scholarly journals A Genome-Scale Model of Shewanella piezotolerans Simulates Mechanisms of Metabolic Diversity and Energy Conservation

mSystems ◽  
2017 ◽  
Vol 2 (2) ◽  
Author(s):  
Keith Dufault-Thompson ◽  
Huahua Jian ◽  
Ruixue Cheng ◽  
Jiefu Li ◽  
Fengping Wang ◽  
...  
Keyword(s):  
Energy Conservation ◽  
Deep Sea ◽  
Carbon Sources ◽  
Scale Model ◽  
Anaerobic Conditions ◽  
A Genome ◽  
Group 2 ◽  
Genome Scale ◽  
Genome Scale Model ◽  
Group 1

ABSTRACT The well-studied nature of the metabolic diversity of Shewanella bacteria makes species from this genus a promising platform for investigating the evolution of carbon metabolism and energy conservation. The Shewanella phylogeny is diverged into two major branches, referred to as group 1 and group 2. While the genotype-phenotype connections of group 2 species have been extensively studied with metabolic modeling, a genome-scale model has been missing for the group 1 species. The metabolic reconstruction of Shewanella piezotolerans strain WP3 represented the first model for Shewanella group 1 and the first model among piezotolerant and psychrotolerant deep-sea bacteria. The model brought insights into the mechanisms of energy conservation in WP3 under anaerobic conditions and highlighted its metabolic flexibility in using diverse carbon sources. Overall, the model opens up new opportunities for investigating energy conservation and metabolic adaptation, and it provides a prototype for systems-level modeling of other deep-sea microorganisms. Shewanella piezotolerans strain WP3 belongs to the group 1 branch of the Shewanella genus and is a piezotolerant and psychrotolerant species isolated from the deep sea. In this study, a genome-scale model was constructed for WP3 using a combination of genome annotation, ortholog mapping, and physiological verification. The metabolic reconstruction contained 806 genes, 653 metabolites, and 922 reactions, including central metabolic functions that represented nonhomologous replacements between the group 1 and group 2 Shewanella species. Metabolic simulations with the WP3 model demonstrated consistency with existing knowledge about the physiology of the organism. A comparison of model simulations with experimental measurements verified the predicted growth profiles under increasing concentrations of carbon sources. The WP3 model was applied to study mechanisms of anaerobic respiration through investigating energy conservation, redox balancing, and the generation of proton motive force. Despite being an obligate respiratory organism, WP3 was predicted to use substrate-level phosphorylation as the primary source of energy conservation under anaerobic conditions, a trait previously identified in other Shewanella species. Further investigation of the ATP synthase activity revealed a positive correlation between the availability of reducing equivalents in the cell and the directionality of the ATP synthase reaction flux. Comparison of the WP3 model with an existing model of a group 2 species, Shewanella oneidensis MR-1, revealed that the WP3 model demonstrated greater flexibility in ATP production under the anaerobic conditions. Such flexibility could be advantageous to WP3 for its adaptation to fluctuating availability of organic carbon sources in the deep sea. IMPORTANCE The well-studied nature of the metabolic diversity of Shewanella bacteria makes species from this genus a promising platform for investigating the evolution of carbon metabolism and energy conservation. The Shewanella phylogeny is diverged into two major branches, referred to as group 1 and group 2. While the genotype-phenotype connections of group 2 species have been extensively studied with metabolic modeling, a genome-scale model has been missing for the group 1 species. The metabolic reconstruction of Shewanella piezotolerans strain WP3 represented the first model for Shewanella group 1 and the first model among piezotolerant and psychrotolerant deep-sea bacteria. The model brought insights into the mechanisms of energy conservation in WP3 under anaerobic conditions and highlighted its metabolic flexibility in using diverse carbon sources. Overall, the model opens up new opportunities for investigating energy conservation and metabolic adaptation, and it provides a prototype for systems-level modeling of other deep-sea microorganisms.

scholarly journals Using a Genome-Scale Model and Genomic Library to Discover the Functions of Metabolic Genes

2017 ◽  
Vol 112 (3) ◽  
pp. 342a
Author(s):  
Shu Pan ◽  
Kiel Nikolakakis ◽  
Edward Ruby ◽  
Jennifer Reed
Keyword(s):  
Genomic Library ◽  
Scale Model ◽  
Metabolic Genes ◽  
A Genome ◽  
Genome Scale ◽  
Genome Scale Model

scholarly journals A comprehensive genome-scale model forRhodosporidium toruloidesIFO0880 accounting for functional genomics and phenotypic data

