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

<p>Abstract</p> <p>Background</p> <p>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.</p> <p>Results</p> <p>We reconstructed a constraint-based metabolic model of <it>Acinetobacter baylyi </it>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.</p> <p>Conclusion</p> <p>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.</p

A complete collection of single-gene deletion mutants of Acinetobacter baylyi ADP1

We have constructed a collection of single-gene deletion mutants for all dispensable genes of the soil bacterium Acinetobacter baylyi ADP1. A total of 2594 deletion mutants were obtained, whereas 499 (16%) were not, and are therefore candidate essential genes for life on minimal medium. This essentiality data set is 88% consistent with the Escherichia coli data set inferred from the Keio mutant collection profiled for growth on minimal medium, while 80% of the orthologous genes described as essential in Pseudomonas aeruginosa are also essential in ADP1. Several strategies were undertaken to investigate ADP1 metabolism by (1) searching for discrepancies between our essentiality data and current metabolic knowledge, (2) comparing this essentiality data set to those from other organisms, (3) systematic phenotyping of the mutant collection on a variety of carbon sources (quinate, 2-3 butanediol, glucose, etc.). This collection provides a new resource for the study of gene function by forward and reverse genetic approaches and constitutes a robust experimental data source for systems biology approaches

Genome-scale models of bacterial metabolism: reconstruction and applications

Genome-scale metabolic models bridge the gap between genome-derived biochemical information and metabolic phenotypes in a principled manner, providing a solid interpretative framework for experimental data related to metabolic states, and enabling simple in silico experiments with whole-cell metabolism. Models have been reconstructed for almost 20 bacterial species, so far mainly through expert curation efforts integrating information from the literature with genome annotation. A wide variety of computational methods exploiting metabolic models have been developed and applied to bacteria, yielding valuable insights into bacterial metabolism and evolution, and providing a sound basis for computer-assisted design in metabolic engineering. Recent advances in computational systems biology and high-throughput experimental technologies pave the way for the systematic reconstruction of metabolic models from genomes of new species, and a corresponding expansion of the scope of their applications. In this review, we provide an introduction to the key ideas of metabolic modeling, survey the methods, and resources that enable model reconstruction and refinement, and chart applications to the investigation of global properties of metabolic systems, the interpretation of experimental results, and the re-engineering of their biochemical capabilities

Elucidation du métabolisme des microorganismes par la modélisation et l'interprétation des données d'essentialité de gènes. Application au métabolisme de la bactérie Acinetobacter baylyi ADP1.

Microbial metabolism has traditionally been investigated at two different scales: the finest involves characterizing individually each reaction occurring in the cell; the largest focuses on global cell physiology. Both scales have recently benefited from technological advances: analyzing sequenced genomes identifies a large fraction of reaction-catalyzing enzymes; cell physiology can be determined at high-throughput for several environmental conditions and genetic perturbations. Combining both scales remains, however, especially complex as the global physiological behavior of a cell results from the coordinated action of a large network of reactions. Mathematical modeling approaches have yet shown recently that genome-scale metabolic models could help in linking both scales.In this thesis, we explore the use of such models to expand the knowledge of reactions with a specific type of high-level data: gene essentiality data, assessed using growth phenotypes of deletion mutants. We will use as model organism the bacterium Acinetobacter baylyi ADP1, for which a genome-wide collection of gene deletion mutants has recently been created.Following a presentation of the key steps and developments that have been required to reconstruct a global metabolic model of A. baylyi, we will show that confronting observed and predicted phenotypes highlight inconsistencies between the two scales. We will then show that a formal interpretation of these inconsistencies can guide model corrections and improvements to the knowledge of metabolism. We will illustrate this claim by presenting model corrections triggered by A. baylyi mutant phenotypes. Finally, we will introduce a method that automates the correction of inconsistencies caused by wrong associations between genes and reactions.Deux échelles d'observations sont traditionnellement utilisées pour étudier le métabolisme des microorganismes: d'une part, à l'échelle locale, la caractérisation individuelle des réactions ayant lieu dans la cellule et d'autre part, à l'échelle globale, l'étude de la physiologie de la cellule. Ces deux échelles ont bénéficié de progrès technologiques récents : l'analyse des génomes séquencés permet d'identifier une large fraction des enzymes catalysant les réactions ; la physiologie des microorganismes peut être étudiée à haut débit pour de nombreux environnements et perturbations génétiques. Cependant, l'exploitation conjointe de ces deux échelles demeure complexe car le comportement physiologique global de la cellule résulte de l'action coordonnée de nombreuses réactions. Les approches de modélisation mathématique ont toutefois récemment permis de relier ces deux échelles à l'aide de modèles globaux du métabolisme. Dans cette thèse, nous explorerons l'utilisation de ces modèles pour compléter la connaissance des réactions à l'aide d'une catégorie particulière de données d'échelle globale : les essentialités de gènes déterminées en observant les phénotypes de croissance de mutants de délétion. Nous nous appuierons pour cela sur la bactérie Acinetobacter baylyi ADP1 pour laquelle une collection complète de mutants de délétion a été récemment constituée au Genoscope. Après avoir présenté les étapes clés et les développements que nous avons effectués pour reconstruire un modèle global du métabolisme d'A. baylyi, nous montrerons que la confrontation entre phénotypes observés et phénotypes prédits permet de mettre en évidence des incohérences entre les deux échelles d'observations. Nous montrerons ensuite qu'une interprétation formelle de ces incohérences permet de corriger le modèle et d'améliorer la connaissance du métabolisme. Nous illustrerons ce propos en présentant les corrections que nous avons réalisées à l'aide des phénotypes de mutants d'A. baylyi. Enfin, dans une dernière partie, nous proposerons une méthode permettant d'automatiser la correction des incohérences causées par des erreurs d'association entre gènes et réactions

