Genome-scale reconstruction of metabolic network in Bacillus subtilis based on high-throughput phenotyping and gene essentiality data

Traceability, reproducibility and wiki-exploration for "à-la-carte" reconstructions of genome-scale metabolic models

Genome-scale metabolic models have become the tool of choice for the global analysis of microorganism metabolism, and their reconstruction has attained high standards of quality and reliability. Improvements in this area have been accompanied by the development of some major platforms and databases, and an explosion of individual bioinformatics methods. Consequently, many recent models result from "à la carte" pipelines, combining the use of platforms, individual tools and biological expertise to enhance the quality of the reconstruction. Although very useful, introducing heterogeneous tools, that hardly interact with each other, causes loss of traceability and reproducibility in the reconstruction process. This represents a real obstacle, especially when considering less studied species whose metabolic reconstruction can greatly benefit from the comparison to good quality models of related organisms. This work proposes an adaptable workspace, AuReMe, for sustainable reconstructions or improvements of genome-scale metabolic models involving personalized pipelines. At each step, relevant information related to the modifications brought to the model by a method is stored. This ensures that the process is reproducible and documented regardless of the combination of tools used. Additionally, the workspace establishes a way to browse metabolic models and their metadata through the automatic generation of ad-hoc local wikis dedicated to monitoring and facilitating the process of reconstruction. AuReMe supports exploration and semantic query based on RDF databases. We illustrate how this workspace allowed handling, in an integrated way, the metabolic reconstructions of non-model organisms such as an extremophile bacterium or eukaryote algae. Among relevant applications, the latter reconstruction led to putative evolutionary insights of a metabolic pathway.

Analysis of metabolic networks of Streptomyces leeuwenhoekii C34 by means of a genome scale model: Prediction of modifications that enhance the production of specialized metabolites

The first genome scale model (GSM) for Streptomyces leeuwenhoekii C34 was developed to study the biosynthesis pathways of specialized metabolites and to find metabolic engineering targets for enhancing their production. The model, iVR1007, consists of 1,722 reactions, 1,463 metabolites, and 1,007 genes, it includes the biosynthesis pathways of chaxamycins, chaxalactins, desferrioxamines, ectoine, and other specialized metabolites. iVR1007 was validated using experimental information of growth on 166 different sources of carbon, nitrogen and phosphorous, showing an 83.7% accuracy. The model was used to predict metabolic engineering targets for enhancing the biosynthesis of chaxamycins and chaxalactins. Gene knockouts, such as sle03600 (L-homoserine O-acetyltransferase), and sle39090 (trehalose-phosphate synthase), that enhance the production of the specialized metabolites by increasing the pool of precursors were identified. Using the algorithm of flux scanning based on enforced objective flux (FSEOF) implemented in python, 35 and 25 over-expression targets for increasing the production of chaxamycin A and chaxalactin A, respectively, that were not directly associated with their biosynthesis routes were identified. Nineteen over-expression targets that were common to the two specialized metabolites studied, like the over-expression of the acetyl carboxylase complex (sle47660 (accA) and any of the following genes: sle44630 (accA_1) or sle39830 (accA_2) or sle27560 (bccA) or sle59710) were identified. The predicted knockouts and over-expression targets will be used to perform metabolic engineering of S. leeuwenhoekii C34 and obtain overproducer strains.

Genome-scale biological models for industrial microbial systems

The primary aims and challenges associated with microbial fermentation include achieving faster cell growth, higher productivity, and more robust production processes. Genome-scale biological models, predicting the formation of an interaction among genetic materials, enzymes, and metabolites, constitute a systematic and comprehensive platform to analyze and optimize the microbial growth and production of biological products. Genome-scale biological models can help optimize microbial growth-associated traits by simulating biomass formation, predicting growth rates, and identifying the requirements for cell growth. With regard to microbial product biosynthesis, genome-scale biological models can be used to design product biosynthetic pathways, accelerate production efficiency, and reduce metabolic side effects, leading to improved production performance. The present review discusses the development of microbial genome-scale biological models since their emergence and emphasizes their pertinent application in improving industrial microbial fermentation of biological products.

Prediction of metabolic fluxes from gene expression data with Huber penalty convex optimization function

As one of the critical parameters of a metabolic pathway, the metabolic flux in a metabolic network serves as an essential role in physiology and pathology. Constraint-based metabolic models are the widely used frameworks for predicting metabolic fluxes in genome-scale metabolic networks. Integrating the transcriptomic data into the constraint-based metabolic models can effectively predict context-specific fluxes across different conditions. However, these methods always need user-defined thresholds to identify the expression levels of metabolic genes or restrain the rate of biomass production, and the predictive results are sensitive to the thresholds. In this work, we present the Huber penalty convex optimization function (HPCOF) combined with the flux minimization principle to predict metabolic fluxes. Our HPCOF method integrates gene expression profiles into the genome-scale metabolic models (GEMs) to reduce the sensitivity to outliers, and uses continuous expression data to avoid selection of arbitrary threshold parameters. In the case studies of Saccharomyces cerevisiae (S. cerevisiae) and Escherichia coli (E. coli) strains under different conditions, the results show that our HPCOF method has a better fit to the experimentally measured values, and has a higher Pearson correlation coefficient, a smaller P-value and a lower sum of squared error than other methods. The HPCOF code can be freely downloaded from for academic users.

Insights into metabolic osmoadaptation of the ectoines-producer bacterium Chromohalobacter salexigens through a high-quality genome scale metabolic model

Background

The halophilic bacterium Chromohalobacter salexigens is a natural producer of ectoines, compatible solutes with current and potential biotechnological applications. As production of ectoines is an osmoregulated process that draws away TCA intermediates, bacterial metabolism needs to be adapted to cope with salinity changes. To explore and use C. salexigens as cell factory for ectoine(s) production, a comprehensive knowledge at the systems level of its metabolism is essential. For this purpose, the construction of a robust and high-quality genome-based metabolic model of C. salexigens was approached.

