Global network reorganization during dynamic adaptations of Bacillus subtilis metabolism

Advancing metabolic models with kinetic information

Kinetic models are crucial to quantitatively understand and predict how functional behavior emerges from dynamic concentration changes of cellular components. The current challenge is on resolving uncertainties about parameter values of reaction kinetics. Additionally, there are also major structural uncertainties due to unknown molecular interactions and only putatively assigned regulatory functions. What if one or few key regulators of biochemical reactions are missing in a metabolic model? By reviewing current advances in building kinetic models of metabolism, we found that such models experience a paradigm shift away from fitting parameters towards identifying key regulatory interactions.

Monitoring intraspecies competition in a bacterial cell population by cocultivation of fluorescently labelled strains

Many microorganisms such as bacteria proliferate extremely fast and the populations may reach high cell densities. Small fractions of cells in a population always have accumulated mutations that are either detrimental or beneficial for the cell. If the fitness effect of a mutation provides the subpopulation with a strong selective growth advantage, the individuals of this subpopulation may rapidly outcompete and even completely eliminate their immediate fellows. Thus, small genetic changes and selection-driven accumulation of cells that have acquired beneficial mutations may lead to a complete shift of the genotype of a cell population. Here we present a procedure to monitor the rapid clonal expansion and elimination of beneficial and detrimental mutations, respectively, in a bacterial cell population over time by cocultivation of fluorescently labeled individuals of the Gram-positive model bacterium Bacillus subtilis. The method is easy to perform and very illustrative to display intraspecies competition among the individuals in a bacterial cell population.

Mitochondria in ageing: there is metabolism beyond the ROS

Mitochondria are responsible for a series of metabolic functions. Superoxide leakage from the respiratory chain and the resulting cascade of reactive oxygen species-induced damage, as well as mitochondrial metabolism in programmed cell death, have been intensively studied during ageing in single-cellular and higher organisms. Changes in mitochondrial physiology and metabolism resulting in ROS are thus considered to be hallmarks of ageing. In this review, we address 'other' metabolic activities of mitochondria, carbon metabolism (the TCA cycle and related underground metabolism), the synthesis of Fe/S clusters and the metabolic consequences of mitophagy. These important mitochondrial activities are hitherto less well-studied in the context of cellular and organismic ageing. In budding yeast, they strongly influence replicative, chronological and hibernating lifespan, connecting the diverse ageing phenotypes studied in this single-cellular model organism. Moreover, there is evidence that similar processes equally contribute to ageing of higher organisms as well. In this scenario, increasing loss of metabolic integrity would be one driving force that contributes to the ageing process. Understanding mitochondrial metabolism may thus be required for achieving a unifying theory of eukaryotic ageing.

Complex formation between malate dehydrogenase and isocitrate dehydrogenase from Bacillus subtilis is regulated by tricarboxylic acid cycle metabolites

In Bacillus subtilis, recent in vivo studies revealed that particular enzymes of the tricarboxylic acid cycle form complexes that allow an efficient transfer of metabolites. Remarkably, a complex of the malate dehydrogenase (Mdh) (EC 1.1.1.37) with isocitrate dehydrogenase (Icd) (EC 1.1.1.42) was identified, although both enzymes do not catalyze subsequent reactions. In the present study, the interactions between these enzymes were characterized in vitro by surface plasmon resonance in the absence and presence of their substrates and cofactors. These analyses revealed a weak but specific interaction between Mdh and Icd, which was specifically stimulated by a mixture of substrates and cofactors of Icd: isocitrate, NADP(+) and Mg(2+). Wild-type Icd converted these substrates too fast, preventing any valid quantitative analysis of the interaction with Mdh. Therefore, binding of the IcdS104P mutant to Mdh was quantified because the mutation reduced the enzymatic activity by 174-fold but did not affect the stimulatory effect of substrates and cofactors on Icd-Mdh complex formation. The analysis of the unstimulated Mdh-IcdS104P interaction revealed kinetic constants of k(a) = 2.0 ± 0.2 × 10(2) m(-1) ·s(-1) and k(d) = 1.0 ± 0.1 × 10(-3) ·s(-1) and a K(D) value of 5.0 ± 0.1 μm. Addition of isocitrate, NADP(+) and Mg(2+) stimulated the affinity of IcdS104P to Mdh by 33-fold (K(D) = 0.15 ± 0.01 μm, k(a) = 1.7 ± 0.7 × 10(3) m(-1) ·s(-1), k(d) = 2.6 ± 0.6 × 10(-4) ·s(-1)). Analyses of the enzymatic activities of wild-type Icd and Mdh showed that Icd activity doubles in the presence of Mdh, whereas Mdh activity was slightly reduced by Icd. In summary, these data indicate substrate control of complex formation in the tricarboxylic acid cycle metabolon assembly and maintenance of the α-ketoglutarate supply for amino acid anabolism in vivo.

Metabolomic analysis of cold acclimation of Arctic Mesorhizobium sp. strain N33

Arctic Mesorhizobium sp. N33 isolated from nodules of Oxytropis arctobia in Canada's eastern Arctic has a growth temperature range from 0 °C to 30 °C and is a well-known cold-adapted rhizobia. The key molecular mechanisms underlying cold adaptation in Arctic rhizobia remains totally unknown. Since the concentration and contents of metabolites are closely related to stress adaptation, we applied GC-MS and NMR to identify and quantify fatty acids and water soluble compounds possibly related to low temperature acclimation in strain N33. Bacterial cells were grown at three different growing temperatures (4 °C, 10 °C and 21 °C). Cells from 21 °C were also cold-exposed to 4°C for different times (2, 4, 8, 60 and 240 minutes). We identified that poly-unsaturated linoleic acids 18:2 (9, 12) & 18:2 (6, 9) were more abundant in cells growing at 4 or 10 °C, than in cells cultivated at 21 °C. The mono-unsaturated phospho/neutral fatty acids myristoleic acid 14:1(11) were the most significantly overexpressed (45-fold) after 1 hour of exposure to 4 °C. As reported in the literature, these fatty acids play important roles in cold adaptability by supplying cell membrane fluidity, and by providing energy to cells. Analysis of water-soluble compounds revealed that isobutyrate, sarcosine, threonine and valine were more accumulated during exposure to 4 °C. These metabolites might play a role in conferring cold acclimation to strain N33 at 4 °C, probably by acting as cryoprotectants. Isobutyrate was highly upregulated (19.4-fold) during growth at 4 °C, thus suggesting that this compound is a precursor for the cold-regulated fatty acids modification to low temperature adaptation.

Inactivation of metabolic genes causes short- and long-range dys-regulation in Escherichia coli metabolic network

The metabolic network in E. coli can be severely affected by the inactivation of metabolic genes that are required to catabolize a nutrient (D-galactose). We hypothesized that the resulting accumulation of small molecules can yield local as well as systemic effects on the metabolic network. Analysis of metabolomics data in wild-type and D-galactose non-utilizing mutants, galT, galU and galE, reveal the large metabolic differences between the wild-type and the mutants when the strains were grown in D-galactose. Network mapping suggested that the enzymatic defects affected the metabolic modules located both at short- and long-ranges from the D-galactose metabolic module. These modules suggested alterations in glutathione, energy, nucleotide and lipid metabolism and disturbed carbon to nitrogen ratio in mutant strains. The altered modules are required for normal cell growth for the wild-type strain, explaining why the cell growth is inhibited in the mutants in the presence of D-galactose. Identification of these distance-based dys-regulations would enhance the systems level understanding of metabolic networks of microorganisms having importance in biomedical and biotechnological research.

