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

2019 ◽  
Author(s):  
Piyush Nanda ◽  
Pradipta Patra ◽  
Manali Das ◽  
Amit Ghosh
Keyword(s):  
Ethyl Acetate ◽  
Metabolic Flux ◽  
Carbon Sources ◽  
Growth Dynamics ◽  
Dry Weight ◽  
Metabolic Model ◽  
Product Formation ◽  
Flux Analysis ◽  
Specific Rate ◽  
Genome Scale

Abstract Background Lachancea kluyveri , a weak Crabtree positive yeast, has been extensively studied for the unique URC pyrimidine catabolism it harbours. It produces more biomass than Saccharomyces cerevisiae due to the underlying Crabtree effect and resorts to fermentation only in oxygen limiting conditions that makes it 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 attempted to reconstruct the metabolism of the yeast from the genome 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 model were manually corrected using literature and published datasets. The model comprises of 1235 reactions, 1179 metabolites and 730 genes distributed in 8 compartments. The in silico viability and the growth rates in various carbon sources show good agreement. The gene essentiality of the metabolic model also performs well in comparison to experimental data confirmed by statistical analysis. 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 provides 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 deeper understanding of metabolism in weakly Crabtree positive yeast.

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

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Piyush Nanda ◽  
Pradipta Patra ◽  
Manali Das ◽  
Amit Ghosh
Keyword(s):  
Ethyl Acetate ◽  
Carbon Sources ◽  
Growth Dynamics ◽  
Metabolic Model ◽  
Product Formation ◽  
Degradation Pathway ◽  
Energetic Cost ◽  
Phase Plane Analysis ◽  
Dynamic Flux Balance Analysis ◽  
Genome Scale

Abstract 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 fermentation only in oxygen limiting conditions that renders it as a suitable industrial host. The yeast also produces ethyl acetate as a major overflow metabolite in aerobic conditions. Here, we report the first genome-scale metabolic model, iPN730, of L. kluyveri comprising of 1235 reactions, 1179 metabolites, and 730 genes distributed in 8 compartments. 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. We have also demonstrated the effect of switching carbon sources on the production of ethyl acetate under varying oxygen uptake rates. A phenotypic phase plane analysis described the energetic cost penalty of ethyl acetate and ethanol production on the specific growth rate of L. kluyveri. We generated the context specific models of L. kluyveri growing on uracil or ammonium salts as the sole nitrogen source. Differential flux calculated using flux variability analysis helped us in highlighting pathways like purine, histidine, riboflavin and pyrimidine metabolism associated with uracil degradation. The genome-scale metabolic construction of L. kluyveri will provide a better understanding of metabolism behind ethyl acetate production as well as uracil catabolism (pyrimidine degradation) pathway. iPN730 is an addition to genome-scale metabolic models of non-conventional yeasts that will facilitate system-wide omics analysis to understand fungal metabolic diversity.

scholarly journals Metabolic control of nitrogen fixation in rhizobium-legume symbioses

2021 ◽  
Vol 7 (31) ◽  
pp. eabh2433
Author(s):  
Carolin C. M. Schulte ◽  
Khushboo Borah ◽  
Rachel M. Wheatley ◽  
Jason J. Terpolilli ◽  
Gerhard Saalbach ◽  
...  
Keyword(s):  
Metabolic Flux ◽  
Tricarboxylic Acid Cycle ◽  
Oxygen Demand ◽  
Metabolic Model ◽  
Redox Balance ◽  
Ammonia Assimilation ◽  
Nodule Formation ◽  
Flux Analysis ◽  
Carbon And Nitrogen ◽  
Genome Scale

Rhizobia induce nodule formation on legume roots and differentiate into bacteroids, which catabolize plant-derived dicarboxylates to reduce atmospheric N2 into ammonia. Despite the agricultural importance of this symbiosis, the mechanisms that govern carbon and nitrogen allocation in bacteroids and promote ammonia secretion to the plant are largely unknown. Using a metabolic model derived from genome-scale datasets, we show that carbon polymer synthesis and alanine secretion by bacteroids facilitate redox balance in microaerobic nodules. Catabolism of dicarboxylates induces not only a higher oxygen demand but also a higher NADH/NAD+ ratio than sugars. Modeling and 13C metabolic flux analysis indicate that oxygen limitation restricts the decarboxylating arm of the tricarboxylic acid cycle, which limits ammonia assimilation into glutamate. By tightly controlling oxygen supply and providing dicarboxylates as the energy and electron source donors for N2 fixation, legumes promote ammonia secretion by bacteroids. This is a defining feature of rhizobium-legume symbioses.

scholarly journals Manually curated genome-scale reconstruction of the metabolic network of Bacillus megaterium DSM319

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Javad Aminian-Dehkordi ◽  
Seyyed Mohammad Mousavi ◽  
Arezou Jafari ◽  
Ivan Mijakovic ◽  
Sayed-Amir Marashi
Keyword(s):  
Experimental Data ◽  
Metabolic Network ◽  
Bacillus Megaterium ◽  
In Silico ◽  
Metabolic Model ◽  
Growth Behavior ◽  
Industrial Biotechnology ◽  
Flux Analysis ◽  
A Genome ◽  
Genome Scale