Results

We generated and validated a high quality genome-based C. salexigens metabolic model (iFP764). This comprised an exhaustive reconstruction process based on experimental information, analysis of genome sequence, manual re-annotation of metabolic genes, and in-depth refinement. The model included three compartments (periplasmic, cytoplasmic and external medium), and two salinity-specific biomass compositions, partially based on experimental results from C. salexigens. Using previous metabolic data as constraints, the metabolic model allowed us to simulate and analyse the metabolic osmoadaptation of C. salexigens under conditions for low and high production of ectoines. The iFP764 model was able to reproduce the major metabolic features of C. salexigens. Flux Balance Analysis (FBA) and Monte Carlo Random sampling analysis showed salinity-specific essential metabolic genes and different distribution of fluxes and variation in the patterns of correlation of reaction sets belonging to central C and N metabolism, in response to salinity. Some of them were related to bioenergetics or production of reducing equivalents, and probably related to demand for ectoines. Ectoines metabolic reactions were distributed according to its correlation in four modules. Interestingly, the four modules were independent both at low and high salinity conditions, as they did not correlate to each other, and they were not correlated with other subsystems.

Conclusions

Our validated model is one of the most complete curated networks of halophilic bacteria. It is a powerful tool to simulate and explore C. salexigens metabolism at low and high salinity conditions, driving to low and high production of ectoines. In addition, it can be useful to optimize the metabolism of other halophilic bacteria for metabolite production.

Electronic supplementary material

The online version of this article (10.1186/s12934-017-0852-0) contains supplementary material, which is available to authorized users.

Low Concentrations of Vitamin C Reduce the Synthesis of Extracellular Polymers and Destabilize Bacterial Biofilms

Extracellular polymeric substances (EPS) produced by bacteria form a matrix supporting the complex three-dimensional architecture of biofilms. This EPS matrix is primarily composed of polysaccharides, proteins and extracellular DNA. In addition to supporting the community structure, the EPS matrix protects bacterial biofilms from the environment. Specifically, it shields the bacterial cells inside the biofilm, by preventing antimicrobial agents from getting in contact with them, thereby reducing their killing effect. New strategies for disrupting the formation of the EPS matrix can therefore lead to a more efficient use of existing antimicrobials. Here we examined the mechanism of the known effect of vitamin C (sodium ascorbate) on enhancing the activity of various antibacterial agents. Our quantitative proteomics analysis shows that non-lethal concentrations of vitamin C inhibit bacterial quorum sensing and other regulatory mechanisms underpinning biofilm development. As a result, the EPS biosynthesis in reduced, and especially the polysaccharide component of the matrix is depleted. Once the EPS content is reduced beyond a critical point, bacterial cells get fully exposed to the medium. At this stage, the cells are more susceptible to killing, either by vitamin C-induced oxidative stress as reported here, or by other antimicrobials or treatments.

Find_tfSBP: find thermodynamics-feasible and smallest balanced pathways with high yield from large-scale metabolic networks

Biologically meaningful metabolic pathways are important references in the design of industrial bacterium. At present, constraint-based method is the only way to model and simulate a genome-scale metabolic network under steady-state criteria. Due to the inadequate assumption of the relationship in gene-enzyme-reaction as one-to-one unique association, computational difficulty or ignoring the yield from substrate to product, previous pathway finding approaches can't be effectively applied to find out the high yield pathways that are mass balanced in stoichiometry. In addition, the shortest pathways may not be the pathways with high yield. At the same time, a pathway, which exists in stoichiometry, may not be feasible in thermodynamics. By using mixed integer programming strategy, we put forward an algorithm to identify all the smallest balanced pathways which convert the source compound to the target compound in large-scale metabolic networks. The resulting pathways by our method can finely satisfy the stoichiometric constraints and non-decomposability condition. Especially, the functions of high yield and thermodynamics feasibility have been considered in our approach. This tool is tailored to direct the metabolic engineering practice to enlarge the metabolic potentials of industrial strains by integrating the extensive metabolic network information built from systems biology dataset.

Metabolic adaptation of two in silico mutants of Mycobacterium tuberculosis during infection

BACKGROUND

Up to date, Mycobacterium tuberculosis (Mtb) remains as the worst intracellular killer pathogen. To establish infection, inside the granuloma, Mtb reprograms its metabolism to support both growth and survival, keeping a balance between catabolism, anabolism and energy supply. Mtb knockouts with the faculty of being essential on a wide range of nutritional conditions are deemed as target candidates for tuberculosis (TB) treatment. Constraint-based genome-scale modeling is considered as a promising tool for evaluating genetic and nutritional perturbations on Mtb metabolic reprogramming. Nonetheless, few in silico assessments of the effect of nutritional conditions on Mtb's vulnerability and metabolic adaptation have been carried out.

