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      A genome-scale metabolic model of the lipid-accumulating yeast Yarrowia lipolytica

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          Abstract

          Background

          Yarrowia lipolytica is an oleaginous yeast which has emerged as an important microorganism for several biotechnological processes, such as the production of organic acids, lipases and proteases. It is also considered a good candidate for single-cell oil production. Although some of its metabolic pathways are well studied, its metabolic engineering is hindered by the lack of a genome-scale model that integrates the current knowledge about its metabolism.

          Results

          Combining in silico tools and expert manual curation, we have produced an accurate genome-scale metabolic model for Y. lipolytica. Using a scaffold derived from a functional metabolic model of the well-studied but phylogenetically distant yeast S. cerevisiae, we mapped conserved reactions, rewrote gene associations, added species-specific reactions and inserted specialized copies of scaffold reactions to account for species-specific expansion of protein families. We used physiological measures obtained under lab conditions to validate our predictions.

          Conclusions

          Y. lipolytica iNL895 represents the first well-annotated metabolic model of an oleaginous yeast, providing a base for future metabolic improvement, and a starting point for the metabolic reconstruction of other species in the Yarrowia clade and other oleaginous yeasts.

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          Most cited references37

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          A protocol for generating a high-quality genome-scale metabolic reconstruction.

          Network reconstructions are a common denominator in systems biology. Bottom-up metabolic network reconstructions have been developed over the last 10 years. These reconstructions represent structured knowledge bases that abstract pertinent information on the biochemical transformations taking place within specific target organisms. The conversion of a reconstruction into a mathematical format facilitates a myriad of computational biological studies, including evaluation of network content, hypothesis testing and generation, analysis of phenotypic characteristics and metabolic engineering. To date, genome-scale metabolic reconstructions for more than 30 organisms have been published and this number is expected to increase rapidly. However, these reconstructions differ in quality and coverage that may minimize their predictive potential and use as knowledge bases. Here we present a comprehensive protocol describing each step necessary to build a high-quality genome-scale metabolic reconstruction, as well as the common trials and tribulations. Therefore, this protocol provides a helpful manual for all stages of the reconstruction process.
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            Automatic clustering of orthologs and in-paralogs from pairwise species comparisons.

            Orthologs are genes in different species that originate from a single gene in the last common ancestor of these species. Such genes have often retained identical biological roles in the present-day organisms. It is hence important to identify orthologs for transferring functional information between genes in different organisms with a high degree of reliability. For example, orthologs of human proteins are often functionally characterized in model organisms. Unfortunately, orthology analysis between human and e.g. invertebrates is often complex because of large numbers of paralogs within protein families. Paralogs that predate the species split, which we call out-paralogs, can easily be confused with true orthologs. Paralogs that arose after the species split, which we call in-paralogs, however, are bona fide orthologs by definition. Orthologs and in-paralogs are typically detected with phylogenetic methods, but these are slow and difficult to automate. Automatic clustering methods based on two-way best genome-wide matches on the other hand, have so far not separated in-paralogs from out-paralogs effectively. We present a fully automatic method for finding orthologs and in-paralogs from two species. Ortholog clusters are seeded with a two-way best pairwise match, after which an algorithm for adding in-paralogs is applied. The method bypasses multiple alignments and phylogenetic trees, which can be slow and error-prone steps in classical ortholog detection. Still, it robustly detects complex orthologous relationships and assigns confidence values for both orthologs and in-paralogs. The program, called INPARANOID, was tested on all completely sequenced eukaryotic genomes. To assess the quality of INPARANOID results, ortholog clusters were generated from a dataset of worm and mammalian transmembrane proteins, and were compared to clusters derived by manual tree-based ortholog detection methods. This study led to the identification with a high degree of confidence of over a dozen novel worm-mammalian ortholog assignments that were previously undetected because of shortcomings of phylogenetic methods.A WWW server that allows searching for orthologs between human and several fully sequenced genomes is installed at http://www.cgb.ki.se/inparanoid/. This is the first comprehensive resource with orthologs of all fully sequenced eukaryotic genomes. Programs and tables of orthology assignments are available from the same location. Copyright 2001 Academic Press.
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              Applications of genome-scale metabolic reconstructions

              The availability and utility of genome-scale metabolic reconstructions have exploded since the first genome-scale reconstruction was published a decade ago. Reconstructions have now been built for a wide variety of organisms, and have been used toward five major ends: (1) contextualization of high-throughput data, (2) guidance of metabolic engineering, (3) directing hypothesis-driven discovery, (4) interrogation of multi-species relationships, and (5) network property discovery. In this review, we examine the many uses and future directions of genome-scale metabolic reconstructions, and we highlight trends and opportunities in the field that will make the greatest impact on many fields of biology.
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                Author and article information

                Journal
                BMC Syst Biol
                BMC Syst Biol
                BMC Systems Biology
                BioMed Central
                1752-0509
                2012
                4 May 2012
                : 6
                : 35
                Affiliations
                [1 ]Inria / Université Bordeaux / CNRS joint project-team MAGNOME, Talence, F-33405, France
                [2 ]INRA, UMR1319 Micalis, Jouy-en-Josas, F-78352, France
                [3 ]CNRS, Micalis, Jouy-en-Josas, F-78352, France
                [4 ]Center for Genome Regulation, Universidad de Chile, Av. Blanco Encalada 2085, 3er piso, Santiago, Chile
                Article
                1752-0509-6-35
                10.1186/1752-0509-6-35
                3443063
                22558935
                da301434-43a6-4ce6-93e7-c84e0255111f
                Copyright ©2012 Loira et al.; licensee BioMed Central Ltd.

                This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 23 November 2011
                : 11 January 2012
                Categories
                Research Article

                Quantitative & Systems biology
                Quantitative & Systems biology

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