scholarly journals Genome-scale metabolic reconstruction of the stress-tolerant hybrid yeast Zygosaccharomyces parabailii

2018 ◽  
Author(s):  
Marzia Di Filippo ◽  
Raúl A. Ortiz-Merino ◽  
Chiara Damiani ◽  
Gianni Frascotti ◽  
Danilo Porro ◽  
...  
Keyword(s):  
Laboratory Data ◽  
Metabolic Model ◽  
Cellular Systems ◽  
Metabolic Reconstruction ◽  
Genome Sequences ◽  
Metabolic Potential ◽  
Cell Factories ◽  
Metabolic Models ◽  
Constraint Based Modeling ◽  
Genome Scale

Genome-scale metabolic models are powerful tools to understand and engineer cellular systems facilitating their use as cell factories. This is especially true for microorganisms with known genome sequences from which nearly complete sets of enzymes and metabolic pathways are determined, or can be inferred. Yeasts are highly diverse eukaryotes whose metabolic traits have long been exploited in industry, and although many of their genome sequences are available, few genome-scale metabolic models have so far been produced. For the first time, we reconstructed the genome-scale metabolic model of the hybrid yeast Zygosaccharomyces parabailii, which is a member of the Z. bailii sensu lato clade notorious for stress-tolerance and therefore relevant to industry. The model comprises 3096 reactions, 2091 metabolites, and 2413 genes. Our own laboratory data were then used to establish a biomass synthesis reaction, and constrain the extracellular environment. Through constraint-based modeling, our model reproduces the co-consumption and catabolism of acetate and glucose posing it as a promising platform for understanding and exploiting the metabolic potential of Z. parabailii.

scholarly journals Genome Scale Constraint-Based Metabolic Reconstruction of Propionibacterium Freudenreichii DSM 20271

2021 ◽  
Author(s):  
Mahsa Sadat Razavi Borghei ◽  
Meysam Mobasheri ◽  
Tabassom Sobati
Keyword(s):  
Metabolic Networks ◽  
Metabolic Model ◽  
Physiological Data ◽  
Metabolic Reconstruction ◽  
Modeling And Analysis ◽  
Microbial Cell Factories ◽  
Culture Optimization ◽  
Cell Factories ◽  
Functional Features ◽  
Genome Scale

Abstract Propionibacterium is an anaerobic bacterium with a history of use in the production of Swiss cheese and, more recently, several industrial bioproducts. While the use of this strain for the production of organic acids and secondary metabolites has gained growing interest, the industrial application of the strain requires further improvement in the yield and productivity of the target products. Systems modeling and analysis of metabolic networks are widely leveraged to gain holistic insights into the metabolic features of biotechnologically important strains and to devise metabolic engineering and culture optimization strategies for economically viable bioprocess development. In the present study, a high-quality genome-scale metabolic model of P. freudenreichii ssp. freudenreichii strain DSM 20271 was developed based on the strain’s genome annotation and biochemical and physiological data. The model covers the functions of 23% of the strain’s ORFs and accounts for 711 metabolic reactions and 647 unique metabolites. Literature-based reconstruction of the central metabolism and rigorous refinement of annotation data for establishing gene-protein-reaction associations renders the model a curated omic-scale knowledge base of the organism. Validation of the model against experimental data indicates that the reconstruction can capture the key structural and functional features of P. freudenreichii metabolism, including the growth rate, the pattern of flux distribution, the strain’s aerotolerance behavior, and the change in the mode of metabolic activity during the transition from an anaerobic to an aerobic growth regime. The model also includes an accurately curated pathway of cobalamin biosynthesis, which was used to examine the capacity of the strain to produce vitamin B12 precursors. Constraint-based reconstruction and analysis of the P. freudenreichii metabolic network also provided novel insights into the complexity and robustness of P. freudenreichii energy metabolism. The developed reconstruction, hence, may be used as a platform for the development of P. freudenreichii-based microbial cell factories and bioprocesses.

scholarly journals Methods for automated genome-scale metabolic model reconstruction

