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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

Distribution of annotation confidence levels for genes included in iAbaylyiv1 model. Confidence levels were assigned according to the type of evidence supporting gene annotation.
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Figure 4: Distribution of annotation confidence levels for genes included in iAbaylyiv1 model. Confidence levels were assigned according to the type of evidence supporting gene annotation.

Mentions: Most A. baylyi genes were annotated by expert curation; a third of the model genes relied on evidence conferring them a medium confidence level, e.g. limited homology with genes of known function, or conservation of amino acid motifs (Figure 4). While the evidence for these genes does not fully prove the existence of associated enzymatic activities, it suggests them with sufficient strength to justify adding the corresponding reactions in the model. The level of evidence of each gene was tracked for later use in interpreting inconsistent behaviors. Out of 262 reactions to which these genes contribute, 85 are solely catalyzed by medium-confidence genes, some of these being essential to the model viability. In addition, 35% of all coding sequences are still of unknown function in A. baylyi, and may leave gaps in the actual metabolic network. Integration of additional experimental data was thus crucial in order to validate the metabolic network and correct it when necessary.


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)

Distribution of annotation confidence levels for genes included in iAbaylyiv1 model. Confidence levels were assigned according to the type of evidence supporting gene annotation.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 4: Distribution of annotation confidence levels for genes included in iAbaylyiv1 model. Confidence levels were assigned according to the type of evidence supporting gene annotation.
Mentions: Most A. baylyi genes were annotated by expert curation; a third of the model genes relied on evidence conferring them a medium confidence level, e.g. limited homology with genes of known function, or conservation of amino acid motifs (Figure 4). While the evidence for these genes does not fully prove the existence of associated enzymatic activities, it suggests them with sufficient strength to justify adding the corresponding reactions in the model. The level of evidence of each gene was tracked for later use in interpreting inconsistent behaviors. Out of 262 reactions to which these genes contribute, 85 are solely catalyzed by medium-confidence genes, some of these being essential to the model viability. In addition, 35% of all coding sequences are still of unknown function in A. baylyi, and may leave gaps in the actual metabolic network. Integration of additional experimental data was thus crucial in order to validate the metabolic network and correct it when necessary.

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