scholarly journals In silico analysis of the fast-growing thermophile Geobacillus sp. LC300 using a novel genome-scale metabolic model

2021 ◽  
Author(s):  
Emil Ljungqvist ◽  
Martin Gustavsson
Keyword(s):  
Metabolic Engineering ◽  
Carbon Sources ◽  
In Silico Analysis ◽  
High Growth ◽  
Metabolic Model ◽  
Thermophilic Microorganisms ◽  
Cell Factories ◽  
Conversion Rates ◽  
A Genome ◽  
Genome Scale

AbstractThermophilic microorganisms show high potential for use as biorefinery cell factories. Their high growth temperatures provide fast conversion rates, lower risk of contaminations, and facilitated purification of volatile products. To date, only a few thermophilic species have been utilized for microbial production purposes, and the development of production strains is impeded by the lack of metabolic engineering tools. In this study, we constructed a genome-scale metabolic model, iGEL601, of Geobacillus sp. LC300, an important part of the metabolic engineering pipeline. The model contains 601 genes, 1240 reactions and 1305 metabolites, and the reaction reversibility is based on thermodynamics at the optimum growth temperature. Using flux sampling, the model shows high similarity to experimentally determined reaction fluxes with both glucose and xylose as sole carbon sources. Furthermore, the model predicts previously unidentified by-products, closing the gap in the carbon balance for both carbon sources. Finally, iGEL601 was used to suggest metabolic engineering strategies to maximise production of five industrially relevant compounds. The suggested strategies have previously been experimentally verified in other microorganisms, and predicted production rates are on par with or higher than those previously achieved experimentally. The results highlight the biotechnological potential of LC300 and the application of iGEL601 for use as a tool in the metabolic engineering workflow.

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 Development of a genome-scale metabolic model of Clostridium thermocellum and its applications for integration of multi-omics datasets and strain design

2020 ◽  
Author(s):  
Sergio Garcia ◽  
R. Adam Thompson ◽  
Richard J. Giannone ◽  
Satyakam Dash ◽  
Costas D. Maranas ◽  
...  
Keyword(s):  
Metabolic Engineering ◽  
Lignocellulosic Biomass ◽  
Clostridium Thermocellum ◽  
System Analysis ◽  
Plant Biomass ◽  
Metabolic Model ◽  
Efficient Production ◽  
Branch Points ◽  
A Genome ◽  
Genome Scale

AbstractSolving environmental and social challenges such as climate change requires a shift from our current non-renewable manufacturing model to a sustainable bioeconomy. To lower carbon emissions in the production of fuels and chemicals, plant biomass feedstocks can replace petroleum using microorganisms as catalysts. The anaerobic thermophile Clostridium thermocellum is a promising bacterium for bioconversion due to its capability to efficiently degrade untreated lignocellulosic biomass. However, the complex metabolism of C. thermocellum is not fully understood, hindering metabolic engineering to achieve high titers, rates, and yields of targeted molecules. In this study, we developed an updated genome-scale metabolic model of C. thermocellum that accounts for recent metabolic findings, has improved prediction accuracy, and is standard-conformant to ensure easy reproducibility. We illustrated two applications of the developed model. We first formulated a multi-omics integration protocol and used it to understand redox metabolism and potential bottlenecks in biofuel (e.g., ethanol) production in C. thermocellum. Second, we used the metabolic model to design modular cells for efficient production of alcohols and esters with broad applications as flavors, fragrances, solvents, and fuels. The proposed designs not only feature intuitive push-and-pull metabolic engineering strategies, but also novel manipulations around important central metabolic branch-points. We anticipate the developed genome-scale metabolic model will provide a useful tool for system analysis of C. thermocellum metabolism to fundamentally understand its physiology and guide metabolic engineering strategies to rapidly generate modular production strains for effective biosynthesis of biofuels and biochemicals from lignocellulosic biomass.

