Genome-scale Metabolic Reconstructions Research Articles

MOOMIN - Mathematical explOration of 'Omics data on a MetabolIc Network.

Analysis of differential expression of genes is often performed to understand how the metabolic activity of an organism is impacted by a perturbation. However, because the system of metabolic regulation is complex and all changes are not directly reflected in the expression levels, interpreting these data can be difficult. In this work, we present a new algorithm and computational tool that uses a genome-scale metabolic reconstruction to infer metabolic changes from differential expression data. Using the framework of constraint-based analysis, our method produces a qualitative hypothesis of a change in metabolic activity. In other words, each reaction of the network is inferred to have increased, decreased, or remained unchanged in flux. In contrast to similar previous approaches, our method does not require a biological objective function and does not assign on/off activity states to genes. An implementation is provided and it is available online. We apply the method to three published datasets to show that it successfully accomplishes its two main goals: confirming or rejecting metabolic changes suggested by differentially expressed genes based on how well they fit in as parts of a coordinated metabolic change, as well as inferring changes in reactions whose genes did not undergo differential expression. github.com/htpusa/moomin. Supplementary data are available at Bioinformatics online.

A systematic assessment of current genome-scale metabolic reconstruction tools

BackgroundSeveral genome-scale metabolic reconstruction software platforms have been developed and are being continuously updated. These tools have been widely applied to reconstruct metabolic models for hundreds of microorganisms ranging from important human pathogens to species of industrial relevance. However, these platforms, as yet, have not been systematically evaluated with respect to software quality, best potential uses and intrinsic capacity to generate high-quality, genome-scale metabolic models. It is therefore unclear for potential users which tool best fits the purpose of their research.ResultsIn this work, we perform a systematic assessment of current genome-scale reconstruction software platforms. To meet our goal, we first define a list of features for assessing software quality related to genome-scale reconstruction. Subsequently, we use the feature list to evaluate the performance of each tool. To assess the similarity of the draft reconstructions to high-quality models, we compare each tool’s output networks with that of the high-quality, manually curated, models of Lactobacillus plantarum and Bordetella pertussis, representatives of gram-positive and gram-negative bacteria, respectively. We additionally compare draft reconstructions with a model of Pseudomonas putida to further confirm our findings. We show that none of the tools outperforms the others in all the defined features.ConclusionsModel builders should carefully choose a tool (or combinations of tools) depending on the intended use of the metabolic model. They can use this benchmark study as a guide to select the best tool for their research. Finally, developers can also benefit from this evaluation by getting feedback to improve their software.

IMet: A graphical user interface software tool to merge metabolic networks

Nowadays, studying microorganisms has become faster and deeper than the last decades, thanks to the modeling of genome-scale metabolic networks. Completed genome sequencing projects of microorganisms and annotating these sequences have provided a worthwhile platform for reconstructing and modeling genome-scale metabolic networks. The genome-scale metabolic network reconstruction is a laborious and time-consuming task which needs an extensive study and search in different types of databases. Furthermore, it also requires an iterative process of creating and curating the obtained network, particularly with experimental methods. Hence, different types of reconstructions and models of a targeted microorganism can be found with different qualities, as the goal and need of researchers differ.Due to these circumstances, scientists have to continue with only one of the reconstructed metabolic networks of each microorganism and ignore the rest in their in silico works. It is clear that having a tool which merges different metabolic networks of a single organism can be a useful and effective way to study them with minimal cost and time. To meet this need, we have developed iMet, the standalone graphical user interface (GUI) software tool to merge multiple reconstructed metabolic networks of microorganisms. As a case study, we merged three reconstructed metabolic networks of a cyanobacterium using iMet, and then all of them (including the new merged one) became modeled. The results of our evaluations including Flux Balance Analysis (FBA), revealed enhancing metabolic network coverage as well as increasing yield of desired products in the new obtained model.

In Vivo Gene Essentiality and Metabolism in Bordetella pertussis.