Transcriptional regulation is insufficient to explain substrate-induced flux changes in Bacillus subtilis

Regulation of enzyme expression is one key mechanism by which cells control their metabolic programs. In this work, a quantitative analysis of metabolism in a model bacterium under different conditions shows that expression alone cannot explain the majority of the observed metabolic changes.

Most enzymes are indeed highly expressed in conditions where they are more active.

Quantitatively, however, the observed changes in expression between conditions do not match the changes in activity for most enzymes.

A good quantitative match is only observed for enzymes involved in the TCA cycle.

Metabolomics reveals that increased substrate availability explains only a few instances of changes in activity.

One of the key ways in which microbes are thought to regulate their metabolism is by modulating the availability of enzymes through transcriptional regulation. However, the limited success of efforts to manipulate metabolic fluxes by rewiring the transcriptional network has cast doubt on the idea that transcript abundance controls metabolic fluxes. In this study, we investigate control of metabolic flux in the model bacterium Bacillus subtilis by quantifying fluxes, transcripts, and metabolites in eight metabolic states enforced by different environmental conditions. We find that most enzymes whose flux switches between on and off states, such as those involved in substrate uptake, exhibit large corresponding transcriptional changes. However, for the majority of enzymes in central metabolism, enzyme concentrations were insufficient to explain the observed fluxes—only for a number of reactions in the tricarboxylic acid cycle were enzyme changes approximately proportional to flux changes. Surprisingly, substrate changes revealed by metabolomics were also insufficient to explain observed fluxes, leaving a large role for allosteric regulation and enzyme modification in the control of metabolic fluxes.

Network component analysis provides quantitative insights on an Arabidopsis transcription factor-gene regulatory network

Background

Gene regulatory networks (GRNs) are models of molecule-gene interactions instrumental in the coordination of gene expression. Transcription factor (TF)-GRNs are an important subset of GRNs that characterize gene expression as the effect of TFs acting on their target genes. Although such networks can qualitatively summarize TF-gene interactions, it is highly desirable to quantitatively determine the strengths of the interactions in a TF-GRN as well as the magnitudes of TF activities. To our knowledge, such analysis is rare in plant biology. A computational methodology developed for this purpose is network component analysis (NCA), which has been used for studying large-scale microbial TF-GRNs to obtain nontrivial, mechanistic insights. In this work, we employed NCA to quantitatively analyze a plant TF-GRN important in floral development using available regulatory information from AGRIS, by processing previously reported gene expression data from four shoot apical meristem cell types.

Results

The NCA model satisfactorily accounted for gene expression measurements in a TF-GRN of seven TFs (LFY, AG, SEPALLATA3 [SEP3], AP2, AGL15, HY5 and AP3/PI) and 55 genes. NCA found strong interactions between certain TF-gene pairs including LFY → MYB17, AG → CRC, AP2 → RD20, AGL15 → RAV2 and HY5 → HLH1, and the direction of the interaction (activation or repression) for some AGL15 targets for which this information was not previously available. The activity trends of four TFs - LFY, AG, HY5 and AP3/PI as deduced by NCA correlated well with the changes in expression levels of the genes encoding these TFs across all four cell types; such a correlation was not observed for SEP3, AP2 and AGL15.

Conclusions

For the first time, we have reported the use of NCA to quantitatively analyze a plant TF-GRN important in floral development for obtaining nontrivial information about connectivity strengths between TFs and their target genes as well as TF activity. However, since NCA relies on documented connectivity information about the underlying TF-GRN, it is currently limited in its application to larger plant networks because of the lack of documented connectivities. In the future, the identification of interactions between plant TFs and their target genes on a genome scale would allow the use of NCA to provide quantitative regulatory information about plant TF-GRNs, leading to improved insights on cellular regulatory programs.

New types of experimental data shape the use of enzyme kinetics for dynamic network modeling

Since the publication of Leonor Michaelis and Maude Menten's paper on the reaction kinetics of the enzyme invertase in 1913, molecular biology has evolved tremendously. New measurement techniques allow in vivo characterization of the whole genome, proteome or transcriptome of cells, whereas the classical enzyme essay only allows determination of the two Michaelis-Menten parameters V and K(m). Nevertheless, Michaelis-Menten kinetics are still commonly used, not only in the in vitro context of enzyme characterization but also as a rate law for enzymatic reactions in larger biochemical reaction networks. In this review, we give an overview of the historical development of kinetic rate laws originating from Michaelis-Menten kinetics over the past 100 years. Furthermore, we briefly summarize the experimental techniques used for the characterization of enzymes, and discuss web resources that systematically store kinetic parameters and related information. Finally, describe the novel opportunities that arise from using these data in dynamic mathematical modeling. In this framework, traditional in vitro approaches may be combined with modern genome-scale measurements to foster thorough understanding of the underlying complex mechanisms.

Elementary flux modes analysis of functional domain networks allows a better metabolic pathway interpretation

Metabolic network analysis is an important step for the functional understanding of biological systems. In these networks, enzymes are made of one or more functional domains often involved in different catalytic activities. Elementary flux mode (EFM) analysis is a method of choice for the topological studies of these enzymatic networks. In this article, we propose to use an EFM approach on networks that encompass available knowledge on structure-function. We introduce a new method that allows to represent the metabolic networks as functional domain networks and provides an application of the algorithm for computing elementary flux modes to analyse them. Any EFM that can be represented using the classical representation can be represented using our functional domain network representation but the fine-grained feature of functional domain networks allows to highlight new connections in EFMs. This methodology is applied to the tricarboxylic acid cycle (TCA cycle) of Bacillus subtilis, and compared to the classical analyses. This new method of analysis of the functional domain network reveals that a specific inhibition on the second domain of the lipoamide dehydrogenase (pdhD) component of pyruvate dehydrogenase complex leads to the loss of all fluxes. Such conclusion was not predictable in the classical approach.

Tpo1-mediated spermine and spermidine export controls cell cycle delay and times antioxidant protein expression during the oxidative stress response

Cellular export of the metabolites spermine and spermidine is induced under oxidative stress to control the timing of antioxidant gene expression and cell cycle delay and to promote cell survival.

Cells counteract oxidative stress by altering metabolism, cell cycle and gene expression. However, the mechanisms that coordinate these adaptations are only marginally understood. Here we provide evidence that timing of these responses in yeast requires export of the polyamines spermidine and spermine. We show that during hydrogen peroxide (H₂O₂) exposure, the polyamine transporter Tpo1 controls spermidine and spermine concentrations and mediates induction of antioxidant proteins, including Hsp70, Hsp90, Hsp104 and Sod1. Moreover, Tpo1 determines a cell cycle delay during adaptation to increased oxidant levels, and affects H₂O₂ tolerance. Thus, central components of the stress response are timed through Tpo1-controlled polyamine export.

