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

Mild Blast Exposure Dysregulates Metabolic Pathways and Correlation Networking as Evident from LC-MS-Based Plasma Profiling

Blast-induced trauma is emerging as a serious threat due to its wide pathophysiology where not only the brain but also a spectrum of organs is being affected. In the present study, we aim to identify the plasma-based metabolic dysregulations along with the associated temporal changes at 5-6 h, day 1 and day 7 post-injury in a preclinical animal model for blast exposure, through liquid chromatography-mass spectrometry (LC-MS). Using significantly advanced metabolomic and statistical bioinformatic platforms, we were able to elucidate better and unravel the complex networks of blast-induced neurotrauma (BINT) and its interlinked systemic effects. Significant changes were evident at 5-6 h with maximal changes at day 1. Temporal analysis also depicted progressive changes which continued till day 7. Significant associations of metabolic markers belonging to the class of amino acids, energy-related molecules, lipids, vitamin, hormone, phenolic acid, keto and histidine derivatives, nucleic acid molecules, uremic toxins, and uronic acids were observed. Also, the present study is the first of its kind where comprehensive, detailed pathway dysregulations of amino acid metabolism and biosynthesis, perturbed nucleotides, lipid peroxidation, and nucleic acid damage followed by correlation networking and multiomics networking were explored on preclinical animal models exposed to mild blast trauma. In addition, markers for systemic changes (renal dysfunction) were also observed. Global pathway predictions of unannotated peaks also presented important insights into BINT pathophysiology. Conclusively, the present study depicts important findings that might help underpin the biological mechanisms of blast-induced brain or systemic trauma.

Genome-scale metabolic network models for industrial microorganisms metabolic engineering: Current advances and future prospects

The construction of high-performance microbial cell factories (MCFs) is the centerpiece of biomanufacturing. However, the complex metabolic regulatory network of microorganisms poses great challenges for the efficient design and construction of MCFs. The genome-scale metabolic network models (GSMs) can systematically simulate the metabolic regulation process of microorganisms in silico, providing effective guidance for the rapid design and construction of MCFs. In this review, we summarized the development status of 16 important industrial microbial GSMs, and further outline the technologies or methods that continuously promote high-quality GSMs construction from five aspects: I) Databases and modeling tools facilitate GSMs reconstruction; II) evolving gap-filling technologies; III) constraint-based model reconstruction; IV) advances in algorithms; and V) developed visualization tools. In addition, we also summarized the applications of GSMs in guiding metabolic engineering from four aspects: I) exploring and explaining metabolic features; II) predicting the effects of genetic perturbations on metabolism; III) predicting the optimal phenotype; IV) guiding cell factories construction in practical experiment. Finally, we discussed the development of GSMs, aiming to provide a reference for efficiently reconstructing GSMs and guiding metabolic engineering.

Identification of primary metabolites in fungal species of Trichophyton mentagrophyte and Trichophyton rubrum by NMR spectroscopy

BACKGROUND

Superficial mycoses are fungal infections limited to the outermost layers of the skin and its appendages. The chief causative agents of these mycoses are dermatophytes and yeasts. The diagnosis of dermatophytosis can be made by direct mycological examination with potassium hydroxide (10%-30%) of biological material obtained from patients with suspected mycosis, providing results more rapid than fungal cultures, which may take days or weeks. This information, together with clinical history and laboratory diagnosis, ensures that the appropriate treatment is initiated promptly. However, false negative results are obtained in 5%-15%, by conventional methods of diagnosis of dermatophytosis.

OBJECTIVES

To study the metabolic profiles of the commonly occurring dermatophytes by NMR spectroscopy.

PATIENTS/MATERIALS

We have used 1D and 2D Nuclear Magnetic Resonance (NMR) experiments along with Human Metabolome Database (HMDB) and Chenomx database search for identification of primary metabolites in the methanol extract of two fungal species: Trichophyton mentagrophyte (T. mentagrophyte) and Trichophyton rubrum (T. rubrum). Both standard strains and representative number of clinical isolates of these two species were investigated. Further, metabolic profiles obtained were analysed using multivariate analysis.

RESULTS

We have identified 23 metabolites in the T. mentagrophyte and another 23 metabolites in T. rubrum. Many important metabolites like trehalose, proline, mannitol, acetate, GABA and several other amino acids were detected, which provide the necessary components for fungal growth and metabolism. Altered metabolites were defined between Trichophyton mentagrophyte and T. rubrum strains.

CONCLUSION

We have detected many metabolites in the two fungal species T. mentagrophyte and T. rubrum by using NMR spectroscopy. NMR spectroscopy provides a holistic snapshot of the metabolome of an organism. Key metabolic differences were identified between the two fungal strains. We need to perform more studies on metabolite profiling of the samples from these species for their rapid diagnosis and prompt treatment.

Targeting the Bottlenecks in Levan Biosynthesis Pathway in Bacillus subtilis and Strain Optimization by Computational Modeling and Omics Integration

Levan is a fructan polymer with many industrial applications such as the formulation of hydrogels, drug delivery, and wound healing, among others. To this end, metabolic systems engineering is a valuable method to improve the yield of a specific metabolite in a wide range of bacterial and eukaryotic organisms. In this study, we report a systems biology approach integrating genomics data for the Bacillus subtilis model, wherein the metabolic pathway for levan biosynthesis is unpacked. We analyzed a revised genome-scale enzyme-constrained metabolic model (ecGEM) and performed simulations to increase levan biopolymer production capacity in B. subtilis. We used the model ec_iYO844_lvn to (1) identify the essential genes and bottlenecks in levan production, and (2) specifically design an engineered B. subtilis strain capable of producing higher levan yields. The FBA and FVA analysis showed the maximal growth rate of the organism up to 0.624 hr-1 at 20 mmol gDw-1 hr-1 of sucrose intake. Gene knockout analyses were performed to identify gene knockout targets to increase the levan flux in B. subtilis. Importantly, we found that the pgk and ctaD genes are the two target genes for the knockout. The perturbation of these two genes has flux gains for levan production reactions with 1.3- and 1.4-fold the relative flux span in the mutant strains, respectively, compared to the wild type. In all, this work identifies the bottlenecks in the production of levan and possible ways to overcome them. Our results provide deeper insights on the bacterium's physiology and new avenues for strain engineering.