RESULTS

A genome-scale model (GEM) of Mtb, modified from the H37Rv iOSDD890, was used to explore the metabolic reprogramming of two Mtb knockout mutants (pfkA- and icl-mutants), lacking key enzymes of central carbon metabolism, while exposed to changing nutritional conditions (oxygen, and carbon and nitrogen sources). A combination of shadow pricing, sensitivity analysis, and flux distributions patterns allowed us to identify metabolic behaviors that are in agreement with phenotypes reported in the literature. During hypoxia, at high glucose consumption, the Mtb pfkA-mutant showed a detrimental growth effect derived from the accumulation of toxic sugar phosphate intermediates (glucose-6-phosphate and fructose-6-phosphate) along with an increment of carbon fluxes towards the reductive direction of the tricarboxylic acid cycle (TCA). Furthermore, metabolic reprogramming of the icl-mutant (icl1&icl2) showed the importance of the methylmalonyl pathway for the detoxification of propionyl-CoA, during growth at high fatty acid consumption rates and aerobic conditions. At elevated levels of fatty acid uptake and hypoxia, we found a drop in TCA cycle intermediate accumulation that might create redox imbalance. Finally, findings regarding Mtb-mutant metabolic adaptation associated with asparagine consumption and acetate, succinate and alanine production, were in agreement with literature reports.

CONCLUSIONS

This study demonstrates the potential application of genome-scale modeling, flux balance analysis (FBA), phenotypic phase plane (PhPP) analysis and shadow pricing to generate valuable insights about Mtb metabolic reprogramming in the context of human granulomas.

Thermodynamic Constraints Improve Metabolic Networks

In pursuit of establishing a realistic metabolic phenotypic space, the reversibility of reactions is thermodynamically constrained in modern metabolic networks. The reversibility constraints follow from heuristic thermodynamic poise approximations that take anticipated cellular metabolite concentration ranges into account. Because constraints reduce the feasible space, draft metabolic network reconstructions may need more extensive reconciliation, and a larger number of genes may become essential. Notwithstanding ubiquitous application, the effect of reversibility constraints on the predictive capabilities of metabolic networks has not been investigated in detail. Instead, work has focused on the implementation and validation of the thermodynamic poise calculation itself. With the advance of fast linear programming-based network reconciliation, the effects of reversibility constraints on network reconciliation and gene essentiality predictions have become feasible and are the subject of this study. Networks with thermodynamically informed reversibility constraints outperformed gene essentiality predictions compared to networks that were constrained with randomly shuffled constraints. Unconstrained networks predicted gene essentiality as accurately as thermodynamically constrained networks, but predicted substantially fewer essential genes. Networks that were reconciled with sequence similarity data and strongly enforced reversibility constraints outperformed all other networks. We conclude that metabolic network analysis confirmed the validity of the thermodynamic constraints, and that thermodynamic poise information is actionable during network reconciliation.

Perspective of ions and messengers: an intricate link between potassium, glutamate, and cyclic di-AMP

Potassium and glutamate are the most abundant ions in every living cell. Whereas potassium plays a major role to keep the cellular turgor and to buffer the negative charges of the nucleic acids, the major function of glutamate is to serve as the universal amino group donor. In addition, both ions are involved in osmoprotection in bacterial cells. Here, we discuss how bacterial cells maintain the homeostasis of both ions and how adaptive evolution allows them to live even at extreme potassium limitation. Interestingly, positively charged amino acids are able to partially replace potassium, likely by buffering the negative charge of DNA. A major factor involved in the control of potassium homeostasis in Gram-positive bacteria is the essential second messenger cyclic di-AMP. This nucleotide is synthesized in response to the potassium concentration and in turn controls the expression and activity of potassium transporters. We discuss the link between the two major ions, DNA and the second messenger c-di-AMP.

Combining Genome-Scale Experimental and Computational Methods To Identify Essential Genes in Rhodobacter sphaeroides

Knowledge about the role of genes under a particular growth condition is required for a holistic understanding of a bacterial cell and has implications for health, agriculture, and biotechnology. We developed the Tn-seq analysis software (TSAS) package to provide a flexible and statistically rigorous workflow for the high-throughput analysis of insertion mutant libraries, advanced the knowledge of gene essentiality in R. sphaeroides, and illustrated how Tn-seq data can be used to more accurately identify genes that play important roles in metabolism and other processes that are essential for cellular survival.

Rhodobacter sphaeroides is one of the best-studied alphaproteobacteria from biochemical, genetic, and genomic perspectives. To gain a better systems-level understanding of this organism, we generated a large transposon mutant library and used transposon sequencing (Tn-seq) to identify genes that are essential under several growth conditions. Using newly developed Tn-seq analysis software (TSAS), we identified 493 genes as essential for aerobic growth on a rich medium. We then used the mutant library to identify conditionally essential genes under two laboratory growth conditions, identifying 85 additional genes required for aerobic growth in a minimal medium and 31 additional genes required for photosynthetic growth. In all instances, our analyses confirmed essentiality for many known genes and identified genes not previously considered to be essential. We used the resulting Tn-seq data to refine and improve a genome-scale metabolic network model (GEM) for R. sphaeroides. Together, we demonstrate how genetic, genomic, and computational approaches can be combined to obtain a systems-level understanding of the genetic framework underlying metabolic diversity in bacterial species.

IMPORTANCE Knowledge about the role of genes under a particular growth condition is required for a holistic understanding of a bacterial cell and has implications for health, agriculture, and biotechnology. We developed the Tn-seq analysis software (TSAS) package to provide a flexible and statistically rigorous workflow for the high-throughput analysis of insertion mutant libraries, advanced the knowledge of gene essentiality in R. sphaeroides, and illustrated how Tn-seq data can be used to more accurately identify genes that play important roles in metabolism and other processes that are essential for cellular survival.

Author Video: An author video summary of this article is available.