The Tat system of Gram-positive bacteria

The twin-arginine protein translocation (Tat) system has a unique ability to translocate folded and co-factor-containing proteins across lipid bilayers. The Tat pathway is present in bacteria, archaea and in the thylakoid membranes of chloroplasts and, depending on the organism and environmental conditions, it can be deemed important for cell survival, virulence or bioproduction. This review provides an overview of the current understanding of the Tat system with specific focus on Gram-positive bacteria. The 'universal minimal Tat system' is composed of a TatA and a TatC protein. However, this pathway is more commonly composed of two TatA-like proteins and one TatC protein. Often the TatA-like proteins have diverged to have two different functions and, in this case, the second TatA-like protein is usually referred to as TatB. The correct folding and/or incorporation of co-factors are requirements for translocation, and the known quality control mechanisms are examined in this review. A number of examples of crosstalk between the Tat system and other protein transport systems, such as the Sec-YidC translocon and signal peptidases or sheddases are also discussed. Further, an overview of specific Gram-positive bacterial Tat systems found in monoderm and diderm species is detailed. Altogether, this review highlights the unique features of Gram-positive bacterial Tat systems and pinpoints key questions that remain to be addressed in future research. This article is part of a Special Issue entitled: Protein trafficking and secretion in bacteria. Guest Editors: Anastassios Economou and Ross Dalbey.

Steady-state metabolite concentrations reflect a balance between maximizing enzyme efficiency and minimizing total metabolite load

Steady-state metabolite concentrations in a microorganism typically span several orders of magnitude. The underlying principles governing these concentrations remain poorly understood. Here, we hypothesize that observed variation can be explained in terms of a compromise between factors that favor minimizing metabolite pool sizes (e.g. limited solvent capacity) and the need to effectively utilize existing enzymes. The latter requires adequate thermodynamic driving force in metabolic reactions so that forward flux substantially exceeds reverse flux. To test this hypothesis, we developed a method, metabolic tug-of-war (mTOW), which computes steady-state metabolite concentrations in microorganisms on a genome-scale. mTOW is shown to explain up to 55% of the observed variation in measured metabolite concentrations in E. coli and C. acetobutylicum across various growth media. Our approach, based strictly on first thermodynamic principles, is the first method that successfully predicts high-throughput metabolite concentration data in bacteria across conditions.

Dissecting the energy metabolism in Mycoplasma pneumoniae through genome-scale metabolic modeling

A new genome-scale metabolic reconstruction of M. pneumonia is used in combination with external metabolite measurement and protein abundance measurements to quantitatively explore the energy metabolism of this genome-reduce human pathogen.

We established a detailed biomass composition for M. pneumoniae, thus allowing for growth simulations.

Using our metabolic model, we corrected the metabolic network topology and the functional annotation of key metabolic enzymes.

M. pneumoniae, unlike other laboratory-grown bacteria, uses a high fraction of energy (up to 89%) for cellular maintenance and not for growth.

Simulating different growth conditions as well as single and double mutant phenotypes, we analyzed pathway connectivity and the impact of gene deletions on the growth performance of M. pneumoniae, highlighting the limited adaptive capabilities of this minimal model organism.

Mycoplasma pneumoniae, a threatening pathogen with a minimal genome, is a model organism for bacterial systems biology for which substantial experimental information is available. With the goal of understanding the complex interactions underlying its metabolism, we analyzed and characterized the metabolic network of M. pneumoniae in great detail, integrating data from different omics analyses under a range of conditions into a constraint-based model backbone. Iterating model predictions, hypothesis generation, experimental testing, and model refinement, we accurately curated the network and quantitatively explored the energy metabolism. In contrast to other bacteria, M. pneumoniae uses most of its energy for maintenance tasks instead of growth. We show that in highly linear networks the prediction of flux distributions for different growth times allows analysis of time-dependent changes, albeit using a static model. By performing an in silico knock-out study as well as analyzing flux distributions in single and double mutant phenotypes, we demonstrated that the model accurately represents the metabolism of M. pneumoniae. The experimentally validated model provides a solid basis for understanding its metabolic regulatory mechanisms.

Flux imbalance analysis and the sensitivity of cellular growth to changes in metabolite pools

Stoichiometric models of metabolism, such as flux balance analysis (FBA), are classically applied to predicting steady state rates - or fluxes - of metabolic reactions in genome-scale metabolic networks. Here we revisit the central assumption of FBA, i.e. that intracellular metabolites are at steady state, and show that deviations from flux balance (i.e. flux imbalances) are informative of some features of in vivo metabolite concentrations. Mathematically, the sensitivity of FBA to these flux imbalances is captured by a native feature of linear optimization, the dual problem, and its corresponding variables, known as shadow prices. First, using recently published data on chemostat growth of Saccharomyces cerevisae under different nutrient limitations, we show that shadow prices anticorrelate with experimentally measured degrees of growth limitation of intracellular metabolites. We next hypothesize that metabolites which are limiting for growth (and thus have very negative shadow price) cannot vary dramatically in an uncontrolled way, and must respond rapidly to perturbations. Using a collection of published datasets monitoring the time-dependent metabolomic response of Escherichia coli to carbon and nitrogen perturbations, we test this hypothesis and find that metabolites with negative shadow price indeed show lower temporal variation following a perturbation than metabolites with zero shadow price. Finally, we illustrate the broader applicability of flux imbalance analysis to other constraint-based methods. In particular, we explore the biological significance of shadow prices in a constraint-based method for integrating gene expression data with a stoichiometric model. In this case, shadow prices point to metabolites that should rise or drop in concentration in order to increase consistency between flux predictions and gene expression data. In general, these results suggest that the sensitivity of metabolic optima to violations of the steady state constraints carries biologically significant information on the processes that control intracellular metabolites in the cell.

Interaction of bacterial fatty-acid-displaced regulators with DNA is interrupted by tyrosine phosphorylation in the helix-turn-helix domain

Bacteria possess transcription regulators (of the TetR family) specifically dedicated to repressing genes for cytochrome P450, involved in oxidation of polyunsaturated fatty acids. Interaction of these repressors with operator sequences is disrupted in the presence of fatty acids, and they are therefore known as fatty-acid-displaced regulators. Here, we describe a novel mechanism of inactivating the interaction of these proteins with DNA, illustrated by the example of Bacillus subtilis regulator FatR. FatR was found to interact in a two-hybrid assay with TkmA, an activator of the protein-tyrosine kinase PtkA. We show that FatR is phosphorylated specifically at the residue tyrosine 45 in its helix-turn-helix domain by the kinase PtkA. Structural modelling reveals that the hydroxyl group of tyrosine 45 interacts with DNA, and we show that this phosphorylation reduces FatR DNA binding capacity. Point mutants mimicking phosphorylation of FatR in vivo lead to a strong derepression of the fatR operon, indicating that this regulatory mechanism works independently of derepression by polyunsaturated fatty acids. Tyrosine 45 is a highly conserved residue, and PtkA from B. subtilis can phosphorylate FatR homologues from other bacteria. This indicates that phosphorylation of tyrosine 45 may be a general mechanism of switching off bacterial fatty-acid-displaced regulators.