Efficient, Formal, Material, and Final Causes in Biology and Technology

This paper considers how a classification of causal effects as comprising efficient, formal, material, and final causation can provide a useful understanding of how emergence takes place in biology and technology, with formal, material, and final causation all including cases of downward causation; they each occur in both synchronic and diachronic forms. Taken together, they underlie why all emergent levels in the hierarchy of emergence have causal powers (which is Noble's principle of biological relativity) and so why causal closure only occurs when the upwards and downwards interactions between all emergent levels are taken into account, contra to claims that some underlying physics level is by itself causality complete. A key feature is that stochasticity at the molecular level plays an important role in enabling agency to emerge, underlying the possibility of final causation occurring in these contexts.

Dynamic Metabolic Control: From the Perspective of Regulation Logic

Establishing microbial cell factories has become a sustainable and increasingly promising approach for the synthesis of valuable chemicals. However, introducing heterologous pathways into these cell factories can disrupt the endogenous cellular metabolism, leading to suboptimal production performance. To address this challenge, dynamic pathway regulation has been developed and proven effective in improving microbial biosynthesis. In this review, we summarized typical dynamic regulation strategies based on their control logic. The applicable scenarios for each control logic were highlighted and perspectives for future research direction in this area were discussed.

Flux balance analysis-based metabolic modeling of microbial secondary metabolism: Current status and outlook

In microorganisms, different from primary metabolism for cellular growth, secondary metabolism is for ecological interactions and stress responses and an important source of natural products widely used in various areas such as pharmaceutics and food additives. With advancements of sequencing technologies and bioinformatics tools, a large number of biosynthetic gene clusters of secondary metabolites have been discovered from microbial genomes. However, due to challenges from the difficulty of genome-scale pathway reconstruction and the limitation of conventional flux balance analysis (FBA) on secondary metabolism, the quantitative modeling of secondary metabolism is poorly established, in contrast to that of primary metabolism. This review first discusses current efforts on the reconstruction of secondary metabolic pathways in genome-scale metabolic models (GSMMs), as well as related FBA-based modeling techniques. Additionally, potential extensions of FBA are suggested to improve the prediction accuracy of secondary metabolite production. As this review posits, biosynthetic pathway reconstruction for various secondary metabolites will become automated and a modeling framework capturing secondary metabolism onset will enhance the predictive power. Expectedly, an improved FBA-based modeling workflow will facilitate quantitative study of secondary metabolism and in silico design of engineering strategies for natural product production.

etiBsu1209: A comprehensive multiscale metabolic model for Bacillus subtilis

Genome-scale metabolic models (GEMs) have been widely used to guide the computational design of microbial cell factories, and to date, seven GEMs have been reported for Bacillus subtilis, a model gram-positive microorganism widely used in bioproduction of functional nutraceuticals and food ingredients. However, none of them are widely used because they often lead to erroneous predictions due to their low predictive power and lack of information on regulatory mechanisms. In this work, we constructed a new version of GEM for B. subtilis (iBsu1209), which contains 1209 genes, 1595 metabolites, and 1948 reactions. We applied machine learning to fill gaps, which formed a relatively complete metabolic network able to predict with high accuracy (89.3%) the growth of 1209 mutants under 12 different culture conditions. In addition, we developed a visualization and code-free software, Model Tool, for multiconstraints model reconstruction and analysis. We used this software to construct etiBsu1209, a multiscale model that integrates enzymatic constraints, thermodynamic constraints, and transcriptional regulatory networks. Furthermore, we used etiBsu1209 to guide a metabolic engineering strategy (knocking out fabI and yfkN genes) for the overproduction of nutraceutical menaquinone-7, and the titer increased to 153.94 mg/L, 2.2-times that of the parental strain. To the best of our knowledge, etiBsu1209 is the first comprehensive multiscale model for B. subtilis and can serve as a solid basis for rational computational design of B. subtilis cell factories for bioproduction.

Evolution of enzyme levels in metabolic pathways: A theoretical approach. Part 2

Metabolism is essential for cell function and adaptation. Because of their central role in metabolism, kinetic parameters and enzyme concentrations are under constant selective pressure to adapt the fluxes of the metabolic networks to the needs of the organism. In line with various studies dealing with enzyme evolution, we recently developed a model of the evolution of enzyme concentrations under selection for increased flux, considered as a proxy for fitness (Coton et al., 2022). With this model, taking into account two realistic cellular constraints, competition for resources and co-regulation, we determined the evolutionary equilibria and range of neutral variations of enzyme concentrations. In this article, we expanded this model by considering that the enzymes in a pathway can belong to different co-regulation groups. We determined the equilibria and showed that the constraints modify the adaptive landscape by limiting the number of independent dimensions. We also showed that any trade-off between enzyme concentrations is sufficient to limit the flux and relax selection for increasing the concentration of other enzymes. Even though this model is based on simplifying assumptions, the complexity of the relationship between enzyme concentrations prevents the formal analysis of the range of neutral variation of enzyme concentrations. However, we could show that selection for maximizing the flux results in selective neutrality for all enzymes regardless the constraints applied, giving generality to the prediction of Hartl et al. (1985).

