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

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dc.contributor.authorMishra, Pranjul-
dc.contributor.authorLee, Na-Rae-
dc.contributor.authorLakshmanan, Meiyappan-
dc.contributor.authorKim, Minsuk-
dc.contributor.authorKim, Byung-Gee-
dc.contributor.authorLee, Dong-Yup-
dc.date.accessioned2018-05-14T07:56:59Z-
dc.date.available2018-05-14T16:58:09Z-
dc.date.issued2018-03-19-
dc.identifier.citationBMC Systems Biology, 12(Suppl 2):12ko_KR
dc.identifier.issn1752-0509-
dc.identifier.urihttps://hdl.handle.net/10371/139758-
dc.description.abstractBackground
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.
ko_KR
dc.description.sponsorshipThis work was supported by the Academic Research Fund (R-279-000-476-112) of the National University of Singapore, Biomedical Research Council of A*STAR (Agency for Science, Technology and Research), Singapore, the Global R&D project program (N011500017), Ministry of Trade, Industry and
Energy (MOTIE), Republic of Korea and the Next-Generation BioGreen 21 Program of the Rural Development Administration, Republic of Korea (Systems and Synthetic Agrobiotech Center; grant no. PJ01334605). Publication cost was covered by NUS SynCTI program.
ko_KR
dc.language.isoenko_KR
dc.publisherBioMed Centralko_KR
dc.subjectYarrowia lipolyticako_KR
dc.subjectDicarboxylic acidko_KR
dc.subjectGenome-scale metabolic modelsko_KR
dc.subjectStrain designko_KR
dc.subjectMetabolic engineeringko_KR
dc.titleGenome-scale model-driven strain design for dicarboxylic acid production in Yarrowia lipolyticako_KR
dc.typeArticleko_KR
dc.contributor.AlternativeAuthor이나래-
dc.contributor.AlternativeAuthor김민석-
dc.contributor.AlternativeAuthor김병기-
dc.contributor.AlternativeAuthor이동엽-
dc.identifier.doi10.1186/s12918-018-0542-5-
dc.language.rfc3066en-
dc.rights.holderThe Author(s).-
dc.date.updated2018-03-25T05:31:10Z-
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