The evolution of intelligent developmental systems

This chapter aims to understand the relations between the evolution and development of complex cognitive functions by emphasizing the context of complex, changeable environments. What evolves and develops in such contexts cannot be achieved by linear deterministic processes based on stable "codes". Rather, what is needed, even in the molecular ensembles of single-cell organisms, are "intelligent" systems with nonlinear dynamic processing, sensitive to informational structures, not just elements, in environments. This is the view emerging in recent molecular biology. The research is also constructing a new "biologic" of both evolution and development, providing a clearer rationale for transitions into more complex forms, including epigenetic, physiological, nervous, cognitive, and human sociocognitive forms. This chapter explains how these transitions form a nested hierarchical system in which the dynamics within and between levels creates emergent abilities so often underestimated or even demeaned in previous accounts, especially regarding human cognition. The implications of the view for human development in modern societies are also briefly considered.

TLM-Quant: an open-source pipeline for visualization and quantification of gene expression heterogeneity in growing microbial cells

Gene expression heterogeneity is a key driver for microbial adaptation to fluctuating environmental conditions, cell differentiation and the evolution of species. This phenomenon has therefore enormous implications, not only for life in general, but also for biotechnological applications where unwanted subpopulations of non-producing cells can emerge in large-scale fermentations. Only time-lapse fluorescence microscopy allows real-time measurements of gene expression heterogeneity. A major limitation in the analysis of time-lapse microscopy data is the lack of fast, cost-effective, open, simple and adaptable protocols. Here we describe TLM-Quant, a semi-automatic pipeline for the analysis of time-lapse fluorescence microscopy data that enables the user to visualize and quantify gene expression heterogeneity. Importantly, our pipeline builds on the open-source packages ImageJ and R. To validate TLM-Quant, we selected three possible scenarios, namely homogeneous expression, highly 'noisy' heterogeneous expression, and bistable heterogeneous expression in the Gram-positive bacterium Bacillus subtilis. This bacterium is both a paradigm for systems-level studies on gene expression and a highly appreciated biotechnological 'cell factory'. We conclude that the temporal resolution of such analyses with TLM-Quant is only limited by the numbers of recorded images.

Coregulation of Terpenoid Pathway Genes and Prediction of Isoprene Production in Bacillus subtilis Using Transcriptomics

The isoprenoid pathway converts pyruvate to isoprene and related isoprenoid compounds in plants and some bacteria. Currently, this pathway is of great interest because of the critical role that isoprenoids play in basic cellular processes, as well as the industrial value of metabolites such as isoprene. Although the regulation of several pathway genes has been described, there is a paucity of information regarding system level regulation and control of the pathway. To address these limitations, we examined Bacillus subtilis grown under multiple conditions and determined the relationship between altered isoprene production and gene expression patterns. We found that with respect to the amount of isoprene produced, terpenoid genes fall into two distinct subsets with opposing correlations. The group whose expression levels positively correlated with isoprene production included dxs, which is responsible for the commitment step in the pathway, ispD, and two genes that participate in the mevalonate pathway, yhfS and pksG. The subset of terpenoid genes that inversely correlated with isoprene production included ispH, ispF, hepS, uppS, ispE, and dxr. A genome-wide partial least squares regression model was created to identify other genes or pathways that contribute to isoprene production. These analyses showed that a subset of 213 regulated genes was sufficient to create a predictive model of isoprene production under different conditions and showed correlations at the transcriptional level. We conclude that gene expression levels alone are sufficiently informative about the metabolic state of a cell that produces increased isoprene and can be used to build a model that accurately predicts production of this secondary metabolite across many simulated environmental conditions.

Selection-driven accumulation of suppressor mutants in bacillus subtilis: the apparent high mutation frequency of the cryptic gudB gene and the rapid clonal expansion of gudB(+) suppressors are due to growth under selection

Soil bacteria like Bacillus subtilis can cope with many growth conditions by adjusting gene expression and metabolic pathways. Alternatively, bacteria can spontaneously accumulate beneficial mutations or shape their genomes in response to stress. Recently, it has been observed that a B. subtilis mutant lacking the catabolically active glutamate dehydrogenase (GDH), RocG, mutates the cryptic gudB^CR gene at a high frequency. The suppressor mutants express the active GDH GudB, which can fully replace the function of RocG. Interestingly, the cryptic gudB^CR allele is stably inherited as long as the bacteria synthesize the functional GDH RocG. Competition experiments revealed that the presence of the cryptic gudB^CR allele provides the bacteria with a selective growth advantage when glutamate is scarce. Moreover, the lack of exogenous glutamate is the driving force for the selection of mutants that have inactivated the active gudB gene. In contrast, two functional GDHs are beneficial for the cells when glutamate was available. Thus, the amount of GDH activity strongly affects fitness of the bacteria depending on the availability of exogenous glutamate. At a first glance the high mutation frequency of the cryptic gudB^CR allele might be attributed to stress-induced adaptive mutagenesis. However, other loci on the chromosome that could be potentially mutated during growth under the selective pressure that is exerted on a GDH-deficient mutant remained unaffected. Moreover, we show that a GDH-proficient B. subtilis strain has a strong selective growth advantage in a glutamate-dependent manner. Thus, the emergence and rapid clonal expansion of the active gudB allele can be in fact explained by spontaneous mutation and growth under selection without an increase of the mutation rate. Moreover, this study shows that the selective pressure that is exerted on a maladapted bacterium strongly affects the apparent mutation frequency of mutational hot spots.

Systems-integration of plant metabolism: means, motive and opportunity

System integration of metabolism is considered in analogy to the investigation of corporate misdemeanour. Motive, or goal-oriented explanation, provides hypotheses that can guide the investigation of network structure. Opportunity can be established by correlative analysis using large-scale omics resources. However, correlative approaches on their own remain inconclusive and seldom identify all the links in a network. Establishment of means, or the ability to act on other network components and contribute to a phenotype, is therefore crucial. This requires functional information. Integration of quantitative data in the context of pathway models provides a powerful approach to establish 'means'. This is illustrated by discussing: first, how protein abundance is regulated by a network including transcript abundance, translation and protein degradation and second, how a combination of experimentation and modelling provides information about pathway flux, an emergent network property that integrates changes in proteins and metabolites and determines composition and biomass.

Charting microbial phenotypes in multiplex nanoliter batch bioreactors

High-throughput growth phenotyping is receiving great attention for establishing the genotype-phenotype map of sequenced organisms owing to the ready availability of complete genome sequences. To date, microbial growth phenotypes have been investigated mostly by the conventional method of batch cultivation using test tubes, Erlenmeyer flasks, or the recently available microwell plates. However, the current batch cultivation methods are time- and labor-intensive and often fail to consider sophisticated environmental changes. The implementation of batch cultures at the nanoliter scale has been difficult because of the quick evaporation of the culture medium inside the reactors. Here, we report a microfluidic system that allows independent cell cultures in evaporation-free multiplex nanoliter reactors under different culture conditions to assess the behavior of cells. The design allows three experimental replicates for each of eight culture environments in a single run. We demonstrate the versatility of the device by performing growth curve experiments with Escherichia coli and microbiological assays of antibiotics against the opportunistic pathogen Pseudomonas aeruginosa. Our study highlights that the microfluidic system can effectively replace the traditional batch culture methods with nanoliter volumes of bacterial cultivations, and it may be therefore promising for high-throughput growth phenotyping as well as for single-cell analyses.

