Reconstruction of the genome-scale metabolic network of Kluyveromyces lactis - Dias, Oscar (2013)
System Biology proposes to study biological components, as well as the interactions between them, to understand and predict systems’ behaviour through the use of mathematical models. Under this scope, Genome-Scale Metabolic Models (GSMMs) can be regarded as mathematical representations of the intrinsic metabolic capabilities of a given organism, encoded in its genome, and can be used in a variety of applications like predicting the phenotypical behaviour of a given organism in different environmental and genetic perturbations. The reconstruction of these models comprehends four fundamental stages, namely Genome Annotation, Assembling of a Metabolic Network from the Genome, the Conversion of the Network to a Stoichiometric Model and finally the Validation of the Metabolic Model. Although this procedure is currently relatively standardized in some stages, a significant amount of work still needs to be done by the community before the reconstruction process becomes semi-automated and reproducible. The present work aims at contributing to this field through the development of several tools for aiding the reconstruction process, while simultaneously applying some of those tools to an industrially relevant organism, the yeast Kluyveromyces lactis. The genome annotation stage is critical, as an inadequate annotation may delay, or even impair, the development of the model. The genome metabolic annotation consists on identifying and attributing functions to metabolic genes, i.e., genes encoding enzymes and transport proteins. While the identification of enzyme encoding genes can be performed by assigning Enzyme Commission numbers to the proteins encoded in the genes, the transport proteins encoding genes annotation is not straightforward. In this work, an automatic system to detect and classify all potential transport proteins from a given genome and integrate the related reactions into GSMMs is proposed, based on the identification and classification of genes that encode transmembrane proteins. The integration of the data provided by this methodology with highly curated models allowed the identification of new transport reactions. This tool was included in the merlin tool, a user-friendly Java application developed under the scope of this thesis that performs the reconstruction of GSMMs for any organism that has its genome sequenced. It performs several steps of the reconstruction process, including the functional genomic annotation of the whole genome. merlin 2.0 also performs the compartmentalisation of the model, predicting the organelle localisation of the proteins encoded in the genome, and thus the localisation of the metabolites involved in the reactions induced by such proteins. Finally, merlin 2.0 expedites the transition from genome-scale data to SBML (the standard Systems Biology Markup Language) metabolic models, allowing the user to have a preliminary view of the biochemical network. The yeast Kluyveromyces lactis has long been considered a model organism for studies in genetics and physiology, mainly due to its ability to metabolize lactose and to express recombinant proteins. Although the genome of Kluyveromyces lactis has been publicly available for some years, until now no complete metabolic functional annotation has been performed to the proteins encoded in the Kluyveromyces lactis genome and consequently no GSMM has been made available. In this work, a new metabolic genome-wide functional re-annotation of the proteins encoded in the Kluyveromyces lactis genome was performed, resulting in the annotation of 1759 genes with metabolic functions, and the development of a methodology supported by merlin. The new annotation includes novelties, such as the assignment of transporter superfamily numbers to genes identified as transporter proteins. The methodology developed throughout this work can be used to re-annotate any yeast or, with a little tweak of the reference organism, the proteins encoded in any sequenced genome. The new annotation provided by this study served as the basis for the reconstruction of a compartmentalized, genome-scale metabolic model for Kluyveromyces lactis. The partially compartmentalised (4 compartments) genome-scale metabolic model of Kluyveromyces lactis, the iOD962 metabolic model, comprises 962 genes, 2038 reactions and 1561 metabolites. Previous chemostat experiments were used to adjust both growth and non-growth associated energy requirements, and the model proved accurate when predicting the biomass, oxygen and carbon dioxide yields. Also, the in silico knockouts predicted accurately the in vivo phenotypes, when compared to published experiments. This model allowed determining a minimal medium for cultivating Kluyveromyces lactis and will surely bring new insights on the milk yeast metabolism, identifying engineering targets for the improvement of the yields of products of interest by performing in silico simulations.