Bordetella pertussis is the causative agent of whooping cough, a serious respiratory illness affecting children and adults, associated with prolonged cough and potential mortality. Whooping cough has reemerged in recent years, emphasizing a need for increased knowledge of basic mechanisms of B. pertussis growth and pathogenicity. While previous studies have provided insight into in vitro gene essentiality of this organism, very little is known about in vivo gene essentiality, a critical gap in knowledge, since B. pertussis has no previously identified environmental reservoir and is isolated from human respiratory tract samples. We hypothesize that the metabolic capabilities of B. pertussis are especially tailored to the respiratory tract and that many of the genes involved in B. pertussis metabolism would be required to establish infection in vivo In this study, we generated a diverse library of transposon mutants and then used it to probe gene essentiality in vivo in a murine model of infection. Using the CON-ARTIST pipeline, 117 genes were identified as conditionally essential at 1 day postinfection, and 169 genes were identified as conditionally essential at 3 days postinfection. Most of the identified genes were associated with metabolism, and we utilized two existing genome-scale metabolic network reconstructions to probe the effects of individual essential genes on biomass synthesis. This analysis suggested a critical role for glucose metabolism and lipooligosaccharide biosynthesis in vivo This is the first genome-wide evaluation of in vivo gene essentiality in B. pertussis and provides tools for future exploration.IMPORTANCE Our study describes the first in vivo transposon sequencing (Tn-seq) analysis of B. pertussis and identifies genes predicted to be essential for in vivo growth in a murine model of intranasal infection, generating key resources for future investigations into B. pertussis pathogenesis and vaccine design.

Systematic assessment of secondary bile acid metabolism in gut microbes reveals distinct metabolic capabilities in inflammatory bowel disease

BackgroundThe human gut microbiome performs important functions in human health and disease. A classic example for host-gut microbial co-metabolism is host biosynthesis of primary bile acids and their subsequent deconjugation and transformation by the gut microbiome. To understand these system-level host-microbe interactions, a mechanistic, multi-scale computational systems biology approach that integrates the different types of omic data is needed. Here, we use a systematic workflow to computationally model bile acid metabolism in gut microbes and microbial communities.ResultsTherefore, we first performed a comparative genomic analysis of bile acid deconjugation and biotransformation pathways in 693 human gut microbial genomes and expanded 232 curated genome-scale microbial metabolic reconstructions with the corresponding reactions (available at https://vmh.life). We then predicted the bile acid biotransformation potential of each microbe and in combination with other microbes. We found that each microbe could produce maximally six of the 13 secondary bile acids in silico, while microbial pairs could produce up to 12 bile acids, suggesting bile acid biotransformation being a microbial community task. To investigate the metabolic potential of a given microbiome, publicly available metagenomics data from healthy Western individuals, as well as inflammatory bowel disease patients and healthy controls, were mapped onto the genomes of the reconstructed strains. We constructed for each individual a large-scale personalized microbial community model that takes into account strain-level abundances. Using flux balance analysis, we found considerable variation in the potential to deconjugate and transform primary bile acids between the gut microbiomes of healthy individuals. Moreover, the microbiomes of pediatric inflammatory bowel disease patients were significantly depleted in their bile acid production potential compared with that of controls. The contributions of each strain to overall bile acid production potential across individuals were found to be distinct between inflammatory bowel disease patients and controls. Finally, bottlenecks limiting secondary bile acid production potential were identified in each microbiome model.ConclusionsThis large-scale modeling approach provides a novel way of analyzing metagenomics data to accelerate our understanding of the metabolic interactions between the host and gut microbiomes in health and diseases states. Our models and tools are freely available to the scientific community.

A genome-scale metabolic network reconstruction of extremely halophilic bacterium Salinibacter ruber.

A genome-scale metabolic network reconstruction of Salinibacter ruber DSM13855 is presented here. To our knowledge, this is the first metabolic model of an organism in the phylum Rhodothermaeota. This model, which will be called iMB631, was reconstructed based on genomic and biochemical data available on the strain Salinibacter ruber DSM13855. This network consists of 1459 reactions, 1363 metabolites and 631 genes. Model evaluation was performed based on existing biochemical data in the literature and also by performing laboratory experiments. For growth on different carbon sources, we show that iMB631 is able to correctly predict the growth in 91% of cases where growth has been observed experimentally and 83% of conditions in which S. ruber did not grow. The F-score was 93%, demonstrating a generally acceptable performance of the model. Based on the predicted flux distributions, we found that under certain autotrophic condition, a reductive tricarboxylic acid cycle (rTCA) has fluxes in all necessary reactions to support autotrophic growth. To include special metabolites of the bacterium, salinixanthin biosynthesis pathway was modeled based on the pathway proposed recently. For years, main glucose consumption pathway has been under debates in S. ruber. Using flux balance analysis, iMB631 predicts pentose phosphate pathway, rather than glycolysis, as the active glucose consumption method in the S. ruber.