The genome organization of Thermotoga maritima reflects its lifestyle

The generation of genome-scale data is becoming more routine, yet the subsequent analysis of omics data remains a significant challenge. Here, an approach that integrates multiple omics datasets with bioinformatics tools was developed that produces a detailed annotation of several microbial genomic features. This methodology was used to characterize the genome of Thermotoga maritima—a phylogenetically deep-branching, hyperthermophilic bacterium. Experimental data were generated for whole-genome resequencing, transcription start site (TSS) determination, transcriptome profiling, and proteome profiling. These datasets, analyzed in combination with bioinformatics tools, served as a basis for the improvement of gene annotation, the elucidation of transcription units (TUs), the identification of putative non-coding RNAs (ncRNAs), and the determination of promoters and ribosome binding sites. This revealed many distinctive properties of the T. maritima genome organization relative to other bacteria. This genome has a high number of genes per TU (3.3), a paucity of putative ncRNAs (12), and few TUs with multiple TSSs (3.7%). Quantitative analysis of promoters and ribosome binding sites showed increased sequence conservation relative to other bacteria. The 5′UTRs follow an atypical bimodal length distribution comprised of “Short” 5′UTRs (11–17 nt) and “Common” 5′UTRs (26–32 nt). Transcriptional regulation is limited by a lack of intergenic space for the majority of TUs. Lastly, a high fraction of annotated genes are expressed independent of growth state and a linear correlation of mRNA/protein is observed (Pearson r = 0.63, p<2.2×10⁻¹⁶ t-test). These distinctive properties are hypothesized to be a reflection of this organism's hyperthermophilic lifestyle and could yield novel insights into the evolutionary trajectory of microbial life on earth.

Genomic studies have greatly benefited from the advent of high-throughput technologies and bioinformatics tools. Here, a methodology integrating genome-scale data and bioinformatics tools is developed to characterize the genome organization of the hyperthermophilic, phylogenetically deep-branching bacterium Thermotoga maritima. This approach elucidates several features of the genome organization and enables comparative analysis of these features across diverse taxa. Our results suggest that the genome of T. maritima is reflective of its hyperthermophilic lifestyle. Ultimately, constraints imposed on the genome have negative impacts on regulatory complexity and phenotypic diversity. Investigating the genome organization of Thermotogae species will help resolve various causal factors contributing to the genome organization such as phylogeny and environment. Applying a similar analysis of the genome organization to numerous taxa will likely provide insights into microbial evolution.

Large-scale metabolome analysis and quantitative integration with genomics and proteomics data in Mycoplasma pneumoniae

Systems metabolomics, the identification and quantification of cellular metabolites and their integration with genomics and proteomics data, promises valuable functional insights into cellular biology. However, technical constraints, sample complexity issues and the lack of suitable complementary quantitative data sets prevented accomplishing such studies in the past. Here, we present an integrative metabolomics study of the genome-reduced bacterium Mycoplasma pneumoniae. We experimentally analysed its metabolome using a cross-platform approach. We explain intracellular metabolite homeostasis by quantitatively integrating our results with the cellular inventory of proteins, DNA and other macromolecules, as well as with available building blocks from the growth medium. We calculated in vivo catalytic parameters of glycolytic enzymes, making use of measured reaction velocities, as well as enzyme and metabolite pool sizes. A quantitative, inter-species comparison of absolute and relative metabolite abundances indicated that metabolic pathways are regulated as functional units, thereby simplifying adaptive responses. Our analysis demonstrates the potential for new scientific insight by integrating different types of large-scale experimental data from a single biological source.

Dissecting specific and global transcriptional regulation of bacterial gene expression

Gene expression is regulated by specific transcriptional circuits but also by the global expression machinery as a function of growth. Simultaneous specific and global regulation thus constitutes an additional--but often neglected--layer of complexity in gene expression. Here, we develop an experimental-computational approach to dissect specific and global regulation in the bacterium Escherichia coli. By using fluorescent promoter reporters, we show that global regulation is growth rate dependent not only during steady state but also during dynamic changes in growth rate and can be quantified through two promoter-specific parameters. By applying our approach to arginine biosynthesis, we obtain a quantitative understanding of both specific and global regulation that allows accurate prediction of the temporal response to simultaneous perturbations in arginine availability and growth rate. We thereby uncover two principles of joint regulation: (i) specific regulation by repression dominates the transcriptional response during metabolic steady states, largely repressing the biosynthesis genes even when biosynthesis is required and (ii) global regulation sets the maximum promoter activity that is exploited during the transition between steady states.

Somewhat in control--the role of transcription in regulating microbial metabolic fluxes

The most common way for microbes to control their metabolism is by controlling enzyme levels through transcriptional regulation. Yet recent studies have shown that in many cases, perturbations to the transcriptional regulatory network do not result in altered metabolic phenotypes on the level of the flux distribution. We suggest that this may be a consequence of cells protecting their metabolism against stochastic fluctuations in expression as well as enabling a fast response for those fluxes that may need to be changed quickly. Furthermore, it is impossible for a regulatory program to guarantee optimal expression levels in all conditions. Several studies have found examples of demonstrably suboptimal regulation of gene expression, and improvements to the regulatory network have been investigated in laboratory evolution experiments.

Bacillus subtilis: from soil bacterium to super-secreting cell factory

The biotechnology industry has become a key element in modern societies. Within this industry, the production of recombinant enzymes and biopharmaceutical proteins is of major importance. The global markets for such recombinant proteins are growing rapidly and, accordingly, there is a continuous need for new production platforms that can deliver protein products in greater yields, with higher quality and at lower costs. This calls for the development of next-generation super-secreting cell factories. One of the microbial cell factories that can meet these challenges is the Gram-positive bacterium Bacillus subtilis, an inhabitant of the upper layers of the soil that has the capacity to secrete proteins in the gram per litre range. The engineering of B. subtilis into a next-generation super-secreting cell factory requires combined Systems and Synthetic Biology approaches. In this way, the bacterial protein secretion machinery can be optimized from the single molecule to the network level while, at the same time, taking into account the balanced use of cellular resources. Although highly ambitious, this is an achievable objective due to recent advances in functional genomics and Systems- and Synthetic Biological analyses of B. subtilis cells.

Merging multiple omics datasets in silico: statistical analyses and data interpretation

By the combinations of high-throughput analytical technologies in the fields of transcriptomics, proteomics, and metabolomics, we are now able to gain comprehensive and quantitative snapshots of the intracellular processes. Dynamic intracellular activities and their regulations can be elucidated by systematic observation of these multi-omics data. On the other hand, careful statistical analysis is necessary for such integration, since each of the omics layers as well as the specific analytical methodologies harbor different levels of noise and variations. Moreover, interpretation of such multitude of data requires an intuitive pathway context. Here we describe such statistical methods for the integration and comparison of multi-omics data, as well as the computational methods for pathway reconstruction, ID conversion, mapping, and visualization that play key roles for the efficient study of multi-omics information.