Conversion of Glycerol to 3-Hydroxypropanoic Acid by Genetically Engineered Bacillus subtilis

3-Hydroxypropanoic acid (3-HP) is an important biomass-derivable platform chemical that can be converted into a number of industrially relevant compounds. There have been several attempts to produce 3-HP from renewable sources in cell factories, focusing mainly on Escherichia coli, Klebsiella pneumoniae, and Saccharomyces cerevisiae. Despite the significant progress made in this field, commercially exploitable large-scale production of 3-HP in microbial strains has still not been achieved. In this study, we investigated the potential of Bacillus subtilis as a microbial platform for bioconversion of glycerol into 3-HP. Our recombinant B. subtilis strains overexpress the two-step heterologous pathway containing glycerol dehydratase and aldehyde dehydrogenase from K. pneumoniae. Genetic engineering, driven by in silico optimization, and optimization of cultivation conditions resulted in a 3-HP titer of 10 g/L, in a standard batch cultivation. Our findings provide the first report of successful introduction of the biosynthetic pathway for conversion of glycerol into 3-HP in B. subtilis. With this relatively high titer in batch, and the robustness of B. subtilis in high density fermentation conditions, we expect that our production strains may constitute a solid basis for commercial production of 3-HP.

IMGMD: A platform for the integration and standardisation of In silico Microbial Genome-scale Metabolic Models

Genome-scale metabolic models (GSMMs) constitute a platform that combines genome sequences and detailed biochemical information to quantify microbial physiology at the system level. To improve the unity, integrity, correctness, and format of data in published GSMMs, a consensus IMGMD database was built in the LAMP (Linux + Apache + MySQL + PHP) system by integrating and standardizing 328 GSMMs constructed for 139 microorganisms. The IMGMD database can help microbial researchers download manually curated GSMMs, rapidly reconstruct standard GSMMs, design pathways, and identify metabolic targets for strategies on strain improvement. Moreover, the IMGMD database facilitates the integration of wet-lab and in silico data to gain an additional insight into microbial physiology. The IMGMD database is freely available, without any registration requirements, at http://imgmd.jiangnan.edu.cn/database.

C. elegans and its bacterial diet as a model for systems-level understanding of host-microbiota interactions

Resident microbes of the human body, particularly the gut microbiota, provide essential functions for the host, and, therefore, have important roles in human health as well as mitigating disease. It is difficult to study the mechanisms by which the microbiota affect human health, especially at a systems-level, due to heterogeneity of human genomes, the complexity and heterogeneity of the gut microbiota, the challenge of growing these bacteria in the laboratory, and the lack of bacterial genetics in most microbiotal species. In the last few years, the interspecies model of the nematode Caenorhabditis elegans and its bacterial diet has proven powerful for studying host-microbiota interactions, as both the animal and its bacterial diet can be subjected to large-scale and high-throughput genetic screening. The high level of homology between many C. elegans and human genes, as well as extensive similarities between human and C. elegans metabolism, indicates that the findings obtained from this interspecies model may be broadly relevant to understanding how the human microbiota affects physiology and disease. In this review, we summarize recent systems studies on how bacteria interact with C. elegans and affect life history traits.

Towards improved genome-scale metabolic network reconstructions: unification, transcript specificity and beyond

Genome-scale metabolic network reconstructions provide a basis for the investigation of the metabolic properties of an organism. There are reconstructions available for multiple organisms, from prokaryotes to higher organisms and methods for the analysis of a reconstruction. One example is the use of flux balance analysis to improve the yields of a target chemical, which has been applied successfully. However, comparison of results between existing reconstructions and models presents a challenge because of the heterogeneity of the available reconstructions, for example, of standards for presenting gene-protein-reaction associations, nomenclature of metabolites and reactions or selection of protonation states. The lack of comparability for gene identifiers or model-specific reactions without annotated evidence often leads to the creation of a new model from scratch, as data cannot be properly matched otherwise. In this contribution, we propose to improve the predictive power of metabolic models by switching from gene-protein-reaction associations to transcript-isoform-reaction associations, thus taking advantage of the improvement of precision in gene expression measurements. To achieve this precision, we discuss available databases that can be used to retrieve this type of information and point at issues that can arise from their neglect. Further, we stress issues that arise from non-standardized building pipelines, like inconsistencies in protonation states. In addition, problems arising from the use of non-specific cofactors, e.g. artificial futile cycles, are discussed, and finally efforts of the metabolic modelling community to unify model reconstructions are highlighted.

A Resource Allocation Trade-Off between Virulence and Proliferation Drives Metabolic Versatility in the Plant Pathogen Ralstonia solanacearum

Bacterial pathogenicity relies on a proficient metabolism and there is increasing evidence that metabolic adaptation to exploit host resources is a key property of infectious organisms. In many cases, colonization by the pathogen also implies an intensive multiplication and the necessity to produce a large array of virulence factors, which may represent a significant cost for the pathogen. We describe here the existence of a resource allocation trade-off mechanism in the plant pathogen R. solanacearum. We generated a genome-scale reconstruction of the metabolic network of R. solanacearum, together with a macromolecule network module accounting for the production and secretion of hundreds of virulence determinants. By using a combination of constraint-based modeling and metabolic flux analyses, we quantified the metabolic cost for production of exopolysaccharides, which are critical for disease symptom production, and other virulence factors. We demonstrated that this trade-off between virulence factor production and bacterial proliferation is controlled by the quorum-sensing-dependent regulatory protein PhcA. A phcA mutant is avirulent but has a better growth rate than the wild-type strain. Moreover, a phcA mutant has an expanded metabolic versatility, being able to metabolize 17 substrates more than the wild-type. Model predictions indicate that metabolic pathways are optimally oriented towards proliferation in a phcA mutant and we show that this enhanced metabolic versatility in phcA mutants is to a large extent a consequence of not paying the cost for virulence. This analysis allowed identifying candidate metabolic substrates having a substantial impact on bacterial growth during infection. Interestingly, the substrates supporting well both production of virulence factors and growth are those found in higher amount within the plant host. These findings also provide an explanatory basis to the well-known emergence of avirulent variants in R. solanacearum populations in planta or in stressful environments.

