Optimization of lipid production with a genome-scale model of Yarrowia lipolytica. Issue 1 (December 2015)

• Record Type: Journal Article
• Authors: Kavšček, Martin; Bhutada, Govindprasad; Madl, Tobias; Natter, Klaus
• Is Part Of: BMC systems biology. Volume 9:Issue 1(2015)
• Page Start: 1
• Page End: 13
• Publication Date: 2015-12
• Subjects: Flux balance analysis -- Citrate -- Oleaginous yeast -- Oxygen limitation -- Fed-batch fermentation; Biological systems -- Periodicals; Biology -- Research -- Periodicals; Cell physiology -- Periodicals; Genes -- Analysis -- Periodicals; 571
• DOI: 10.1186/s12918-015-0217-4
• Languages: English
• ISSNs: 1752-0509

Abstract

Abstract 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 ofY. 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 ofY. 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 ofY. 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 ofY. lipolytica to optimize growth conditions for lipid accumulation are demonstrated. Based on these findings, further strategies for engineeringY. lipolytica towards higher efficiency in single cell oil production are discussed.