Dataset Information

In silico identification of metabolic engineering strategies for improved lipid production in Yarrowia lipolytica by genome-scale metabolic modeling.

ABSTRACT:

Background

Yarrowia lipolytica, an oleaginous yeast, is a promising platform strain for production of biofuels and oleochemicals as it can accumulate a high level of lipids in response to nitrogen limitation. Accordingly, many metabolic engineering efforts have been made to develop engineered strains of Y. lipolytica with higher lipid yields. Genome-scale model of metabolism (GEM) is a powerful tool for identifying novel genetic designs for metabolic engineering. Several GEMs for Y. lipolytica have recently been developed; however, not many applications of the GEMs have been reported for actual metabolic engineering of Y. lipolytica. The major obstacle impeding the application of Y. lipolytica GEMs is the lack of proper methods for predicting phenotypes of the cells in the nitrogen-limited condition, or more specifically in the stationary phase of a batch culture.

Results

In this study, we showed that environmental version of minimization of metabolic adjustment (eMOMA) can be used for predicting metabolic flux distribution of Y. lipolytica under the nitrogen-limited condition and identifying metabolic engineering strategies to improve lipid production in Y. lipolytica. Several well-characterized overexpression targets, such as diglyceride acyltransferase, acetyl-CoA carboxylase, and stearoyl-CoA desaturase, were successfully rediscovered by our eMOMA-based design method, showing the relevance of prediction results. Interestingly, the eMOMA-based design method also suggested non-intuitive knockout targets, and we experimentally validated the prediction with a mutant lacking YALI0F30745g, one of the predicted targets involved in one-carbon/methionine metabolism. The mutant accumulated 45% more lipids compared to the wild-type.

Conclusion

This study demonstrated that eMOMA is a powerful computational method for understanding and engineering the metabolism of Y. lipolytica and potentially other oleaginous microorganisms.

SUBMITTER: Kim M

PROVIDER: S-EPMC6657051 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Publications

In silico identification of metabolic engineering strategies for improved lipid production in <i>Yarrowia lipolytica</i> by genome-scale metabolic modeling.

Kim Minsuk M Park Beom Gi BG Kim Eun-Jung EJ Kim Joonwon J Kim Byung-Gee BG

Biotechnology for biofuels 20190724

<h4>Background</h4><i>Yarrowia lipolytica</i>, an oleaginous yeast, is a promising platform strain for production of biofuels and oleochemicals as it can accumulate a high level of lipids in response to nitrogen limitation. Accordingly, many metabolic engineering efforts have been made to develop engineered strains of <i>Y. lipolytica</i> with higher lipid yields. Genome-scale model of metabolism (GEM) is a powerful tool for identifying novel genetic designs for metabolic engineering. Several GE ...[more]

PMID: 31367232

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Project description:The oleaginous yeast Yarrowia lipolytica has attracted much attention due to its ability to utilize a wide range of substrates to accumulate high lipid content and its flexibility for genetic manipulation. In this study, intracellular lipid metabolism in Y. lipolytica was tailored to produce fatty acid, a renewable oleochemical and precursor for production of advanced biofuels. Two main strategies, including blocking activation and peroxisomal uptake of fatty acids and elimination of biosynthesis of lipids, were employed to reduce fatty acid consumption by the native pathways in Y. lipolytica. Both genetic modifications improved fatty acid production. However, disruption of the genes responsible for assembly of nonpolar lipid molecules including triacylglycerols (TAGs) and steryl esters resulted in the deleterious effects on the cell growth. The gene tesA encoding thioesterase from Escherichia coli was expressed in the strain with disrupted faa genes encoding fatty acyl-CoA synthetases and pxa1 encoding peroxisomal acyl-CoA transporter, and the titer of fatty acids resulted in 2.3 g/L in shake flask culture, representing 11-fold improvement compared with the parent strain. Expressing the native genes encoding acetyl-CoA carboxylase (ACC) and hexokinase also increased fatty acid production, although the improvement was not as significant as that with tesA expression. Saturated fatty acids including palmitic acid (C16:0) and stearic acid (C18:0) increased remarkably in the fatty acid composition of the recombinant bearing tesA compared with the parent strain. The recombinant expressing tesA gene resulted in high lipid content, indicating the great fatty acid producing potential of Y. lipolytica. The results highlight the achievement of fatty acid overproduction without adverse effect on growth of the strain. Results of this study provided insight into the relationship between fatty acid and lipid metabolism in Y. lipolytica, confirming the avenue to reprogram lipid metabolism of this host for overproduction of renewable fatty acids.

Project 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.