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Optimization of lipid production with a genome-scale model of Yarrowia lipolytica

BACKGROUND: Yarrowia lipolytica is a non-conventional yeast that is extensively investigated for its ability to excrete citrate or to accumulate large amounts of storage lipids, which is of great significance for single cell oil production. Both traits are thus of interest for basic research as well...

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Autores principales: Kavšček, Martin, Bhutada, Govindprasad, Madl, Tobias, Natter, Klaus
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4623914/
https://www.ncbi.nlm.nih.gov/pubmed/26503450
http://dx.doi.org/10.1186/s12918-015-0217-4
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author Kavšček, Martin
Bhutada, Govindprasad
Madl, Tobias
Natter, Klaus
author_facet Kavšček, Martin
Bhutada, Govindprasad
Madl, Tobias
Natter, Klaus
author_sort Kavšček, Martin
collection PubMed
description BACKGROUND: Yarrowia lipolytica is a non-conventional yeast that is extensively investigated for its ability to excrete citrate or to accumulate large amounts of storage lipids, which is of great significance for single cell oil production. Both traits are thus of interest for basic research as well as for biotechnological applications but they typically occur simultaneously thus lowering the respective yields. Therefore, engineering of strains with high lipid content relies on novel concepts such as computational simulation to better understand the two competing processes and to eliminate citrate excretion. RESULTS: Using a genome-scale model (GSM) of baker's yeast as a scaffold, we reconstructed the metabolic network of Y. lipolytica and optimized it for use in flux balance analysis (FBA), with the aim to simulate growth and lipid production phases of this yeast. We validated our model and found the predictions of the growth behavior of Y. lipolytica in excellent agreement with experimental data. Based on these data, we successfully designed a fed-batch strategy to avoid citrate excretion during the lipid production phase. Further analysis of the network suggested that the oxygen demand of Y. lipolytica is reduced upon induction of lipid synthesis. According to this finding we hypothesized that a reduced aeration rate might induce lipid accumulation. This prediction was indeed confirmed experimentally. In a fermentation combining these two strategies lipid content of the biomass was increased by 80 %, and lipid yield was improved more than four-fold, compared to standard conditions. CONCLUSIONS: Genome scale network reconstructions provide a powerful tool to predict the effects of genetic modifications and the metabolic response to environmental conditions. The high accuracy and the predictive value of a newly reconstructed GSM of Y. lipolytica to optimize growth conditions for lipid accumulation are demonstrated. Based on these findings, further strategies for engineering Y. lipolytica towards higher efficiency in single cell oil production are discussed. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12918-015-0217-4) contains supplementary material, which is available to authorized users.
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spelling pubmed-46239142015-10-29 Optimization of lipid production with a genome-scale model of Yarrowia lipolytica Kavšček, Martin Bhutada, Govindprasad Madl, Tobias Natter, Klaus BMC Syst Biol Research Article BACKGROUND: Yarrowia lipolytica is a non-conventional yeast that is extensively investigated for its ability to excrete citrate or to accumulate large amounts of storage lipids, which is of great significance for single cell oil production. Both traits are thus of interest for basic research as well as for biotechnological applications but they typically occur simultaneously thus lowering the respective yields. Therefore, engineering of strains with high lipid content relies on novel concepts such as computational simulation to better understand the two competing processes and to eliminate citrate excretion. RESULTS: Using a genome-scale model (GSM) of baker's yeast as a scaffold, we reconstructed the metabolic network of Y. lipolytica and optimized it for use in flux balance analysis (FBA), with the aim to simulate growth and lipid production phases of this yeast. We validated our model and found the predictions of the growth behavior of Y. lipolytica in excellent agreement with experimental data. Based on these data, we successfully designed a fed-batch strategy to avoid citrate excretion during the lipid production phase. Further analysis of the network suggested that the oxygen demand of Y. lipolytica is reduced upon induction of lipid synthesis. According to this finding we hypothesized that a reduced aeration rate might induce lipid accumulation. This prediction was indeed confirmed experimentally. In a fermentation combining these two strategies lipid content of the biomass was increased by 80 %, and lipid yield was improved more than four-fold, compared to standard conditions. CONCLUSIONS: Genome scale network reconstructions provide a powerful tool to predict the effects of genetic modifications and the metabolic response to environmental conditions. The high accuracy and the predictive value of a newly reconstructed GSM of Y. lipolytica to optimize growth conditions for lipid accumulation are demonstrated. Based on these findings, further strategies for engineering Y. lipolytica towards higher efficiency in single cell oil production are discussed. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12918-015-0217-4) contains supplementary material, which is available to authorized users. BioMed Central 2015-10-26 /pmc/articles/PMC4623914/ /pubmed/26503450 http://dx.doi.org/10.1186/s12918-015-0217-4 Text en © Kavšček et al. 2015 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research Article
Kavšček, Martin
Bhutada, Govindprasad
Madl, Tobias
Natter, Klaus
Optimization of lipid production with a genome-scale model of Yarrowia lipolytica
title Optimization of lipid production with a genome-scale model of Yarrowia lipolytica
title_full Optimization of lipid production with a genome-scale model of Yarrowia lipolytica
title_fullStr Optimization of lipid production with a genome-scale model of Yarrowia lipolytica
title_full_unstemmed Optimization of lipid production with a genome-scale model of Yarrowia lipolytica
title_short Optimization of lipid production with a genome-scale model of Yarrowia lipolytica
title_sort optimization of lipid production with a genome-scale model of yarrowia lipolytica
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4623914/
https://www.ncbi.nlm.nih.gov/pubmed/26503450
http://dx.doi.org/10.1186/s12918-015-0217-4
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