scholarly journals Uncovering novel pathways for enhancing hyaluronan synthesis in recombinantLactococcus lactis: Genome-scale metabolic modelling and experimental validation

2018 ◽  
Author(s):  
Abinaya Badri ◽  
Karthik Raman ◽  
Guhan Jayaraman
Keyword(s):  
Metabolic Network ◽  
Fold Increase ◽  
Experimental Results ◽  
Metabolic Modelling ◽  
Knock Out ◽  
Cell Factories ◽  
Naturally Occurring ◽  
A Genome ◽  
Recombinant Cell ◽  
Genome Scale

AbstractHyaluronan (HA) is a naturally occurring high-value polysaccharide with important medical applications. HA is commercially produced from pathogenic microbial sources. HA-producing recombinant cell factories that are being developed with GRAS organisms are comparatively less productive than the best natural producers. The metabolism of these recombinant systems needs to be more strategically engineered to achieve significant improvement. Here, we use a genome-scale metabolic network model to account for the entire metabolic network of the cell to predict strategies for improving HA production. We here analyze the metabolic network ofLactococcus lactisadapted to produce HA, and identify non-conventional overexpression and knock-out strategies to enhance HA flux.To experimentally validate our predictions, we identify an alternate route for enhancement of HA synthesis, originating from the nucleoside inosine, which has the capacity to function in parallel with the traditionally known route from glucose. Adopting this strategy resulted in a 2.8-fold increase in HA yield. The strategies identified and the experimental results show that the cell is capable of involving a larger subset of metabolic pathways in HA production. Apart from being the first report of the use of a nucleoside to improve HA production, our study shows how experimental results enable model refinement. Overall, we point out that well-constructed genome-scale metabolic models could be very potent tools to derive efficient strategies to improve biosynthesis of important high-value products.

scholarly journals Uncovering Novel Pathways for Enhancing Hyaluronan Synthesis in Recombinant Lactococcus lactis: Genome-Scale Metabolic Modeling and Experimental Validation

Processes ◽  
2019 ◽  
Vol 7 (6) ◽  
pp. 343 ◽  
Author(s):  
Abinaya Badri ◽  
Karthik Raman ◽  
Guhan Jayaraman
Keyword(s):  
Metabolic Network ◽  
Lactococcus Lactis ◽  
Experimental Validation ◽  
Fold Increase ◽  
Scale Model ◽  
A Genome ◽  
Recombinant Lactococcus Lactis ◽  
Genome Scale ◽  
Genome Scale Model

Hyaluronan (HA), a glycosaminoglycan with important medical applications, is commercially produced from pathogenic microbial sources. The metabolism of HA-producing recombinant generally regarded as safe (GRAS) systems needs to be more strategically engineered to achieve yields higher than native producers. Here, we use a genome-scale model (GEM) to account for the entire metabolic network of the cell while predicting strategies to improve HA production. We analyze the metabolic network of Lactococcus lactis adapted to produce HA and identify non-conventional strategies to enhance HA flux. We also show experimental verification of one of the predicted strategies. We thus identified an alternate route for enhancement of HA synthesis, originating from the nucleoside inosine, that can function in parallel with the traditionally known route from glucose. Adopting this strategy resulted in a 2.8-fold increase in HA yield. The strategies identified and the experimental results show that the cell is capable of involving a larger subset of metabolic pathways in HA production. Apart from being the first report to use a nucleoside to improve HA production, we demonstrate the role of experimental validation in model refinement and strategy improvisation. Overall, we point out that well-constructed GEMs could be used to derive efficient strategies to improve the biosynthesis of high-value products.

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 Network Analysis of the Opportunistic Pathogen Pseudomonas aeruginosa PAO1

2008 ◽  
Vol 190 (8) ◽  
pp. 2790-2803 ◽  
Author(s):  
Matthew A. Oberhardt ◽  
Jacek Puchałka ◽  
Kimberly E. Fryer ◽  
Vítor A. P. Martins dos Santos ◽  
Jason A. Papin
Keyword(s):  
Pseudomonas Aeruginosa ◽  
Metabolic Network ◽  
Opportunistic Pathogen ◽  
Scale Model ◽  
Modeling Framework ◽  
Major Life ◽  
Metabolic Versatility ◽  
A Genome ◽  
Scale Properties ◽  
Genome Scale

ABSTRACT Pseudomonas aeruginosa is a major life-threatening opportunistic pathogen that commonly infects immunocompromised patients. This bacterium owes its success as a pathogen largely to its metabolic versatility and flexibility. A thorough understanding of P. aeruginosa's metabolism is thus pivotal for the design of effective intervention strategies. Here we aim to provide, through systems analysis, a basis for the characterization of the genome-scale properties of this pathogen's versatile metabolic network. To this end, we reconstructed a genome-scale metabolic network of Pseudomonas aeruginosa PAO1. This reconstruction accounts for 1,056 genes (19% of the genome), 1,030 proteins, and 883 reactions. Flux balance analysis was used to identify key features of P. aeruginosa metabolism, such as growth yield, under defined conditions and with defined knowledge gaps within the network. BIOLOG substrate oxidation data were used in model expansion, and a genome-scale transposon knockout set was compared against in silico knockout predictions to validate the model. Ultimately, this genome-scale model provides a basic modeling framework with which to explore the metabolism of P. aeruginosa in the context of its environmental and genetic constraints, thereby contributing to a more thorough understanding of the genotype-phenotype relationships in this resourceful and dangerous pathogen.

scholarly journals A genome‐scale metabolic network reconstruction of tomato ( Solanum lycopersicum L.) and its application to photorespiratory metabolism

2016 ◽  
Vol 85 (2) ◽  
pp. 289-304 ◽  
Author(s):  
Huili Yuan ◽  
C.Y. Maurice Cheung ◽  
Mark G. Poolman ◽  
Peter A. J. Hilbers ◽  
Natal A. W. Riel
Keyword(s):  
Metabolic Network ◽  
Solanum Lycopersicum ◽  
Network Reconstruction ◽  
Metabolic Network Reconstruction ◽  
Solanum Lycopersicum L ◽  
A Genome ◽  
Genome Scale

scholarly journals A genome-scale metabolic network of the aroma bacterium Leuconostoc mesenteroides subsp. cremoris

2019 ◽  
Vol 103 (7) ◽  
pp. 3153-3165 ◽  
Author(s):  
Emrah Özcan ◽  
S. Selvin Selvi ◽  
Emrah Nikerel ◽  
Bas Teusink ◽  
Ebru Toksoy Öner ◽  
...  
Keyword(s):  
Metabolic Network ◽  
Leuconostoc Mesenteroides ◽  
A Genome ◽  
Genome Scale

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 AraGEM, a Genome-Scale Reconstruction of the Primary Metabolic Network in Arabidopsis

2009 ◽  
Vol 152 (2) ◽  
pp. 579-589 ◽  
Author(s):  
Cristiana Gomes de Oliveira Dal'Molin ◽  
Lake-Ee Quek ◽  
Robin William Palfreyman ◽  
Stevens Michael Brumbley ◽  
Lars Keld Nielsen
Keyword(s):  
Metabolic Network ◽  
A Genome ◽  
Genome Scale
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