Are microbes and metabolites influencing the parental consumption of nestlings' feces in gray-backed shrikes?

The behavioral video recordings of the gray-backed shrike Lanius tephronotus revealed that parent birds eat the feces produced by their nestlings. "Parental nutrition hypothesis" attributes the origin of this behavior to nutrition-recovery and cost-saving, respectively. However, the presence of usable nutrients in the nestlings' feces is unknown because of traditional technology. In this study, we analyzed all the metabolites and the variations in the diversity and content of microbes in the feces of gray-backed shrike nestlings. We aimed to report the changes in microbes and metabolites with the age of nestlings and point out that the parent birds that eat the feces may gain potential nutrition benefits. The results showed that the relative abundances of Proteobacteria, Firmicutes, and Bacteroidota, changed significantly when the nestlings were 6 days old. The relative abundances of 6 probiotics, which are involved in digestion, metabolism, and immunity-related physiological functions, decreased in the nestlings' feces gradually with age; therefore, these probiotics may be obtained by parent birds upon ingestion of the feces of young nestlings. Among the metabolites that were detected, 20 were lipids and some had a role in anti-parasitic functions and wound healing; however, their relative contents decreased with age. These beneficial substances in the nestlings' feces may stimulate the parents to swallow the feces. Moreover, there were many aromatic metabolites in the newly hatched nestlings' feces, but the content of bitter metabolites increased as they grew up. Therefore, our results are in accordance with the nutritional hypothesis.

Predicting synchronized gene coexpression patterns from fibration symmetries in gene regulatory networks in bacteria

BACKGROUND

Gene regulatory networks coordinate the expression of genes across physiological states and ensure a synchronized expression of genes in cellular subsystems, critical for the coherent functioning of cells. Here we address the question whether it is possible to predict gene synchronization from network structure alone. We have recently shown that synchronized gene expression can be predicted from symmetries in the gene regulatory networks described by the concept of symmetry fibrations. We showed that symmetry fibrations partition the genes into groups called fibers based on the symmetries of their 'input trees', the set of paths in the network through which signals can reach a gene. In idealized dynamic gene expression models, all genes in a fiber are perfectly synchronized, while less idealized models-with gene input functions differencing between genes-predict symmetry breaking and desynchronization.

RESULTS

To study the functional role of gene fibers and to test whether some of the fiber-induced coexpression remains in reality, we analyze gene fibrations for the gene regulatory networks of E. coli and B. subtilis and confront them with expression data. We find approximate gene coexpression patterns consistent with symmetry fibrations with idealized gene expression dynamics. This shows that network structure alone provides useful information about gene synchronization, and suggest that gene input functions within fibers may be further streamlined by evolutionary pressures to realize a coexpression of genes.

CONCLUSIONS

Thus, gene fibrations provide a sound conceptual tool to describe tunable coexpression induced by network topology and shaped by mechanistic details of gene expression.

Statistical modelling of bacterial promoter sequences for regulatory motif discovery with the help of transcriptome data: application to Listeria monocytogenes

Automatic de novo identification of the main regulons of a bacterium from genome and transcriptome data remains a challenge. To address this task, we propose a statistical model that can use information on exact positions of the transcription start sites and condition-dependent expression profiles. The central idea of this model is to improve the probabilistic representation of the promoter DNA sequences by incorporating covariates summarizing expression profiles (e.g. coordinates in projection spaces or hierarchical clustering trees). A dedicated trans-dimensional Markov chain Monte Carlo algorithm adjusts the width and palindromic properties of the corresponding position-weight matrices, the number of parameters to describe exact position relative to the transcription start site, and chooses the expression covariates relevant for each motif. All parameters are estimated simultaneously, for many motifs and many expression covariates. The method is applied to a dataset of transcription start sites and expression profiles available for Listeria monocytogenes. The results validate the approach and provide a new global view of the transcription regulatory network of this important pathogen. Remarkably, a previously unreported motif is found in promoter regions of ribosomal protein genes, suggesting a role in the regulation of growth.

Genome scale metabolic models and analysis for evaluating probiotic potentials

Probiotics are live beneficial microorganisms that can be consumed in the form of dairy and food products as well as dietary supplements to promote a healthy balance of gut bacteria in humans. Practically, the main challenge is to identify and select promising strains and formulate multi-strain probiotic blends with consistent efficacy which is highly dependent on individual dietary regimes, gut environments, and health conditions. Limitations of current in vivo and in vitro methods for testing probiotic strains can be overcome by in silico model guided systems biology approaches where genome scale metabolic models (GEMs) can be used to describe their cellular behaviors and metabolic states of probiotic strains under various gut environments. Here, we summarize currently available GEMs of microbial strains with probiotic potentials and propose a knowledge-based framework to evaluate metabolic capabilities on the basis of six probiotic criteria. They include metabolic characteristics, stability, safety, colonization, postbiotics, and interaction with the gut microbiome which can be assessed by in silico approaches. As such, the most suitable strains can be identified to design personalized multi-strain probiotics in the future.

Current status and applications of genome-scale metabolic models

Genome-scale metabolic models (GEMs) computationally describe gene-protein-reaction associations for entire metabolic genes in an organism, and can be simulated to predict metabolic fluxes for various systems-level metabolic studies. Since the first GEM for Haemophilus influenzae was reported in 1999, advances have been made to develop and simulate GEMs for an increasing number of organisms across bacteria, archaea, and eukarya. Here, we review current reconstructed GEMs and discuss their applications, including strain development for chemicals and materials production, drug targeting in pathogens, prediction of enzyme functions, pan-reactome analysis, modeling interactions among multiple cells or organisms, and understanding human diseases.