2019 ◽  
Author(s):  
Hoang V. Dinh ◽  
Patrick F. Suthers ◽  
Siu Hung Joshua Chan ◽  
Yihui Shen ◽  
Tianxia Xiao ◽  
...  
Keyword(s):  
Functional Genomics ◽  
Current Knowledge ◽  
Biomass Composition ◽  
Scale Model ◽  
Phenotypic Data ◽  
Gene Essentiality ◽  
A Genome ◽  
Genetic Interventions ◽  
Genome Scale ◽  
Genome Scale Model

AbstractBackgroundRhodosporidium toruloidesis a basidiomycetes yeast that can accumulate large amount of lipids and natively produce carotenoids. To better assess this non-model yeast’s metabolic capabilities, we reconstruct a genome-scale model ofR. toruloidesIFO0880’s metabolic network (iRhto1108) using recent functional genomics and phenotypic data in literature or generated herein.ResultsThe modeliRhto1108 accounts for 2,203 reactions, 1,985 metabolites and 1,108 genes. In this work, we integrate and supplement the current knowledge with in-house generated biomass composition and experimental measurements pertaining to the organism’s metabolic capabilities. Phenotype-genotype relationship predictions were improved through manual curation of gene-protein-reaction rules for 543 reactions and validations with gene essentiality data leading to correct recapitulations of 84.5% of gene essentiality data (sensitivity of 94.3% and specificity of 53.8%). Organism-specific macromolecular composition and ATP maintenance requirements were experimentally measured for two separate growth conditions: (i) carbon and (ii) nitrogen limitations. Overall,iRhto1108 reproducedR. toruloides’s utilization capabilities for 18 alternate substrates, matched measured wild-type growth yield, and recapitulated the viability of 772 out of 819 deletion mutants. As a demonstration to the model’s fidelity in guiding engineering interventions, the OptForce procedure was applied oniRhto1108 for the overproduction of triacylglycerol. Suggested interventions recapitulated many of the previously successfully implemented genetic modifications and put forth a few new ones.ConclusioniRhto1108 offers a highly curated model for a non-model yeast supported by multiple layers of experimental data that can be used to inform genetic interventions.

Pantograph: A template-based method for genome-scale metabolic model reconstruction

2015 ◽  
Vol 13 (02) ◽  
pp. 1550006 ◽  
Author(s):  
Nicolas Loira ◽  
Anna Zhukova ◽  
David James Sherman
Keyword(s):  
Metabolic Model ◽  
Scale Model ◽  
Model Reconstruction ◽  
Specific Knowledge ◽  
Manual Curation ◽  
A Genome ◽  
Iterative Improvement ◽  
Genome Scale ◽  
Genome Scale Model ◽  
Template Reaction

Genome-scale metabolic models are a powerful tool to study the inner workings of biological systems and to guide applications. The advent of cheap sequencing has brought the opportunity to create metabolic maps of biotechnologically interesting organisms. While this drives the development of new methods and automatic tools, network reconstruction remains a time-consuming process where extensive manual curation is required. This curation introduces specific knowledge about the modeled organism, either explicitly in the form of molecular processes, or indirectly in the form of annotations of the model elements. Paradoxically, this knowledge is usually lost when reconstruction of a different organism is started. We introduce the Pantograph method for metabolic model reconstruction. This method combines a template reaction knowledge base, orthology mappings between two organisms, and experimental phenotypic evidence, to build a genome-scale metabolic model for a target organism. Our method infers implicit knowledge from annotations in the template, and rewrites these inferences to include them in the resulting model of the target organism. The generated model is well suited for manual curation. Scripts for evaluating the model with respect to experimental data are automatically generated, to aid curators in iterative improvement. We present an implementation of the Pantograph method, as a toolbox for genome-scale model reconstruction, curation and validation. This open source package can be obtained from: http://pathtastic.gforge.inria.fr .

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 Optimization of lipid production with a genome-scale model of Yarrowia lipolytica

2015 ◽  
Vol 9 (1) ◽  
Author(s):  
Martin Kavšček ◽  
Govindprasad Bhutada ◽  
Tobias Madl ◽  
Klaus Natter
Keyword(s):  
Yarrowia Lipolytica ◽  
Lipid Production ◽  
Scale Model ◽  
A Genome ◽  
Genome Scale ◽  
Genome Scale Model

scholarly journals Application of a genome-scale model in tandem with enzyme assays for identification of metabolic signatures of high and low CHO cell producers

2019 ◽  
Vol 9 ◽  
pp. e00097 ◽  
Author(s):  
Cyrielle Calmels ◽  
Solène Arnoult ◽  
Bassem Ben Yahia ◽  
Laetitia Malphettes ◽  
Mikael Rørdam Andersen
Keyword(s):  
Scale Model ◽  
Enzyme Assays ◽  
Cho Cell ◽  
A Genome ◽  
Genome Scale ◽  
Genome Scale Model ◽  
Metabolic Signatures

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.

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