scholarly journals A genome-scale metabolic reconstruction provides insight into the metabolism of the thermophilic bacterium Rhodothermus marinus

2021 ◽  
Author(s):  
Thordis Kristjansdottir ◽  
Gudmundur O. Hreggvidsson ◽  
Sigmar K. Stefansson ◽  
Elisabet E. Gudmundsdottir ◽  
Snaedis H. Bjornsdottir ◽  
...  
Keyword(s):  
Growth Rates ◽  
Shuttle Vector ◽  
Thermophilic Bacterium ◽  
Metabolic Reconstruction ◽  
Cell Factory ◽  
Rhodothermus Marinus ◽  
Reconstruction Process ◽  
A Genome ◽  
Wide Range ◽  
Genome Scale

The thermophilic bacterium Rhodothermus marinus has mainly been studied for its thermostable enzymes. More recently, the potential of using the species as a cell factory and in biorefinery platforms has been explored, due to the elevated growth temperature, native production of compounds such as carotenoids and EPSs, the ability to grow on a wide range of carbon sources including polysaccharides, and available genetic tools. A comprehensive understanding of the metabolism of production organisms is crucial. Here, we report a genome-scale metabolic model of R. marinus DSM 4252T. Moreover, the genome of the genetically amenable R. marinus ISCaR-493 was sequenced and the analysis of the core genome indicated that the model could be used for both strains. Bioreactor growth data was obtained, used for constraining the model and the predicted and experimental growth rates were compared. The model correctly predicted the growth rates of both strains. During the reconstruction process, different aspects of the R. marinus metabolism were reviewed and subsequently, both cell densities and carotenoid production were investigated for strain ISCaR-493 under different growth conditions. Additionally, the dxs gene, which was not found in the R. marinus genomes, from Thermus thermophilus was cloned on a shuttle vector into strain ISCaR-493 resulting in a higher yield of carotenoids.

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 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.

scholarly journals Genome-scale metabolic modelling underscores the potential of Cutaneotrichosporon oleaginosus ATCC 20509 as a cell factory for biofuel production

2020 ◽  
Author(s):  
Nhung TT Pham ◽  
Maarten Reijnders ◽  
Maria Suarez-Diez ◽  
Bart Nijsse ◽  
Jan Springer ◽  
...  
Keyword(s):  
Carbon Source ◽  
Lipid Accumulation ◽  
Crude Glycerol ◽  
Unsaturated Fatty Acids ◽  
Biodiesel Production ◽  
Biofuel Production ◽  
Cell Factory ◽  
A Genome ◽  
Wide Range ◽  
Genome Scale

Abstract Background: Cutaneotrichosporon oleaginosus ATCC 20509 is a fast growing oleaginous basidiomycete yeast that is able to grow in a wide range of low-cost carbon sources including crude glycerol, a byproduct of biodiesel production. When glycerol is used as a carbon source, this yeast can accumulate more than 50% lipids (w/w) with high concentrations of mono-unsaturated fatty acids. Results: To increase our understanding of this yeast and to provide a knowledge base for further industrial use, a FAIR re-annotated genome was used to build a genome-scale, constraint-based metabolic model containing 1553 reactions involving 1373 metabolites in 11 compartments. A new description of the biomass synthesis reaction was introduced to account for massive lipid accumulation in conditions with high carbon to nitrogen (C/N) ratio in the media. This condition-specific biomass objective function is shown to better predict conditions with high lipid accumulation using glucose, fructose, sucrose, xylose, ethanol and glycerol as sole carbon source. Conclusion: Contributing to the economic viability of biodiesel as renewable fuel, C. oleaginosus ATCC 20509 can effectively convert crude glycerol waste streams in lipids as a potential bioenergy source. Performance simulations are essential to identify optimal production conditions and to develop and fine tune a cost-effective production process. Our model suggests ATP-citrate lyase as a target for overexpression to further improve lipid production.

scholarly journals Genome-scale metabolic modelling underscores the potential of Cutaneotrichosporon oleaginosus ATCC 20509 as a cell factory for biofuel production

2020 ◽  
Author(s):  
Nhung TT Pham ◽  
Maarten Reijnders ◽  
Maria Suarez-Diez ◽  
Bart Nijsse ◽  
Jan Springer ◽  
...  
Keyword(s):  
Lipid Accumulation ◽  
Crude Glycerol ◽  
Lipid Production ◽  
Biodiesel Production ◽  
Biofuel Production ◽  
Cell Factory ◽  
Atp Citrate Lyase ◽  
A Genome ◽  
Wide Range ◽  
Genome Scale