Leveraging the effects of chloroquine on resistant malaria parasites for combination therapies

BackgroundMalaria is a major global health problem, with the Plasmodium falciparum protozoan parasite causing the most severe form of the disease. Prevalence of drug-resistant P. falciparum highlights the need to understand the biology of resistance and to identify novel combination therapies that are effective against resistant parasites. Resistance has compromised the therapeutic use of many antimalarial drugs, including chloroquine, and limited our ability to treat malaria across the world. Fortunately, chloroquine resistance comes at a fitness cost to the parasite; this can be leveraged in developing combination therapies or to reinstate use of chloroquine.ResultsTo understand biological changes induced by chloroquine treatment, we compared transcriptomics data from chloroquine-resistant parasites in the presence or absence of the drug. Using both linear models and a genome-scale metabolic network reconstruction of the parasite to interpret the expression data, we identified targetable pathways in resistant parasites. This study identified an increased importance of lipid synthesis, glutathione production/cycling, isoprenoids biosynthesis, and folate metabolism in response to chloroquine.ConclusionsWe identified potential drug targets for chloroquine combination therapies. Significantly, our analysis predicts that the combination of chloroquine and sulfadoxine-pyrimethamine or fosmidomycin may be more effective against chloroquine-resistant parasites than either drug alone; further studies will explore the use of these drugs as chloroquine resistance blockers. Additional metabolic weaknesses were found in glutathione generation and lipid synthesis during chloroquine treatment. These processes could be targeted with novel inhibitors to reduce parasite growth and reduce the burden of malaria infections. Thus, we identified metabolic weaknesses of chloroquine-resistant parasites and propose targeted chloroquine combination therapies.

Reconciling high-throughput gene essentiality data with metabolic network reconstructions.

The identification of genes essential for bacterial growth and survival represents a promising strategy for the discovery of antimicrobial targets. Essential genes can be identified on a genome-scale using transposon mutagenesis approaches; however, variability between screens and challenges with interpretation of essentiality data hinder the identification of both condition-independent and condition-dependent essential genes. To illustrate the scope of these challenges, we perform a large-scale comparison of multiple published Pseudomonas aeruginosa gene essentiality datasets, revealing substantial differences between the screens. We then contextualize essentiality using genome-scale metabolic network reconstructions and demonstrate the utility of this approach in providing functional explanations for essentiality and reconciling differences between screens. Genome-scale metabolic network reconstructions also enable a high-throughput, quantitative analysis to assess the impact of media conditions on the identification of condition-independent essential genes. Our computational model-driven analysis provides mechanistic insight into essentiality and contributes novel insights for design of future gene essentiality screens and the identification of core metabolic processes.

Casting light on Asgardarchaeota metabolism in a sunlit microoxic niche.

Recent advances in phylogenomic analyses and increased genomic sampling of uncultured prokaryotic lineages have brought compelling evidence in support of the emergence of eukaryotes from within the archaeal domain of life (eocyte hypothesis)1,2. The discovery of Asgardarchaeota and its supposed position at the base of the eukaryotic tree of life3,4 provided cues about the long-awaited identity of the eocytic lineage from which the nucleated cells (Eukaryota) emerged. While it is apparent that Asgardarchaeota encode a plethora of eukaryotic-specific proteins (the highest number identified yet in prokaryotes)5, the lack of genomic information and metabolic characterization has precluded inferences about their lifestyles and the metabolic landscape that favoured the emergence of the protoeukaryote ancestor. Here, we use advanced phylogenetic analyses for inferring the deep ancestry of eukaryotes, and genome-scale metabolic reconstructions for shedding light on the metabolic milieu of Asgardarchaeota. In doing so, we: (1) show that Heimdallarchaeia (the closest eocytic lineage to eukaryotes to date) are likely to have a microoxic niche, based on their genomic potential, with aerobic metabolic pathways that are unique among Archaea (that is, the kynurenine pathway); (2) provide evidence of mixotrophy within Asgardarchaeota; and (3) describe a previously unknown family of rhodopsins encoded within the recovered genomes.