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 First Genome-Scale Metabolic Model of Dolosigranulum pigrum Confirms Multiple Auxotrophies

Metabolites ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 232
Author(s):  
Alina Renz ◽  
Lina Widerspick ◽  
Andreas Dräger
Keyword(s):  
Minimal Medium ◽  
Laboratory Experiments ◽  
Carbon Sources ◽  
Metabolic Model ◽  
High Quality ◽  
High Importance ◽  
Manual Curation ◽  
A Genome ◽  
Severe Infections ◽  
Genome Scale

Dolosigranulum pigrum is a quite recently discovered Gram-positive coccus. It has gained increasing attention due to its negative correlation with Staphylococcus aureus, which is one of the most successful modern pathogens causing severe infections with tremendous morbidity and mortality due to its multiple resistances. As the possible mechanisms behind its inhibition of S. aureus remain unclear, a genome-scale metabolic model (GEM) is of enormous interest and high importance to better study its role in this fight. This article presents the first GEM of D. pigrum, which was curated using automated reconstruction tools and extensive manual curation steps to yield a high-quality GEM. It was evaluated and validated using all currently available experimental data of D. pigrum. With this model, already predicted auxotrophies and biosynthetic pathways could be verified. The model was used to define a minimal medium for further laboratory experiments and to predict various carbon sources’ growth capacities. This model will pave the way to better understand D. pigrum’s role in the fight against S. aureus.

scholarly journals Reconstruction and analysis of genome-scale metabolic model of weak Crabtree positive yeast Lachancea kluyveri

2020 ◽  
Author(s):  
Piyush Nanda ◽  
Pradipta Patra ◽  
Manali Das ◽  
Amit Ghosh
Keyword(s):  
Ethyl Acetate ◽  
Metabolic Flux ◽  
Carbon Sources ◽  
Growth Dynamics ◽  
Dry Weight ◽  
Metabolic Model ◽  
Flux Analysis ◽  
Specific Rate ◽  
A Genome ◽  
Genome Scale

Abstract Background Lachancea kluyveri, a weak Crabtree positive yeast, has been extensively studied for its unique URC pyrimidine catabolism pathway. It produces more biomass than Saccharomyces cerevisiae due to the underlying weak Crabtree effect and resorts to optimal fermentation only in oxygen limiting conditions that render it a suitable host for industrial-scale protein production. Ethyl acetate, an important industrial chemical, has been demonstrated to be a major overflow metabolite during aerobic batch cultivation with a specific rate of 0.12 g per g dry weight per hour. Here, we reconstruct a genome-scale metabolic model of the yeast to better explain the observed phenotypes and aid further hypothesis generation. Results We report the first genome-scale metabolic model, iPN730, using Build Fungal Model in KBase workspace. The inconsistencies in the draft model were semi-automatically corrected using literature and published datasets. The curated model comprises of 1235 reactions, 1179 metabolites, and 730 genes distributed in 8 compartments (organelles). The in silico viability in different media conditions and the growth characteristics in various carbon sources show good agreement with experimental data. Dynamic flux balance analysis describes the growth dynamics, substrate utilization and product formation kinetics in various oxygen-limited conditions. The URC pyrimidine degradation pathway incorporated into the model enables it to grow on uracil or urea as the sole nitrogen source. Conclusion The genome-scale metabolic construction of L. kluyveri will provide a better understanding of metabolism, particularly that of pyrimidine metabolism and ethyl acetate production. Metabolic flux analysis using the model will enable hypotheses generation to gain a deeper understanding of metabolism in weakly Crabtree positive yeast and in fungal biodiversity in general.

scholarly journals A Genome-Scale Metabolic Model of 2,3-Butanediol Production by Thermophilic Bacteria Geobacillus icigianus

2020 ◽  
Vol 8 (7) ◽  
pp. 1002
Author(s):  
Mikhail Kulyashov ◽  
Sergey E. Peltek ◽  
Ilya R. Akberdin
Keyword(s):  
Citrate Synthase ◽  
Thermophilic Bacteria ◽  
Carbon Sources ◽  
Metabolic Model ◽  
Biofuel Production ◽  
Biotechnological Applications ◽  
Metabolic Potential ◽  
A Genome ◽  
Wide Range ◽  
Genome Scale

The thermophilic strain of the genus Geobacillus, Geobacillus icigianus is a promising bacterial chassis for a wide range of biotechnological applications. In this study, we explored the metabolic potential of Geobacillus icigianus for the production of 2,3-butanediol (2,3-BTD), one of the cost-effective commodity chemicals. Here we present a genome-scale metabolic model iMK1321 for Geobacillus icigianus constructed using an auto-generating pipeline with consequent thorough manual curation. The model contains 1321 genes and includes 1676 reactions and 1589 metabolites, representing the most-complete and publicly available model of the genus Geobacillus. The developed model provides new insights into thermophilic bacterial metabolism and highlights new strategies for biotechnological applications of the strain. Our analysis suggests that Geobacillus icigianus has a potential for 2,3-butanediol production from a variety of utilized carbon sources, including glycerine, a common byproduct of biofuel production. We identified a set of solutions for enhancing 2,3-BTD production, including cultivation under anaerobic or microaerophilic conditions and decreasing the TCA flux to succinate via reducing citrate synthase activity. Both in silico predicted metabolic alternatives have been previously experimentally verified for closely related strains including the genus Bacillus.