Gene network requirements for regulation of metabolic gene expression to a desired state

Gene circuits that control metabolism should restore metabolic functions upon environmental changes. Whether gene networks are capable of steering metabolism to optimal states is an open question. Here we present a method to identify such optimal gene networks. We show that metabolic network optimisation over a range of environments results in an input-output relationship for the gene network that guarantees optimal metabolic states. Optimal control is possible if the gene network can achieve this input-output relationship. We illustrate our approach with the best-studied regulatory network in yeast, the galactose network. We find that over the entire range of external galactose concentrations, the regulatory network is able to optimally steer galactose metabolism. Only a few gene network parameters affect this optimal regulation. The other parameters can be tuned independently for optimisation of other functions, such as fast and low-noise gene expression. This study highlights gene network plasticity, evolvability, and modular functionality.

The Bacillus subtilis response regulator gene degU is positively regulated by CcpA and by catabolite-repressed synthesis of ClpC

In Bacillus subtilis, the response regulator DegU and its cognate kinase, DegS, constitute a two-component system that regulates many cellular processes, including exoprotease production and genetic competence. Phosphorylated DegU (DegU-P) activates its own promoter and is degraded by the ClpCP protease. We observed induction of degU by glucose in sporulation medium. This was abolished in two mutants: the ccpA (catabolite control protein A) and clpC disruptants. Transcription of the promoter of the operon containing clpC (PclpC) decreased in the presence of glucose, and the disruption of ccpA resulted in derepression of PclpC. However, this was not directly mediated by CcpA, because we failed to detect binding of CcpA to PclpC. Glucose decreased the expression of clpC, leading to low cellular concentrations of the ClpCP protease. Thus, degU is induced through activation of autoregulation by a decrease in ClpCP-dependent proteolysis of DegU-P. An electrophoretic mobility shift assay showed that CcpA bound directly to the degU upstream region, indicating that CcpA activates degU through binding. The bound region was narrowed down to 27 bases, which contained a cre (catabolite-responsive element) sequence with a low match to the cre consensus sequence. In a footprint analysis, CcpA specifically protected a region containing the cre sequence from DNase I digestion. The induction of degU by glucose showed complex regulation of the degU gene.

Malate metabolism in Bacillus subtilis: distinct roles for three classes of malate-oxidizing enzymes

The Gram-positive soil bacterium Bacillus subtilis uses glucose and malate as the preferred carbon sources. In the presence of either glucose or malate, the expression of genes and operons for the utilization of secondary carbon sources is subject to carbon catabolite repression. While glucose is a preferred substrate in many organisms from bacteria to man, the factors that contribute to the preference for malate have so far remained elusive. In this work, we have studied the contribution of the different malate-metabolizing enzymes in B. subtilis, and we have elucidated their distinct functions. The malate dehydrogenase and the phosphoenolpyruvate carboxykinase are both essential for malate utilization; they introduce malate into gluconeogenesis. The NADPH-generating malic enzyme YtsJ is important to establish the cellular pools of NADPH for anabolic reactions. Finally, the NADH-generating malic enzymes MaeA, MalS, and MleA are involved in keeping the ATP levels high. Together, this unique array of distinct activities makes malate a preferred carbon source for B. subtilis.

Coping with complexity in metabolic engineering

In the past decade, systems biology has revealed great metabolic and regulatory complexity even in seemingly simple microbial systems. Metabolic engineering aims to control this complexity in order to establish sustainable and economically viable production routes for valuable chemicals. Recent advances in systems-level data generation and modeling of cellular metabolism and regulation together with tremendous progress in synthetic biology will provide the tools to put biotechnologists on the fast track for implementing novel production processes. Great potential lies in the reduction of cellular complexity by orthogonalization of metabolic modules. Here, we review recent advances that will eventually enable metabolic engineers to predict, design, and build streamlined microbial cell factories with reduced time and effort.

Error propagation analysis for quantitative intracellular metabolomics

Model-based analyses have become an integral part of modern metabolic engineering and systems biology in order to gain knowledge about complex and not directly observable cellular processes. For quantitative analyses, not only experimental data, but also measurement errors, play a crucial role. The total measurement error of any analytical protocol is the result of an accumulation of single errors introduced by several processing steps. Here, we present a framework for the quantification of intracellular metabolites, including error propagation during metabolome sample processing. Focusing on one specific protocol, we comprehensively investigate all currently known and accessible factors that ultimately impact the accuracy of intracellular metabolite concentration data. All intermediate steps are modeled, and their uncertainty with respect to the final concentration data is rigorously quantified. Finally, on the basis of a comprehensive metabolome dataset of Corynebacterium glutamicum, an integrated error propagation analysis for all parts of the model is conducted, and the most critical steps for intracellular metabolite quantification are detected.

Metabolic flux ratio analysis and multi-objective optimization revealed a globally conserved and coordinated metabolic response of E. coli to paraquat-induced oxidative stress

The ability of a microorganism to adapt to changes in the environment, such as in nutrient or oxygen availability, is essential for its competitive fitness and survival. The cellular objective and the strategy of the metabolic response to an extreme environment are therefore of tremendous interest and, thus, have been increasingly explored. However, the cellular objective of the complex regulatory structure of the metabolic changes has not yet been fully elucidated and more details regarding the quantitative behaviour of the metabolic flux redistribution are required to understand the systems-wide biological significance of this response. In this study, the intracellular metabolic flux ratios involved in the central carbon metabolism were determined by fractional (13)C-labeling and metabolic flux ratio analysis (MetaFoR) of the wild-type E. coli strain JM101 at an oxidative environment in a chemostat. We observed a significant increase in the flux through phosphoenolpyruvate carboxykinase (PEPCK), phosphoenolpyruvate carboxylase (PEPC), malic enzyme (MEZ) and serine hydroxymethyltransferase (SHMT). We applied an ε-constraint based multi-objective optimization to investigate the trade-off relationships between the biomass yield and the generation of reductive power using the in silico iJR904 genome-scale model of E. coli K-12. The theoretical metabolic redistribution supports that the trans-hydrogenase pathway should not play a direct role in the defence mounted by E. coli against oxidative stress. The agreement between the measured ratio and the theoretical redistribution established the significance of NADPH synthesis as the goal of the metabolic reprogramming that occurs in response to oxidative stress. Our work presents a framework that combines metabolic flux ratio analysis and multi-objective optimization to investigate the metabolic trade-offs that occur under varied environmental conditions. Our results led to the proposal that the metabolic response of E. coli to paraquat-induced oxidative stress is globally conserved and coordinated.

The LIKE system, a novel protein expression toolbox for Bacillus subtilis based on the liaI promoter

Background

Bacillus subtilis is a very important Gram-positive model organism of high biotechnological relevance, which is widely used as a host for the production of both secreted and cytoplasmic proteins. We developed a novel and efficient expression system, based on the liaI promoter (P_liaI) from B. subtilis, which is under control of the LiaRS antibiotic-inducible two-component system. In the absence of a stimulus, this promoter is kept tightly inactive. Upon induction by cell wall antibiotics, it shows an over 100-fold increase in activity within 10 min.