Metabolic versatility is a critical element for pathogen’s virulence and their ability to survive in the host. Beyond the necessity to collect resources during infection, pathogens face a resource allocation dilemma: they have to use nutritional resources to proliferate inside the host, and in the other hand they need to mobilize matter and energy for the production of essential virulence factors. In this study, we provide evidence of that such a trade-off constrains antagonistically bacterial proliferation and virulence in the bacterial plant pathogen Ralstonia solanacearum. We determined the energetic cost required by R. solanacearum to produce and secrete exopolysaccharide, which is a major virulence factor required for wilting symptom appearance. We validated this result by showing that bacterial mutants defective for exopolysaccharide production or other virulence factor indeed have an increased growth rate compared to the wild-type strain. We provide evidence that this trade-off mechanism is orchestrated by the phcA master regulatory gene, which directly connects quorum-sensing regulation to metabolic versatility and virulence. Our results also support the view that R. solanacearum specializes towards a restricted number of substrates used during in planta growth.

Intrinsic noise and deviations from criticality in Boolean gene-regulatory networks

Gene regulatory networks can be successfully modeled as Boolean networks. A much discussed hypothesis says that such model networks reproduce empirical findings the best if they are tuned to operate at criticality, i.e. at the borderline between their ordered and disordered phases. Critical networks have been argued to lead to a number of functional advantages such as maximal dynamical range, maximal sensitivity to environmental changes, as well as to an excellent tradeoff between stability and flexibility. Here, we study the effect of noise within the context of Boolean networks trained to learn complex tasks under supervision. We verify that quasi-critical networks are the ones learning in the fastest possible way -even for asynchronous updating rules- and that the larger the task complexity the smaller the distance to criticality. On the other hand, when additional sources of intrinsic noise in the network states and/or in its wiring pattern are introduced, the optimally performing networks become clearly subcritical. These results suggest that in order to compensate for inherent stochasticity, regulatory and other type of biological networks might become subcritical rather than being critical, all the most if the task to be performed has limited complexity.

Efficient Reconstruction of Predictive Consensus Metabolic Network Models

Understanding cellular function requires accurate, comprehensive representations of metabolism. Genome-scale, constraint-based metabolic models (GSMs) provide such representations, but their usability is often hampered by inconsistencies at various levels, in particular for concurrent models. COMMGEN, our tool for COnsensus Metabolic Model GENeration, automatically identifies inconsistencies between concurrent models and semi-automatically resolves them, thereby contributing to consolidate knowledge of metabolic function. Tests of COMMGEN for four organisms showed that automatically generated consensus models were predictive and that they substantially increased coherence of knowledge representation. COMMGEN ought to be particularly useful for complex scenarios in which manual curation does not scale, such as for eukaryotic organisms, microbial communities, and host-pathogen interactions.

Genome-scale reconstruction of the metabolic network in Pseudomonas stutzeri A1501

Pseudomonas stutzeri A1501 is an endophytic bacterium capable of nitrogen fixation. This strain has been isolated from the rice rhizosphere and provides the plant with fixed nitrogen and phytohormones. These interesting features encouraged us to study the metabolism of this microorganism at the systems-level. In this work, we present the first genome-scale metabolic model (iPB890) for P. stutzeri, involving 890 genes, 1135 reactions, and 813 metabolites. A combination of automatic and manual approaches was used in the reconstruction process. Briefly, using the metabolic networks of Pseudomonas aeruginosa and Pseudomonas putida as templates, a draft metabolic network of P. stutzeri was reconstructed. Then, the draft network was driven through an iterative and curative process of gap filling. In the next step, the model was evaluated using different experimental data such as specific growth rate, Biolog substrate utilization data and other experimental observations. In most of the evaluation cases, the model was successful in correctly predicting the cellular phenotypes. Thus, we posit that the iPB890 model serves as a suitable platform to explore the metabolism of P. stutzeri.

Comparative genome-scale modelling of Staphylococcus aureus strains identifies strain-specific metabolic capabilities linked to pathogenicity

Staphylococcus aureus is a preeminent bacterial pathogen capable of colonizing diverse ecological niches within its human host. We describe here the pangenome of S. aureus based on analysis of genome sequences from 64 strains of S. aureus spanning a range of ecological niches, host types, and antibiotic resistance profiles. Based on this set, S. aureus is expected to have an open pangenome composed of 7,411 genes and a core genome composed of 1,441 genes. Metabolism was highly conserved in this core genome; however, differences were identified in amino acid and nucleotide biosynthesis pathways between the strains. Genome-scale models (GEMs) of metabolism were constructed for the 64 strains of S. aureus These GEMs enabled a systems approach to characterizing the core metabolic and panmetabolic capabilities of the S. aureus species. All models were predicted to be auxotrophic for the vitamins niacin (vitamin B3) and thiamin (vitamin B1), whereas strain-specific auxotrophies were predicted for riboflavin (vitamin B2), guanosine, leucine, methionine, and cysteine, among others. GEMs were used to systematically analyze growth capabilities in more than 300 different growth-supporting environments. The results identified metabolic capabilities linked to pathogenic traits and virulence acquisitions. Such traits can be used to differentiate strains responsible for mild vs. severe infections and preference for hosts (e.g., animals vs. humans). Genome-scale analysis of multiple strains of a species can thus be used to identify metabolic determinants of virulence and increase our understanding of why certain strains of this deadly pathogen have spread rapidly throughout the world.