Model-based investigation of microbial metabolism to interpret gene essentiality results : illustrated on Acinetobacter baylyi ADP1 metabolism

Le métabolisme des microorganismes est traditionnellement étudié à deux échelles: d’une part, à l’échelle locale, la description des réactions métaboliques et d’autre part, à l’échelle globale, l’étude de la physiologie de la cellule. Malgré des progrès technologiques récents facilitant les études à ces deux échelles, leur exploitation conjointe demeure complexe car le comportement physiologique de la cellule résulte de l’action coordonnée de nombreuses réactions. Les modèles mathématiques globaux du métabolisme ont toutefois récemment permis de relier ces deux échelles. Dans cette thèse, nous explorerons l’utilisation de ces modèles pour compléter la connaissance des réactions à l’aide d’une catégorie particulière de données d’échelle globale : les essentialités de gènes déterminées à partir des phénotypes de croissance de mutants de délétion. Nous nous appuierons pour cela sur la bactérie Acinetobacter baylyi ADP1. Après avoir présenté les développements effectués pour reconstruire un modèle global du métabolisme d’A. baylyi, nous montrerons que la confrontation entre phénotypes observés et phénotypes prédits permet de mettre en évidence des incohérences entre les deux échelles d’observations. Nous montrerons ensuite qu’une interprétation formelle de ces incohérences permet de corriger le modèle et d’améliorer la connaissance du métabolisme. Nous illustrerons ce propos en présentant les corrections que nous avons réalisées à l’aide de phénotypes de mutants d’A. baylyi. Enfin, dans une dernière partie, nous proposerons une méthode permettant d’automatiser la correction des incohérences causées par des erreurs d’association entre gènes et réactions.Microbial metabolism has traditionally been investigated at two different scales: the finest involves characterizing individually each reaction occurring in the cell; the largest focuses on global cell physiology. While both scales have recently benefited from technological advances, combining them remains, however, especially complex as the global physiological behavior of a cell results from the coordinated action of a large network of reactions. Mathematical modeling approaches have yet shown recently that genome-scale metabolic models could help in linking both scales. In this thesis, we explore the use of such models to expand the knowledge of reactions with a specific type of high-level data: gene essentiality data, assessed using growth phenotypes of deletion mutants. We will use as model organism the bacterium Acinetobacter baylyi ADP1, for which a genome-wide collection of gene deletion mutants has recently been created. Following a presentation of the key steps and developments that have been required to reconstruct a global metabolic model of A. baylyi, we will show that confronting observed and predicted phenotypes highlight inconsistencies between the two scales. We will then show that a formal interpretation of these inconsistencies can guide model corrections and improvements to the knowledge of metabolism. We will illustrate this claim by presenting model corrections triggered by A. baylyi mutant phenotypes. Finally, we will introduce a method that automates the correction of inconsistencies caused by wrong associations between genes and reactions

Elucidation du métabolisme des microorganismes par la modélisation et l'interprétation des données d'essentialités de gènes (application au métabolisme de la bactérie Acinetobacter baylyi ADP1)