A synthetic ecosystem for the multi-level modelling of heterotroph-phototroph metabolic interactions

Marine ecosystems are characterized by an intricate set of interactions. One of the most important occurs through the exchange of dissolved organic matter (DOM) provided by phototrophs and used by heterotrophic bacteria as their main carbon and energy source. This metabolic interaction represents the foundation of the entire ocean food-web.Here we have assembled a synthetic ecosystem to assist the systems-level investigation of this biological association. This was achieved by building an integrated, genome-scale metabolic reconstruction using two model organisms (a diatom Phaeodactylum tricornutum and an heterotrophic bacterium, Pseudoalteromonas haloplanktis). The model was initially analysed using a constraint-based approach (Flux Balance Analysis, FBA) and then turned into a dynamic (dFBA) model to simulate a diatom-bacteria co-culture and to study the effect of changes in growth parameters on such a system. Furthermore, we developed a simpler dynamic Ordinary Differential Equations (ODEs) system that, fed with dFBA results, was able to qualitatively describe this synthetic ecosystem and allowed performing stochastic simulations to assess the effect of noise on the overall balance of this co-culture.We show that our model represents known metabolic cross-talks of a phototroph-heterotroph system, including mutualism and competition for inorganic ions (i.e., phosphate and sulphate). In addition, the dynamic simulation predicts realistic growth rates for both the diatom and the bacterium and a steady-state balance between diatom and bacterial cell concentration that matches those determined in experimental co-cultures. This steady state, however, is reached following an oscillatory trend, a behaviour that is typically observed in the presence of metabolic co-dependencies. Finally, we show that, at high diatom/bacteria cell concentration ratio, stochastic fluctuations can lead to the extinction of bacteria from the co-culture, causing an explosion of diatom population. We anticipate that the developed synthetic ecosystem will serve as a basis for the generation of testable hypotheses and as a scaffold for integrating and interpreting -omics data from experimental co-cultures.

Metabolic Modeling of Clostridium difficile Associated Dysbiosis of the Gut Microbiota

Recent in vitro experiments have demonstrated the ability of the pathogen Clostridium difficile and commensal gut bacteria to form biofilms on surfaces, and biofilm development in vivo is likely. Various studies have reported that 3%–15% of healthy adults are asymptomatically colonized with C. difficile, with commensal species providing resistance against C. difficile pathogenic colonization. C. difficile infection (CDI) is observed at a higher rate in immunocompromised patients previously treated with broad spectrum antibiotics that disrupt the commensal microbiota and reduce competition for available nutrients, resulting in imbalance among commensal species and dysbiosis conducive to C. difficile propagation. To investigate the metabolic interactions of C. difficile with commensal species from the three dominant phyla in the human gut, we developed a multispecies biofilm model by combining genome-scale metabolic reconstructions of C. difficile, Bacteroides thetaiotaomicron from the phylum Bacteroidetes, Faecalibacterium prausnitzii from the phylum Firmicutes, and Escherichia coli from the phylum Proteobacteria. The biofilm model was used to identify gut nutrient conditions that resulted in C. difficile-associated dysbiosis characterized by large increases in C. difficile and E. coli abundances and large decreases in F. prausnitzii abundance. We tuned the model to produce species abundances and short-chain fatty acid levels consistent with available data for healthy individuals. The model predicted that experimentally-observed host-microbiota perturbations resulting in decreased carbohydrate/increased amino acid levels and/or increased primary bile acid levels would induce large increases in C. difficile abundance and decreases in F. prausnitzii abundance. By adding the experimentally-observed perturbation of increased host nitrate secretion, the model also was able to predict increased E. coli abundance associated with C. difficile dysbiosis. In addition to rationalizing known connections between nutrient levels and disease progression, the model generated hypotheses for future testing and has the capability to support the development of new treatment strategies for C. difficile gut infections.