Abstract Background: Cutaneotrichosporon oleaginosus ATCC 20509 is a fast growing oleaginous basidiomycete yeast that is able to grow in a wide range of low-cost carbon sources including crude glycerol, a byproduct of biodiesel production. When glycerol is used as a carbon source, this yeast can accumulate more than 50% lipids (w/w) with high concentrations of mono-unsaturated fatty acids.Results: To increase our understanding of this yeast and to provide a knowledge base for further industrial use, a FAIR re-annotated genome was used to build a genome-scale, constraint-based metabolic model containing 1553 reactions involving 1373 metabolites in 11 compartments. A new description of the biomass synthesis reaction was introduced to account for massive lipid accumulation in conditions with high carbon to nitrogen (C/N) ratio in the media. This condition-specific biomass objective function is shown to better predict conditions with high lipid accumulation using glucose, fructose, sucrose, xylose, and glycerol as sole carbon source.Conclusion: Contributing to the economic viability of biodiesel as renewable fuel, C. oleaginosus ATCC 20509 can effectively convert crude glycerol waste streams in lipids as a potential bioenergy source. Performance simulations are essential to identify optimal production conditions and to develop and fine tune a cost-effective production process. Our model suggests ATP-citrate lyase as a possible target to further improve lipid production.

scholarly journals Genome-scale metabolic modelling underscores the potential of Cutaneotrichosporon oleaginosus ATCC 20509 as a cell factory for biofuel production

2020 ◽  
Author(s):  
Nhung TT Pham ◽  
Maarten Reijnders ◽  
Maria Suarez-Diez ◽  
Bart Nijsse ◽  
Jan Springer ◽  
...  
Keyword(s):  
Carbon Source ◽  
Lipid Accumulation ◽  
Crude Glycerol ◽  
Metabolic Model ◽  
Biodiesel Production ◽  
Biofuel Production ◽  
Cell Factory ◽  
A Genome ◽  
Wide Range ◽  
Genome Scale

Abstract Background: Cutaneotrichosporon oleaginosus ATCC 20509 is a fast growing oleaginous basidiomycete yeast that is able to grow in a wide range of low-cost carbon sources including crude glycerol, a byproduct of biodiesel production. When glycerol is used as a carbon source, this yeast can accumulate more than 50% lipids (w/w) with high concentrations of mono-unsaturated fatty acids. Results: To increase our understanding of this yeast and to provide a knowledge base for further industrial use, a FAIR re-annotated genome was used to build a genome-scale, constraint-based metabolic model containing 1553 reactions involving 1373 metabolites in 11 compartments. A new description of the biomass synthesis reaction was introduced to account for massive lipid accumulation in conditions with high carbon to nitrogen (C/N) ratio in the media. This condition-specific biomass objective function is shown to better predict conditions with high lipid accumulation using glucose, fructose, sucrose, xylose and glycerol as sole carbon source. Conclusion: Contributing to the economic viability of biodiesel as renewable fuel, C. oleaginosus ATCC 20509 can effectively convert crude glycerol waste streams in lipids as a potential bioenergy source. Performance simulations are essential to identify optimal production conditions and to develop and fine tune a cost-effective production process. Our model suggests ATP-citrate lyase as a target for further improve lipid production. Keywords: Genome-scale metabolic model; Cutaneotrichosporon oleaginosus ATCC 20509; lipid accumulation; Crude glycerol; biodiesel production; flux balance analysis; oleaginous yeast

scholarly journals Genome-scale metabolic modeling underscores the potential of Cutaneotrichosporon oleaginosus ATCC 20509 as a cell factory for biofuel production

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Nhung Pham ◽  
Maarten Reijnders ◽  
Maria Suarez-Diez ◽  
Bart Nijsse ◽  
Jan Springer ◽  
...  
Keyword(s):  
Carbon Source ◽  
Lipid Accumulation ◽  
Crude Glycerol ◽  
Unsaturated Fatty Acids ◽  
Biodiesel Production ◽  
Biofuel Production ◽  
Cell Factory ◽  
A Genome ◽  
Wide Range ◽  
Genome Scale

