabolic Genes across All Bacterial Culture Media

One of the basic postulates of molecular evolution is that functionally important genes should evolve slower than genes of lesser significance. Essential genes, whose knockout leads to a lethal phenotype are considered of high functional importance, yet whether they are truly more conserved than nonessential genes has been the topic of much debate, fuelled by a host of contradictory findings. Here we conduct the first large-scale study utilizing genome-scale metabolic modeling and spanning many bacterial species, which aims to answer this question. Using the novel Media Variation Analysis, we examine the range of conservation of essential vs. nonessential metabolic genes in a given species across all possible media. We are thus able to obtain for the first time, exact upper and lower bounds on the levels of differential conservation of essential genes for each of the species studied. The results show that bacteria do exhibit an overall tendency for differential conservation of their essential genes vs. their non-essential ones, yet this tendency is highly variable across species. We show that the model bacterium E. coli K12 may or may not exhibit differential conservation of essential genes depending on its growth medium, shedding light on previous experimental studies showing opposite trends.

Inferring causal metabolic signals that regulate the dynamic TORC1-dependent transcriptome

Cells react to nutritional cues in changing environments via the integrated action of signaling, transcriptional, and metabolic networks. Mechanistic insight into signaling processes is often complicated because ubiquitous feedback loops obscure causal relationships. Consequently, the endogenous inputs of many nutrient signaling pathways remain unknown. Recent advances for system-wide experimental data generation have facilitated the quantification of signaling systems, but the integration of multi-level dynamic data remains challenging. Here, we co-designed dynamic experiments and a probabilistic, model-based method to infer causal relationships between metabolism, signaling, and gene regulation. We analyzed the dynamic regulation of nitrogen metabolism by the target of rapamycin complex 1 (TORC1) pathway in budding yeast. Dynamic transcriptomic, proteomic, and metabolomic measurements along shifts in nitrogen quality yielded a consistent dataset that demonstrated extensive re-wiring of cellular networks during adaptation. Our inference method identified putative downstream targets of TORC1 and putative metabolic inputs of TORC1, including the hypothesized glutamine signal. The work provides a basis for further mechanistic studies of nitrogen metabolism and a general computational framework to study cellular processes.

Small regulatory RNA-induced growth rate heterogeneity of Bacillus subtilis

Isogenic bacterial populations can consist of cells displaying heterogeneous physiological traits. Small regulatory RNAs (sRNAs) could affect this heterogeneity since they act by fine-tuning mRNA or protein levels to coordinate the appropriate cellular behavior. Here we show that the sRNA RnaC/S1022 from the Gram-positive bacterium Bacillus subtilis can suppress exponential growth by modulation of the transcriptional regulator AbrB. Specifically, the post-transcriptional abrB-RnaC/S1022 interaction allows B. subtilis to increase the cell-to-cell variation in AbrB protein levels, despite strong negative autoregulation of the abrB promoter. This behavior is consistent with existing mathematical models of sRNA action, thus suggesting that induction of protein expression noise could be a new general aspect of sRNA regulation. Importantly, we show that the sRNA-induced diversity in AbrB levels generates heterogeneity in growth rates during the exponential growth phase. Based on these findings, we hypothesize that the resulting subpopulations of fast- and slow-growing B. subtilis cells reflect a bet-hedging strategy for enhanced survival of unfavorable conditions.

Bacterial cells that share the same genetic information can display very different phenotypes, even if they grow under identical conditions. Despite the relevance of this population heterogeneity for processes like drug resistance and development, the molecular players that induce heterogenic phenotypes are often not known. Here we report that in the Gram-positive model bacterium Bacillus subtilis a small regulatory RNA (sRNA) can induce heterogeneity in growth rates by increasing cell-to-cell variation in the levels of the transcriptional regulator AbrB, which is important for rapid growth. Remarkably, the observed variation in AbrB levels is induced post-transcriptionally because of AbrB’s negative autoregulation, and is not observed at the abrB promoter level. We show that our observations are consistent with mathematical models of sRNA action, thus suggesting that induction of protein expression noise could be a new general aspect of sRNA regulation. Since a low growth rate can be beneficial for cellular survival, we propose that the observed subpopulations of fast- and slow-growing B. subtilis cells reflect a bet-hedging strategy for enhanced survival of unfavorable conditions.