ModelExplorer - software for visual inspection and inconsistency correction of genome-scale metabolic reconstructions

BackgroundGenome-scale metabolic network reconstructions are low level chemical representations of biological organisms. These models allow the system-level investigation of metabolic phenotypes using a variety of computational approaches. The link between a metabolic network model and an organisms’ higher-level behaviour is usually found using a constraint-based analysis approach, such as FBA (Flux Balance Analysis). However, the process of model reconstruction rarely proceeds without error. Often, considerable parts of a model cannot carry flux under any condition. This is termed model inconsistency and is caused by faulty topology and/or stoichiometry of the underlying reconstructed network. While there exist several automated gap-filling tools that may solve some of the inconsistencies, much of the work still needs to be carried out manually. The common “linear list” format of writing biochemical reactions makes it difficult to intuit what is at the root of the inconsistent behaviour. Unfortunately, we have frequently observed that model builders do not correct their models past the abilities of automated tools, leaving many widely used models significantly inconsistent.ResultsWe have developed the software ModelExplorer, which main purpose is to fill this gap by providing an intuitive and visual framework that allows the user to explore and correct inconsistencies in genome-scale metabolic models. The software will automatically visualize metabolic networks as graphs with distinct separation and delineation of cellular compartments. ModelExplorer highlights reactions and species that are unable to carry flux (blocked), with several different consistency checking modes available. Our software also allows the automatic identification of neighbours and production pathways of any species or reaction. Additionally, the user may focus on any chosen inconsistent part of the model on its own. This facilitates a rapid and visual identification of reactions and species responsible for model inconsistencies. Finally, ModelExplorer lets the user freely edit, add or delete model elements, allowing straight-forward correction of discovered issues.ConclusionOverall, ModelExplorer is currently the fastest real-time metabolic network visualization program available. It implements several consistency checking algorithms, which in combination with its set of tracking tools, gives an efficient and systematic model-correction process.

Genomic sequencing, genome-scale metabolic network reconstruction, and in silico flux analysis of the grape endophytic fungus Alternaria sp. MG1

BackgroundAlternaria sp. MG1, an endophytic fungus isolated from grape, is a native producer of resveratrol, which has important application potential. However, the metabolic characteristics and physiological behavior of MG1 still remains mostly unraveled. In addition, the resveratrol production of the strain is low. Thus, the whole-genome sequencing is highly required for elucidating the resveratrol biosynthesis pathway. Furthermore, the metabolic network model of MG1 was constructed to provide a computational guided approach for improving the yield of resveratrol.ResultsFirstly, a draft genomic sequence of MG1 was generated with a size of 34.7 Mbp and a GC content of 50.96%. Genome annotation indicated that MG1 possessed complete biosynthesis pathways for stilbenoids, flavonoids, and lignins. Eight secondary metabolites involved in these pathways were detected by GC–MS analysis, confirming the metabolic diversity of MG1. Furthermore, the first genome-scale metabolic network of Alternaria sp. MG1 (named iYL1539) was reconstructed, accounting for 1539 genes, 2231 metabolites, and 2255 reactions. The model was validated qualitatively and quantitatively by comparing the in silico simulation with experimental data, and the results showed a high consistency. In iYL1539, 56 genes were identified as growth essential in rich medium. According to constraint-based analysis, the importance of cofactors for the resveratrol biosynthesis was successfully demonstrated. Ethanol addition was predicted in silico to be an effective method to improve resveratrol production by strengthening acetyl-CoA synthesis and pentose phosphate pathway, and was verified experimentally with a 26.31% increase of resveratrol. Finally, 6 genes were identified as potential targets for resveratrol over-production by the recently developed methodology. The target-genes were validated using salicylic acid as elicitor, leading to an increase of resveratrol yield by 33.32% and the expression of gene 4CL and CHS by 1.8- and 1.6-fold, respectively.ConclusionsThis study details the diverse capability and key genes of Alternaria sp. MG1 to produce multiple secondary metabolites. The first model of the species Alternaria was constructed, providing an overall understanding of the physiological behavior and metabolic characteristics of MG1. The model is a highly useful tool for enhancing productivity by rational design of the metabolic pathway for resveratrol and other secondary metabolites.

Predicting Strain Engineering Strategies Using iKS1317: A Genome-Scale Metabolic Model of Streptomyces coelicolor.

