A Genome-Scale Metabolic Reconstruction of Mycoplasma genitalium, iPS189

ABSTRACT System-level modeling is beginning to be used to decipher high throughput data in the context of disease. In this study, we present an integration of expression microarray data with a genome-scale metabolic reconstruction of P seudomonas aeruginosa in the context of a chronic cystic fibrosis (CF) lung infection. A genome-scale reconstruction of P. aeruginosa metabolism was tailored to represent the metabolic states of two clonally related lineages of P. aeruginosa isolated from the lungs of a CF patient at different points over a 44-month time course, giving a mechanistic glimpse into how the bacterial metabolism adapts over time in the CF lung. Metabolic capacities were analyzed to determine how tradeoffs between growth and other important cellular processes shift during disease progression. Genes whose knockouts were either significantly growth reducing or lethal in silico were also identified for each time point and serve as hypotheses for future drug targeting efforts specific to the stages of disease progression.

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A metabolic reconstruction of Lactobacillus reuteri JCM 1112 and analysis of its potential as a cell factory

Microbial Cell Factories ◽

10.1186/s12934-019-1229-3 ◽

2019 ◽

Vol 18 (1) ◽

Cited By ~ 2

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.

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Modeling methanogenesis with a genome‐scale metabolic reconstruction ofMethanosarcina barkeri

Molecular Systems Biology ◽

10.1038/msb4100046 ◽

2006 ◽

Vol 2 (1) ◽

Cited By ~ 113

Author(s):

Adam M Feist ◽

Johannes C M Scholten ◽

Bernhard Ø Palsson ◽

Fred J Brockman ◽

Trey Ideker

Keyword(s):

Metabolic Reconstruction ◽

A Genome ◽

Genome Scale

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A metabolic reconstruction of Lactobacillus reuteri JCM 1112 and analysis of its potential as a cell factory

10.1101/708875 ◽

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.

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Exploring Hydrogenotrophic Methanogenesis: a Genome Scale Metabolic Reconstruction of Methanococcus maripaludis

Journal of Bacteriology ◽

10.1128/jb.00571-16 ◽

2016 ◽

Vol 198 (24) ◽

pp. 3379-3390 ◽

Cited By ~ 18

Author(s):

Matthew A. Richards ◽

Thomas J. Lie ◽

Juan Zhang ◽

Stephen W. Ragsdale ◽

John A. Leigh ◽

...

Keyword(s):

Energy Conservation ◽

Metabolic Network ◽

Hot Springs ◽

Gene Knockout ◽

Metabolic Reconstruction ◽

Methanococcus Maripaludis ◽

Content Type ◽

Hydrogenotrophic Methanogenesis ◽

A Genome ◽

Genome Scale

ABSTRACTHydrogenotrophic methanogenesis occurs in multiple environments, ranging from the intestinal tracts of animals to anaerobic sediments and hot springs. Energy conservation in hydrogenotrophic methanogens was long a mystery; only within the last decade was it reported that net energy conservation for growth depends on electron bifurcation. In this work, we focus onMethanococcus maripaludis, a well-studied hydrogenotrophic marine methanogen. To better understand hydrogenotrophic methanogenesis and compare it with methylotrophic methanogenesis that utilizes oxidative phosphorylation rather than electron bifurcation, we have built iMR539, a genome scale metabolic reconstruction that accounts for 539 of the 1,722 protein-coding genes ofM. maripaludisstrain S2. Our reconstructed metabolic network uses recent literature to not only represent the central electron bifurcation reaction but also incorporate vital biosynthesis and assimilation pathways, including unique cofactor and coenzyme syntheses. We show that our model accurately predicts experimental growth and gene knockout data, with 93% accuracy and a Matthews correlation coefficient of 0.78. Furthermore, we use our metabolic network reconstruction to probe the implications of electron bifurcation by showing its essentiality, as well as investigating the infeasibility of aceticlastic methanogenesis in the network. Additionally, we demonstrate a method of applying thermodynamic constraints to a metabolic model to quickly estimate overall free-energy changes between what comes in and out of the cell. Finally, we describe a novel reconstruction-specific computational toolbox we created to improve usability. Together, our results provide a computational network for exploring hydrogenotrophic methanogenesis and confirm the importance of electron bifurcation in this process.IMPORTANCEUnderstanding and applying hydrogenotrophic methanogenesis is a promising avenue for developing new bioenergy technologies around methane gas. Although a significant portion of biological methane is generated through this environmentally ubiquitous pathway, existing methanogen models portray the more traditional energy conservation mechanisms that are found in other methanogens. We have constructed a genome scale metabolic network ofMethanococcus maripaludisthat explicitly accounts for all major reactions involved in hydrogenotrophic methanogenesis. Our reconstruction demonstrates the importance of electron bifurcation in central metabolism, providing both a window into hydrogenotrophic methanogenesis and a hypothesis-generating platform to fuel metabolic engineering efforts.