Abstract Background Cutaneotrichosporon oleaginosus ATCC 20509 is a fast-growing oleaginous basidiomycete yeast that is able to grow in a wide range of low-cost carbon sources including crude glycerol, a byproduct of biodiesel production. When glycerol is used as a carbon source, this yeast can accumulate more than 50% lipids (w/w) with high concentrations of mono-unsaturated fatty acids. Results To increase our understanding of this yeast and to provide a knowledge base for further industrial use, a FAIR re-annotated genome was used to build a genome-scale, constraint-based metabolic model containing 1553 reactions involving 1373 metabolites in 11 compartments. A new description of the biomass synthesis reaction was introduced to account for massive lipid accumulation in conditions with high carbon-to-nitrogen (C/N) ratio in the media. This condition-specific biomass objective function is shown to better predict conditions with high lipid accumulation using glucose, fructose, sucrose, xylose, and glycerol as sole carbon source. Conclusion Contributing to the economic viability of biodiesel as renewable fuel, C. oleaginosus ATCC 20509 can effectively convert crude glycerol waste streams in lipids as a potential bioenergy source. Performance simulations are essential to identify optimal production conditions and to develop and fine tune a cost-effective production process. Our model suggests ATP-citrate lyase as a possible target to further improve lipid production.

scholarly journals A genome-scale metabolic model of Saccharomyces cerevisiae that integrates expression constraints and reaction thermodynamics

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Omid Oftadeh ◽  
Pierre Salvy ◽  
Maria Masid ◽  
Maxime Curvat ◽  
Ljubisa Miskovic ◽  
...  
Keyword(s):  
Saccharomyces Cerevisiae ◽  
Expression System ◽  
Biomass Composition ◽  
Industrial Biotechnology ◽  
Overflow Metabolism ◽  
Cellular Expression ◽  
A Genome ◽  
Wide Range ◽  
Eukaryotic Organisms ◽  
Genome Scale

AbstractEukaryotic organisms play an important role in industrial biotechnology, from the production of fuels and commodity chemicals to therapeutic proteins. To optimize these industrial systems, a mathematical approach can be used to integrate the description of multiple biological networks into a single model for cell analysis and engineering. One of the most accurate models of biological systems include Expression and Thermodynamics FLux (ETFL), which efficiently integrates RNA and protein synthesis with traditional genome-scale metabolic models. However, ETFL is so far only applicable for E. coli. To adapt this model for Saccharomyces cerevisiae, we developed yETFL, in which we augmented the original formulation with additional considerations for biomass composition, the compartmentalized cellular expression system, and the energetic costs of biological processes. We demonstrated the ability of yETFL to predict maximum growth rate, essential genes, and the phenotype of overflow metabolism. We envision that the presented formulation can be extended to a wide range of eukaryotic organisms to the benefit of academic and industrial research.

scholarly journals A Genome-Scale Metabolic Reconstruction of Mycoplasma genitalium, iPS189

2009 ◽  
Vol 5 (2) ◽  
pp. e1000285 ◽  
Author(s):  
Patrick F. Suthers ◽  
Madhukar S. Dasika ◽  
Vinay Satish Kumar ◽  
Gennady Denisov ◽  
John I. Glass ◽  
...  
Keyword(s):  
Mycoplasma Genitalium ◽  
Metabolic Reconstruction ◽  
A Genome ◽  
Genome Scale

scholarly journals Metabolic Network Analysis of Pseudomonas aeruginosa during Chronic Cystic Fibrosis Lung Infection

2010 ◽  
Vol 192 (20) ◽  
pp. 5534-5548 ◽  
Author(s):  
Matthew A. Oberhardt ◽  
Joanna B. Goldberg ◽  
Michael Hogardt ◽  
Jason A. Papin
Keyword(s):  
Cystic Fibrosis ◽  
Disease Progression ◽  
Time Course ◽  
Lung Infection ◽  
System Level ◽  
Metabolic Reconstruction ◽  
Metabolic Network Analysis ◽  
A Genome ◽  
Future Drug ◽  
Genome Scale

ABSTRACT System-level modeling is beginning to be used to decipher high throughput data in the context of disease. In this study, we present an integration of expression microarray data with a genome-scale metabolic reconstruction of P seudomonas aeruginosa in the context of a chronic cystic fibrosis (CF) lung infection. A genome-scale reconstruction of P. aeruginosa metabolism was tailored to represent the metabolic states of two clonally related lineages of P. aeruginosa isolated from the lungs of a CF patient at different points over a 44-month time course, giving a mechanistic glimpse into how the bacterial metabolism adapts over time in the CF lung. Metabolic capacities were analyzed to determine how tradeoffs between growth and other important cellular processes shift during disease progression. Genes whose knockouts were either significantly growth reducing or lethal in silico were also identified for each time point and serve as hypotheses for future drug targeting efforts specific to the stages of disease progression.

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