Systematic development and optimization of chemically defined medium supporting high cell density growth of Bacillus coagulans

With determined components and experimental reducibility, the chemically defined medium (CDM) and the minimal chemically defined medium (MCDM) are used in many metabolism and regulation studies. This research aimed to develop the chemically defined medium supporting high cell density growth of Bacillus coagulans, which is a promising producer of lactic acid and other bio-chemicals. In this study, a systematic methodology combining the experimental technique with flux balance analysis (FBA) was proposed to design and simplify a CDM. The single omission technique and single addition technique were employed to determine the essential and stimulatory compounds, before the optimization of their concentrations by the statistical method. In addition, to improve the growth rationally, in silico omission and addition were performed by FBA based on the construction of a medium-size metabolic model of B. coagulans 36D1. Thus, CDMs were developed to obtain considerable biomass production of at least five B. coagulans strains, in which two model strains B. coagulans 36D1 and ATCC 7050 were involved.

A Caenorhabditis elegans Genome-Scale Metabolic Network Model

Caenorhabditis elegans is a powerful model to study metabolism and how it relates to nutrition, gene expression, and life history traits. However, while numerous experimental techniques that enable perturbation of its diet and gene function are available, a high-quality metabolic network model has been lacking. Here, we reconstruct an initial version of the C. elegans metabolic network. This network model contains 1,273 genes, 623 enzymes, and 1,985 metabolic reactions and is referred to as iCEL1273. Using flux balance analysis, we show that iCEL1273 is capable of representing the conversion of bacterial biomass into C. elegans biomass during growth and enables the predictions of gene essentiality and other phenotypes. In addition, we demonstrate that gene expression data can be integrated with the model by comparing metabolic rewiring in dauer animals versus growing larvae. iCEL1273 is available at a dedicated website (wormflux.umassmed.edu) and will enable the unraveling of the mechanisms by which different macro- and micronutrients contribute to the animal's physiology.

Investigating Moorella thermoacetica metabolism with a genome-scale constraint-based metabolic model

Moorella thermoacetica is a strictly anaerobic, endospore-forming, and metabolically versatile acetogenic bacterium capable of conserving energy by both autotrophic (acetogenesis) and heterotrophic (homoacetogenesis) modes of metabolism. Its metabolic diversity and the ability to efficiently convert a wide range of compounds, including syngas (CO + H2) into acetyl-CoA have made this thermophilic bacterium a promising host for industrial biotechnology applications. However, lack of detailed information on M. thermoacetica's metabolism is a major impediment to its use as a microbial cell factory. In order to overcome this issue, a genome-scale constraint-based metabolic model of Moorella thermoacetica, iAI558, has been developed using its genome sequence and physiological data from published literature. The reconstructed metabolic network of M. thermoacetica comprises 558 metabolic genes, 705 biochemical reactions, and 698 metabolites. Of the total 705 model reactions, 680 are gene-associated while the rest are non-gene associated reactions. The model, in addition to simulating both autotrophic and heterotrophic growth of M. thermoacetica, revealed degeneracy in its TCA-cycle, a common characteristic of anaerobic metabolism. Furthermore, the model helped elucidate the poorly understood energy conservation mechanism of M. thermoacetica during autotrophy. Thus, in addition to generating experimentally testable hypotheses regarding its physiology, such a detailed model will facilitate rapid strain designing and metabolic engineering of M. thermoacetica for industrial applications.

Genome-scale reconstruction of the Streptococcus pyogenes M49 metabolic network reveals growth requirements and indicates potential drug targets

Genome-scale metabolic models comprise stoichiometric relations between metabolites, as well as associations between genes and metabolic reactions and facilitate the analysis of metabolism. We computationally reconstructed the metabolic network of the lactic acid bacterium Streptococcus pyogenes M49. Initially, we based the reconstruction on genome annotations and already existing and curated metabolic networks of Bacillus subtilis, Escherichia coli, Lactobacillus plantarum and Lactococcus lactis. This initial draft was manually curated with the final reconstruction accounting for 480 genes associated with 576 reactions and 558 metabolites. In order to constrain the model further, we performed growth experiments of wild type and arcA deletion strains of S. pyogenes M49 in a chemically defined medium and calculated nutrient uptake and production fluxes. We additionally performed amino acid auxotrophy experiments to test the consistency of the model. The established genome-scale model can be used to understand the growth requirements of the human pathogen S. pyogenes and define optimal and suboptimal conditions, but also to describe differences and similarities between S. pyogenes and related lactic acid bacteria such as L. lactis in order to find strategies to reduce the growth of the pathogen and propose drug targets.

Metabolic modeling of clostridia: current developments and applications

Anaerobic Clostridium spp. is an important bioproduction microbial genus that can produce solvents and utilize a broad spectrum of substrates including cellulose and syngas. Genome-scale metabolic (GSM) models are increasingly being put forth for various clostridial strains to explore their respective metabolic capabilities and suitability for various bioconversions. In this study, we have selected representative GSM models for six different clostridia (Clostridium acetobutylicum, C. beijerinckii, C. butyricum, C. cellulolyticum, C. ljungdahlii and C. thermocellum) and performed a detailed model comparison contrasting their metabolic repertoire. We also discuss various applications of these GSM models to guide metabolic engineering interventions as well as assessing cellular physiology.