The Dynamical Emergence of Biology From Physics: Branching Causation via Biomolecules

Biology differs fundamentally from the physics that underlies it. This paper proposes that the essential difference is that while physics at its fundamental level is Hamiltonian, in biology, once life has come into existence, causation of a contextual branching nature occurs at every level of the hierarchy of emergence at each time. The key feature allowing this to happen is the way biomolecules such as voltage-gated ion channels can act to enable branching logic to arise from the underlying physics, despite that physics per se being of a deterministic nature. Much randomness occurs at the molecular level, which enables higher level functions to select lower level outcomes according to higher level needs. Intelligent causation occurs when organisms engage in deduction, enabling prediction and planning. This is possible because ion channels enable action potentials to propagate in axons. The further key feature is that such branching biological behavior acts down to cause the underlying physical interactions to also exhibit a contextual branching behavior.

Feedbacks from the metabolic network to the genetic network reveal regulatory modules in E. coli and B. subtilis

The genetic regulatory network (GRN) plays a key role in controlling the response of the cell to changes in the environment. Although the structure of GRNs has been the subject of many studies, their large scale structure in the light of feedbacks from the metabolic network (MN) has received relatively little attention. Here we study the causal structure of the GRNs, namely the chain of influence of one component on the other, taking into account feedback from the MN. First we consider the GRNs of E. coli and B. subtilis without feedback from MN and illustrate their causal structure. Next we augment the GRNs with feedback from their respective MNs by including (a) links from genes coding for enzymes to metabolites produced or consumed in reactions catalyzed by those enzymes and (b) links from metabolites to genes coding for transcription factors whose transcriptional activity the metabolites alter by binding to them. We find that the inclusion of feedback from MN into GRN significantly affects its causal structure, in particular the number of levels and relative positions of nodes in the hierarchy, and the number and size of the strongly connected components (SCCs). We then study the functional significance of the SCCs. For this we identify condition specific feedbacks from the MN into the GRN by retaining only those enzymes that are essential for growth in specific environmental conditions simulated via the technique of flux balance analysis (FBA). We find that the SCCs of the GRN augmented by these feedbacks can be ascribed specific functional roles in the organism. Our algorithmic approach thus reveals relatively autonomous subsystems with specific functionality, or regulatory modules in the organism. This automated approach could be useful in identifying biologically relevant modules in other organisms for which network data is available, but whose biology is less well studied.

Capacity for instantaneous catabolism of preferred and non-preferred carbon sources in Escherichia coli and Bacillus subtilis

Making the right choice for nutrient consumption in an ever-changing environment is a key factor for evolutionary success of bacteria. Here we investigate the regulatory mechanisms that enable dynamic adaptation between non-preferred and preferred carbon sources for the model Gram-negative and -positive species Escherichia coli and Bacillus subtilis, respectively. We focus on the ability for instantaneous catabolism of a gluconeogenic carbon source upon growth on a glycolytic carbon source and vice versa. By following isotopic tracer dynamics on a 1–2 minute scale, we show that flux reversal from the preferred glucose to non-preferred pyruvate as the sole carbon source is primarily transcriptionally regulated. In the opposite direction, however, E. coli can reverse its flux instantaneously by means of allosteric regulation, whereas in B. subtilis this flux reversal is transcriptionally regulated. Upon removal of transcriptional regulation, B. subtilis assumes the ability of instantaneous glucose catabolism. Using an approach that combines quantitative metabolomics and kinetic modelling, we then identify the additionally necessary key metabolite-enzyme interactions that implement the instantaneous flux reversal in the transcriptionally deregulated B. subtilis, and validate the most relevant allosteric interactions.

Tri11, tri3, and tri4 genes are required for trichodermin biosynthesis of Trichoderma brevicompactum

Trichoderma brevicompactum and T. arundinaceum both can synthesize trichodermin with strong antifungal activity and high biotechnological value. The two Trichoderma species have a tri cluster, which includes seven genes (tri14, tri12, tri11, tri10, tri3, tri4, and tri6) that encode transport and regulatory enzymes required for the biosynthesis of trichodermin. Here, we isolated T. brevicompactum 0248 transformants with disrupted tri11, tri4, or tri3 gene. We also described the effect of tri11, tri3, or tri4 deletion on the expression of other genes in the tri cluster. Targeted Δtri3 knockout mutant exhibited a sharp decline in the production of trichodermin, and trichodermol, which is a substrate for trichodermin production, accumulated. Thus, the results demonstrated that tri3 was responsible for the biosynthesis of trichodermin, and the tri3 gene-encoded enzyme catalyzed the acetylation reaction of the hydroxy group at C-4 of the trichodermin skeleton. In addition, tri4 and tri11 deletion mutants were generated to evaluate the roles of tri4 and tri11 in trichodermin biosynthesis, respectively. Deletion mutant strain Δtri4 or Δtri11 did not produce trichodermin in T. brevicompactum, indicating that tri4 and tri11 are essential for trichodermin biosynthesis. This is the first to report the function of tri3, tri4 and tri11 in T. brevicompactum, although the role of tri4 and tri11 has already been described for T. arundinaceum by Cardoza et al. (Appl Environ Microbiol 77:4867-4877, 2011).

The Genome-Scale Integrated Networks in Microorganisms

The genome-scale cellular network has become a necessary tool in the systematic analysis of microbes. In a cell, there are several layers (i.e., types) of the molecular networks, for example, genome-scale metabolic network (GMN), transcriptional regulatory network (TRN), and signal transduction network (STN). It has been realized that the limitation and inaccuracy of the prediction exist just using only a single-layer network. Therefore, the integrated network constructed based on the networks of the three types attracts more interests. The function of a biological process in living cells is usually performed by the interaction of biological components. Therefore, it is necessary to integrate and analyze all the related components at the systems level for the comprehensively and correctly realizing the physiological function in living organisms. In this review, we discussed three representative genome-scale cellular networks: GMN, TRN, and STN, representing different levels (i.e., metabolism, gene regulation, and cellular signaling) of a cell’s activities. Furthermore, we discussed the integration of the networks of the three types. With more understanding on the complexity of microbial cells, the development of integrated network has become an inevitable trend in analyzing genome-scale cellular networks of microorganisms.

