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Iterative reconstruction of a global metabolic model of Acinetobacter baylyi ADP1 using high-throughput growth phenotype and gene essentiality data.

Durot M, Le Fèvre F, de Berardinis V, Kreimeyer A, Vallenet D, Combe C, Smidtas S, Salanoubat M, Weissenbach J, Schachter V - BMC Syst Biol (2008)

Bottom Line: The predictions of the final version of the model, which included three rounds of refinements, are consistent with the experimental results for (1) 91% of the wild-type growth phenotypes, (2) 94% of the gene essentiality results, and (3) 94% of the mutant growth phenotypes.To facilitate the exploitation of the metabolic model, we provide a web interface allowing online predictions and visualization of results on metabolic maps.The iterative reconstruction procedure led to significant model improvements, showing that genome-wide mutant phenotypes on several media can significantly facilitate the transition from genome annotation to a high-quality model.

View Article: PubMed Central - HTML - PubMed

Affiliation: Genoscope (Commissariat à l'Energie Atomique) and UMR 8030 CNRS-Genoscope-Université d'Evry, 2 rue Gaston Crémieux, CP5706, 91057 Evry, Cedex, France. mdurot@genoscope.cns.fr

ABSTRACT

Background: Genome-scale metabolic models are powerful tools to study global properties of metabolic networks. They provide a way to integrate various types of biological information in a single framework, providing a structured representation of available knowledge on the metabolism of the respective species.

Results: We reconstructed a constraint-based metabolic model of Acinetobacter baylyi ADP1, a soil bacterium of interest for environmental and biotechnological applications with large-spectrum biodegradation capabilities. Following initial reconstruction from genome annotation and the literature, we iteratively refined the model by comparing its predictions with the results of large-scale experiments: (1) high-throughput growth phenotypes of the wild-type strain on 190 distinct environments, (2) genome-wide gene essentialities from a knockout mutant library, and (3) large-scale growth phenotypes of all mutant strains on 8 minimal media. Out of 1412 predictions, 1262 were initially consistent with our experimental observations. Inconsistencies were systematically examined, leading in 65 cases to model corrections. The predictions of the final version of the model, which included three rounds of refinements, are consistent with the experimental results for (1) 91% of the wild-type growth phenotypes, (2) 94% of the gene essentiality results, and (3) 94% of the mutant growth phenotypes. To facilitate the exploitation of the metabolic model, we provide a web interface allowing online predictions and visualization of results on metabolic maps.

Conclusion: The iterative reconstruction procedure led to significant model improvements, showing that genome-wide mutant phenotypes on several media can significantly facilitate the transition from genome annotation to a high-quality model.

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Related in: MedlinePlus

A. baylyi metabolic model refinement process. A. baylyi metabolic model was iteratively refined in three steps using datasets of experimental results. The initial reconstruction iAbaylyiv1 was assessed and improved using dataset 1; the resulting model iAbaylyiv2 was then assessed and refined using dataset 2, yielding iAbaylyiv3 which was again evaluated and refined using dataset 3, leading to the final model iAbaylyiv4. Since only mutants corresponding to dispensable genes in dataset 2 could be phenotyped in dataset 3, gene essentialities revealed in dataset 3 are medium-specific, i.e. conditionally essential. Genes classified as conditionally essential in dataset 3 are conditionally essential on at least one environment. Genes classified as dispensable are dispensable on all tested environments. Model accuracy figures indicates for each dataset and its corresponding models the counts of consistent and inconsistent predictions. Accuracy is computed as the fraction of consistent predictions among all predictions. For dataset 1, Biolog results for metabolites that were not in the model were counted as consistent with predictions if the metabolite was not a carbon source and inconsistent if the metabolite was a carbon source. Model corrections figures summarize the corrections performed on each model component.
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Figure 1: A. baylyi metabolic model refinement process. A. baylyi metabolic model was iteratively refined in three steps using datasets of experimental results. The initial reconstruction iAbaylyiv1 was assessed and improved using dataset 1; the resulting model iAbaylyiv2 was then assessed and refined using dataset 2, yielding iAbaylyiv3 which was again evaluated and refined using dataset 3, leading to the final model iAbaylyiv4. Since only mutants corresponding to dispensable genes in dataset 2 could be phenotyped in dataset 3, gene essentialities revealed in dataset 3 are medium-specific, i.e. conditionally essential. Genes classified as conditionally essential in dataset 3 are conditionally essential on at least one environment. Genes classified as dispensable are dispensable on all tested environments. Model accuracy figures indicates for each dataset and its corresponding models the counts of consistent and inconsistent predictions. Accuracy is computed as the fraction of consistent predictions among all predictions. For dataset 1, Biolog results for metabolites that were not in the model were counted as consistent with predictions if the metabolite was not a carbon source and inconsistent if the metabolite was a carbon source. Model corrections figures summarize the corrections performed on each model component.