AbstractBacillus megaterium is a microorganism widely used in industrial biotechnology for production of enzymes and recombinant proteins, as well as in bioleaching processes. Precise understanding of its metabolism is essential for designing engineering strategies to further optimize B. megaterium for biotechnology applications. Here, we present a genome-scale metabolic model for B. megaterium DSM319, iJA1121, which is a result of a metabolic network reconciliation process. The model includes 1709 reactions, 1349 metabolites, and 1121 genes. Based on multiple-genome alignments and available genome-scale metabolic models for other Bacillus species, we constructed a draft network using an automated approach followed by manual curation. The refinements were performed using a gap-filling process. Constraint-based modeling was used to scrutinize network features. Phenotyping assays were performed in order to validate the growth behavior of the model using different substrates. To verify the model accuracy, experimental data reported in the literature (growth behavior patterns, metabolite production capabilities, metabolic flux analysis using 13C glucose and formaldehyde inhibitory effect) were confronted with model predictions. This indicated a very good agreement between in silico results and experimental data. For example, our in silico study of fatty acid biosynthesis and lipid accumulation in B. megaterium highlighted the importance of adopting appropriate carbon sources for fermentation purposes. We conclude that the genome-scale metabolic model iJA1121 represents a useful tool for systems analysis and furthers our understanding of the metabolism of B. megaterium.

scholarly journals Advances in metabolic flux analysis toward genome-scale profiling of higher organisms

2018 ◽  
Vol 38 (6) ◽  
Author(s):  
Georg Basler ◽  
Alisdair R. Fernie ◽  
Zoran Nikoloski
Keyword(s):  
Large Scale ◽  
Metabolic Flux ◽  
Cell Types ◽  
Flux Analysis ◽  
Metabolic Labeling ◽  
Modeling Framework ◽  
Metabolomics Data ◽  
Technological Advances ◽  
A Genome ◽  
Genome Scale

Methodological and technological advances have recently paved the way for metabolic flux profiling in higher organisms, like plants. However, in comparison with omics technologies, flux profiling has yet to provide comprehensive differential flux maps at a genome-scale and in different cell types, tissues, and organs. Here we highlight the recent advances in technologies to gather metabolic labeling patterns and flux profiling approaches. We provide an opinion of how recent local flux profiling approaches can be used in conjunction with the constraint-based modeling framework to arrive at genome-scale flux maps. In addition, we point at approaches which use metabolomics data without introduction of label to predict either non-steady state fluxes in a time-series experiment or flux changes in different experimental scenarios. The combination of these developments allows an experimentally feasible approach for flux-based large-scale systems biology studies.

scholarly journals 13C metabolic flux analysis at a genome-scale

2015 ◽  
Vol 32 ◽  
pp. 12-22 ◽  
Author(s):  
Saratram Gopalakrishnan ◽  
Costas D. Maranas
Keyword(s):  
Metabolic Flux Analysis ◽  
Metabolic Flux ◽  
Flux Analysis ◽  
13C Metabolic Flux Analysis ◽  
A Genome ◽  
Genome Scale

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.

Pathway-flux based biomarker for immunotherapy response in melanoma.

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. e22064-e22064
Author(s):  
Jian Li ◽  
Qi Mei ◽  
Weiting Cheng ◽  
Guangyuan Hu ◽  
Xianglin Yuan ◽  
...  
Keyword(s):  
Immune System ◽  
Metabolic Flux ◽  
Expression Profiles ◽  
Objective Response ◽  
Superior Performance ◽  
Flux Analysis ◽  
Signaling Transduction ◽  
A Genome ◽  
Genome Scale ◽  
Pathway Flux

e22064 Background: Immune checkpoint therapy (ICT) refers to therapeutic interventions that specifically target immune evasion mechanisms to restore the host immunity with anti-tumor ability. The ICT has revolutionized the immune-based treatment across > 30 different cancers including solid tumors and hematopoietic malignancies, with an ORR of 30% and a 7%-12% grades 3-5 irAEs in average. However, a substantial unmet point is the development of a biomarker with which response of ICT can be predicted before treatment for individual patients. Methods: In order to face this challenge this study developed an advanced genome-scale pathway flux analysis (GPFA) to evaluate the strength of signaling transduction and metabolic flux in immune system. The input of GPFA is the gene expression profiles of individual objects and the output of GPFA can be summarized in a index system, IM.Index, with following definition: IM.Index = 1.78E-4 * Σ flux(P) + 2.37E-4 * Σ flux(P) p ∈ signaling transduction p ∈ energy metabolism. Subsequently, the IM.Index was applied to analyze genetic data of two independent cohorts of melanoma patients treated with anti-PD-1 therapy (nivolumab a. pembrolizumab). Results: The IM.Index predicted the response of anti-PD-1 therapy (nivolumab) in the first cohort with an odds ratio (OR) of 3.14 (95%CI: 1.16-8.45; p = 3.10E-3; AUC = 0.82) and with a sensitivity 89% and specificity 76%. The prediction on overall survival (OS) of this cohort achieved an hazard ratio (HR) of 1.53 (95%CI: 1.22-1.92; p = 7.8E-3). Subsequently, the prediction result for the anti-PD-1 therapy (pembrolizumab) in the second cohort achieved an OR of 2.12 (95%CI: 1.22-3.66; p = 4.50E-4; AUC = 0.87) and the OS prediction in this cohort reached an HR of 1.24 (95%CI: 1.04-1.47; p = 1.40E-2). Comparison with other four potential biomarkers (TMB, TNB, neo-peptide load and cytolytic score) related to immunotherapy showed a comparative outcome of the IM.Index regarding diagnosis and prognosis in melanoma. For instance, IM.Index showed a superior performance on objective response rate (ORR) of 70% and AUC of 0.83. Conclusions: In conclusion this study demonstrated that a pathway flux analysis at a genome-scale may be explorative in biomarker research in immunotherapy, since this type of analysis could reflect the strength or functional status of the immune system. The IM.Index developed in this study may also be applied to investigation the treatment response of immunotherapy in other types of cancer.

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.

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