Gene expression of an arthrobacter in surfactant-enhanced biodegradation of a hydrophobic organic compound

Surfactants can affect the biodegradation process and the fate of hydrophobic organic compounds (HOCs) in the environment. Previous studies have shown that surfactants can enhance the biodegradation of HOCs by increasing cell surface hydrophobicity (CSH) and membrane fluidity. In this study, we took this work one step further by investigating the expression levels of three genes of Arthrobacter sp. SA02 in the biodegradation of phenanthrene as a typical HOC at different concentrations of sodium dodecyl benzenesulfonate (SDBS), which is a widely used surfactant. The Δ9 fatty acid desaturase gene codes for Δ9 fatty acid desaturase, which can convert saturated fatty acid to its unsaturated form. The ring-hydroxylating dioxygenase (RHDase) and the 1-hydroxyl-2-naphthoate dioxygenase (1H2Nase) genes code for the RHDase and 1H2Nase enzymes, respectively, which play a key role in decomposing doubly hydroxylated aromatic compounds. The results show that these three genes were upregulated in the presence of SDBS. On the basis of the genetic and physiological changes, we proposed a pathway that links the gene expression with the physiological phenomena, including CSH, membrane fluidity, and intracellular degradation. This study advances our understanding of the surfactant-enhanced biodegradation of HOCs at the gene level, and the proposed pathway should be further validated in the future.

Genome-wide mapping of TnrA-binding sites provides new insights into the TnrA regulon in Bacillus subtilis

Under nitrogen limitation conditions, Bacillus subtilis induces a sophisticated network of adaptation responses. More precisely, the B. subtilis TnrA regulator represses or activates directly or indirectly the expression of a hundred genes in response to nitrogen availability. The global TnrA regulon have already been identified among which some directly TnrA-regulated genes have been characterized. However, a genome-wide mapping of in vivo TnrA-binding sites was still needed to clearly define the set of genes directly regulated by TnrA. Using chromatin immunoprecipitation coupled with hybridization to DNA tiling arrays (ChIP-on-chip), we now provide in vivo evidence that TnrA reproducibly binds to 42 regions on the chromosome. Further analysis with real-time in vivo transcriptional profiling, combined with results from previous reports, allowed us to define the TnrA primary regulon. We identified 35 promoter regions fulfilling three criteria necessary to be part of this primary regulon: (i) TnrA binding in ChIP-on-chip experiments and/or in previous in vitro studies; (ii) the presence of a TnrA box; (iii) TnrA-dependent expression regulation. In addition, the TnrA primary regulon delimitation allowed us to improve the TnrA box consensus. Finally, our results reveal new interconnections between the nitrogen regulatory network and other cellular processes.

Mechanistic links between cellular trade-offs, gene expression, and growth

Intracellular processes rarely work in isolation but continually interact with the rest of the cell. In microbes, for example, we now know that gene expression across the whole genome typically changes with growth rate. The mechanisms driving such global regulation, however, are not well understood. Here we consider three trade-offs that, because of limitations in levels of cellular energy, free ribosomes, and proteins, are faced by all living cells and we construct a mechanistic model that comprises these trade-offs. Our model couples gene expression with growth rate and growth rate with a growing population of cells. We show that the model recovers Monod's law for the growth of microbes and two other empirical relationships connecting growth rate to the mass fraction of ribosomes. Further, we can explain growth-related effects in dosage compensation by paralogs and predict host-circuit interactions in synthetic biology. Simulating competitions between strains, we find that the regulation of metabolic pathways may have evolved not to match expression of enzymes to levels of extracellular substrates in changing environments but rather to balance a trade-off between exploiting one type of nutrient over another. Although coarse-grained, the trade-offs that the model embodies are fundamental, and, as such, our modeling framework has potentially wide application, including in both biotechnology and medicine.

A modular modulation method for achieving increases in metabolite production

Increasing the production of overproducing strains represents a great challenge. Here, we develop a modular modulation method to determine the key steps for genetic manipulation to increase metabolite production. The method consists of three steps: (i) modularization of the metabolic network into two modules connected by linking metabolites, (ii) change in the activity of the modules using auxiliary rates producing or consuming the linking metabolites in appropriate proportions and (iii) determination of the key modules and steps to increase production. The mathematical formulation of the method in matrix form shows that it may be applied to metabolic networks of any structure and size, with reactions showing any kind of rate laws. The results are valid for any type of conservation relationships in the metabolite concentrations or interactions between modules. The activity of the module may, in principle, be changed by any large factor. The method may be applied recursively or combined with other methods devised to perform fine searches in smaller regions. In practice, it is implemented by integrating to the producer strain heterologous reactions or synthetic pathways producing or consuming the linking metabolites. The new procedure may contribute to develop metabolic engineering into a more systematic practice.

A haploproficient interaction of the transaldolase paralogue NQM1 with the transcription factor VHR1 affects stationary phase survival and oxidative stress resistance

BACKGROUND

Studying the survival of yeast in stationary phase, known as chronological lifespan, led to the identification of molecular ageing factors conserved from yeast to higher organisms. To identify functional interactions among yeast chronological ageing genes, we conducted a haploproficiency screen on the basis of previously identified long-living mutants. For this, we created a library of heterozygous Saccharomyces cerevisiae double deletion strains and aged them in a competitive manner.

