Reconstruction Of Genome-scale Metabolic Models Research Articles

Pan-Draft: automated reconstruction of species-representative metabolic models from multiple genomes

The accurate reconstruction of genome-scale metabolic models (GEMs) for unculturable species poses challenges due to the incomplete and fragmented genetic information typical of metagenome-assembled genomes (MAGs). While existing tools leverage sequence homology from single genomes, this study introduces pan-Draft, a pan-reactome-based approach exploiting recurrent genetic evidence to determine the solid core structure of species-level GEMs. By comparing MAGs clustered at the species-level, pan-Draft addresses the issues due to the incompleteness and contamination of individual genomes, providing high-quality draft models and an accessory reactions catalog supporting the gapfilling step. This approach will improve our comprehension of metabolic functions of uncultured species.

Genome-Scale Metabolic Model Reconstruction and Investigation into the Fluxome of the Fast-Growing Cyanobacterium Synechococcus sp. PCC 11901.

The ability to convert atmospheric CO2 and light into biomass and value-added chemicals makes cyanobacteria a promising resource microbial host for biotechnological applications. A newly discovered fastest-growing cyanobacterial strain, Synechococcus sp. PCC 11901, has been reported to have the highest biomass accumulation rate, making it a preferred target host for producing renewable fuels, value-added biochemicals, and natural products. System-level knowledge of an organism is imperative to understand the metabolic potential of the strain, which can be attained by developing genome-scale metabolic models (GEMs). We present the first genome-scale metabolic model of Synechococcus sp. PCC 11901 (iRS840), which contains 840 genes, 1001 reactions, and 944 metabolites. The model has been optimized and validated under different trophic modes, i.e., autotrophic and mixotrophic, by conducting an in vivo growth experiment. The robustness of the metabolic network was evaluated by changing the biomass coefficient of the model, which showed a higher sensitivity toward pigments under the photoautotrophic condition, whereas under the heterotrophic condition, amino acids were found to be more influential. Furthermore, it was discovered that PCC 11901 synthesizes succinyl-CoA via succinic semialdehyde due to its imperfect TCA cycle. Subsequent flux balance analysis (FBA) revealed a quantum yield of 0.16 in silico, which is higher compared to that of PCC 6803. Under mixotrophic conditions (with glycerol and carbon dioxide), the flux through the Calvin cycle increased compared to autotrophic conditions. This model will be useful for gaining insights into the metabolic potential of PCC 11901 and developing effective metabolic engineering strategies for product development.

BioISO: An Objective-Oriented Application for Assisting the Curation of Genome-Scale Metabolic Models.

As the reconstruction of Genome-Scale Metabolic Models (GEMs) becomes standard practice in systems biology, the number of organisms having at least one metabolic model is peaking at an unprecedented scale. The automation of laborious tasks, such as gap-finding and gap-filling, allowed the development of GEMs for poorly described organisms. However, the quality of these models can be compromised by the automation of several steps, which may lead to erroneous phenotype simulations. Biological networks constraint-based In Silico Optimisation (BioISO) is a computational tool aimed at accelerating the reconstruction of GEMs. This tool facilitates manual curation steps by reducing the large search spaces often met when debugging in silico biological models. BioISO uses a recursive relation-like algorithm and Flux Balance Analysis (FBA) to evaluate and guide debugging of in silico phenotype simulations. The potential of BioISO to guide the debugging of model reconstructions was showcased and compared with the results of two other state-of-the-art gap-filling tools (Meneco and fastGapFill). In this assessment, BioISO is better suited to reducing the search space for errors and gaps in metabolic networks by identifying smaller ratios of dead-end metabolites. Furthermore, BioISO was used as Meneco's gap-finding algorithm to reduce the number of proposed solutions for filling the gaps.

Reconstruction of genome-scale metabolic models of non-conventional yeasts: current state, challenges, and perspectives

Reconstruction of genome-scale metabolic models of non-conventional yeasts: current state, challenges, and perspectives

Genome-scale metabolic models in translational medicine: the current status and potential of machine learning in improving the effectiveness of the models.