Mentions: The genome scale model of A. baylyi was iteratively reconstructed following a process depicted in Figure 1. We first built an initial draft model iAbaylyiv1 using information from the genome annotation, metabolic pathways databases, and the literature. Although facilitated by the automated network reconstruction software PathoLogic [21], this initial reconstruction still required extensive manual curation (see Methods). The draft metabolic network generated by PathoLogic was first inspected to filter out and correct wrongly predicted pathways and reactions, and then completed by reviewing the expert genome annotations and the metabolic information contained in the literature. For instance, specific efforts were dedicated to properly include pathways accounting for the particular degradation capabilities of A. baylyi. Physiological information on A. baylyi was especially helpful to build the set of transport processes, as substrate specificities of transporters are difficult to deduce from genome annotation only. For each metabolite shown to be consumed by A. baylyi we added a corresponding transport reaction to the model. Out of 133 transporters, 23 were initially included in the model using this type of evidence only. The dependency between genes and reactions was modeled using Boolean rules, known as GPR (Gene-Protein-Reaction associations) [22]. These rules encode the presence of isozymes or enzymatic complexes for the catalysis of reactions, and predict the effect of genetic perturbations on the activity of reactions. GPR rules were first derived using homology with E. coli enzyme complexes [23] and then completed by manual curation. In order to model the metabolic and energetic demands associated with growth, we introduced a set of intermediary biomass reactions that synthesize generic cell constituents (e.g. protein, DNA, RNA, or lipid) from precursor metabolites, and a global growth reaction consuming them in proportion defined by studies of biomass composition [24,25]. Energetic parameters required to predict quantitative growth rate using Flux Balance Analysis (FBA) were assumed to be similar to those of E. coli model (see Methods)[22]. No accurate measurement of A. baylyi growth yields could be used to validate these parameters, however. While such validation would be required to get more accurate predictions of growth yields, the current parameters already provide good approximate values (see Additional file 1 for a sensitivity analysis on these parameters). For the purpose of qualitatively predicting growth ability using Metabolite Producibility analysis (see Figure 2) [26], we designed a reduced list of biomass precursors which are all essential for growth in in vitro conditions. We used this list to predict qualitative growth phenotypes and compare them with those of phenotyping experiments on in vitro environments. In vivo environments may impose harsher conditions requiring additional metabolic responses; this list therefore represents a minimal set of essential precursors that may need to be expanded to properly predict growth phenotypes on more realistic environments [27]. The Methods section provides more details on the reconstruction process.


Iterative reconstruction of a global metabolic model of Acinetobacter baylyi ADP1 using high-throughput growth phenotype and gene essentiality data.

Durot M, Le Fèvre F, de Berardinis V, Kreimeyer A, Vallenet D, Combe C, Smidtas S, Salanoubat M, Weissenbach J, Schachter V - BMC Syst Biol (2008)

A. baylyi metabolic model refinement process. A. baylyi metabolic model was iteratively refined in three steps using datasets of experimental results. The initial reconstruction iAbaylyiv1 was assessed and improved using dataset 1; the resulting model iAbaylyiv2 was then assessed and refined using dataset 2, yielding iAbaylyiv3 which was again evaluated and refined using dataset 3, leading to the final model iAbaylyiv4. Since only mutants corresponding to dispensable genes in dataset 2 could be phenotyped in dataset 3, gene essentialities revealed in dataset 3 are medium-specific, i.e. conditionally essential. Genes classified as conditionally essential in dataset 3 are conditionally essential on at least one environment. Genes classified as dispensable are dispensable on all tested environments. Model accuracy figures indicates for each dataset and its corresponding models the counts of consistent and inconsistent predictions. Accuracy is computed as the fraction of consistent predictions among all predictions. For dataset 1, Biolog results for metabolites that were not in the model were counted as consistent with predictions if the metabolite was not a carbon source and inconsistent if the metabolite was a carbon source. Model corrections figures summarize the corrections performed on each model component.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2606687&req=5