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MOOMIN – Mathematical explOration of ’Omics data on a MetabolIc Network

Bioinformatics ◽

10.1093/bioinformatics/btz584 ◽

2019 ◽

Author(s):

Taneli Pusa ◽

Mariana Galvão Ferrarini ◽

Ricardo Andrade ◽

Arnaud Mary ◽

Alberto Marchetti-Spaccamela ◽

...

Keyword(s):

Differential Expression ◽

Metabolic Activity ◽

Metabolic Regulation ◽

Metabolic Changes ◽

Supplementary Information ◽

Metabolic Reconstruction ◽

Expression Of Genes ◽

A Genome ◽

Mathematical Exploration ◽

Genome Scale

Abstract Motivation Analysis of differential expression of genes is often performed to understand how the metabolic activity of an organism is impacted by a perturbation. However, because the system of metabolic regulation is complex and all changes are not directly reflected in the expression levels, interpreting these data can be difficult. Results In this work, we present a new algorithm and computational tool that uses a genome-scale metabolic reconstruction to infer metabolic changes from differential expression data. Using the framework of constraint-based analysis, our method produces a qualitative hypothesis of a change in metabolic activity. In other words, each reaction of the network is inferred to have increased, decreased, or remained unchanged in flux. In contrast to similar previous approaches, our method does not require a biological objective function and does not assign on/off activity states to genes. An implementation is provided and it is available online. We apply the method to three published datasets to show that it successfully accomplishes its two main goals: confirming or rejecting metabolic changes suggested by differentially expressed genes based on how well they fit in as parts of a coordinated metabolic change, as well as inferring changes in reactions whose genes did not undergo differential expression. Availability and implementation github.com/htpusa/moomin. Supplementary information Supplementary data are available at Bioinformatics online.

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ChiMera: An easy to use pipeline for Bacterial Genome Based Metabolic Network Reconstruction, Evaluation and Visualization

10.1101/2021.11.30.470608 ◽

2021 ◽

Author(s):

Gustavo Tamasco ◽

Ricardo Roberto da Silva ◽

Rafael Silva-Rocha

Keyword(s):

Genetic Engineering ◽

Bacterial Genome ◽

Draft Genome ◽

Scale Model ◽

Metabolic Reconstruction ◽

Acid Oxidation ◽

Model Driven ◽

A Genome ◽

High Level ◽

Genome Scale

Several genome scale metabolic reconstruction tools have been developed in the last decades. They have helped to construct many metabolic models, which have contributed to a variety of fields, e.g., genetic engineering, drug discovery, prediction of phenotypes, and other model-driven discoveries. However, the use of these programs requires a higher level of bioinformatic skills, and most of them are not scalable for multiple genomes. Moreover, the functionalities required to build models are generally scattered through multiple tools, requiring knowledge of their utilization. Here, we present ChiMera, which combines the most efficient tools in model reconstruction, prediction, and visualization. ChiMera uses CarveMe top-down approach based on genomic evidence to prune a global model with a high level of curation, generating a draft genome able to produce growth predictions using flux balance analysis for gram-positive and gram-negative bacteria. ChiMera also contains two modules of visualization implemented, predefined and universal. The first generates maps for the most important pathways, e.g., core-metabolism, fatty acid oxidation and biosynthesis, nucleotides and amino acids biosynthesis, glycolysis, and others. The second module produces a genome-wide metabolic map, which can be used to harvest KEGG pathway information for each compound in the model. A module of gene essentiality and knockout is also present. Overall, ChiMera combines model creation, gap-filling, FBA and metabolic visualization to create a simulation ready genome-scale model, helping genetic engineering projects, prediction of phenotypes, and other model-driven discoveries in a friendly manner.

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