The genome-scale metabolic model (GEM) has emerged as one of the leading modeling approaches for systems-level metabolic studies and has been widely explored for a broad range of organisms and applications. Owing to the development of genome sequencing technologies and available biochemical data, it is possible to reconstruct GEMs for model and non-model microorganisms as well as for multicellular organisms such as humans and animal models. GEMs will evolve in parallel with the availability of biological data, new mathematical modeling techniques and the development of automated GEM reconstruction tools. The use of high-quality, context-specific GEMs, a subset of the original GEM in which inactive reactions are removed while maintaining metabolic functions in the extracted model, for model organisms along with machine learning (ML) techniques could increase their applications and effectiveness in translational research in the near future. Here, we briefly review the current state of GEMs, discuss the potential contributions of ML approaches for more efficient and frequent application of these models in translational research, and explore the extension of GEMs to integrative cellular models.

The media composition as a crucial element in high-throughput metabolic network reconstruction.

In recent years, metagenome-assembled genomes (MAGs) have provided glimpses into the intra- and interspecies genetic diversity and interactions that form the bases of complex microbial communities. High-throughput reconstruction of genome-scale metabolic networks (GEMs) from MAGs is a promising avenue to disentangle the myriad trophic interactions stabilizing these communities. However, high-throughput reconstruction of GEMs relies on accurate gap filling of metabolic pathways using automated algorithms. Here, we systematically explore how the composition of the media (specification of the available nutrients and metabolites) during gap filling influences the resulting GEMs concerning predicted auxotrophies for fully sequenced model organisms and environmental isolates. We expand this analysis by using 106 MAGs from the same species with differing quality. We find that although the completeness of MAGs influences the fraction of gap-filled reactions, the composition of the media plays the dominant role in the accurate prediction of auxotrophies that form the basis of myriad community interactions. We propose that constraining the media composition for gap filling through both experimental approaches and computational approaches will increase the reliability of high-throughput reconstruction of genome-scale metabolic models from MAGs and paves the way for culture independent prediction of trophic interactions in complex microbial communities.

Engineered yeast for efficient de novo synthesis of 7-dehydrocholesterol.

The synthesis of vitamin D3 precursor 7-dehydrocholesterol (7-DHC) by microbial fermentation has much attracted attention owing to its advantages of environmental protection. In this study, Saccharomyces cerevisiae was engineered for a de novo biosynthesis of 7-DHC. First, seven essential genes (six endogenous genes and one heterologous gene) were overexpressed, and the ROX1 gene (heme-dependent repressor of hypoxic genes) was knocked out. The resulting strain produced 82.6 mg/L 7-DHC from glucose. Then, we predicted five gene knockout targets for 7-DHC overproduction by the reconstruction of genome-scale metabolic model. GDH1 gene knockout increased the 7-DHC titer from 82.6 to 101.5 mg/L, and the specific growth rate of the ΔGDH1 mutant was also increased by 28%. Next, Ty1 transposon in S. cerevisiae was applied to increase the copies of the ERG1 gene and DHCR24 gene, resulting in a 120% increase in 7-DHC titer to 223.3 mg/L. Besides, to optimize the metabolic flux distribution, Clustered Regularly Interspaced Short Palindromic Repeats interference (CRISPRi) system was used to dynamically inhibit the competitive pathway, and the best binding site of ERG6 (delta (24)-sterol C-methyltransferase) promoter was screened out. The OD600 value of ERG6 regulated cells increased by 43% than knocking out ERG6 directly, and 7-DHC titer increased to 365.5 mg/L in a shake flask. Finally, the 7-DHC titer reached 1328 mg/L in 3-L bioreactor and the specific titer of 7-DHC reached up to 114.7 mg/g dry cell weight). Overall, this study constructed a yeast chassis for the highly efficient production of 7-DHC by systems metabolic engineering.