RESULTS

Stationary phase survival was prolonged in a double heterozygous mutant of the metabolic enzyme non-quiescent mutant 1 (NQM1), a paralogue to the pentose phosphate pathway enzyme transaldolase (TAL1), and the transcription factor vitamin H response transcription factor 1 (VHR1). We find that cells deleted for the two genes possess increased clonogenicity at late stages of stationary phase survival, but find no indication that the mutations delay initial mortality upon reaching stationary phase, canonically defined as an extension of chronological lifespan. We show that both genes influence the concentration of metabolites of glycolysis and the pentose phosphate pathway, central metabolic players in the ageing process, and affect osmolality of growth media in stationary phase cultures. Moreover, NQM1 is glucose repressed and induced in a VHR1 dependent manner upon caloric restriction, on non-fermentable carbon sources, as well as under osmotic and oxidative stress. Finally, deletion of NQM1 is shown to confer resistance to oxidizing substances.

CONCLUSIONS

The transaldolase paralogue NQM1 and the transcription factor VHR1 interact haploproficiently and affect yeast stationary phase survival. The glucose repressed NQM1 gene is induced under various stress conditions, affects stress resistance and this process is dependent on VHR1. While NQM1 appears not to function in the pentose phosphate pathway, the interplay of NQM1 with VHR1 influences the yeast metabolic homeostasis and stress tolerance during stationary phase, processes associated with yeast ageing.

Genome-wide identification of Bacillus subtilis Zur-binding sites associated with a Zur box expands its known regulatory network

Background

The Bacillus subtilis Zur transcription factor recognizes a specific DNA motif, the Zur box, to repress expression of genes in response to zinc availability. Although several Zur-regulated genes are well characterized, a genome-wide mapping of Zur-binding sites is needed to define further the set of genes directly regulated by this protein.

Results

Using chromatin immunoprecipitation coupled with hybridization to DNA tiling arrays (ChIP-on-chip), we reported the identification of 80 inter- and intragenic chromosomal sites bound by Zur. Seven Zur-binding regions constitute the Zur primary regulon while 35 newly identified targets were associated with a predicted Zur box. Using transcriptional fusions an intragenic Zur box was showed to promote a full Zur-mediated repression when placed within a promoter region. In addition, intragenic Zur boxes appeared to mediate a transcriptional cis-repressive effect (4- to 9-fold) but the function of Zur at these sites remains unclear. Zur binding to intragenic Zur boxes could prime an intricate mechanisms of regulation of the transcription elongation, possibly with other transcriptional factors. However, the disruption of zinc homeostasis in Δzur cells likely affects many cellular processes masking direct Zur-dependent effects. Finally, most Zur-binding sites were located near or within genes responsive to disulfide stress. These findings expand the potential Zur regulon and reveal unknown interconnections between zinc and redox homeostasis regulatory networks.

Conclusions

Our findings considerably expand the potential Zur regulon, and reveal a new level of complexity in Zur binding to its targets via a Zur box motif and via a yet unknown mechanism that remains to be characterized.

Electronic supplementary material

The online version of this article (doi:10.1186/s12866-015-0345-4) contains supplementary material, which is available to authorized users.

A systems biology approach toward understanding seed composition in soybean

BACKGROUND

The molecular, biochemical, and genetic mechanisms that regulate the complex metabolic network of soybean seed development determine the ultimate balance of protein, lipid, and carbohydrate stored in the mature seed. Many of the genes and metabolites that participate in seed metabolism are unknown or poorly defined; even more remains to be understood about the regulation of their metabolic networks. A global omics analysis can provide insights into the regulation of seed metabolism, even without a priori assumptions about the structure of these networks.

RESULTS

With the future goal of predictive biology in mind, we have combined metabolomics, transcriptomics, and metabolic flux technologies to reveal the global developmental and metabolic networks that determine the structure and composition of the mature soybean seed. We have coupled this global approach with interactive bioinformatics and statistical analyses to gain insights into the biochemical programs that determine soybean seed composition. For this purpose, we used Plant/Eukaryotic and Microbial Metabolomics Systems Resource (PMR, http://www.metnetdb.org/pmr, a platform that incorporates metabolomics data to develop hypotheses concerning the organization and regulation of metabolic networks, and MetNet systems biology tools http://www.metnetdb.org for plant omics data, a framework to enable interactive visualization of metabolic and regulatory networks.

