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 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 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 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 Model and Omics Analysis of Metabolic Capacities of Akkermansia muciniphila Reveal a Preferential Mucin-Degrading Lifestyle

2017 ◽  
Vol 83 (18) ◽  
Author(s):  
Noora Ottman ◽  
Mark Davids ◽  
Maria Suarez-Diez ◽  
Sjef Boeren ◽  
Peter J. Schaap ◽  
...  
Keyword(s):  
Proteome Analysis ◽  
Metabolic Model ◽  
Scale Model ◽  
Bacterial Cells ◽  
Akkermansia Muciniphila ◽  
A Genome ◽  
Wide Range ◽  
Genome Scale ◽  
Genome Scale Model

ABSTRACT The composition and activity of the microbiota in the human gastrointestinal tract are primarily shaped by nutrients derived from either food or the host. Bacteria colonizing the mucus layer have evolved to use mucin as a carbon and energy source. One of the members of the mucosa-associated microbiota is Akkermansia muciniphila, which is capable of producing an extensive repertoire of mucin-degrading enzymes. To further study the substrate utilization abilities of A. muciniphila, we constructed a genome-scale metabolic model to test amino acid auxotrophy, vitamin biosynthesis, and sugar-degrading capacities. The model-supported predictions were validated by in vitro experiments, which showed A. muciniphila to be able to utilize the mucin-derived monosaccharides fucose, galactose, and N-acetylglucosamine. Growth was also observed on N-acetylgalactosamine, even though the metabolic model did not predict this. The uptake of these sugars, as well as the nonmucin sugar glucose, was enhanced in the presence of mucin, indicating that additional mucin-derived components are needed for optimal growth. An analysis of whole-transcriptome sequencing (RNA-Seq) comparing the gene expression of A. muciniphila grown on mucin with that of the same bacterium grown on glucose confirmed the activity of the genes involved in mucin degradation and revealed most of these to be upregulated in the presence of mucin. The transcriptional response was confirmed by a proteome analysis, altogether revealing a hierarchy in the use of sugars and reflecting the adaptation of A. muciniphila to the mucosal environment. In conclusion, these findings provide molecular insights into the lifestyle of A. muciniphila and further confirm its role as a mucin specialist in the gut. IMPORTANCE Akkermansia muciniphila is among the most abundant mucosal bacteria in humans and in a wide range of other animals. Recently, A. muciniphila has attracted considerable attention because of its capacity to protect against diet-induced obesity in mouse models. However, the physiology of A. muciniphila has not been studied in detail. Hence, we constructed a genome-scale model and describe its validation by transcriptomic and proteomic approaches on bacterial cells grown on mucus and glucose, a nonmucus sugar. The results provide detailed molecular insight into the mucus-degrading lifestyle of A. muciniphila and further confirm the role of this mucin specialist in producing propionate and acetate under conditions of the intestinal tract.

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 Strategies for Enhancing in vitro Degradation of Linuron by Variovorax sp. Strain SRS 16 Under the Guidance of Metabolic Modeling

2021 ◽  
Vol 9 ◽  
Author(s):  
Kusum Dhakar ◽  
Raphy Zarecki ◽  
Daniella van Bommel ◽  
Nadav Knossow ◽  
Shlomit Medina ◽  
...  
Keyword(s):  
Genetic Manipulation ◽  
Nitrogen Sources ◽  
Metabolic Model ◽  
Scale Model ◽  
Promoting Effect ◽  
Metabolic Potential ◽  
Carbon And Nitrogen ◽  
A Genome ◽  
Genome Scale

Phenyl urea herbicides are being extensively used for weed control in both agricultural and non-agricultural applications. Linuron is one of the key herbicides in this family and is in wide use. Like other phenyl urea herbicides, it is known to have toxic effects as a result of its persistence in the environment. The natural removal of linuron from the environment is mainly carried through microbial biodegradation. Some microorganisms have been reported to mineralize linuron completely and utilize it as a carbon and nitrogen source. Variovorax sp. strain SRS 16 is one of the known efficient degraders with a recently sequenced genome. The genomic data provide an opportunity to use a genome-scale model for improving biodegradation. The aim of our study is the construction of a genome-scale metabolic model following automatic and manual protocols and its application for improving its metabolic potential through iterative simulations. Applying flux balance analysis (FBA), growth and degradation performances of SRS 16 in different media considering the influence of selected supplements (potential carbon and nitrogen sources) were simulated. Outcomes are predictions for the suitable media modification, allowing faster degradation of linuron by SRS 16. Seven metabolites were selected for in vitro validation of the predictions through laboratory experiments confirming the degradation-promoting effect of specific amino acids (glutamine and asparagine) on linuron degradation and SRS 16 growth. Overall, simulations are shown to be efficient in predicting the degradation potential of SRS 16 in the presence of specific supplements. The generated information contributes to the understanding of the biochemistry of linuron degradation and can be further utilized for the development of new cleanup solutions without any genetic manipulation.

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 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.

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