Handling the heterogeneity of genomic and metabolic networks data within flexible workflows with the PADMet toolbox

National audienceA main challenge of the era of fast and massive genome sequencing is to transform sequences into biological knowledge. The reconstruction of metabolic networks that include all biochemical reactions of a cell is a way to understand physiology interactions from genomic data. In 2010, Thiele and Palsson described a general protocol enabling the reconstruction of high-quality metabolic networks. Since then several approaches have been implemented for this purpose. They all rely mainly on drafting a first metabolic network from genome annotations and orthology information followed by a gap-filling step. More precisely, in the case of exotic species the lack of good annotations and poor biological information result in incomplete networks. Reference databases of metabolic reactions guide the filling process in order to check whether adding reactions to a network allows compounds of interest to be produced from a given growth media. As a final objective, as soon as the network is considered to be complete enough, functional studies are undergone, often relying on the constraint-based paradigm derived from the Flux Balance Analysis (FBA) framework (Orth et al., 2010). The high diversity of input files and tools required to run any metabolic networks reconstruction protocol represents an important drawback. In addition, most approaches require reference metabolic networks of a template organism. Dictionaries mapping the reference metabolic databases to the gene identifiers corresponding to the studied organism may be required. As a main issue, it appears very difficult to ensure that input files agree among them. Such a heterogeneity produces loss of information during the use of the protocols and generates uncertainty in the final metabolic model. Here we introduce the PADMet-toolbox which allows conciliating genomic and metabolic network information. The toolbox centralizes all this information in a new graph-based format: PADMet (PortAble Database for Metabolism) and provides methods to import, update and export information. For the sake of illustration, the toolbox was used to create a workflow, named AuReMe, aiming to produce high-quality genome-scale metabolic networks and eventually input files to feed most platforms involved in metabolic network analyses. We applied this approach to two exotic organisms and our results evidenced the need of combining approaches and reconciling information to obtain a functional metabolic network to produce biomass

KIT is required for hepatic function during mouse post-natal development

<p>Abstract</p> <p>Background</p> <p>The <it>Kit </it>gene encodes a receptor tyrosine kinase involved in various biological processes including melanogenesis, hematopoiesis and gametogenesis in mice and human. A large number of <it>Kit </it>mutants has been described so far showing the pleiotropic phenotypes associated with partial loss-of-function of the gene. Hypomorphic mutations can induce a light coat color phenotype while complete lack of KIT function interferes with embryogenesis. Interestingly several intermediate hypomorphic mutations induced in addition growth retardation and post-natal mortality.</p> <p>Results</p> <p>In this report we investigated the post-natal role of <it>Kit </it>by using a panel of chemically-induced hypomorphic mutations recently isolated in the mouse. We found that, in addition to the classical phenotypes, mutations of <it>Kit </it>induced juvenile steatosis, associated with the downregulation of the three genes, <it>VldlR</it>, <it>Lpin1 </it>and <it>Lpl</it>, controlling lipid metabolism in the post-natal liver. Hence, <it>Kit </it>loss-of-functions mimicked the inactivation of genes controlling the hepatic metabolism of triglycerides, the major source of energy from maternal milk, leading to growth and viability defects during neonatal development.</p> <p>Conclusion</p> <p>This is a first report involving KIT in the control of lipid metabolism in neonates and opening new perspectives for understanding juvenile steatosis. Moreover, it reinforces the role of Kit during development of the liver and underscores the caution that should be exerted in using KIT inhibitors during anti-cancer treatment.</p

Analyse de l'effet de dose dans des modèles murins de trisomie 21 (contributions monogéniques ou multigéniques ?)