CONCLUSIONS

This combination of high-throughput experimental data and bioinformatics analyses has revealed sets of specific genes, genetic perturbations and mechanisms, and metabolic changes that are associated with the developmental variation in soybean seed composition. Researchers can explore these metabolomics and transcriptomics data interactively at PMR.

A time resolved metabolomics study: the influence of different carbon sources during growth and starvation of Bacillus subtilis

In its natural environment, the soil, the Gram-positive model bacterium Bacillus subtilis frequently encounters nutrient limitation and other stress factors. Efficient adaptation mechanisms are necessary to cope with this wide range of environmental challenges. The ability to utilize diverse carbon sources represents a key adaptation process that allows B. subtilis to thrive in its natural habitat. To gain a comprehensive insight into the metabolism of B. subtilis, global metabolite analyses were performed during growth with glucose alone or glucose with either malate, fumarate or citrate as carbon/energy sources. Furthermore, to achieve a comprehensive coverage of a wide range of chemically different metabolites, complementary GC-MS, LC-MS and (1)H-NMR analyses were applied. This study reveals that the availability of different carbon sources results in different extracellular metabolite profiles whereas a regulated intracellular metabolite equilibrium was observed. In addition, the typical energy-starvation induced activation of the general stress sigma factor σ(B) was only observed upon entry into the stationary phase with glucose or glucose and malate as carbon sources.

Reconstructing genome-wide protein-protein interaction networks using multiple strategies with homologous mapping

BACKGROUND

One of the crucial steps toward understanding the biological functions of a cellular system is to investigate protein-protein interaction (PPI) networks. As an increasing number of reliable PPIs become available, there is a growing need for discovering PPIs to reconstruct PPI networks of interesting organisms. Some interolog-based methods and homologous PPI families have been proposed for predicting PPIs from the known PPIs of source organisms.

RESULTS

Here, we propose a multiple-strategy scoring method to identify reliable PPIs for reconstructing the mouse PPI network from two well-known organisms: human and fly. We firstly identified the PPI candidates of target organisms based on homologous PPIs, sharing significant sequence similarities (joint E-value ≤ 1 × 10(-40)), from source organisms using generalized interolog mapping. These PPI candidates were evaluated by our multiple-strategy scoring method, combining sequence similarities, normalized ranks, and conservation scores across multiple organisms. According to 106,825 PPI candidates in yeast derived from human and fly, our scoring method can achieve high prediction accuracy and outperform generalized interolog mapping. Experiment results show that our multiple-strategy score can avoid the influence of the protein family size and length to significantly improve PPI prediction accuracy and reflect the biological functions. In addition, the top-ranked and conserved PPIs are often orthologous/essential interactions and share the functional similarity. Based on these reliable predicted PPIs, we reconstructed a comprehensive mouse PPI network, which is a scale-free network and can reflect the biological functions and high connectivity of 292 KEGG modules, including 216 pathways and 76 structural complexes.

CONCLUSIONS

Experimental results show that our scoring method can improve the predicting accuracy based on the normalized rank and evolutionary conservation from multiple organisms. Our predicted PPIs share similar biological processes and cellular components, and the reconstructed genome-wide PPI network can reflect network topology and modularity. We believe that our method is useful for inferring reliable PPIs and reconstructing a comprehensive PPI network of an interesting organism.

Applications of computational modeling in metabolic engineering of yeast

Generally, a microorganism's phenotype can be described by its pattern of metabolic fluxes. Although fluxes cannot be measured directly, inference of fluxes is well established. In biotechnology the aim is often to increase the capacity of specific fluxes. For this, metabolic engineering methods have been developed and applied extensively. Many of these rely on balancing of intracellular metabolites, redox, and energy fluxes, using genome-scale models (GEMs) that in combination with appropriate objective functions and constraints can be used to predict potential gene targets for obtaining a preferred flux distribution. These methods point to strategies for altering gene expression; however, fluxes are often controlled by post-transcriptional events. Moreover, GEMs are usually not taking into account metabolic regulation, thermodynamics and enzyme kinetics. To facilitate metabolic engineering, tools from synthetic biology have emerged, enabling integration and assembly of naturally nonexistent, but well-characterized components into a living organism. To describe these systems kinetic models are often used and to integrate these systems with the standard metabolic engineering approach, it is necessary to expand the modeling of metabolism to consider kinetics of individual processes. This review will give an overview about models available for metabolic engineering of yeast and discusses their applications.

Bacterial economics: adaptation to stress conditions via stage-wise changes in the response mechanism

Common features of microbial adaptation are analysed with mathematical models and extended to stress conditions when the bacterial population declines before growing again. A parallel is drawn between bacterial and human communities in terms of non-mutation-based adaptation (acclimation) to stress. For a case study, the behaviour of Escherichia coli under osmotic stress, is detailed. It is suggested that stress modelling adaptation should be the focus of further developments in predictive food microbiology.