Figure 1: A. baylyi metabolic model refinement process. A. baylyi metabolic model was iteratively refined in three steps using datasets of experimental results. The initial reconstruction iAbaylyiv1 was assessed and improved using dataset 1; the resulting model iAbaylyiv2 was then assessed and refined using dataset 2, yielding iAbaylyiv3 which was again evaluated and refined using dataset 3, leading to the final model iAbaylyiv4. Since only mutants corresponding to dispensable genes in dataset 2 could be phenotyped in dataset 3, gene essentialities revealed in dataset 3 are medium-specific, i.e. conditionally essential. Genes classified as conditionally essential in dataset 3 are conditionally essential on at least one environment. Genes classified as dispensable are dispensable on all tested environments. Model accuracy figures indicates for each dataset and its corresponding models the counts of consistent and inconsistent predictions. Accuracy is computed as the fraction of consistent predictions among all predictions. For dataset 1, Biolog results for metabolites that were not in the model were counted as consistent with predictions if the metabolite was not a carbon source and inconsistent if the metabolite was a carbon source. Model corrections figures summarize the corrections performed on each model component.
Mentions: The genome scale model of A. baylyi was iteratively reconstructed following a process depicted in Figure 1. We first built an initial draft model iAbaylyiv1 using information from the genome annotation, metabolic pathways databases, and the literature. Although facilitated by the automated network reconstruction software PathoLogic [21], this initial reconstruction still required extensive manual curation (see Methods). The draft metabolic network generated by PathoLogic was first inspected to filter out and correct wrongly predicted pathways and reactions, and then completed by reviewing the expert genome annotations and the metabolic information contained in the literature. For instance, specific efforts were dedicated to properly include pathways accounting for the particular degradation capabilities of A. baylyi. Physiological information on A. baylyi was especially helpful to build the set of transport processes, as substrate specificities of transporters are difficult to deduce from genome annotation only. For each metabolite shown to be consumed by A. baylyi we added a corresponding transport reaction to the model. Out of 133 transporters, 23 were initially included in the model using this type of evidence only. The dependency between genes and reactions was modeled using Boolean rules, known as GPR (Gene-Protein-Reaction associations) [22]. These rules encode the presence of isozymes or enzymatic complexes for the catalysis of reactions, and predict the effect of genetic perturbations on the activity of reactions. GPR rules were first derived using homology with E. coli enzyme complexes [23] and then completed by manual curation. In order to model the metabolic and energetic demands associated with growth, we introduced a set of intermediary biomass reactions that synthesize generic cell constituents (e.g. protein, DNA, RNA, or lipid) from precursor metabolites, and a global growth reaction consuming them in proportion defined by studies of biomass composition [24,25]. Energetic parameters required to predict quantitative growth rate using Flux Balance Analysis (FBA) were assumed to be similar to those of E. coli model (see Methods)[22]. No accurate measurement of A. baylyi growth yields could be used to validate these parameters, however. While such validation would be required to get more accurate predictions of growth yields, the current parameters already provide good approximate values (see Additional file 1 for a sensitivity analysis on these parameters). For the purpose of qualitatively predicting growth ability using Metabolite Producibility analysis (see Figure 2) [26], we designed a reduced list of biomass precursors which are all essential for growth in in vitro conditions. We used this list to predict qualitative growth phenotypes and compare them with those of phenotyping experiments on in vitro environments. In vivo environments may impose harsher conditions requiring additional metabolic responses; this list therefore represents a minimal set of essential precursors that may need to be expanded to properly predict growth phenotypes on more realistic environments [27]. The Methods section provides more details on the reconstruction process.

Bottom Line: The predictions of the final version of the model, which included three rounds of refinements, are consistent with the experimental results for (1) 91% of the wild-type growth phenotypes, (2) 94% of the gene essentiality results, and (3) 94% of the mutant growth phenotypes.To facilitate the exploitation of the metabolic model, we provide a web interface allowing online predictions and visualization of results on metabolic maps.The iterative reconstruction procedure led to significant model improvements, showing that genome-wide mutant phenotypes on several media can significantly facilitate the transition from genome annotation to a high-quality model.

View Article: PubMed Central - HTML - PubMed

Affiliation: Genoscope (Commissariat à l'Energie Atomique) and UMR 8030 CNRS-Genoscope-Université d'Evry, 2 rue Gaston Crémieux, CP5706, 91057 Evry, Cedex, France. mdurot@genoscope.cns.fr

ABSTRACT

Background: Genome-scale metabolic models are powerful tools to study global properties of metabolic networks. They provide a way to integrate various types of biological information in a single framework, providing a structured representation of available knowledge on the metabolism of the respective species.

Results: We reconstructed a constraint-based metabolic model of Acinetobacter baylyi ADP1, a soil bacterium of interest for environmental and biotechnological applications with large-spectrum biodegradation capabilities. Following initial reconstruction from genome annotation and the literature, we iteratively refined the model by comparing its predictions with the results of large-scale experiments: (1) high-throughput growth phenotypes of the wild-type strain on 190 distinct environments, (2) genome-wide gene essentialities from a knockout mutant library, and (3) large-scale growth phenotypes of all mutant strains on 8 minimal media. Out of 1412 predictions, 1262 were initially consistent with our experimental observations. Inconsistencies were systematically examined, leading in 65 cases to model corrections. The predictions of the final version of the model, which included three rounds of refinements, are consistent with the experimental results for (1) 91% of the wild-type growth phenotypes, (2) 94% of the gene essentiality results, and (3) 94% of the mutant growth phenotypes. To facilitate the exploitation of the metabolic model, we provide a web interface allowing online predictions and visualization of results on metabolic maps.

Conclusion: The iterative reconstruction procedure led to significant model improvements, showing that genome-wide mutant phenotypes on several media can significantly facilitate the transition from genome annotation to a high-quality model.

Show MeSH
Related in: MedlinePlus