2018 ◽  
Vol 46 (4) ◽  
pp. 931-936 ◽  
Author(s):  
José P. Faria ◽  
Miguel Rocha ◽  
Isabel Rocha ◽  
Christopher S. Henry
Keyword(s):  
Next Generation Sequencing ◽  
Sequence Data ◽  
Metabolic Model ◽  
Model Reconstruction ◽  
Next Generation ◽  
Genome Sequences ◽  
Metabolic Models ◽  
Genome Scale ◽  
Generation Sequencing

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.

scholarly journals Genome-scale metabolic reconstruction and metabolic versatility of an obligate methanotrophMethylococcus capsulatusstr. Bath

2018 ◽  
Author(s):  
Ankit Gupta ◽  
Ahmad Ahmad ◽  
Dipesh Chothwe ◽  
Midhun K. Madhu ◽  
Shireesh Srivastava ◽  
...  
Keyword(s):  
Amino Acids ◽  
Metabolic Model ◽  
Metabolic Reconstruction ◽  
Methylococcus Capsulatus ◽  
Double Knockout ◽  
Metabolic Potential ◽  
Methane Mitigation ◽  
Metabolic Versatility ◽  
A Genome ◽  
Genome Scale

AbstractThe increase in greenhouse gases with high global warming potential such as methane is a matter of concern and requires multifaceted efforts to reduce its emission and increase its mitigation from the environment. Microbes such as methanotrophs can assist in methane mitigation. To understand the metabolic capabilities of methanotrophs, a complete genome-scale metabolic model of an obligate methanotroph,Methylococcus capsulatusstr. Bath was reconstructed. The model contains 535 genes, 898 reactions and 784 unique metabolites and is namediMC535. The predictive potential of the model was validated using previously-reported experimental data. The model predicted the Entner-Duodoroff (ED) pathway to be essential for the growth of this bacterium, whereas the Embden-Meyerhof-Parnas (EMP) pathway was found non-essential. The performance of the model was simulated on various carbon and nitrogen sources and found thatM. capsulatuscan grow on amino acids. The analysis of network topology of the model identified that six amino acids were in the top-ranked metabolic hubs. Using flux balance analysis (FBA), 29% of the metabolic genes were predicted to be essential, and 76 double knockout combinations involving 92 unique genes were predicted to be lethal. In conclusion, we have reconstructed a genome-scale metabolic model of a unique methanotrophMethylococcus capsulatusstr. Bath. The model will serve as a knowledge-base for deeper understanding, as a platform for exploring the metabolic potential, and as a tool to engineer this bacterium for methane mitigation and industrial applications.

scholarly journals Genome-Scale Metabolic Model of the Human Pathogen Candida albicans: A Promising Platform for Drug Target Prediction

2020 ◽  
Vol 6 (3) ◽  
pp. 171
Author(s):  
Romeu Viana ◽  
Oscar Dias ◽  
Davide Lagoa ◽  
Mónica Galocha ◽  
Isabel Rocha ◽  
...  
Keyword(s):  
Candida Albicans ◽  
Drug Targets ◽  
Fungal Pathogens ◽  
Software Tool ◽  
Metabolic Model ◽  
New Drugs ◽  
Metabolic Reconstruction ◽  
Metabolic Potential ◽  
New Drug ◽  
Genome Scale

Candida albicans is one of the most impactful fungal pathogens and the most common cause of invasive candidiasis, which is associated with very high mortality rates. With the rise in the frequency of multidrug-resistant clinical isolates, the identification of new drug targets and new drugs is crucial in overcoming the increase in therapeutic failure. In this study, the first validated genome-scale metabolic model for Candida albicans, iRV781, is presented. The model consists of 1221 reactions, 926 metabolites, 781 genes, and four compartments. This model was reconstructed using the open-source software tool merlin 4.0.2. It is provided in the well-established systems biology markup language (SBML) format, thus, being usable in most metabolic engineering platforms, such as OptFlux or COBRA. The model was validated, proving accurate when predicting the capability of utilizing different carbon and nitrogen sources when compared to experimental data. Finally, this genome-scale metabolic reconstruction was tested as a platform for the identification of drug targets, through the comparison between known drug targets and the prediction of gene essentiality in conditions mimicking the human host. Altogether, this model provides a promising platform for global elucidation of the metabolic potential of C. albicans, possibly guiding the identification of new drug targets to tackle human candidiasis.