The bacterial interlocked process ONtology (BiPON): a systemic multi-scale unified representation of biological processes in prokaryotes

BACKGROUND

High-throughput technologies produce huge amounts of heterogeneous biological data at all cellular levels. Structuring these data together with biological knowledge is a critical issue in biology and requires integrative tools and methods such as bio-ontologies to extract and share valuable information. In parallel, the development of recent whole-cell models using a systemic cell description opened alternatives for data integration. Integrating a systemic cell description within a bio-ontology would help to progress in whole-cell data integration and modeling synergistically.

RESULTS

We present BiPON, an ontology integrating a multi-scale systemic representation of bacterial cellular processes. BiPON consists in of two sub-ontologies, bioBiPON and modelBiPON. bioBiPON organizes the systemic description of biological information while modelBiPON describes the mathematical models (including parameters) associated with biological processes. bioBiPON and modelBiPON are related using bridge rules on classes during automatic reasoning. Biological processes are thus automatically related to mathematical models. 37% of BiPON classes stem from different well-established bio-ontologies, while the others have been manually defined and curated. Currently, BiPON integrates the main processes involved in bacterial gene expression processes.

CONCLUSIONS

BiPON is a proof of concept of the way to combine formally systems biology and bio-ontology. The knowledge formalization is highly flexible and generic. Most of the known cellular processes, new participants or new mathematical models could be inserted in BiPON. Altogether, BiPON opens up promising perspectives for knowledge integration and sharing and can be used by biologists, systems and computational biologists, and the emerging community of whole-cell modeling.

Resource allocation in living organisms

Quantitative prediction of resource allocation for living systems has been an intensive area of research in the field of biology. Resource allocation was initially investigated in higher organisms by using empirical mathematical models based on mass distribution. A challenge is now to go a step further by reconciling the cellular scale to the individual scale. In the present paper, we review the foundations of modelling of resource allocation, particularly at the cellular scale: from small macro-molecular models to genome-scale cellular models. We enlighten how the combination of omic measurements and computational advances together with systems biology has contributed to dramatic progresses in the current understanding and prediction of cellular resource allocation. Accurate genome-wide predictive methods of resource allocation based on the resource balance analysis (RBA) framework have been developed and ensure a good trade-off between the complexity/tractability and the prediction capability of the model. The RBA framework shows promise for a wide range of applications in metabolic engineering and synthetic biology, and for pursuing investigations of the design principles of cellular and multi-cellular organisms.

Optimal resource allocation enables mathematical exploration of microbial metabolic configurations

Central to the functioning of any living cell, the metabolic network is a complex network of biochemical reactions. It may also be viewed as an elaborate production system, integrating a diversity of internal and external signals in order to efficiently produce the energy and the biochemical precursors to ensure all cellular functions. Even in simple organisms like bacteria, it shows a striking level of coordination, adapting to very different growth media. Constraint-based models constitute an efficient mathematical framework to compute optimal metabolic configurations, at the scale of a whole genome. Combining the constraint-based approach "Resource Balance Analysis" with combinatorial optimization techniques, we propose a general method to explore these configurations, based on the inference of logical rules governing the activation of metabolic fluxes in response to diverse extracellular media. Using the concept of partial Boolean functions, we notably introduce a novel tractable algorithm to infer monotone Boolean functions on a minimal support. Monotonicity seems particularly relevant in this context, since the orderliness exhibited by the metabolic network's dynamical behavior is expected to give rise to relatively simple rules. First results are promising, as the application of the method on Bacillus subtilis central carbon metabolism allows to recover known regulations as well as to investigate lesser known parts of the global regulatory network.

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

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

Understanding Metabolic Regulation at a Systems Level: Metabolite Sensing, Mathematical Predictions, and Model Organisms

Metabolic networks are extensively regulated to facilitate tissue-specific metabolic programs and robustly maintain homeostasis in response to dietary changes. Homeostatic metabolic regulation is achieved through metabolite sensing coupled to feedback regulation of metabolic enzyme activity or expression. With a wealth of transcriptomic, proteomic, and metabolomic data available for different cell types across various conditions, we are challenged with understanding global metabolic network regulation and the resulting metabolic outputs. Stoichiometric metabolic network modeling integrated with "omics" data has addressed this challenge by generating nonintuitive, testable hypotheses about metabolic flux rewiring. Model organism studies have also yielded novel insight into metabolic networks. This review covers three topics: the feedback loops inherent in metabolic regulatory networks, metabolic network modeling, and interspecies studies utilizing Caenorhabditis elegans and various bacterial diets that have revealed novel metabolic paradigms.

Advances in the integration of transcriptional regulatory information into genome-scale metabolic models

A major goal of systems biology is to build predictive computational models of cellular metabolism. Availability of complete genome sequences and wealth of legacy biochemical information has led to the reconstruction of genome-scale metabolic networks in the last 15 years for several organisms across the three domains of life. Due to paucity of information on kinetic parameters associated with metabolic reactions, the constraint-based modelling approach, flux balance analysis (FBA), has proved to be a vital alternative to investigate the capabilities of reconstructed metabolic networks. In parallel, advent of high-throughput technologies has led to the generation of massive amounts of omics data on transcriptional regulation comprising mRNA transcript levels and genome-wide binding profile of transcriptional regulators. A frontier area in metabolic systems biology has been the development of methods to integrate the available transcriptional regulatory information into constraint-based models of reconstructed metabolic networks in order to increase the predictive capabilities of computational models and understand the regulation of cellular metabolism. Here, we review the existing methods to integrate transcriptional regulatory information into constraint-based models of metabolic networks.

