Abstract
BackgroundYarrowia 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.ResultsIn 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.ConclusionThis study demonstrated that eMOMA is a powerful computational method for understanding and engineering the metabolism of Y. lipolytica and potentially other oleaginous microorganisms.
Highlights
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
In this study, we showed that metabolic flux distributions of Y. lipolytica in nitrogen-limited conditions, which cannot be predicted by conventional Flux balance analysis (FBA), could be predicted by environmental version of minimization of metabolic adjustment (eMOMA)
We demonstrated that eMOMA could be further applied to identify metabolic engineering strategies for improving lipid production in Y. lipolytica
Summary
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. 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 in the stationary phase of a batch culture. Several proof-of-concept studies have demonstrated that engineered Y. lipolytica strains can be an efficient production platform for a variety of fuels and oleochemicals [9]. Oleaginous yeasts including Y. lipolytica start to accumulate lipids in response to nutrient depletion. The lipid production has most commonly been studied in batch cultivation systems using culture media with high carbon-to-nitrogen ratios to conveniently establish nitrogen-limited stationary phase conditions. Further understanding of the lipid production mechanism is required for sophisticated design of engineered strains and control of the lipid production process
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