Streptomyces coelicolor is a model organism for the Actinobacteria, a phylum known to produce an extensive range of different bioactive compounds that include antibiotics currently used in the clinic. Biosynthetic gene clusters discovered in genomes of other Actinobacteria can be transferred to and expressed in S. coelicolor, making it a factory for heterologous production of secondary metabolites. Genome-scale metabolic reconstructions have successfully been used in several biotechnology applications to facilitate the over-production of target metabolites. Here, the authors present iKS1317, the most comprehensive and accurate reconstructed genome-scale metabolic model (GEM) for S. coelicolor. The model reconstruction is based on previous models, publicly available databases, and published literature and includes 1317 genes, 2119 reactions, and 1581 metabolites. It correctly predicts wild-type growth in 96.5% of the evaluated growth environments and gene knockout predictions in 78.4% when comparing with observed mutant growth phenotypes, with a total accuracy of 83.3%. However, using a minimal nutrient environment for the gene knockout predictions, iKS1317 has an accuracy of 87.1% in predicting mutant growth phenotypes. Furthermore, we used iKS1317 and existing strain design algorithms to suggest robust gene-knockout strategies to increase the production of acetyl-CoA. Since acetyl-CoA is the most important precursor for polyketide antibiotics, the suggested strategies may be implemented in vivo to improve the function of S. coelicolor as a heterologous expression host.

Systems-level engineering and characterisation of Clostridium autoethanogenum through heterologous production of poly-3-hydroxybutyrate (PHB)

Gas fermentation is emerging as an economically attractive option for the sustainable production of fuels and chemicals from gaseous waste feedstocks. Clostridium autoethanogenum can use CO and/or CO2 + H2 as its sole carbon and energy sources. Fermentation of C. autoethanogenum is currently being deployed on a commercial scale for ethanol production. Expanding the product spectrum of acetogens will enhance the economics of gas fermentation. To achieve efficient heterologous product synthesis, limitations in redox and energy metabolism must be overcome. Here, we engineered and characterised at a systems-level, a recombinant poly-3-hydroxybutyrate (PHB)-producing strain of C. autoethanogenum. Cells were grown in CO-limited steady-state chemostats on two gas mixtures, one resembling syngas (20% H2) and the other steel mill off-gas (2% H2). Results were characterised using metabolomics and transcriptomics, and then integrated using a genome-scale metabolic model reconstruction. PHB-producing cells had an increased expression of the Rnf complex, suggesting energy limitations for heterologous production. Subsequent optimisation of the bioprocess led to a 12-fold increase in the cellular PHB content. The data suggest that the cellular redox state, rather than the acetyl-CoA pool, was limiting PHB production. Integration of the data into the genome-scale metabolic model showed that ATP availability limits PHB production. Altogether, the data presented here advances the fundamental understanding of heterologous product synthesis in gas-fermenting acetogens.

Insights into Dynamic Network States Using Metabolomic Data.

Metabolomic data is the youngest of the high-throughput data types; however, it is potentially one of the most informative, as it provides a direct, quantitative biochemical phenotype. There are a number of ways in which metabolomic data can be analyzed in systems biology; however, the thermodynamic and kinetic relevance of these data cannot be overstated. Genome-scale metabolic network reconstructions provide a natural context to incorporate metabolomic data in order to provide insight into the condition-specific kinetic characteristics of metabolic networks. Herein we discuss how metabolomic data can be incorporated into constraint-based models in a flexible framework that enables scaling from small pathways to cell-scale models, while being able to accommodate coarse-grained to more detailed, allosteric interactions, all using the well-known principle of mass action.

Integrated Experimental and Computational Analyses Reveal Differential Metabolic Functionality in Antibiotic-Resistant Pseudomonas aeruginosa.

Metabolic adaptations accompanying the development of antibiotic resistance in bacteria remain poorly understood. To study this relationship, we profiled the growth of lab-evolved antibiotic-resistantlineages of the opportunistic pathogen Pseudomonas aeruginosa across 190 unique carbon sources. Our data revealed that the evolution of antibiotic resistance resulted in systems-level changes to growth dynamics and metabolic phenotype. A genome-scale metabolic network reconstruction of P.aeruginosa was paired with whole-genome sequencing data to predict genes contributing to observed changes in metabolism. We experimentally validated computational predictions to identify mutations in resistant P.aeruginosa affecting loss of catabolic function. Finally, we found a shared metabolic phenotype between lab-evolved P.aeruginosa and clinical isolates with similar mutational landscapes. Our results build upon previous knowledge of antibiotic-induced metabolic adaptation and provide a framework for the identification of metabolic limitations in antibiotic-resistant pathogens.

A Model Integration Pipeline for the Improvement of Human Genome-Scale Metabolic Reconstructions.