The Landscape of Evolution: Reconciling Structural and Dynamic Properties of Metabolic Networks in Adaptive Diversifications

The network of the interactions among genes, proteins, and metabolites delineates a range of potential phenotypic diversifications in a lineage, and realized phenotypic changes are the result of differences in the dynamics of the expression of the elements and interactions in this deterministic network. Regulatory mechanisms, such as hormones, mediate the relationship between the structural and dynamic properties of networks by determining how and when the elements are expressed and form a functional unit or state. Changes in regulatory mechanisms lead to variable expression of functional states of a network within and among generations. Functional properties of network elements, and the magnitude and direction of evolutionary change they determine, depend on their location within a network. Here, we examine the relationship between network structure and the dynamic mechanisms that regulate flux through a metabolic network. We review the mechanisms that control metabolic flux in enzymatic reactions and examine structural properties of the network locations that are targets of flux control. We aim to establish a predictive framework to test the contributions of structural and dynamic properties of deterministic networks to evolutionary diversifications.

Conserved Units of Co-Expression in Bacterial Genomes: An Evolutionary Insight into Transcriptional Regulation

Genome-wide measurements of transcriptional activity in bacteria indicate that the transcription of successive genes is strongly correlated beyond the scale of operons. Here, we analyze hundreds of bacterial genomes to identify supra-operonic segments of genes that are proximal in a large number of genomes. We show that these synteny segments correspond to genomic units of strong transcriptional co-expression. Structurally, the segments contain operons with specific relative orientations (co-directional or divergent) and nucleoid-associated proteins are found to bind at their boundaries. Functionally, operons inside a same segment are highly co-expressed even in the apparent absence of regulatory factors at their promoter regions. Remote operons along DNA can also be co-expressed if their corresponding segments share a transcriptional or sigma factor, without requiring these factors to bind directly to the promoters of the operons. As evidence that these results apply across the bacterial kingdom, we demonstrate them both in the Gram-negative bacterium Escherichia coli and in the Gram-positive bacterium Bacillus subtilis. The underlying process that we propose involves only RNA-polymerases and DNA: it implies that the transcription of an operon mechanically enhances the transcription of adjacent operons. In support of a primary role of this regulation by facilitated co-transcription, we show that the transcription en bloc of successive operons as a result of transcriptional read-through is strongly and specifically enhanced in synteny segments. Finally, our analysis indicates that facilitated co-transcription may be evolutionary primitive and may apply beyond bacteria.

Translation elicits a growth rate-dependent, genome-wide, differential protein production in Bacillus subtilis

Complex regulatory programs control cell adaptation to environmental changes by setting condition-specific proteomes. In balanced growth, bacterial protein abundances depend on the dilution rate, transcript abundances and transcript-specific translation efficiencies. We revisited the current theory claiming the invariance of bacterial translation efficiency. By integrating genome-wide transcriptome datasets and datasets from a library of synthetic gfp-reporter fusions, we demonstrated that translation efficiencies in Bacillus subtilis decreased up to fourfold from slow to fast growth. The translation initiation regions elicited a growth rate-dependent, differential production of proteins without regulators, hence revealing a unique, hard-coded, growth rate-dependent mode of regulation. We combined model-based data analyses of transcript and protein abundances genome-wide and revealed that this global regulation is extensively used in B. subtilis We eventually developed a knowledge-based, three-step translation initiation model, experimentally challenged the model predictions and proposed that a growth rate-dependent drop in free ribosome abundance accounted for the differential protein production.

Reconstruction of the Regulatory Network for Bacillus subtilis and Reconciliation with Gene Expression Data

We introduce a manually constructed and curated regulatory network model that describes the current state of knowledge of transcriptional regulation of Bacillus subtilis. The model corresponds to an updated and enlarged version of the regulatory model of central metabolism originally proposed in 2008. We extended the original network to the whole genome by integration of information from DBTBS, a compendium of regulatory data that includes promoters, transcription factors (TFs), binding sites, motifs, and regulated operons. Additionally, we consolidated our network with all the information on regulation included in the SporeWeb and Subtiwiki community-curated resources on B. subtilis. Finally, we reconciled our network with data from RegPrecise, which recently released their own less comprehensive reconstruction of the regulatory network for B. subtilis. Our model describes 275 regulators and their target genes, representing 30 different mechanisms of regulation such as TFs, RNA switches, Riboswitches, and small regulatory RNAs. Overall, regulatory information is included in the model for ∼2500 of the ∼4200 genes in B. subtilis 168. In an effort to further expand our knowledge of B. subtilis regulation, we reconciled our model with expression data. For this process, we reconstructed the Atomic Regulons (ARs) for B. subtilis, which are the sets of genes that share the same “ON” and “OFF” gene expression profiles across multiple samples of experimental data. We show how ARs for B. subtilis are able to capture many sets of genes corresponding to regulated operons in our manually curated network. Additionally, we demonstrate how ARs can be used to help expand or validate the knowledge of the regulatory networks by looking at highly correlated genes in the ARs for which regulatory information is lacking. During this process, we were also able to infer novel stimuli for hypothetical genes by exploring the genome expression metadata relating to experimental conditions, gaining insights into novel biology.