Construction and analysis of a genome-scale metabolic network for Bacillus licheniformis WX-02

We constructed the genome-scale metabolic network of Bacillus licheniformis (B. licheniformis) WX-02 by combining genomic annotation, high-throughput phenotype microarray (PM) experiments and literature-based metabolic information. The accuracy of the metabolic network was assessed by an OmniLog PM experiment. The final metabolic model iWX1009 contains 1009 genes, 1141 metabolites and 1762 reactions, and the predicted metabolic phenotypes showed an agreement rate of 76.8% with experimental PM data. In addition, key metabolic features such as growth yield, utilization of different substrates and essential genes were identified by flux balance analysis. A total of 195 essential genes were predicted from LB medium, among which 149 were verified with the experimental essential gene set of B. subtilis 168. With the removal of 5 reactions from the network, pathways for poly-γ-glutamic acid (γ-PGA) synthesis were optimized and the γ-PGA yield reached 83.8 mmol/h. Furthermore, the important metabolites and pathways related to γ-PGA synthesis and bacterium growth were comprehensively analyzed. The present study provides valuable clues for exploring the metabolisms and metabolic regulation of γ-PGA synthesis in B. licheniformis WX-02.

Reconstruction of a charge balanced genome-scale metabolic model to study the energy-uncoupled growth of Zymomonas mobilis ZM1

Zymomonas mobilisis an ethanologenic bacterium and is known to be an example microorganism with energy-uncoupled growth. The reconstructed metabolic model indicate that resistance to intracellular pH reduction could be the main reason for uncoupled growth.

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.

Metabolic engineering of Bacillus subtilis for terpenoid production

Terpenoids are the largest group of small-molecule natural products, with more than 60,000 compounds made from isopentenyl diphosphate (IPP) and its isomer dimethylallyl diphosphate (DMAPP). As the most diverse group of small-molecule natural products, terpenoids play an important role in the pharmaceutical, food, and cosmetic industries. For decades, Escherichia coli (E. coli) and Saccharomyces cerevisiae (S. cerevisiae) were extensively studied to biosynthesize terpenoids, because they are both fully amenable to genetic modifications and have vast molecular resources. On the other hand, our literature survey (20 years) revealed that terpenoids are naturally more widespread in Bacillales. In the mid-1990s, an inherent methylerythritol phosphate (MEP) pathway was discovered in Bacillus subtilis (B. subtilis). Since B. subtilis is a generally recognized as safe (GRAS) organism and has long been used for the industrial production of proteins, attempts to biosynthesize terpenoids in this bacterium have aroused much interest in the scientific community. This review discusses metabolic engineering of B. subtilis for terpenoid production, and encountered challenges will be discussed. We will summarize some major advances and outline future directions for exploiting the potential of B. subtilis as a desired "cell factory" to produce terpenoids.

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.

Mapping high-growth phenotypes in the flux space of microbial metabolism

Experimental and empirical observations on cell metabolism cannot be understood as a whole without their integration into a consistent systematic framework. However, the characterization of metabolic flux phenotypes is typically reduced to the study of a single optimal state, such as maximum biomass yield that is by far the most common assumption. Here, we confront optimal growth solutions to the whole set of feasible flux phenotypes (FFPs), which provides a benchmark to assess the likelihood of optimal and high-growth states and their agreement with experimental results. In addition, FFP maps are able to uncover metabolic behaviours, such as aerobic fermentation accompanying exponential growth on sugars at nutrient excess conditions, that are unreachable using standard models based on optimality principles. The information content of the full FFP space provides us with a map to explore and evaluate metabolic behaviour and capabilities, and so it opens new avenues for biotechnological and biomedical applications.

In silico model-driven cofactor engineering strategies for improving the overall NADP(H) turnover in microbial cell factories

Optimizing the overall NADPH turnover is one of the key challenges in various value-added biochemical syntheses. In this work, we first analyzed the NADPH regeneration potentials of common cell factories, including Escherichia coli, Saccharomyces cerevisiae, Bacillus subtilis, and Pichia pastoris across multiple environmental conditions and determined E. coli and glycerol as the best microbial chassis and most suitable carbon source, respectively. In addition, we identified optimal cofactor specificity engineering (CSE) enzyme targets, whose cofactors when switched from NAD(H) to NADP(H) improve the overall NADP(H) turnover. Among several enzyme targets, glyceraldehyde-3-phosphate dehydrogenase was recognized as a global candidate since its CSE improved the NADP(H) regeneration under most of the conditions examined. Finally, by analyzing the protein structures of all CSE enzyme targets via homology modeling, we established that the replacement of conserved glutamate or aspartate with serine in the loop region could change the cofactor dependence from NAD(H) to NADP(H).

Modeling cancer metabolism on a genome scale

Cancer cells have fundamentally altered cellular metabolism that is associated with their tumorigenicity and malignancy. In addition to the widely studied Warburg effect, several new key metabolic alterations in cancer have been established over the last decade, leading to the recognition that altered tumor metabolism is one of the hallmarks of cancer. Deciphering the full scope and functional implications of the dysregulated metabolism in cancer requires both the advancement of a variety of omics measurements and the advancement of computational approaches for the analysis and contextualization of the accumulated data. Encouragingly, while the metabolic network is highly interconnected and complex, it is at the same time probably the best characterized cellular network. Following, this review discusses the challenges that genome-scale modeling of cancer metabolism has been facing. We survey several recent studies demonstrating the first strides that have been done, testifying to the value of this approach in portraying a network-level view of the cancer metabolism and in identifying novel drug targets and biomarkers. Finally, we outline a few new steps that may further advance this field.

Data-driven integration of genome-scale regulatory and metabolic network models

Microbes are diverse and extremely versatile organisms that play vital roles in all ecological niches. Understanding and harnessing microbial systems will be key to the sustainability of our planet. One approach to improving our knowledge of microbial processes is through data-driven and mechanism-informed computational modeling. Individual models of biological networks (such as metabolism, transcription, and signaling) have played pivotal roles in driving microbial research through the years. These networks, however, are highly interconnected and function in concert-a fact that has led to the development of a variety of approaches aimed at simulating the integrated functions of two or more network types. Though the task of integrating these different models is fraught with new challenges, the large amounts of high-throughput data sets being generated, and algorithms being developed, means that the time is at hand for concerted efforts to build integrated regulatory-metabolic networks in a data-driven fashion. In this perspective, we review current approaches for constructing integrated regulatory-metabolic models and outline new strategies for future development of these network models for any microbial system.