High-Quality Genome-Scale Reconstruction of Corynebacterium glutamicum ATCC 13032.

Corynebacterium glutamicum belongs to the microbes of enormous biotechnological relevance. In particular, its strain ATCC 13032 is a widely used producer of L-amino acids at an industrial scale. Its apparent robustness also turns it into a favorable platform host for a wide range of further compounds, mainly because of emerging bio-based economies. A deep understanding of the biochemical processes in C. glutamicum is essential for a sustainable enhancement of the microbe's productivity. Computational systems biology has the potential to provide a valuable basis for driving metabolic engineering and biotechnological advances, such as increased yields of healthy producer strains based on genome-scale metabolic models (GEMs). Advanced reconstruction pipelines are now available that facilitate the reconstruction of GEMs and support their manual curation. This article presents iCGB21FR, an updated and unified GEM of C. glutamicum ATCC 13032 with high quality regarding comprehensiveness and data standards, built with the latest modeling techniques and advanced reconstruction pipelines. It comprises 1042 metabolites, 1539 reactions, and 805 genes with detailed annotations and database cross-references. The model validation took place using different media and resulted in realistic growth rate predictions under aerobic and anaerobic conditions. The new GEM produces all canonical amino acids, and its phenotypic predictions are consistent with laboratory data. The in silico model proved fruitful in adding knowledge to the metabolism of C. glutamicum: iCGB21FR still produces L-glutamate with the knock-out of the enzyme pyruvate carboxylase, despite the common belief to be relevant for the amino acid's production. We conclude that integrating high standards into the reconstruction of GEMs facilitates replicating validated knowledge, closing knowledge gaps, and making it a useful basis for metabolic engineering. The model is freely available from BioModels Database under identifier MODEL2102050001.

Advances in Genome-Scale Metabolic Modeling toward Microbial Community Analysis of the Human Microbiome

A genome-scale metabolic model (GEM) represents metabolic pathways of an organism in a mathematical form and can be built using biochemistry and genome annotation data. GEMs are invaluable for understanding organisms since they analyze the metabolic capabilities and behaviors quantitatively and can predict phenotypes. The development of high-throughput data collection techniques led to an immense increase in omics data such as metagenomics, which expand our knowledge on the human microbiome, but this also created a need for systematic analysis of these data. In recent years, GEMs have also been reconstructed for microbial species, including human gut microbiota, and methods for the analysis of microbial communities have been developed to examine the interaction between the organisms or the host. The purpose of this review is to provide a comprehensive guide for the applications of GEMs in microbial community analysis. Starting with GEM repositories, automatic GEM reconstruction tools, and quality control of models, this review will give insights into microbe-microbe and microbe-host interaction predictions and optimization of microbial community models. Recent studies that utilize microbial GEMs and personalized models to infer the influence of microbiota on human diseases such as inflammatory bowel diseases (IBD) or Parkinson's disease are exemplified. Being powerful system biology tools for both species-level and community-level analysis of microbes, GEMs integrated with omics data and machine learning techniques will be indispensable for studying the microbiome and their effects on human physiology as well as for deciphering the mechanisms behind human diseases.

Analyzing Metabolic States of Adipogenic and Osteogenic Differentiation in Human Mesenchymal Stem Cells via Genome Scale Metabolic Model Reconstruction.

Since their initial discovery in 1976, mesenchymal stem cells (MSCs) have been gathering interest as a possible tool to further the development and enhancement of various therapeutics within regenerative medicine. However, our current understanding of both metabolic function and existing differences within the varying cell lineages (e.g., cells in either osteogenesis or adipogenesis) is severely lacking making it more difficult to fully realize the therapeutic potential of MSCs. Here, we reconstruct the MSC metabolic network to understand the activity of various metabolic pathways and compare their usage under different conditions and use these models to perform experimental design. We present three new genome-scale metabolic models (GEMs) each representing a different MSC lineage (proliferation, osteogenesis, and adipogenesis) that are biologically feasible and have distinctive cell lineage characteristics that can be used to explore metabolic function and increase our understanding of these phenotypes. We present the most distinctive differences between these lineages when it comes to enriched metabolic subsystems and propose a possible osteogenic enhancer. Taken together, we hope these mechanistic models will aid in the understanding and therapeutic potential of MSCs.