Metabolism has been a major field of study in the last years, mainly due to its importance in understanding cell physiology and certain disease phenotypes due to its deregulation. Genome-scale metabolic models (GSMMs) have been established as important tools to help achieve a better understanding of human metabolism. Towards this aim, advances in systems biology and bioinformatics have allowed the reconstruction of several human GSMMs, although some limitations and challenges remain, such as the lack of external identifiers for both metabolites and reactions. A pipeline was developed to integrate multiple GSMMs, starting by retrieving information from the main human GSMMs and evaluating the presence of external database identifiers and annotations for both metabolites and reactions. Information from metabolites was included into a graph database with omics data repositories, allowing clustering of metabolites through their similarity regarding database cross-referencing. Metabolite annotation of several older GSMMs was enriched, allowing the identification and integration of common entities. Using this information, as well as other metrics, we successfully integrated reactions from these models. These methods can be leveraged towards the creation of a unified consensus model of human metabolism.

Tn-Core: A Toolbox for Integrating Tn-seq Gene Essentiality Data and Constraint-Based Metabolic Modeling.

The design of synthetic cells requires a detailed understanding of the relevance of genes and gene networks underlying complex cellular phenotypes. Transposon-sequencing (Tn-seq) and constraint-based metabolic modeling can be used to probe the core genetic and metabolic networks underlying a biological process. Integrating these highly complementary experimental and in silico approaches has the potential to yield a highly comprehensive understanding of the core networks of a cell. Specifically, it can facilitate the interpretation of Tn-seq data sets and identify gaps in the data that could hinder the engineering of the cellular system, while also providing refined models for the accurate predictions of cellular metabolism. Here, we present Tn-Core, the first easy-to-use computational pipeline specifically designed for integrating Tn-seq data with metabolic modeling, prepared for use by both experimental and computational biologists. Tn-Core is a MATLAB toolbox that contains several custom functions, and it is built upon existing functions within the COBRA Toolbox and the TIGER Toolbox. Tn-Core takes as input a genome-scale metabolic model, Tn-seq data, and optionally RNA-seq data, and returns: (i) a context-specific core metabolic model; (ii) an evaluation of redundancies within core metabolic pathways, and optionally (iii) a refined genome-scale metabolic model. A simple, user-friendly workflow, requiring limited knowledge of metabolic modeling, is provided that allows users to run the analyses and export the data as easy-to-explore files of value to both experimental and computational biologists. We demonstrate the utility of Tn-Core using Sinorhizobium meliloti, Pseudomonas aeruginosa, and Rhodobacter sphaeroides genome-scale metabolic reconstructions as case studies.

Automated generation of genome-scale metabolic draft reconstructions based on KEGG

BackgroundConstraint-based modeling is a widely used and powerful methodology to assess the metabolic phenotypes and capabilities of an organism. The starting point and cornerstone of all such modeling is a genome-scale metabolic network reconstruction. The creation, further development, and application of such networks is a growing field of research thanks to a plethora of readily accessible computational tools. While the majority of studies are focused on single-species analyses, typically of a microbe, the computational study of communities of organisms is gaining attention. Similarly, reconstructions that are unified for a multi-cellular organism have gained in popularity. Consequently, the rapid generation of genome-scale metabolic reconstructed networks is crucial. While multiple web-based or stand-alone tools are available for automated network reconstruction, there is, however, currently no publicly available tool that allows the swift assembly of draft reconstructions of community metabolic networks and consolidated metabolic networks for a specified list of organisms.ResultsHere, we present AutoKEGGRec, an automated tool that creates first draft metabolic network reconstructions of single organisms, community reconstructions based on a list of organisms, and finally a consolidated reconstruction for a list of organisms or strains. AutoKEGGRec is developed in Matlab and works seamlessly with the COBRA Toolbox v3, and it is based on only using the KEGG database as external input. The generated first draft reconstructions are stored in SBML files and consist of all reactions for a KEGG organism ID and corresponding linked genes. This provides a comprehensive starting point for further refinement and curation using the host of COBRA toolbox functions or other preferred tools. Through the data structures created, the tool also facilitates a comparative analysis of metabolic content in any given number of organisms present in the KEGG database.ConclusionAutoKEGGRec provides a first step in a metabolic network reconstruction process, filling a gap for tools creating community and consolidated metabolic networks. Based only on KEGG data as external input, the generated reconstructions consist of data with a directly traceable foundation and pedigree. With AutoKEGGRec, this kind of modeling is made accessible to a wider part of the genome-scale metabolic analysis community.