La trisomie 21 (T21), est l'anomalie chromosomique la plus fréquente, associée à un large ensemble d altérations morphologiques, physiologiques et neurologiques. Nous avons entrepris d'analyser la surexpression d'un gène candidat, CBS, intervenant dans le contrôle de l'homocystéine et impliqué dans une neuropathologie humaine. Nous avons produit un modèle de surexpression conditionnelle dans le cerveau pour le gène humain de la CBS, avec la stratégie Cre-loxP, afin d'étudier sa contribution dans le phénotype du retard mental. Sur les huit lignées transgéniques, seulement deux d'entre elles expriment le transgène dans différentes parties du cerveau. Cette surexpression a également un impact sur la viabilité des embryons de souris. Toutefois, nous avons obtenu une lignée nous permettant d'étudier ses effets sur le comportement. Afin de décoder les rapports complexes qui existent entre le génotype et le phénotype dans la T21, le laboratoire a créé de nouveaux modèles murins d'aneuploïdies pour des régions homologues au chromosome 21: Ts1Yah et Ts2Yah. L'objectif de ce travail est d'étudier les interactions génétiques entre les deux nouveaux modèles. L'analyse phénotypique détaillée des doubles trisomiques révèle des modifications dans le tonus musculaire ainsi que dans l'anxiété, d'autres études comportementales et de morphologie du crâne compléteront ces résultats. Avec le développement, ces dernières années, des modèles murins, du séquençage du chromosome 21 et l'essor des outils de mesure de l'expression globale des gènes, il est probable que dans un proche avenir nous aurons une meilleure connaissance de l origine moléculaire fondamentale de la T21.ORLEANS-BU Sciences (452342104) / SudocPARIS-BIUP (751062107) / SudocSudocFranceF

Toward the study of metabolic functions in algal holobionts

National audienceIn the era of metagenomics, more and more studies consider ecosystems rather than individual organisms to elucidate biological mechanisms. This enabled to associate two previously independent research fields: genomics and ecology. A host organism can be highly dependent on its microbial environment and new methods are needed to decipher the interactions occurring at this level. The microbiome is an interesting support from the ecological and evolutionary point of view as it may impact the growth ability, the fitness and even the survival of the host. Theories about holobionts suggest that the microbiome is faster to adapt than the host, further emphasizing the need to focus on this cornerstone of ecosystems studies. A major concern is to understand the reasons determining the diversity of the communities as well as the mechanisms underlying the general ecosystem functionalities. Here we focus on Ectocarpus subulatus str. BFT along with its bacterial community. Multi-omics studies were performed to gain better insights into the organization of the holobiont. Functional annotation, metabolic network modeling and biological pathway completion inform on potential interaction mechanisms at the metabolic scale. The hypothesis according to which microbial communities gather preferentially in order to optimize functional complementarity can explain the metabolic richness and diversity of the microbiome observed in the algal holobiont

Toward the study of metabolic functions in algal holobionts

National audienceIn the era of metagenomics, more and more studies consider ecosystems rather than individual organisms to elucidate biological mechanisms. This enabled to associate two previously independent research fields: genomics and ecology. A host organism can be highly dependent on its microbial environment and new methods are needed to decipher the interactions occurring at this level. The microbiome is an interesting support from the ecological and evolutionary point of view as it may impact the growth ability, the fitness and even the survival of the host. Theories about holobionts suggest that the microbiome is faster to adapt than the host, further emphasizing the need to focus on this cornerstone of ecosystems studies. A major concern is to understand the reasons determining the diversity of the communities as well as the mechanisms underlying the general ecosystem functionalities. Here we focus on Ectocarpus subulatus str. BFT along with its bacterial community. Multi-omics studies were performed to gain better insights into the organization of the holobiont. Functional annotation, metabolic network modeling and biological pathway completion inform on potential interaction mechanisms at the metabolic scale. The hypothesis according to which microbial communities gather preferentially in order to optimize functional complementarity can explain the metabolic richness and diversity of the microbiome observed in the algal holobiont

Toward the study of metabolic functions in algal holobionts

National audienceIn the era of metagenomics, more and more studies consider ecosystems rather than individual organisms to elucidate biological mechanisms. This enabled to associate two previously independent research fields: genomics and ecology. A host organism can be highly dependent on its microbial environment and new methods are needed to decipher the interactions occurring at this level. The microbiome is an interesting support from the ecological and evolutionary point of view as it may impact the growth ability, the fitness and even the survival of the host. Theories about holobionts suggest that the microbiome is faster to adapt than the host, further emphasizing the need to focus on this cornerstone of ecosystems studies. A major concern is to understand the reasons determining the diversity of the communities as well as the mechanisms underlying the general ecosystem functionalities. Here we focus on Ectocarpus subulatus str. BFT along with its bacterial community. Multi-omics studies were performed to gain better insights into the organization of the holobiont. Functional annotation, metabolic network modeling and biological pathway completion inform on potential interaction mechanisms at the metabolic scale. The hypothesis according to which microbial communities gather preferentially in order to optimize functional complementarity can explain the metabolic richness and diversity of the microbiome observed in the algal holobiont

