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Iterative reconstruction of a global metabolic model of Acinetobacter baylyi ADP1 using high-throughput growth phenotype and gene essentiality data
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Edité par CCSD ; BioMed Central -
A utilisé MicroScope Platform. International audience. Background: Genome-scale metabolic models are powerful tools to study global properties ofmetabolic networks. They provide a way to integrate various types of biological information in asingle framework, providing a structured representation of available knowledge on the metabolismof the respective species.Results: We reconstructed a constraint-based metabolic model of Acinetobacter baylyi ADP1, a soilbacterium of interest for environmental and biotechnological applications with large-spectrumbiodegradation capabilities. Following initial reconstruction from genome annotation and theliterature, we iteratively refined the model by comparing its predictions with the results of largescaleexperiments: (1) high-throughput growth phenotypes of the wild-type strain on 190 distinctenvironments, (2) genome-wide gene essentialities from a knockout mutant library, and (3) largescalegrowth phenotypes of all mutant strains on 8 minimal media. Out of 1412 predictions, 1262were initially consistent with our experimental observations. Inconsistencies were systematicallyexamined, leading in 65 cases to model corrections. The predictions of the final version of themodel, which included three rounds of refinements, are consistent with the experimental resultsfor (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, weprovide 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 thetransition from genome annotation to a high-quality model.
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