Bacillus subtilis and Escherichia coli essential genes and minimal cell factories after one decade of genome engineering

Investigation of essential genes, besides contributing to understanding the fundamental principles of life, has numerous practical applications. Essential genes can be exploited as building blocks of a tightly controlled cell ‘chassis’. Bacillus subtilis and Escherichia coli K-12 are both well-characterized model bacteria used as hosts for a plethora of biotechnological applications. Determination of the essential genes that constitute the B. subtilis and E. coli minimal genomes is therefore of the highest importance. Recent advances have led to the modification of the original B. subtilis and E. coli essential gene sets identified 10 years ago. Furthermore, significant progress has been made in the area of genome minimization of both model bacteria. This review provides an update, with particular emphasis on the current essential gene sets and their comparison with the original gene sets identified 10 years ago. Special attention is focused on the genome reduction analyses in B. subtilis and E. coli and the construction of minimal cell factories for industrial applications.

The return of metabolism: biochemistry and physiology of the pentose phosphate pathway

The pentose phosphate pathway (PPP) is a fundamental component of cellular metabolism. The PPP is important to maintain carbon homoeostasis, to provide precursors for nucleotide and amino acid biosynthesis, to provide reducing molecules for anabolism, and to defeat oxidative stress. The PPP shares reactions with the Entner–Doudoroff pathway and Calvin cycle and divides into an oxidative and non-oxidative branch. The oxidative branch is highly active in most eukaryotes and converts glucose 6-phosphate into carbon dioxide, ribulose 5-phosphate and NADPH. The latter function is critical to maintain redox balance under stress situations, when cells proliferate rapidly, in ageing, and for the ‘Warburg effect’ of cancer cells. The non-oxidative branch instead is virtually ubiquitous, and metabolizes the glycolytic intermediates fructose 6-phosphate and glyceraldehyde 3-phosphate as well as sedoheptulose sugars, yielding ribose 5-phosphate for the synthesis of nucleic acids and sugar phosphate precursors for the synthesis of amino acids. Whereas the oxidative PPP is considered unidirectional, the non-oxidative branch can supply glycolysis with intermediates derived from ribose 5-phosphate and vice versa, depending on the biochemical demand. These functions require dynamic regulation of the PPP pathway that is achieved through hierarchical interactions between transcriptome, proteome and metabolome. Consequently, the biochemistry and regulation of this pathway, while still unresolved in many cases, are archetypal for the dynamics of the metabolic network of the cell. In this comprehensive article we review seminal work that led to the discovery and description of the pathway that date back now for 80 years, and address recent results about genetic and metabolic mechanisms that regulate its activity. These biochemical principles are discussed in the context of PPP deficiencies causing metabolic disease and the role of this pathway in biotechnology, bacterial and parasite infections, neurons, stem cell potency and cancer metabolism.

The multidrug ABC transporter BmrC/BmrD of Bacillus subtilis is regulated via a ribosome-mediated transcriptional attenuation mechanism

Expression of particular drug transporters in response to antibiotic pressure is a critical element in the development of bacterial multidrug resistance, and represents a serious concern for human health. To obtain a better understanding of underlying regulatory mechanisms, we have dissected the transcriptional activation of the ATP-binding cassette (ABC) transporter BmrC/BmrD of the Gram-positive model bacterium Bacillus subtilis. By using promoter-GFP fusions and live cell array technology, we demonstrate a temporally controlled transcriptional activation of the bmrCD genes in response to antibiotics that target protein synthesis. Intriguingly, bmrCD expression only occurs during the late-exponential and stationary growth stages, irrespective of the timing of the antibiotic challenge. We show that this is due to tight transcriptional control by the transition state regulator AbrB. Moreover, our results show that the bmrCD genes are co-transcribed with bmrB (yheJ), a small open reading frame immediately upstream of bmrC that harbors three alternative stem-loop structures. These stem-loops are apparently crucial for antibiotic-induced bmrCD transcription. Importantly, the antibiotic-induced bmrCD expression requires translation of bmrB, which implies that BmrB serves as a regulatory leader peptide. Altogether, we demonstrate for the first time that a ribosome-mediated transcriptional attenuation mechanism can control the expression of a multidrug ABC transporter.