Systems modeling approaches for microbial community studies: from metagenomics to inference of the community structure

Microbial communities play important roles in health, industrial applications and earth's ecosystems. With current molecular techniques we can characterize these systems in unprecedented detail. However, such methods provide little mechanistic insight into how the genetic properties and the dynamic couplings between individual microorganisms give rise to their dynamic activities. Neither do they give insight into what we call “the community state”, that is the fluxes and concentrations of nutrients within the community. This knowledge is a prerequisite for rational control and intervention in microbial communities. Therefore, the inference of the community structure from experimental data is a major current challenge. We will argue that this inference problem requires mathematical models that can integrate heterogeneous experimental data with existing knowledge. We propose that two types of models are needed. Firstly, mathematical models that integrate existing genomic, physiological, and physicochemical information with metagenomics data so as to maximize information content and predictive power. This can be achieved with the use of constraint-based genome-scale stoichiometric modeling of community metabolism which is ideally suited for this purpose. Next, we propose a simpler coarse-grained model, which is tailored to solve the inference problem from the experimental data. This model unambiguously relate to the more detailed genome-scale stoichiometric models which act as heterogeneous data integrators. The simpler inference models are, in our opinion, key to understanding microbial ecosystems, yet until now, have received remarkably little attention. This has led to the situation where the modeling of microbial communities, using only genome-scale models is currently more a computational, theoretical exercise than a method useful to the experimentalist.

Multi -omics and metabolic modelling pipelines: challenges and tools for systems microbiology

Integrated -omics approaches are quickly spreading across microbiology research labs, leading to (i) the possibility of detecting previously hidden features of microbial cells like multi-scale spatial organization and (ii) tracing molecular components across multiple cellular functional states. This promises to reduce the knowledge gap between genotype and phenotype and poses new challenges for computational microbiologists. We underline how the capability to unravel the complexity of microbial life will strongly depend on the integration of the huge and diverse amount of information that can be derived today from -omics experiments. In this work, we present opportunities and challenges of multi -omics data integration in current systems biology pipelines. We here discuss which layers of biological information are important for biotechnological and clinical purposes, with a special focus on bacterial metabolism and modelling procedures. A general review of the most recent computational tools for performing large-scale datasets integration is also presented, together with a possible framework to guide the design of systems biology experiments by microbiologists.

Computational prediction of essential metabolic genes using constraint-based approaches

In this chapter, we describe the application of constraint-based modeling to predict the impact of gene deletions on a metabolic phenotype. The metabolic reactions taking place inside cells form large networks, which have been reconstructed at a genome-scale for several organisms at increasing levels of detail. By integrating mathematical modeling techniques with biochemical principles, constraint-based approaches enable predictions of metabolite fluxes and growth under specific environmental conditions or for genetically modified microorganisms. Similar to the experimental knockout of a gene, predicting the essentiality of a metabolic gene for a phenotype further allows to generate hypotheses on its biological function and design of genetic engineering strategies for biotechnological applications. Here, we summarize the principles of constraint-based approaches and provide a detailed description of the procedure to predict the essentiality of metabolic genes with respect to a specific metabolic function. We exemplify the approach by predicting the essentiality of reactions in the citric acid cycle for the production of glucose from fatty acids.

Comparative Genomics and Evolutionary Modularity of Prokaryotes

The soaring number of high-quality genomic sequences has ushered in the era of post-genomic research where our understanding of organisms has dramatically shifted towards defining the function of genes within their larger biological contexts. As a result, novel high-throughput experimental technologies are being increasingly employed to uncover physical and functional associations of genes and proteins in complex biological processes. Through the construction and analysis of physical, genetic and metabolic networks generated for the model organisms, such as Escherichia coli, organizational principles of the genome have been deduced, such as modularity, which has important implications toward understanding prokaryotic evolution and adaptation to novel lifestyles.

Using a genome-scale metabolic model of Enterococcus faecalis V583 to assess amino acid uptake and its impact on central metabolism

Increasing antibiotic resistance in pathogenic bacteria necessitates the development of new medication strategies. Interfering with the metabolic network of the pathogen can provide novel drug targets but simultaneously requires a deeper and more detailed organism-specific understanding of the metabolism, which is often surprisingly sparse. In light of this, we reconstructed a genome-scale metabolic model of the pathogen Enterococcus faecalis V583. The manually curated metabolic network comprises 642 metabolites and 706 reactions. We experimentally determined metabolic profiles of E. faecalis grown in chemically defined medium in an anaerobic chemostat setup at different dilution rates and calculated the net uptake and product fluxes to constrain the model. We computed growth-associated energy and maintenance parameters and studied flux distributions through the metabolic network. Amino acid auxotrophies were identified experimentally for model validation and revealed seven essential amino acids. In addition, the important metabolic hub of glutamine/glutamate was altered by constructing a glutamine synthetase knockout mutant. The metabolic profile showed a slight shift in the fermentation pattern toward ethanol production and increased uptake rates of multiple amino acids, especially l-glutamine and l-glutamate. The model was used to understand the altered flux distributions in the mutant and provided an explanation for the experimentally observed redirection of the metabolic flux. We further highlighted the importance of gene-regulatory effects on the redirection of the metabolic fluxes upon perturbation. The genome-scale metabolic model presented here includes gene-protein-reaction associations, allowing a further use for biotechnological applications, for studying essential genes, proteins, or reactions, and the search for novel drug targets.