Genome-Scale Metabolic Modelling of the <i>Drosophila Melanogaster</i> Gut Microbial Community Uncovers Growth Promoting Metabolite Exchange

Most if not all multicellular organisms are inhabited by a vast number of microorganisms, the so-called microbiome. The bacteria present in the digestive system are usually the most abundant microbiome members and important for various aspects of the hosts’ physiology. On top of making food components accessible, microbiome members produce e.g. micronutrients required by the host as well as metabolites, which affect several of the hosts’ signaling pathways. A lot of knowledge exists concerning varying microbiome compositions. How the gut bacteria affect each other, however, remains elusive. In mammals, this is at least in part based on the sheer complexity of the microbiome with hundreds to thousands of different species. Thus, model organisms are commonly used to investigate mechanistic questions. The gut microbiome of Drosophila melanogaster, for example, turned out to be an exquisite model based on its low complexity of only about 10 leading bacterial species in laboratory flies. Here, we isolated abundant gut bacteria from laboratory-reared Drosophila, sequenced their respective genomes and used this information for the reconstruction of genome-scale metabolic models. With these, we simulated growth in mono- and co-culture conditions and different media including a synthetic diet designed to grow Drosophila melanogaster. Our simulations reveal a synergistic growth of some but not all gut microbiome members, which stems on the exchange of distinct metabolites including Tricarboxylic acid cycle intermediates. Culturing experiments confirmed our predictions, thus demonstrating the existence of microbiome-derived prebiotics.

Genome-Scale Metabolic Model Reconstruction and in Silico Investigations of Methane Metabolism in Methylosinus trichosporium OB3b.

Methylosinus trichosporium OB3b is an obligate aerobic methane-utilizing alpha-proteobacterium. Since its isolation, M. trichosporium OB3b has been established as a model organism to study methane metabolism in type II methanotrophs. M. trichosporium OB3b utilizes soluble and particulate methane monooxygenase (sMMO and pMMO respectively) for methane oxidation. While the source of electrons is known for sMMO, there is less consensus regarding electron donor to pMMO. To investigate this and other questions regarding methane metabolism, the genome-scale metabolic model for M. trichosporium OB3b (model ID: iMsOB3b) was reconstructed. The model accurately predicted oxygen: methane molar uptake ratios and specific growth rates on nitrate-supplemented medium with methane as carbon and energy source. The redox-arm mechanism which links methane oxidation with complex I of electron transport chain has been found to be the most optimal mode of electron transfer. The model was also qualitatively validated on ammonium-supplemented medium indicating its potential to accurately predict methane metabolism in different environmental conditions. Finally, in silico investigations regarding flux distribution in central carbon metabolism of M. trichosporium OB3b were performed. Overall, iMsOB3b can be used as an organism-specific knowledgebase and a platform for hypothesis-driven theoretical investigations of methane metabolism.

Genome-Scale Metabolic Reconstruction of Acetobacter pasteurianus 386B, a Candidate Functional Starter Culture for Cocoa Bean Fermentation.

Acetobacter pasteurianus 386B is a candidate functional starter culture for the cocoa bean fermentation process. To allow in silico simulations of its related metabolism in response to different environmental conditions, a genome-scale metabolic model for A. pasteurianus 386B was reconstructed. This is the first genome-scale metabolic model reconstruction for a member of the genus Acetobacter. The metabolic network reconstruction process was based on extensive genome re-annotation and comparative genomics analyses. The information content related to the functional annotation of metabolic enzymes and transporters was placed in a metabolic context by exploring and curating a Pathway/Genome Database of A. pasteurianus 386B using the Pathway Tools software. Metabolic reactions and curated gene-protein-reaction associations were bundled into a genome-scale metabolic model of A. pasteurianus 386B, named iAp386B454, containing 454 genes, 322 reactions, and 296 metabolites embedded in two cellular compartments. The reconstructed model was validated by performing growth experiments in a defined medium, which revealed that lactic acid as the sole carbon source could sustain growth of this strain. Further, the reconstruction of the A. pasteurianus 386B genome-scale metabolic model revealed knowledge gaps concerning the metabolism of this strain, especially related to the biosynthesis of its cell envelope and the presence or absence of metabolite transporters.