Reconstruction of insulin signal flow from phosphoproteome and metabolome data

Cellular homeostasis is regulated by signals through multiple molecular networks that include protein phosphorylation and metabolites. However, where and when the signal flows through a network and regulates homeostasis has not been explored. We have developed a reconstruction method for the signal flow based on time-course phosphoproteome and metabolome data, using multiple databases, and have applied it to acute action of insulin, an important hormone for metabolic homeostasis. An insulin signal flows through a network, through signaling pathways that involve 13 protein kinases, 26 phosphorylated metabolic enzymes, and 35 allosteric effectors, resulting in quantitative changes in 44 metabolites. Analysis of the network reveals that insulin induces phosphorylation and activation of liver-type phosphofructokinase 1, thereby controlling a key reaction in glycolysis. We thus provide a versatile method of reconstruction of signal flow through the network using phosphoproteome and metabolome data.

Tuning transcription of nutrient utilization genes to catabolic rate promotes growth in a gut bacterium

Cells respond to nutrient availability by expressing nutrient catabolic genes. We report that the regulator controlling utilization of chondroitin sulphate (CS) in the mammalian gut symbiont Bacteroides thetaiotaomicron is activated by an intermediate in CS breakdown rather than CS itself. We determine that the rate-determining enzyme in CS breakdown is responsible for degrading this intermediate and establish that the levels of the enzyme increase 100-fold, whereas those of the regulator remain constant upon exposure to CS. Because enzyme and regulator compete for the intermediate, B. thetaiotaomicron tunes transcription of CS utilization genes to CS catabolic rate. This tuning results in a transient increase in CS utilization transcripts upon exposure to excess CS. Constitutive expression of the rate-determining enzyme hindered activation of CS utilization genes and growth on CS. An analogous mechanism regulates heparin utilization genes, suggesting that the identified strategy aids B. thetaiotaomicron in the competitive gut environment.

Source and regulation of flux variability in Escherichia coli

BACKGROUND

Metabolic responses are essential for the adaptation of microorganisms to changing environmental conditions. The repertoire of flux responses that the metabolic network can display in different external conditions may be quantified applying flux variability analysis to genome-scale metabolic reconstructions.

RESULTS

A procedure is developed to classify and quantify the sources of flux variability. We apply the procedure to the latest Escherichia coli metabolic reconstruction, in glucose minimal medium, with an additional constraint to account for the mechanism coordinating carbon and nitrogen utilization mediated by α-ketoglutarate. Flux variability can be decomposed into three components: internal, external and growth variability. Unexpectedly, growth variability is the only significant component of flux variability in the physiological ranges of glucose, oxygen and ammonia uptake rates. To obtain substantial increases in metabolic flexibility, E. coli must decrease growth rate to suboptimal values. This growth-flexibility trade-off gives a straightforward interpretation to recent work showing that most overall cell-to-cell flux variability in a population of E. coli can be attained sampling a small number of enzymes most likely to constrain cell growth. Importantly, it provides an explanation for the global reorganization occurring in metabolic networks during adaptations to environmental challenges. The calculations were repeated with a pathogenic strain and an old reconstruction of the commensal strain, having less than 50% of the reactions of the latest reconstruction, obtaining the same general conclusions.

CONCLUSIONS

In E. coli growing on glucose, growth variability is the only significant component of flux variability for all physiological conditions explored. Increasing flux variability requires reducing growth to suboptimal values. The growth-flexibility trade-off operates in physiological and evolutionary adaptations, and provides an explanation for the global reorganization occurring during adaptations to environmental challenges. The results obtained do not rely on the knowledge of kinetic and regulatory details of the system and are highly robust to incomplete or incorrect knowledge of the reaction network.

Protein-to-mRNA ratios are conserved between Pseudomonas aeruginosa strains

Recent studies have shown that the concentrations of proteins expressed from orthologous genes are often conserved across organisms and to a greater extent than the abundances of the corresponding mRNAs. However, such studies have not distinguished between evolutionary (e.g., sequence divergence) and environmental (e.g., growth condition) effects on the regulation of steady-state protein and mRNA abundances. Here, we systematically investigated the transcriptome and proteome of two closely related Pseudomonas aeruginosa strains, PAO1 and PA14, under identical experimental conditions, thus controlling for environmental effects. For 703 genes observed by both shotgun proteomics and microarray experiments, we found that the protein-to-mRNA ratios are highly correlated between orthologous genes in the two strains to an extent comparable to protein and mRNA abundances. In spite of this high molecular similarity between PAO1 and PA14, we found that several metabolic, virulence, and antibiotic resistance genes are differentially expressed between the two strains, mostly at the protein but not at the mRNA level. Our data demonstrate that the magnitude and direction of the effect of protein abundance regulation occurring after the setting of mRNA levels is conserved between bacterial strains and is important for explaining the discordance between mRNA and protein abundances.