scholarly journals Systematically gap-filling the genome-scale metabolic model of CHO cells

2020 ◽  
Author(s):  
Hamideh Fouladiha ◽  
Sayed-Amir Marashi ◽  
Shangzhong Li ◽  
Zerong Li ◽  
Helen O. Masson ◽  
...  
Keyword(s):  
Cho Cells ◽  
Predictive Power ◽  
Cell Metabolism ◽  
Chinese Hamster ◽  
Metabolic Model ◽  
Gap Filling ◽  
Cell Factories ◽  
New Genes ◽  
Metabolic Models ◽  
Genome Scale

AbstractObjectiveChinese hamster ovary (CHO) cells are the leading cell factories for producing recombinant proteins in the biopharmaceutical industry. In this regard, constraint-based metabolic models are useful platforms to perform computational analysis of cell metabolism. These models need to be regularly updated in order to include the latest biochemical data of the cells, and to increase their predictive power. Here, we provide an update to iCHO1766, the metabolic model of CHO cells.ResultsWe expanded the existing model of Chinese hamster metabolism with the help of four gap-filling approaches, leading to the addition of 773 new reactions and 335 new genes. We incorporated these into an updated genome-scale metabolic network model of CHO cells, named iCHO2101. In this updated model, the number of reactions and pathways capable of carrying flux is substantially increased.ConclusionsThe present CHO model is an important step towards more complete metabolic models of CHO cells.

Genome-scale metabolic reconstruction and analysis for Clostridium kluyveri

Genome ◽  
2018 ◽  
Vol 61 (8) ◽  
pp. 605-613 ◽  
Author(s):  
Wei Zou ◽  
Guangbin Ye ◽  
Jing Zhang ◽  
Changqing Zhao ◽  
Xingxiu Zhao ◽  
...  
Keyword(s):  
Metabolic Model ◽  
Metabolic Reconstruction ◽  
Comprehensive Understanding ◽  
Metabolic Potential ◽  
Anaerobic Microorganism ◽  
Flux Balance ◽  
Carbon Substrates ◽  
Balance Analysis ◽  
Clostridium Kluyveri ◽  
Genome Scale

Clostridium kluyveri is an anaerobic microorganism that is well-known for producing butyrate and hexanoate using ethanol and acetate. It is also an important bacterium in the production of Chinese strong flavour baijiu (SFB). To obtain a comprehensive understanding of its metabolism, a curated genome-scale metabolic model (GSMM) of C. kluyveri, including 708 genes, 994 reactions, and 804 metabolites, was constructed and named iCKL708. This model was used to simulate the growth of C. kluyveri on different carbon substrates and the results agreed well with the experimental data. The butyrate, pentanoate, and hexanoate biosynthesis pathways were also elucidated. Flux balance analysis indicated that the ratio of ethanol to acetate, as well as the uptake rate of carbon dioxide, affected hexanoate production. The GSMM iCKL708 described here provides a platform to further our understanding and exploration of the metabolic potential of C. kluyveri.

scholarly journals A metabolic reconstruction of Lactobacillus reuteri JCM 1112 and analysis of its potential as a cell factory

2019 ◽  
Vol 18 (1) ◽  
Author(s):  
Thordis Kristjansdottir ◽  
Elleke F. Bosma ◽  
Filipe Branco dos Santos ◽  
Emre Özdemir ◽  
Markus J. Herrgård ◽  
...  
Keyword(s):  
Experimental Data ◽  
Metabolic Engineering ◽  
Growth Rates ◽  
Lactobacillus Reuteri ◽  
Metabolic Model ◽  
Metabolic Reconstruction ◽  
Cell Factory ◽  
A Genome ◽  
A Cell ◽  
Genome Scale