A consensus S. cerevisiae metabolic model Yeast8 and its ecosystem for comprehensively probing cellular metabolism

Genome-scale metabolic models (GEMs) represent extensive knowledgebases that provide a platform for model simulations and integrative analysis of omics data. This study introduces Yeast8 and an associated ecosystem of models that represent a comprehensive computational resource for performing simulations of the metabolism of Saccharomyces cerevisiae––an important model organism and widely used cell-factory. Yeast8 tracks community development with version control, setting a standard for how GEMs can be continuously updated in a simple and reproducible way. We use Yeast8 to develop the derived models panYeast8 and coreYeast8, which in turn enable the reconstruction of GEMs for 1,011 different yeast strains. Through integration with enzyme constraints (ecYeast8) and protein 3D structures (proYeast8DB), Yeast8 further facilitates the exploration of yeast metabolism at a multi-scale level, enabling prediction of how single nucleotide variations translate to phenotypic traits.

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.

RAVEN 2.0: A versatile toolbox for metabolic network reconstruction and a case study on Streptomyces coelicolor.

RAVEN is a commonly used MATLAB toolbox for genome-scale metabolic model (GEM) reconstruction, curation and constraint-based modelling and simulation. Here we present RAVEN Toolbox 2.0 with major enhancements, including: (i) de novo reconstruction of GEMs based on the MetaCyc pathway database; (ii) a redesigned KEGG-based reconstruction pipeline; (iii) convergence of reconstructions from various sources; (iv) improved performance, usability, and compatibility with the COBRA Toolbox. Capabilities of RAVEN 2.0 are here illustrated through de novo reconstruction of GEMs for the antibiotic-producing bacterium Streptomyces coelicolor. Comparison of the automated de novo reconstructions with the iMK1208 model, a previously published high-quality S. coelicolor GEM, exemplifies that RAVEN 2.0 can capture most of the manually curated model. The generated de novo reconstruction is subsequently used to curate iMK1208 resulting in Sco4, the most comprehensive GEM of S. coelicolor, with increased coverage of both primary and secondary metabolism. This increased coverage allows the use of Sco4 to predict novel genome editing targets for optimized secondary metabolites production. As such, we demonstrate that RAVEN 2.0 can be used not only for de novo GEM reconstruction, but also for curating existing models based on up-to-date databases. Both RAVEN 2.0 and Sco4 are distributed through GitHub to facilitate usage and further development by the community (https://github.com/SysBioChalmers/RAVEN and https://github.com/SysBioChalmers/Streptomyces_coelicolor-GEM).

Methods for automated genome-scale metabolic model reconstruction.

In the era of next-generation sequencing and ubiquitous assembly and binning of metagenomes, new putative genome sequences are being produced from isolate and microbiome samples at ever-increasing rates. Genome-scale metabolic models have enormous utility for supporting the analysis and predictive characterization of these genomes based on sequence data. As a result, tools for rapid automated reconstruction of metabolic models are becoming critically important for supporting the analysis of new genome sequences. Many tools and algorithms have now emerged to support rapid model reconstruction and analysis. Here, we are comparing and contrasting the capabilities and output of a variety of these tools, including ModelSEED, Raven Toolbox, PathwayTools, SuBliMinal Toolbox and merlin.

Fast automated reconstruction of genome-scale metabolic models for microbial species and communities.