An evolutionarily conserved RNase-based mechanism for repression of transcriptional positive autoregulation

It is known that environmental context influences the degree of regulation at the transcriptional and post-transcriptional levels. However, the principles governing the differential usage and interplay of regulation at these two levels are not clear. Here, we show that the integration of transcriptional and post-transcriptional regulatory mechanisms in a characteristic network motif drives efficient environment-dependent state transitions. Through phenotypic screening, systems analysis, and rigorous experimental validation, we discovered an RNase (VNG2099C) in Halobacterium salinarum that is transcriptionally co-regulated with genes of the aerobic physiologic state but acts on transcripts of the anaerobic state. Through modelling and experimentation we show that this arrangement generates an efficient state-transition switch, within which RNase-repression of a transcriptional positive autoregulation (RPAR) loop is critical for shutting down ATP-consuming active potassium uptake to conserve energy required for salinity adaptation under aerobic, high potassium, or dark conditions. Subsequently, we discovered that many Escherichia coli operons with energy-associated functions are also putatively controlled by RPAR indicating that this network motif may have evolved independently in phylogenetically distant organisms. Thus, our data suggest that interplay of transcriptional and post-transcriptional regulation in the RPAR motif is a generalized principle for efficient environment-dependent state transitions across prokaryotes.

Co-factor insertion and disulfide bond requirements for twin-arginine translocase-dependent export of the Bacillus subtilis Rieske protein QcrA

The twin-arginine translocation (Tat) pathway can transport folded and co-factor-containing cargo proteins over bacterial cytoplasmic membranes. Functional Tat machinery components, a folded state of the cargo protein and correct co-factor insertion in the cargo protein are generally considered as prerequisites for successful translocation. The present studies were aimed at a dissection of these requirements with regard to the Rieske iron-sulfur protein QcrA of Bacillus subtilis. Notably, QcrA is a component of the cytochrome bc1 complex, which is conserved from bacteria to man. Single amino acid substitutions were introduced into the Rieske domain of QcrA to prevent either co-factor binding or disulfide bond formation. Both types of mutations precluded QcrA translocation. Importantly, a proofreading hierarchy was uncovered, where a QcrA mutant defective in disulfide bonding was quickly degraded, whereas mutant QcrA proteins defective in co-factor binding accumulated in the cytoplasm and membrane. Altogether, these are the first studies on Tat-dependent protein translocation where both oxidative folding and co-factor attachment have been addressed in a single native molecule.

Bacillus subtilis Systems Biology: Applications of -Omics Techniques to the Study of Endospore Formation

Endospore-forming bacteria, with Bacillus subtilis being the prevalent model organism, belong to the phylum Firmicutes. Although the last common ancestor of all Firmicutes is likely to have been an endospore-forming species, not every lineage in the phylum has maintained the ability to produce endospores (hereafter, spores). In 1997, the release of the full genome sequence for B. subtilis strain 168 marked the beginning of the genomic era for the study of spore formation (sporulation). In this original genome sequence, 139 of the 4,100 protein-coding genes were annotated as sporulation genes. By the time a revised genome sequence with updated annotations was published in 2009, that number had increased significantly, especially since transcriptional profiling studies (transcriptomics) led to the identification of several genes expressed under the control of known sporulation transcription factors. Over the past decade, genome sequences for multiple spore-forming species have been released (including several strains in the Bacillus anthracis / Bacillus cereus group and many Clostridium species), and phylogenomic analyses have revealed many conserved sporulation genes. Parallel advances in transcriptomics led to the identification of small untranslated regulatory RNAs (sRNAs), including some that are expressed during sporulation. An extended array of -omics techniques, i.e., techniques designed to probe gene function on a genome-wide scale, such as proteomics, metabolomics, and high-throughput protein localization studies, have been implemented in microbiology. Combined with the use of new computational methods for predicting gene function and inferring regulatory relationships on a global scale, these -omics approaches are uncovering novel information about sporulation and a variety of other bacterial cell processes.

Escherichia coli achieves faster growth by increasing catalytic and translation rates of proteins

Regulation levels of the gene expression cascade controlling protein levels and metabolic fluxes for cells to achieve faster growth have not been elaborated in acceptable detail. Furthermore, there is need for specific growth rate (μ) dependent absolute quantitative transcriptome and proteome data to understand the molecular relationships for enabling cells to modify μ. We address these questions, for the first time, by presenting regulatory strategies for more efficient metabolism of Escherichia coli at higher μ by statistical covariance analysis of genome-wide intracellular mRNA and protein concentrations coupled to metabolic flux analysis in the steady state range of μ = 0.11-0.49 h(-1). Our analyses show dominating post-transcriptional control of protein abundances and post-translational control of flux rates. On average, E. coli achieved five-times faster growth through 3.7-fold increase of apparent catalytic rates of enzymes (kapp) and 2.5-fold increased translation rates, demonstrating the relevance of post-translational regulation for increasing flux throughput. Interestingly, pathways carrying the highest flux showed both high protein abundance and kapp values. Furthermore, co-regulation analysis of enzymatic capacities revealed tightly coupled regulatory dependencies of protein synthesis and RNA precursor synthesis, substrate utilization, biosynthetic and energy generation pathways carrying the highest flux. We also observed metabolic pathway and COG specific protein and metabolic flux control levels, protein expression costs and genome-wide principles for translation efficiency and transcription unit polarity. This work contributes to the much needed quantitative understanding of coordinated gene expression regulation and metabolic flux control. Our findings will also advance modeling and metabolic engineering of industrial strains.