Allantoin transport protein, PucI, from Bacillus subtilis: evolutionary relationships, amplified expression, activity and specificity

This work reports the evolutionary relationships, amplified expression, functional characterization and purification of the putative allantoin transport protein, PucI, from Bacillus subtilis. Sequence alignments and phylogenetic analysis confirmed close evolutionary relationships between PucI and membrane proteins of the nucleobase-cation-symport-1 family of secondary active transporters. These include the sodium-coupled hydantoin transport protein, Mhp1, from Microbacterium liquefaciens, and related proteins from bacteria, fungi and plants. Membrane topology predictions for PucI were consistent with 12 putative transmembrane-spanning α-helices with both N- and C-terminal ends at the cytoplasmic side of the membrane. The pucI gene was cloned into the IPTG-inducible plasmid pTTQ18 upstream from an in-frame hexahistidine tag and conditions determined for optimal amplified expression of the PucI(His6) protein in Escherichia coli to a level of about 5 % in inner membranes. Initial rates of inducible PucI-mediated uptake of 14C-allantoin into energized E. coli whole cells conformed to Michaelis-Menten kinetics with an apparent affinity (Kmapp) of 24 ± 3 μM, therefore confirming that PucI is a medium-affinity transporter of allantoin. Dependence of allantoin transport on sodium was not apparent. Competitive uptake experiments showed that PucI recognizes some additional hydantoin compounds, including hydantoin itself, and to a lesser extent a range of nucleobases and nucleosides. PucI(His6) was solubilized from inner membranes using n-dodecyl-β-d-maltoside and purified. The isolated protein contained a substantial proportion of α-helix secondary structure, consistent with the predictions, and a 3D model was therefore constructed on a template of the Mhp1 structure, which aided localization of the potential ligand binding site in PucI.

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.

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.

Coordination of microbial metabolism

Beyond fuelling cellular activities with building blocks and energy, metabolism also integrates environmental conditions into intracellular signals. The underlying regulatory network is complex and multifaceted: it ranges from slow interactions, such as changing gene expression, to rapid ones, such as the modulation of protein activity via post-translational modification or the allosteric binding of small molecules. In this Review, we outline the coordination of common metabolic tasks, including nutrient uptake, central metabolism, the generation of energy, the supply of amino acids and protein synthesis. Increasingly, a set of key metabolites is recognized to control individual regulatory circuits, which carry out specific functions of information input and regulatory output. Such a modular view of microbial metabolism facilitates an intuitive understanding of the molecular mechanisms that underlie cellular decision making.

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.

Comprehensive absolute quantification of the cytosolic proteome of Bacillus subtilis by data independent, parallel fragmentation in liquid chromatography/mass spectrometry (LC/MS(E))

In the growing field of systems biology, the knowledge of protein concentrations is highly required to truly understand metabolic and adaptational networks within the cells. Therefore we established a workflow relying on long chromatographic separation and mass spectrometric analysis by data independent, parallel fragmentation of all precursor ions at the same time (LC/MS(E)). By prevention of discrimination of co-eluting low and high abundant peptides a high average sequence coverage of 40% could be achieved, resulting in identification of almost half of the predicted cytosolic proteome of the Gram-positive model organism Bacillus subtilis (>1,050 proteins). Absolute quantification was achieved by correlation of average MS signal intensities of the three most intense peptides of a protein to the signal intensity of a spiked standard protein digest. Comparative analysis with heavily labeled peptides (AQUA approach) showed the use of only one standard digest is sufficient for global quantification. The quantification results covered almost four orders of magnitude, ranging roughly from 10 to 150,000 copies per cell. To prove this method for its biological relevance selected physiological aspects of B. subtilis cells grown under conditions requiring either amino acid synthesis or alternatively amino acid degradation were analyzed. This allowed both in particular the validation of the adjustment of protein levels by known regulatory events and in general a perspective of new insights into bacterial physiology. Within new findings the analysis of "protein costs" of cellular processes is extremely important. Such a comprehensive and detailed characterization of cellular protein concentrations based on data independent, parallel fragmentation in liquid chromatography/mass spectrometry (LC/MS(E)) data has been performed for the first time and should pave the way for future comprehensive quantitative characterization of microorganisms as physiological entities.

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.

(Im)Perfect robustness and adaptation of metabolic networks subject to metabolic and gene-expression regulation: marrying control engineering with metabolic control analysis

Background

Metabolic control analysis (MCA) and supply–demand theory have led to appreciable understanding of the systems properties of metabolic networks that are subject exclusively to metabolic regulation. Supply–demand theory has not yet considered gene-expression regulation explicitly whilst a variant of MCA, i.e. Hierarchical Control Analysis (HCA), has done so. Existing analyses based on control engineering approaches have not been very explicit about whether metabolic or gene-expression regulation would be involved, but designed different ways in which regulation could be organized, with the potential of causing adaptation to be perfect.

Results

This study integrates control engineering and classical MCA augmented with supply–demand theory and HCA. Because gene-expression regulation involves time integration, it is identified as a natural instantiation of the ‘integral control’ (or near integral control) known in control engineering. This study then focuses on robustness against and adaptation to perturbations of process activities in the network, which could result from environmental perturbations, mutations or slow noise. It is shown however that this type of ‘integral control’ should rarely be expected to lead to the ‘perfect adaptation’: although the gene-expression regulation increases the robustness of important metabolite concentrations, it rarely makes them infinitely robust. For perfect adaptation to occur, the protein degradation reactions should be zero order in the concentration of the protein, which may be rare biologically for cells growing steadily.

Conclusions

A proposed new framework integrating the methodologies of control engineering and metabolic and hierarchical control analysis, improves the understanding of biological systems that are regulated both metabolically and by gene expression. In particular, the new approach enables one to address the issue whether the intracellular biochemical networks that have been and are being identified by genomics and systems biology, correspond to the ‘perfect’ regulatory structures designed by control engineering vis-à-vis optimal functions such as robustness. To the extent that they are not, the analyses suggest how they may become so and this in turn should facilitate synthetic biology and metabolic engineering.

