Journal
BMC SYSTEMS BIOLOGY
Volume 12, Issue -, Pages -Publisher
BMC
DOI: 10.1186/s12918-018-0542-5
Keywords
Yarrowia lipolytica; Dicarboxylic acid; Genome-scale metabolic models; Strain design; Metabolic engineering
Categories
Funding
- Academic Research Fund of the National University of Singapore [R-279-000-476-112]
- Biomedical Research Council of A*STAR (Agency for Science, Technology and Research), Singapore
- Global R&D project program, Ministry of Trade, Industry and Energy (MOTIE), Republic of Korea [N011500017]
- NUS SynCTI program
- Next-Generation BioGreen 21 Program of the Rural Development Administration, Republic of Korea (Systems and Synthetic Agrobiotech Center) [PJ01334605]
- Ministry of Knowledge Economy (MKE), Republic of Korea [N011500017] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
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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.
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