4.0 Article

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

期刊

BMC SYSTEMS BIOLOGY
卷 9, 期 -, 页码 -

出版社

BMC
DOI: 10.1186/s12918-015-0217-4

关键词

Flux balance analysis; Citrate; Oleaginous yeast; Oxygen limitation; Fed-batch fermentation

资金

  1. Bavarian Ministry of Sciences, Research and the Arts (Bavarian Molecular Biosystems Research Network)
  2. Deutsche Forschungsgemeinschaft (Emmy Noether program) [MA 5703/1-1]
  3. President's International Fellowship Initiative of CAS [2015VBB045]
  4. National Natural Science Foundation of China [31450110423]
  5. Austrian Science Fund, FWF [TRP 240-B21]
  6. NAWI Graz
  7. Austrian Science Fund (FWF) [TRP 240] Funding Source: researchfish

向作者/读者索取更多资源

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.

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