Results

Based on these traits of P_liaI, we developed a novel LiaRS-controlled gene expression system for B. subtilis (the “LIKE" system). Two expression vectors, the integrative pLIKE-int and the replicative pLIKE-rep, were constructed. To enhance the performance of the P_liaI-derived system, site-directed mutagenesis was employed to optimize the ribosome binding site and alter its spacing to the initiation codon used for the translational fusion. The impact of these genetic modifications on protein production yield was measured using GFP as a model protein. Moreover, a number of tailored B. subtilis expression strains containing different markerless chromosomal deletions of the liaIH region were constructed to circumvent undesired protein production, enhance the positive autoregulation of the LiaRS system and thereby increase target gene expression strength from the P_liaI promoter.

Conclusions

The LIKE protein expression system is a novel protein expression system, which offers a number of advantages over existing systems. Its major advantages are (i) a tightly switched-off promoter during exponential growth in the absence of a stimulus, (ii) a concentration-dependent activation of P_liaI in the presence of suitable inducers, (iii) a very fast but transient response with a very high dynamic range of over 100-fold (up to 1,000-fold) induction, (iv) a choice from a range of well-defined, commercially available, and affordable inducers and (v) the convenient conversion of LIKE-derived inducible expression strains into strong constitutive protein production factories.

A gene-phenotype network based on genetic variability for drought responses reveals key physiological processes in controlled and natural environments

Identifying the connections between molecular and physiological processes underlying the diversity of drought stress responses in plants is key for basic and applied science. Drought stress response involves a large number of molecular pathways and subsequent physiological processes. Therefore, it constitutes an archetypical systems biology model. We first inferred a gene-phenotype network exploiting differences in drought responses of eight sunflower (Helianthus annuus) genotypes to two drought stress scenarios. Large transcriptomic data were obtained with the sunflower Affymetrix microarray, comprising 32423 probesets, and were associated to nine morpho-physiological traits (integrated transpired water, leaf transpiration rate, osmotic potential, relative water content, leaf mass per area, carbon isotope discrimination, plant height, number of leaves and collar diameter) using sPLS regression. Overall, we could associate the expression patterns of 1263 probesets to six phenotypic traits and identify if correlations were due to treatment, genotype and/or their interaction. We also identified genes whose expression is affected at moderate and/or intense drought stress together with genes whose expression variation could explain phenotypic and drought tolerance variability among our genetic material. We then used the network model to study phenotypic changes in less tractable agronomical conditions, i.e. sunflower hybrids subjected to different watering regimes in field trials. Mapping this new dataset in the gene-phenotype network allowed us to identify genes whose expression was robustly affected by water deprivation in both controlled and field conditions. The enrichment in genes correlated to relative water content and osmotic potential provides evidence of the importance of these traits in agronomical conditions.

Single cell analysis of gene expression patterns during carbon starvation in Bacillus subtilis reveals large phenotypic variation

How cells dynamically respond to fluctuating environmental conditions depends on the architecture and noise of the underlying genetic circuits. Most work characterizing stress pathways in the model bacterium Bacillus subtilis has been performed on bulk cultures using ensemble assays. However, investigating the single cell response to stress is important since noise might generate significant phenotypic heterogeneity. Here, we study the stress response to carbon source starvation and compare both population and single cell data. Using a top-down approach, we investigate the transcriptional dynamics of various stress-related genes of B. subtilis in response to carbon source starvation and to increased cell density. Our data reveal that most of the tested gene-regulatory networks respond highly heterogeneously to starvation and cells show a large degree of variation in gene expression. The level of highly dynamic diversification within B. subtilis populations under changing environments reflects the necessity to study cells at the single cell level.

Direct regulation of GTP homeostasis by (p)ppGpp: a critical component of viability and stress resistance

Cells constantly adjust their metabolism in response to environmental conditions, yet major mechanisms underlying survival remain poorly understood. We discover a posttranscriptional mechanism that integrates starvation response with GTP homeostasis to allow survival, enacted by the nucleotide (p)ppGpp, a key player in bacterial stress response and persistence. We reveal that (p)ppGpp activates global metabolic changes upon starvation, allowing survival by regulating GTP. Combining metabolomics with biochemical demonstrations, we find that (p)ppGpp directly inhibits the activities of multiple GTP biosynthesis enzymes. This inhibition results in robust and rapid GTP regulation in Bacillus subtilis, which we demonstrate is essential to maintaining GTP levels within a range that supports viability even in the absence of starvation. Correspondingly, without (p)ppGpp, gross GTP dysregulation occurs, revealing a vital housekeeping function of (p)ppGpp; in fact, loss of (p)ppGpp results in death from rising GTP, a severe and previously unknown consequence of GTP dysfunction.

Lipidomic profiling of model organisms and the world's major pathogens

Lipidomics is a subspecialty of metabolomics that focuses on water insoluble metabolites that form membrane barriers. Most lipidomic databases catalog lipids from common model organisms, like humans or Escherichia coli. However, model organisms' lipid profiles show surprisingly little overlap with those of specialized pathogens, creating the need for organism-specific lipidomic databases. Here we review rapid progress in lipidomic platform development with regard to chromatography, detection and bioinformatics. We emphasize new methods of comparative lipidomics, which use aligned datasets to identify lipids changed after introducing a biological variable. These new methods provide an unprecedented ability to broadly and quantitatively describe lipidic change during biological processes and identify changed lipids with low error rates.

High- and low-affinity cre boxes for CcpA binding in Bacillus subtilis revealed by genome-wide analysis

BACKGROUND

In Bacillus subtilis and its relatives carbon catabolite control, a mechanism enabling to reach maximal efficiency of carbon and energy sources metabolism, is achieved by the global regulator CcpA (carbon catabolite protein A). CcpA in a complex with HPr-Ser-P (seryl-phosphorylated form of histidine-containing protein, HPr) binds to operator sites called catabolite responsive elements, cre. Depending on the cre box position relative to the promoter, the CcpA/HPr-Ser-P complex can either act as a positive or a negative regulator. The cre boxes are highly degenerate semi-palindromes with a lowly conserved consensus sequence. So far, studies aimed at revealing how CcpA can bind such diverse sites were focused on the analysis of single cre boxes. In this study, a genome-wide analysis of cre sites was performed in order to identify differences in cre sequence and position, which determine their binding affinity.

RESULTS

The transcriptomes of B. subtilis cultures with three different CcpA expression levels were compared. The higher the amount of CcpA in the cells, the more operons possessing cre sites were differentially regulated. The cre boxes that mediated regulation at low CcpA levels were designated as strong (high affinity) and those which responded only to high amounts of CcpA, as weak (low affinity). Differences in the sequence and position in relation to the transcription start site between strong and weak cre boxes were revealed.

CONCLUSIONS

Certain residues at specific positions in the cre box as well as, to a certain extent, a more palindromic nature of cre sequences and the location of cre in close vicinity to the transcription start site contribute to the strength of CcpA-dependent regulation. The main factors contributing to cre regulatory efficiencies, enabling subtle differential control of various subregulons of the CcpA regulon, are identified.