Genome-scale metabolic models are instrumental in uncovering operating principles of cellular metabolism, for model-guided re-engineering, and unraveling cross-feeding in microbial communities. Yet, the application of genome-scale models, especially to microbial communities, is lagging behind the availability of sequenced genomes. This is largely due to the time-consuming steps of manual curation required to obtain good quality models. Here, we present an automated tool, CarveMe, for reconstruction of species and community level metabolic models. We introduce the concept of a universal model, which is manually curated and simulation ready. Starting with this universal model and annotated genome sequences, CarveMe uses a top-down approach to build single-species and community models in a fast and scalable manner. We show that CarveMe models perform closely to manually curated models in reproducing experimental phenotypes (substrate utilization and gene essentiality). Additionally, we build a collection of 74 models for human gut bacteria and test their ability to reproduce growth on a set of experimentally defined media. Finally, we create a database of 5587 bacterial models and demonstrate its potential for fast generation of microbial community models. Overall, CarveMe provides an open-source and user-friendly tool towards broadening the use of metabolic modeling in studying microbial species and communities.

Reconstruction of genome-scale metabolic model of Yarrowia lipolytica and its application in overproduction of triacylglycerol

BackgroundYarrowia lipolytica is widely studied as a non-conventional model yeast owing to the high level of lipid accumulation. Therein, triacylglycerol (TAG) is a major component of liposome. In order to investigate the TAG biosynthesis mechanism at a systematic level, a novel genome-scale metabolic model of Y. lipolytica was reconstructed based on a previous model iYL619_PCP published by our lab and another model iYali4 published by Kerkhoven et al.ResultsThe novel model iYL_2.0 contains 645 genes, 1083 metabolites, and 1471 reactions, which was validated more effective on simulations of specific growth rate. The precision of 29 carbon sources utilities reached up to 96.6% when simulated by iYL_2.0. In minimal growth medium, 111 genes were identified as essential for cell growth, whereas 66 essential genes were identified in yeast extract medium, which were verified by database of essential genes, suggesting a better prediction ability of iYL_2.0 in comparison with other existing models. In addition, potential metabolic engineering targets of improving TAG production were predicted by three in silico methods developed in-house, and the effects of amino acids supplementation were investigated based on model iYL_2.0.ConclusionsThe reconstructed model iYL_2.0 is a powerful platform for efficiently optimizing the metabolism of TAG and systematically understanding the physiological mechanism of Y. lipolytica.

Analyses of extracellular protein production in Bacillus subtilis – I: Genome-scale metabolic model reconstruction based on updated gene-enzyme-reaction data

Bacillus subtilis genome-scale model (GEM) reconstruction was stimulated by the recent sequencing and consequent re-annotations. The updated gene-enzyme-reaction data were collected from databases to reconstruct B. subtilis reaction network BsRN-2016 containing 1144 genes linked to 1955 reactions and 1103 metabolites. Thermodynamic analysis was conducted to identify reversibility and directionality of the reactions. By elimination of unconnected-reactions from BsRN-2016, reconstruction process of the first third-generation GEM iBsu1144 employing 1083 reactions linked to 719 genes was completed. The stoichiometric flux-balance based model was solved using time-profiles of serine alkaline protease fermentation-data at three different oxygen-transfer conditions creating perturbations on the intracellular reaction-network. Testing iBsu1144 dignity with three different objective functions indicated superior robustness of the GEM. Comparison of iBsu1144 results with a second-generation GEM was demonstrated. Insights obtained from flux distributions were used to determine metabolic engineering sites. Asparagine, isoleucine, threonine, and aspartic acid were determined as the primary rate-limiting amino acids to be considered as corresponding metabolic engineering sites in SAP synthesis in B. subtilis. Flux variability analysis carried out for the optimum condition reveals that 288 reactions are active and linked to 317 genes, so called substantial genes. The blocked 735 reactions linked to 533 genes, formed a platform for guided gene deletions in B. subtilis to generate simplified host strains.