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Genome-scale model-driven strain design for dicarboxylic acid production in Yarrowia lipolytica

Cited 35 time in Web of Science Cited 41 time in Scopus
Authors

Mishra, Pranjul; Lee, Na-Rae; Lakshmanan, Meiyappan; Kim, Minsuk; Kim, Byung-Gee; Lee, Dong-Yup

Issue Date
2018-03-19
Publisher
BioMed Central
Citation
BMC Systems Biology, 12(Suppl 2):12
Keywords
Yarrowia lipolyticaDicarboxylic acidGenome-scale metabolic modelsStrain designMetabolic engineering
Abstract
Background
Recently, there have been several attempts to produce long-chain dicarboxylic acids (DCAs) in various microbial hosts. Of these, Yarrowia lipolytica has great potential due to its oleaginous characteristics and unique ability to utilize hydrophobic substrates. However, Y. lipolytica should be further engineered to make it more competitive: the current approaches are mostly intuitive and cumbersome, thus limiting its industrial application.

Results
In this study, we proposed model-guided metabolic engineering strategies for enhanced production of DCAs in Y. lipolytica. At the outset, we reconstructed genome-scale metabolic model (GSMM) of Y. lipolytica (iYLI647) by substantially expanding the previous models. Subsequently, the model was validated using three sets of published culture experiment data. It was finally exploited to identify genetic engineering targets for overexpression, knockout, and cofactor modification by applying several in silico strain design methods, which potentially give rise to high yield production of the industrially relevant long-chain DCAs, e.g., dodecanedioic acid (DDDA). The resultant targets include (1) malate dehydrogenase and malic enzyme genes and (2) glutamate dehydrogenase gene, in silico overexpression of which generated additional NADPH required for fatty acid synthesis, leading to the increased DDDA fluxes by 48% and 22% higher, respectively, compared to wild-type. We further investigated the effect of supplying branched-chain amino acids on the acetyl-CoA turn-over rate which is key metabolite for fatty acid synthesis, suggesting their significance for production of DDDA in Y. lipolytica.

Conclusion
In silico model-based strain design strategies allowed us to identify several metabolic engineering targets for overproducing DCAs in lipid accumulating yeast, Y. lipolytica. Thus, the current study can provide a methodological framework that is applicable to other oleaginous yeasts for value-added biochemical production.
ISSN
1752-0509
Language
English
URI
https://hdl.handle.net/10371/139758
DOI
https://doi.org/10.1186/s12918-018-0542-5
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College of Engineering/Engineering Practice School (공과대학/대학원)Dept. of Chemical and Biological Engineering (화학생물공학부)Journal Papers (저널논문_화학생물공학부)
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