Inhibition of triosephosphate isomerase by phosphoenolpyruvate in the feedback-regulation of glycolysis

The inhibition of triosephosphate isomerase (TPI) in glycolysis by the pyruvate kinase (PK) substrate phosphoenolpyruvate (PEP) results in a newly discovered feedback loop that counters oxidative stress in cancer and actively respiring cells. The mechanism underlying this inhibition is illuminated by the co-crystal structure of TPI with bound PEP at 1.6 Å resolution, and by mutational studies guided by the crystallographic results. PEP is bound to the catalytic pocket of TPI and occludes substrate, which accounts for the observation that PEP competitively inhibits the interconversion of glyceraldehyde-3-phosphate and dihydroxyacetone phosphate. Replacing an isoleucine residue located in the catalytic pocket of TPI with valine or threonine altered binding of substrates and PEP, reducing TPI activity in vitro and in vivo. Confirming a TPI-mediated activation of the pentose phosphate pathway (PPP), transgenic yeast cells expressing these TPI mutations accumulate greater levels of PPP intermediates and have altered stress resistance, mimicking the activation of the PK-TPI feedback loop. These results support a model in which glycolytic regulation requires direct catalytic inhibition of TPI by the pyruvate kinase substrate PEP, mediating a protective metabolic self-reconfiguration of central metabolism under conditions of oxidative stress.

The CcpA regulon of Streptococcus suis reveals novel insights into the regulation of the streptococcal central carbon metabolism by binding of CcpA to two distinct binding motifs

Streptococcus suis (S. suis) is a neglected zoonotic streptococcus causing fatal diseases in humans and in pigs. The transcriptional regulator CcpA (catabolite control protein A) is involved in the metabolic adaptation to different carbohydrate sources and virulence of S. suis and other pathogenic streptococci. In this study, we determined the DNA binding characteristics of CcpA and identified the CcpA regulon during growth of S. suis. Electrophoretic mobility shift analyses showed promiscuous DNA binding of CcpA to cognate cre sites in vitro. In contrast, sequencing of immunoprecipitated chromatin revealed two specific consensus motifs, a pseudo-palindromic cre motif (WWGAAARCGYTTTCWW) and a novel cre2 motif (TTTTYHWDHHWWTTTY), within the regulatory elements of the genes directly controlled by CcpA. Via these elements CcpA regulates expression of genes involved in carbohydrate uptake and conversion, and in addition in important metabolic pathways of the central carbon metabolism, like glycolysis, mixed-acid fermentation, and the fragmentary TCA cycle. Furthermore, our analyses provide evidence that CcpA regulates the genes of the central carbon metabolism by binding either the pseudo-palindromic cre motif or the cre2 motif in a HPr(Ser)∼P independent conformation.

Environmental dependence of stationary-phase metabolism in Bacillus subtilis and Escherichia coli

When microbes lack the nutrients necessary for growth, they enter stationary phase. In cases when energy sources are still present in the environment, they must decide whether to continue to use their metabolic program to harvest the available energy. Here we characterized the metabolic response to a variety of types of nutrient starvation in Escherichia coli and Bacillus subtilis. We found that E. coli exhibits a range of phenotypes, with the lowest metabolic rates under nitrogen starvation and highest rates under magnesium starvation. In contrast, the phenotype of B. subtilis was dominated by its decision to form metabolically inactive endospores. While its metabolic rates under most conditions were thus lower than those of E. coli, when sporulation was suppressed by a genetic perturbation or an unnatural starvation condition, the situation was reversed. To further probe stationary-phase metabolism, we used quantitative metabolomics to investigate possible small-molecule signals that may regulate the metabolic rate of E. coli and initiate sporulation in B. subtilis. We hypothesize a role for phosphoenolpyruvate (PEP) in regulating E. coli glucose uptake and for the redox cofactors NAD(H) and NADP(H) in initiation of sporulation. Our work is directly relevant to synthetic biology and metabolic engineering, where active metabolism during stationary phase, which uncouples production from growth, remains an elusive goal.