Le métabolisme des microorganismes est traditionnellement étudié à deux échelles: d une part, à l échelle locale, la description des réactions métaboliques et d autre part, à l échelle globale, l étude de la physiologie de la cellule. Malgré des progrès technologiques récents facilitant les études à ces deux échelles, leur exploitation conjointe demeure complexe car le comportement physiologique de la cellule résulte de l action coordonnée de nombreuses réactions. Les modèles mathématiques globaux du métabolisme ont toutefois récemment permis de relier ces deux échelles. Dans cette thèse, nous explorerons l utilisation de ces modèles pour compléter la connaissance des réactions à l aide d une catégorie particulière de données d échelle globale : les essentialités de gènes déterminées à partir des phénotypes de croissance de mutants de délétion. Nous nous appuierons pour cela sur la bactérie Acinetobacter baylyi ADP1. Après avoir présenté les développements effectués pour reconstruire un modèle global du métabolisme d A. baylyi, nous montrerons que la confrontation entre phénotypes observés et phénotypes prédits permet de mettre en évidence des incohérences entre les deux échelles d observations. Nous montrerons ensuite qu une interprétation formelle de ces incohérences permet de corriger le modèle et d améliorer la connaissance du métabolisme. Nous illustrerons ce propos en présentant les corrections que nous avons réalisées à l aide de phénotypes de mutants d A. baylyi. Enfin, dans une dernière partie, nous proposerons une méthode permettant d automatiser la correction des incohérences causées par des erreurs d association entre gènes et réactions.Microbial metabolism has traditionally been investigated at two different scales: the finest involves characterizing individually each reaction occurring in the cell; the largest focuses on global cell physiology. While both scales have recently benefited from technological advances, combining them remains, however, especially complex as the global physiological behavior of a cell results from the coordinated action of a large network of reactions. Mathematical modeling approaches have yet shown recently that genome-scale metabolic models could help in linking both scales. In this thesis, we explore the use of such models to expand the knowledge of reactions with a specific type of high-level data: gene essentiality data, assessed using growth phenotypes of deletion mutants. We will use as model organism the bacterium Acinetobacter baylyi ADP1, for which a genome-wide collection of gene deletion mutants has recently been created. Following a presentation of the key steps and developments that have been required to reconstruct a global metabolic model of A. baylyi, we will show that confronting observed and predicted phenotypes highlight inconsistencies between the two scales. We will then show that a formal interpretation of these inconsistencies can guide model corrections and improvements to the knowledge of metabolism. We will illustrate this claim by presenting model corrections triggered by A. baylyi mutant phenotypes. Finally, we will introduce a method that automates the correction of inconsistencies caused by wrong associations between genes and reactions.EVRY-Bib. électronique (912289901) / SudocSudocFranceF

Core and Panmetabolism in Escherichia coli▿ †

Escherichia coli exhibits a wide range of lifestyles encompassing commensalism and various pathogenic behaviors which its highly dynamic genome contributes to develop. How environmental and host factors shape the genetic structure of E. coli strains remains, however, largely unknown. Following a previous study of E. coli genomic diversity, we investigated its diversity at the metabolic level by building and analyzing the genome-scale metabolic networks of 29 E. coli strains (8 commensal and 21 pathogenic strains, including 6 Shigella strains). Using a tailor-made reconstruction strategy, we significantly improved the completeness and accuracy of the metabolic networks over default automatic reconstruction processes. Among the 1,545 reactions forming E. coli panmetabolism, 885 reactions were common to all strains. This high proportion of core reactions (57%) was found to be in sharp contrast to the low proportion (13%) of core genes in the E. coli pangenome, suggesting less diversity of metabolic functions compared to that of all gene functions. Core reactions were significantly overrepresented among biosynthetic reactions compared to the more variable degradation processes. Differences between metabolic networks were found to follow E. coli phylogeny rather than pathogenic phenotypes, except for Shigella networks, which were significantly more distant from the others. This suggests that most metabolic changes in non-Shigella strains were not driven by their pathogenic phenotypes. Using a supervised method, we were yet able to identify small sets of reactions related to pathogenicity or commensalism. The quality of our reconstructed networks also makes them reliable bases for building metabolic models

Novel metabolic features in Acinetobacter baylyi ADP1 revealed by a multiomics approach

Expansive knowledge of bacterial metabolism has been gained from genome sequencing output, but the high proportion of genes lacking a proper functional annotation in a given genome still impedes the accurate prediction of the metabolism of a cell. To access to a more global view of the functioning of the soil bacterium Acinetobacter baylyi ADP1, we adopted a multi ‘omics’ approach. Application of RNA-seq transcriptomics and LC/MS-based metabolomics, along with the systematic phenotyping of the complete collection of single-gene deletion mutants of A. baylyi ADP1 made possible to interrogate on the metabolic perturbations encountered by the bacterium upon a biotic change. Shifting the sole carbon source from succinate to quinate elicited in the cell not only a specific transcriptional response, necessary to catabolize the new carbon source, but also a major reorganization of the transcription pattern. Here, the expression of more than 12 % of the total number of genes was affected, most of them being of unknown function. These perturbations were ultimately reflected in the metabolome, in which the concentration of about 50 % of the LC/MS-detected metabolites was impacted. And the differential regulation of many genes of unknown function is probably related to the synthesis of the numerous unidentified compounds that were present exclusively in quinate-grown cells. Together, these data suggest that A. baylyi ADP1 metabolism involves unsuspected enzymatic reactions that await discovery. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s11306-014-0662-x) contains supplementary material, which is available to authorized users