Section: New Results
Inferring metabolic models
Participants : David James Sherman [correspondant] , Pascal Durrens, Razanne Issa, Anna Zhukova.
In collaboration with Prof Jean-Marc Nicaud's lab at the INRA Grignon, we developed the first functional genome-scale metabolic model of an oleaginous yeast. Most work in producing genome-scale metabolic models has focused on model organisms, in part due to the cost of obtaining well-annotated genome sequences and sufficiently complete experimental data for refining and verifying the models. However, for many fungal genomes of biotechnological interest, the combination of large-scale sequencing projects and in-depth experimental studies has made it feasible to undertake metabolic network reconstruction for a wider range of organisms.
An excellent representative of this new class of organisms is Yarrowia lipolytica, an oleaginous yeast studied experimentally for its role as a food contaminant and its use in bioremediation and cell factory applications. As one of the hemiascomycetous yeasts completely sequenced in the Génolevures program it enjoys a high quality manual annotation by a network of experts. It is also an ideal subject for studying the role of species-specific expansion of paralogous families, a considerable challenge for eukaryotes in genome-scale metabolic construction. To these ends, we undertook a complete reconstruction of the Y. lipolytica metabolic network.
Methods: A draft model was extrapolated from the S. cerevisiae model iIN800, using in silico methods including enzyme conservation predicted using Génolevures and reaction mapping maintaining compartments. This draft was curated by a group of experts in Y. lipolytica metabolism, and iteratively improved and validated through comparison with experimental data by flux balance analysis. Gap filling, species-specific reactions, and the addition of compartments with the corresponding transport reactions were among the improvements that most affected accuracy. These methods, initially implemented in an ad hoc way in the Pathtastic software tool, have been redefined and formalized by Razanne Issa using a novel logical framework.
Results: We produced an accurate functional model for Y. lipolytica, MODEL1111190000 in Biomodels.net , that has been qualitatively validated against gene knockouts. This model has been enriched by Anna Zhukova with ontology terms from ChEBI and GO.