Abstract Background Lactobacillus reuteri is a heterofermentative Lactic Acid Bacterium (LAB) that is commonly used for food fermentations and probiotic purposes. Due to its robust properties, it is also increasingly considered for use as a cell factory. It produces several industrially important compounds such as 1,3-propanediol and reuterin natively, but for cell factory purposes, developing improved strategies for engineering and fermentation optimization is crucial. Genome-scale metabolic models can be highly beneficial in guiding rational metabolic engineering. Reconstructing a reliable and a quantitatively accurate metabolic model requires extensive manual curation and incorporation of experimental data. Results A genome-scale metabolic model of L. reuteri JCM 1112T was reconstructed and the resulting model, Lreuteri_530, was validated and tested with experimental data. Several knowledge gaps in the metabolism were identified and resolved during this process, including presence/absence of glycolytic genes. Flux distribution between the two glycolytic pathways, the phosphoketolase and Embden–Meyerhof–Parnas pathways, varies considerably between LAB species and strains. As these pathways result in different energy yields, it is important to include strain-specific utilization of these pathways in the model. We determined experimentally that the Embden–Meyerhof–Parnas pathway carried at most 7% of the total glycolytic flux. Predicted growth rates from Lreuteri_530 were in good agreement with experimentally determined values. To further validate the prediction accuracy of Lreuteri_530, the predicted effects of glycerol addition and adhE gene knock-out, which results in impaired ethanol production, were compared to in vivo data. Examination of both growth rates and uptake- and secretion rates of the main metabolites in central metabolism demonstrated that the model was able to accurately predict the experimentally observed effects. Lastly, the potential of L. reuteri as a cell factory was investigated, resulting in a number of general metabolic engineering strategies. Conclusion We have constructed a manually curated genome-scale metabolic model of L. reuteri JCM 1112T that has been experimentally parameterized and validated and can accurately predict metabolic behavior of this important platform cell factory.

scholarly journals High-quality genome-scale metabolic model of Aurantiochytrium sp. T66

2020 ◽  
Author(s):  
Vetle Simensen ◽  
André Voigt ◽  
Eivind Almaas
Keyword(s):  
Primary Source ◽  
Metabolic Model ◽  
Cellular Growth ◽  
Fish Oils ◽  
Growth Data ◽  
High Quality ◽  
Cell Factories ◽  
Genome Scale ◽  
High Quality Genome

AbstractThe long-chain, ω-3 polyunsaturated fatty acids (PUFAs) eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA) are essential for humans and animals, including marine fish species. Presently, the primary source of these PUFAs is fish oils. As the global production of fish oils appears to be reaching its limits, alternative sources of high-quality ω-3 PUFAs is paramount to support the growing aquaculture industry. Thraustochytrids are a group of heterotrophic protists able to synthesize and accrue large amounts of essential ω-3 PUFAs, including EPA and DHA. Thus, the thraustochytrids are prime candidates to solve the increasing demand for ω-3 PUFAs using microbial cell factories. However, a systems-level understanding of their metabolic shift from cellular growth into lipid accumulation is, to a large extent, unclear. Here, we reconstructed a high-quality genome-scale metabolic model of the thraustochytrid Aurantiochytrium sp. T66 termed iVS1191. Through iterative rounds of model refinement and extensive manual curation, we significantly enhanced the metabolic scope and coverage of the reconstruction from that of previously published models, making considerable improvements with stoichiometric consistency, metabolic connectivity, and model annotations. We show that iVS1191 is highly consistent with experimental growth data, reproducing in vivo growth phenotypes as well as specific growth rates on minimal carbon media. The availability of iVS1191 provides a solid framework for further developing our understanding of T66’s metabolic properties, as well as exploring metabolic engineering and process-optimization strategies in silico for increased ω-3 PUFA production.

scholarly journals metaGEM: reconstruction of genome scale metabolic models directly from metagenomes

2021 ◽  
Author(s):  
Francisco Zorrilla ◽  
Kiran R. Patil ◽  
Aleksej Zelezniak
Keyword(s):  
Microbial Communities ◽  
Metabolic Modeling ◽  
Large Degree ◽  
Metabolic Model ◽  
Community Level ◽  
Starting Point ◽  
Metabolic Models ◽  
Context Specific ◽  
Genome Scale ◽  
Reference Genomes