Genome-wide identification of genes directly regulated by the pleiotropic transcription factor Spx in Bacillus subtilis

The transcriptional regulator Spx plays a key role in maintaining the redox homeostasis of Bacillus subtilis cells exposed to disulfide stress. Defects in Spx were previously shown to lead to differential expression of numerous genes but direct and indirect regulatory effects could not be distinguished. Here we identified 283 discrete chromosomal sites potentially bound by the Spx–RNA polymerase (Spx–RNAP) complex using chromatin immunoprecipitation of Spx. Three quarters of these sites were located near Sigma(A)-dependent promoters, and upon diamide treatment, the fraction of the Spx–RNAP complex increased in parallel with the number and occupancy of DNA sites. Correlation of Spx–RNAP-binding sites with gene differential expression in wild-type and Δspx strains exposed or not to diamide revealed that 144 transcription units comprising 275 genes were potentially under direct Spx regulation. Spx-controlled promoters exhibited an extended −35 box in which nucleotide composition at the −43/−44 positions strongly correlated with observed activation. In vitro transcription confirmed activation by oxidized Spx of seven newly identified promoters, of which one was also activated by reduced Spx. Our study globally characterized the Spx regulatory network, revealing its role in the basal expression of some genes and its complex interplay with other stress responses.

Recent advances in mapping environmental microbial metabolisms through 13C isotopic fingerprints

After feeding microbes with a defined (13)C substrate, unique isotopic patterns (isotopic fingerprints) can be formed in their metabolic products. Such labelling information not only can provide novel insights into functional pathways but also can determine absolute carbon fluxes through the metabolic network via metabolic modelling approaches. This technique has been used for finding pathways that may have been mis-annotated in the past, elucidating new enzyme functions, and investigating cell metabolisms in microbial communities. In this review paper, we summarize the applications of (13)C approaches to analyse novel cell metabolisms for the past 3 years. The isotopic fingerprints (defined as unique isotopomers useful for pathway identifications) have revealed the operations of the Entner-Doudoroff pathway, the reverse tricarboxylic acid cycle, new enzymes for biosynthesis of central metabolites, diverse respiration routes in phototrophic metabolism, co-metabolism of carbon nutrients and novel CO(2) fixation pathways. This review also discusses new isotopic methods to map carbon fluxes in global metabolisms, as well as potential factors influencing the metabolic flux quantification (e.g. metabolite channelling, the isotopic purity of (13)C substrates and the isotopic effect). Although (13)C labelling is not applicable to all biological systems (e.g. microbial communities), recent studies have shown that this method has a significant value in functional characterization of poorly understood micro-organisms, including species relevant for biotechnology and human health.

Dynamic expression of the translational machinery during Bacillus subtilis life cycle at a single cell level

The ability of bacteria to responsively regulate the expression of translation components is crucial for rapid adaptation to fluctuating environments. Utilizing Bacillus subtilis (B. subtilis) as a model organism, we followed the dynamics of the translational machinery at a single cell resolution during growth and differentiation. By comprehensive monitoring the activity of the major rrn promoters and ribosomal protein production, we revealed diverse dynamics between cells grown in rich and poor medium, with the most prominent dissimilarities exhibited during deep stationary phase. Further, the variability pattern of translational activity varied among the cells, being affected by nutrient availability. We have monitored for the first time translational dynamics during the developmental process of sporulation within the two distinct cellular compartments of forespore and mother-cell. Our study uncovers a transient forespore specific increase in expression of translational components. Finally, the contribution of each rrn promoter throughout the bacterium life cycle was found to be relatively constant, implying that differential expression is not the main purpose for the existence of multiple rrn genes. Instead, we propose that coordination of the rrn operons serves as a strategy to rapidly fine tune translational activities in a synchronized fashion to achieve an optimal translation level for a given condition.

Successes and failures in modular genetic engineering

Synthetic biology relies on engineering concepts such as abstraction, standardization, and decoupling to develop systems that address environmental, clinical, and industrial needs. Recent advances in applying modular design to system development have enabled creation of increasingly complex systems. However, several challenges to module and system development remain, including syntactic errors, semantic errors, parameter mismatches, contextual sensitivity, noise and evolution, and load and stress. To combat these challenges, researchers should develop a framework for describing and reasoning about biological information, design systems with modularity in mind, and investigate how to predictively describe the diverse sources and consequences of metabolic load and stress.

13C-flux analysis reveals NADPH-balancing transhydrogenation cycles in stationary phase of nitrogen-starving Bacillus subtilis

In their natural habitat, microorganisms are typically confronted with nutritional limitations that restrict growth and force them to persevere in a stationary phase. Despite the importance of this phase, little is known about the metabolic state(s) that sustains it. Here, we investigate metabolically active but non-growing Bacillus subtilis during nitrogen starvation. In the absence of biomass formation as the major NADPH sink, the intracellular flux distribution in these resting B. subtilis reveals a large apparent catabolic NADPH overproduction of 5.0 ± 0.6 mmol g(-1)h(-1) that was partly caused by high pentose phosphate pathway fluxes. Combining transcriptome analysis, stationary (13)C-flux analysis in metabolic deletion mutants, (2)H-labeling experiments, and kinetic flux profiling, we demonstrate that about half of the catabolic excess NADPH is oxidized by two transhydrogenation cycles, i.e. isoenzyme pairs of dehydrogenases with different cofactor specificities that operate in reverse directions. These transhydrogenation cycles were constituted by the combined activities of the glyceraldehyde 3-phosphate dehydrogenases GapA/GapB and the malic enzymes MalS/YtsJ. At least an additional 6% of the overproduced NADPH is reoxidized by continuous cycling between ana- and catabolism of glutamate. Furthermore, in vitro enzyme data show that a not yet identified transhydrogenase could potentially reoxidize ∼20% of the overproduced NADPH. Overall, we demonstrate the interplay between several metabolic mechanisms that concertedly enable network-wide NADPH homeostasis under conditions of high catabolic NADPH production in the absence of cell growth in B. subtilis.

Synthesis, release, and recapture of compatible solute proline by osmotically stressed Bacillus subtilis cells

Bacillus subtilis synthesizes large amounts of the compatible solute proline as a cellular defense against high osmolarity to ensure a physiologically appropriate level of hydration of the cytoplasm and turgor. It also imports proline for this purpose via the osmotically inducible OpuE transport system. Unexpectedly, an opuE mutant was at a strong growth disadvantage in high-salinity minimal media lacking proline. Appreciable amounts of proline were detected in the culture supernatant of the opuE mutant strain, and they rose concomitantly with increases in the external salinity. We found that the intracellular proline pool of severely salinity-stressed cells of the opuE mutant was considerably lower than that of its opuE(+) parent strain. This loss of proline into the medium and the resulting decrease in the intracellular proline content provide a rational explanation for the observed salt-sensitive growth phenotype of cells lacking OpuE. None of the known MscL- and MscS-type mechanosensitive channels of B. subtilis participated in the release of proline under permanently imposed high-salinity growth conditions. The data reported here show that the OpuE transporter not only possesses the previously reported role for the scavenging of exogenously provided proline as an osmoprotectant but also functions as a physiologically highly important recapturing device for proline that is synthesized de novo and subsequently released by salt-stressed B. subtilis cells. The wider implications of our findings for the retention of compatible solutes by osmotically challenged microorganisms and the roles of uptake systems for compatible solutes are considered.