scholarly journals Yeast optimizes metal utilization based on metabolic network and enzyme kinetics

2021 ◽  
Vol 118 (12) ◽  
pp. e2020154118
Author(s):  
Yu Chen ◽  
Feiran Li ◽  
Jiwei Mao ◽  
Yun Chen ◽  
Jens Nielsen
Keyword(s):  
Metal Ions ◽  
Enzyme Kinetics ◽  
Metabolic Network ◽  
Metal Ion ◽  
Metabolic Flux ◽  
Metabolic Model ◽  
Enzymatic Reactions ◽  
Cell Factories ◽  
A Genome ◽  
Genome Scale

Metal ions are vital to metabolism, as they can act as cofactors on enzymes and thus modulate individual enzymatic reactions. Although many enzymes have been reported to interact with metal ions, the quantitative relationships between metal ions and metabolism are lacking. Here, we reconstructed a genome-scale metabolic model of the yeast Saccharomyces cerevisiae to account for proteome constraints and enzyme cofactors such as metal ions, named CofactorYeast. The model is able to estimate abundances of metal ions binding on enzymes in cells under various conditions, which are comparable to measured metal ion contents in biomass. In addition, the model predicts distinct metabolic flux distributions in response to reduced levels of various metal ions in the medium. Specifically, the model reproduces changes upon iron deficiency in metabolic and gene expression levels, which could be interpreted by optimization principles (i.e., yeast optimizes iron utilization based on metabolic network and enzyme kinetics rather than preferentially targeting iron to specific enzymes or pathways). At last, we show the potential of using the model for understanding cell factories that harbor heterologous iron-containing enzymes to synthesize high-value compounds such as p-coumaric acid. Overall, the model demonstrates the dependence of enzymes on metal ions and links metal ions to metabolism on a genome scale.

scholarly journals Genome-scale metabolic modelling of P. thermoglucosidasius NCIMB 11955 reveals metabolic bottlenecks in anaerobic metabolism.

2021 ◽  
Author(s):  
Viviënne Mol ◽  
Martyn Bennett ◽  
Benjamín J. Sánchez ◽  
Beata K. Lisowska ◽  
Markus J. Herrgård ◽  
...  
Keyword(s):  
Metabolic Engineering ◽  
Anaerobic Metabolism ◽  
Carbon Sources ◽  
Anaerobic Growth ◽  
Scale Model ◽  
High Yield ◽  
A Genome ◽  
Depth Analysis ◽  
Genome Scale ◽  
Parageobacillus Thermoglucosidasius

Parageobacillus thermoglucosidasius represents a thermophilic, facultative anaerobic bacterial chassis, with several desirable traits for metabolic engineering and industrial production. To further optimize strain productivity, a systems level understanding of its metabolism is needed, which can be facilitated by a genome-scale metabolic model. Here, we present p-thermo, the most complete, curated and validated genome-scale model (to date) of Parageobacillus thermoglucosidasius NCIMB 11955. It spans a total of 890 metabolites, 1175 reactions and 917 metabolic genes, forming an extensive knowledge base for P. thermoglucosidasius NCIMB 11955 metabolism. The model accurately predicts aerobic utilization of 22 carbon sources, and the predictive quality of internal fluxes was validated with previously published 13C-fluxomics data. In an application case, p-thermo was used to facilitate more in-depth analysis of reported metabolic engineering efforts, giving additional insight into fermentative metabolism. Finally, p-thermo was used to resolve a previously uncharacterised bottleneck in anaerobic metabolism, by identifying the minimal required supplemented nutrients (thiamin, biotin and iron(III)) needed to sustain anaerobic growth. This highlights the usefulness of p-thermo for guiding the generation of experimental hypotheses and for facilitating data-driven metabolic engineering, expanding the use of P. thermoglucosidasius as a high yield production platform.

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