AbstractAdvances in genome-resolved metagenomic analysis of complex microbial communities have revealed a large degree of interspecies and intraspecies genetic diversity through the reconstruction of metagenome assembled genomes (MAGs). Yet, metabolic modeling efforts still tend to rely on reference genomes as the starting point for reconstruction and simulation of genome scale metabolic models (GEMs), neglecting the immense intra- and inter-species diversity present in microbial communities. Here we present metaGEM (https://github.com/franciscozorrilla/metaGEM), an end-to-end highly scalable pipeline enabling metabolic modeling of multi-species communities directly from metagenomic samples. The pipeline automates all steps from the extraction of context-specific prokaryotic GEMs from metagenome assembled genomes to community level flux balance simulations. To demonstrate the capabilities of the metaGEM pipeline, we analyzed 483 samples spanning lab culture, human gut, plant associated, soil, and ocean metagenomes, to reconstruct over 14 000 prokaryotic GEMs. We show that GEMs reconstructed from metagenomes have fully represented metabolism comparable to the GEMs reconstructed from reference genomes. We further demonstrate that metagenomic GEMs capture intraspecies metabolic diversity by identifying the differences between pathogenicity levels of type 2 diabetes at the level of gut bacterial metabolic exchanges. Overall, our pipeline enables simulation-ready metabolic model reconstruction directly from individual metagenomes, provides a resource of all reconstructed metabolic models, and showcases community-level modeling of microbiomes associated with disease conditions allowing generation of mechanistic hypotheses.

scholarly journals A metabolic reconstruction of Lactobacillus reuteri JCM 1112 and analysis of its potential as a cell factory

2019 ◽  
Author(s):  
Thordis Kristjansdottir ◽  
Elleke F. Bosma ◽  
Filipe Branco dos Santos ◽  
Emre Özdemir ◽  
Markus J. Herrgård ◽  
...  
Keyword(s):  
Experimental Data ◽  
Metabolic Engineering ◽  
Growth Rates ◽  
Lactobacillus Reuteri ◽  
Metabolic Model ◽  
Metabolic Reconstruction ◽  
Cell Factory ◽  
A Genome ◽  
A Cell ◽  
Genome Scale

AbstractBackgroundLactobacillus reuteri is a heterofermentative Lactic Acid Bacterium (LAB) that is commonly used for food fermentations and probiotic purposes. Due to its robust properties, it is also increasingly considered for use as a cell factory. It produces several industrially important compounds such as 1,3-propanediol and reuterin natively, but for cell factory purposes, developing improved strategies for engineering and fermentation optimization is crucial. Genome-scale metabolic models can be highly beneficial in guiding rational metabolic engineering. Reconstructing a reliable and a quantitatively accurate metabolic model requires extensive manual curation and incorporation of experimental data.ResultsA genome-scale metabolic model of L. reuteri JCM 1112T was reconstructed and the resulting model, Lreuteri_530, was validated and tested with experimental data. Several knowledge gaps in the metabolism were identified and resolved during this process, including presence/absence of glycolytic genes. Flux distribution between the two glycolytic pathways, the phosphoketolase and Embden-Meyerhof-Parnas pathways, varies considerably between LAB species and strains. As these pathways result in different energy yields, it is important to include strain-specific utilization of these pathways in the model. We determined experimentally that the Embden-Meyerhof-Parnas pathway carried at most 7% of the total glycolytic flux. Predicted growth rates from Lreuteri_530 were in good agreement with experimentally determined values. To further validate the prediction accuracy of Lreuteri_530, the predicted effects of glycerol addition and adhE gene knock-out, which results in impaired ethanol production, were compared to in vivo data. Examination of both growth rates and uptake- and secretion rates of the main metabolites in central metabolism demonstrated that the model was able to accurately predict the experimentally observed effects. Lastly, the potential of L. reuteri as a cell factory was investigated, resulting in a number of general metabolic engineering strategies.ConclusionWe have constructed a manually curated genome-scale metabolic model of L. reuteri JCM 1112T that has been experimentally parameterized and validated and can accurately predict metabolic behavior of this important platform cell factory.

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