Handling the heterogeneity of genomic and metabolic networks data within flexible workflows with the PADMet toolbox

National audienceA main challenge of the era of fast and massive genome sequencing is to transform sequences into biological knowledge. The reconstruction of metabolic networks that include all biochemical reactions of a cell is a way to understand physiology interactions from genomic data. In 2010, Thiele and Palsson described a general protocol enabling the reconstruction of high-quality metabolic networks. Since then several approaches have been implemented for this purpose. They all rely mainly on drafting a first metabolic network from genome annotations and orthology information followed by a gap-filling step. More precisely, in the case of exotic species the lack of good annotations and poor biological information result in incomplete networks. Reference databases of metabolic reactions guide the filling process in order to check whether adding reactions to a network allows compounds of interest to be produced from a given growth media. As a final objective, as soon as the network is considered to be complete enough, functional studies are undergone, often relying on the constraint-based paradigm derived from the Flux Balance Analysis (FBA) framework (Orth et al., 2010). The high diversity of input files and tools required to run any metabolic networks reconstruction protocol represents an important drawback. In addition, most approaches require reference metabolic networks of a template organism. Dictionaries mapping the reference metabolic databases to the gene identifiers corresponding to the studied organism may be required. As a main issue, it appears very difficult to ensure that input files agree among them. Such a heterogeneity produces loss of information during the use of the protocols and generates uncertainty in the final metabolic model. Here we introduce the PADMet-toolbox which allows conciliating genomic and metabolic network information. The toolbox centralizes all this information in a new graph-based format: PADMet (PortAble Database for Metabolism) and provides methods to import, update and export information. For the sake of illustration, the toolbox was used to create a workflow, named AuReMe, aiming to produce high-quality genome-scale metabolic networks and eventually input files to feed most platforms involved in metabolic network analyses. We applied this approach to two exotic organisms and our results evidenced the need of combining approaches and reconciling information to obtain a functional metabolic network to produce biomass

Traceability, reproducibility and wiki-exploration for "à-la-carte" reconstructions of genome-scale metabolic models

International audienceGenome-scale metabolic models have become the tool of choice for the global analysis of microorganism metabolism, and their reconstruction has attained high standards of quality and reliability. Improvements in this area have been accompanied by the development of some major platforms and databases, and an explosion of individual bioinformatics methods. Consequently, many recent models result from "à la carte" pipelines, combining the use of platforms, individual tools and biological expertise to enhance the quality of the reconstruction. Although very useful, introducing heterogeneous tools, that hardly interact with each other, causes loss of traceability and reproducibility in the reconstruction process. This represents a real obstacle, especially when considering less studied species whose metabolic reconstruction can greatly benefit from the comparison to good quality models of related organisms. This work proposes an adaptable workspace, AuReMe, for sustainable reconstructions or improvements of genome-scale metabolic models involving personalized pipelines. At each step, relevant information related to the modifications brought to the model by a method is stored. This ensures that the process is reproducible and documented regardless of the combination of tools used. Additionally, the workspace establishes a way to browse metabolic models and their metadata through the automatic generation of ad-hoc local wikis dedicated to monitoring and facilitating the process of reconstruction. AuReMe supports exploration and semantic query based on RDF databases. We illustrate how this workspace allowed handling, in an integrated way, the metabolic reconstructions of non-model organisms such as an extremophile bacterium or eukaryote algae. Among relevant applications, the latter reconstruction led to putative evolutionary insights of a metabolic pathway

Interest of heterogeneous methods in pathway completion and filling thanks to tracking of process metadata.

<p>Completion of the 6-hydroxymethyl-dihydropterin diphosphate biosynthesis I and the tetrahydrofolate biosynthesis pathways in <i>E</i>. <i>siliculosus</i> via the combination of annotation (yellow), orthology (green) and gap-filling (blue). The dihydrofolate compound with the dotted line is an instance of the dihydrofolate-glu-n class, following MetaCyc classes ontology structure. The class compound is the original reactant of the dihydrofolatereduct-rxn reaction retrieved with annotation, whereas the previous reaction of the pathway (dihydrofolatesynth-rxn) produces the instance dihydrofolate. Hence the gap-filling step that, using an extended version of MetaCyc, selects an instantiated version of dihydrofolatesynth-rxn that consumes the instance dihydrofolate.</p