Global stability of enzymatic chains of full reversible Michaelis-Menten reactions

We consider a chain of metabolic reactions catalyzed by enzymes, of reversible Michaelis-Menten type with full dynamics, i.e. not reduced with any quasi-steady state approximations. We study the corresponding dynamical system and show its global stability if the equilibrium exists. If the system is open, the equilibrium may not exist. The main tool is monotone systems theory. Finally we study the implications of these results for the study of coupled genetic-metabolic systems.

Genomic reconstruction of the transcriptional regulatory network in Bacillus subtilis

The adaptation of microorganisms to their environment is controlled by complex transcriptional regulatory networks (TRNs), which are still only partially understood even for model species. Genome scale annotation of regulatory features of genes and TRN reconstruction are challenging tasks of microbial genomics. We used the knowledge-driven comparative-genomics approach implemented in the RegPredict Web server to infer TRN in the model Gram-positive bacterium Bacillus subtilis and 10 related Bacillales species. For transcription factor (TF) regulons, we combined the available information from the DBTBS database and the literature with bioinformatics tools, allowing inference of TF binding sites (TFBSs), comparative analysis of the genomic context of predicted TFBSs, functional assignment of target genes, and effector prediction. For RNA regulons, we used known RNA regulatory motifs collected in the Rfam database to scan genomes and analyze the genomic context of new RNA sites. The inferred TRN in B. subtilis comprises regulons for 129 TFs and 24 regulatory RNA families. First, we analyzed 66 TF regulons with previously known TFBSs in B. subtilis and projected them to other Bacillales genomes, resulting in refinement of TFBS motifs and identification of novel regulon members. Second, we inferred motifs and described regulons for 28 experimentally studied TFs with previously unknown TFBSs. Third, we discovered novel motifs and reconstructed regulons for 36 previously uncharacterized TFs. The inferred collection of regulons is available in the RegPrecise database (http://regprecise.lbl.gov/) and can be used in genetic experiments, metabolic modeling, and evolutionary analysis.

Global stability of reversible enzymatic metabolic chains

We consider metabolic networks with reversible enzymatic reactions. The model is written as a system of ordinary differential equations, possibly with inputs and outputs. We prove the global stability of the equilibrium (if it exists), using techniques of monotone systems and compartmental matrices. We show that the equilibrium does not always exist. Finally, we consider a metabolic system coupled with a genetic network, and we study the dependence of the metabolic equilibrium (if it exists) with respect to concentrations of enzymes. We give some conclusions concerning the dynamical behavior of coupled genetic/metabolic systems.

Genomic reconstruction of transcriptional regulatory networks in lactic acid bacteria

Background

Genome scale annotation of regulatory interactions and reconstruction of regulatory networks are the crucial problems in bacterial genomics. The Lactobacillales order of bacteria collates various microorganisms having a large economic impact, including both human and animal pathogens and strains used in the food industry. Nonetheless, no systematic genome-wide analysis of transcriptional regulation has been previously made for this taxonomic group.

Results

A comparative genomics approach was used for reconstruction of transcriptional regulatory networks in 30 selected genomes of lactic acid bacteria. The inferred networks comprise regulons for 102 orthologous transcription factors (TFs), including 47 novel regulons for previously uncharacterized TFs. Numerous differences between regulatory networks of the Streptococcaceae and Lactobacillaceae groups were described on several levels. The two groups are characterized by substantially different sets of TFs encoded in their genomes. Content of the inferred regulons and structure of their cognate TF binding motifs differ for many orthologous TFs between the two groups. Multiple cases of non-orthologous displacements of TFs that control specific metabolic pathways were reported.

Conclusions

The reconstructed regulatory networks substantially expand the existing knowledge of transcriptional regulation in lactic acid bacteria. In each of 30 studied genomes the obtained regulatory network contains on average 36 TFs and 250 target genes that are mostly involved in carbohydrate metabolism, stress response, metal homeostasis and amino acids biosynthesis. The inferred networks can be used for genetic experiments, functional annotations of genes, metabolic reconstruction and evolutionary analysis. All reconstructed regulons are captured within the Streptococcaceae and Lactobacillaceae collections in the RegPrecise database (http://regprecise.lbl.gov).

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.

Global transcriptional control by glucose and carbon regulator CcpA in Clostridium difficile

The catabolite control protein CcpA is a pleiotropic regulator that mediates the global transcriptional response to rapidly catabolizable carbohydrates, like glucose in Gram-positive bacteria. By whole transcriptome analyses, we characterized glucose-dependent and CcpA-dependent gene regulation in Clostridium difficile. About 18% of all C. difficile genes are regulated by glucose, for which 50% depend on CcpA for regulation. The CcpA regulon comprises genes involved in sugar uptake, fermentation and amino acids metabolism, confirming the role of CcpA as a link between carbon and nitrogen pathways. Using combination of chromatin immunoprecipitation and genome sequence analysis, we detected 55 CcpA binding sites corresponding to ∼140 genes directly controlled by CcpA. We defined the C. difficile CcpA consensus binding site (cre_CD motif), that is, ‘RRGAAAANGTTTTCWW’. Binding of purified CcpA protein to 19 target cre_CD sites was demonstrated by electrophoretic mobility shift assay. CcpA also directly represses key factors in early steps of sporulation (Spo0A and SigF). Furthermore, the C. difficile toxin genes (tcdA and tcdB) and their regulators (tcdR and tcdC) are direct CcpA targets. Finally, CcpA controls a complex and extended regulatory network through the modulation of a large set of regulators.