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Genome-scale model-driven strain design for dicarboxylic acid production in Yarrowia lipolytica
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 shou...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5861505/ https://www.ncbi.nlm.nih.gov/pubmed/29560822 http://dx.doi.org/10.1186/s12918-018-0542-5 |
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author | Mishra, Pranjul Lee, Na-Rae Lakshmanan, Meiyappan Kim, Minsuk Kim, Byung-Gee Lee, Dong-Yup |
author_facet | Mishra, Pranjul Lee, Na-Rae Lakshmanan, Meiyappan Kim, Minsuk Kim, Byung-Gee Lee, Dong-Yup |
author_sort | Mishra, Pranjul |
collection | PubMed |
description | 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. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12918-018-0542-5) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-5861505 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-58615052018-03-26 Genome-scale model-driven strain design for dicarboxylic acid production in Yarrowia lipolytica Mishra, Pranjul Lee, Na-Rae Lakshmanan, Meiyappan Kim, Minsuk Kim, Byung-Gee Lee, Dong-Yup BMC Syst Biol Research 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. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12918-018-0542-5) contains supplementary material, which is available to authorized users. BioMed Central 2018-03-19 /pmc/articles/PMC5861505/ /pubmed/29560822 http://dx.doi.org/10.1186/s12918-018-0542-5 Text en © The Author(s). 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Mishra, Pranjul Lee, Na-Rae Lakshmanan, Meiyappan Kim, Minsuk Kim, Byung-Gee Lee, Dong-Yup Genome-scale model-driven strain design for dicarboxylic acid production in Yarrowia lipolytica |
title | Genome-scale model-driven strain design for dicarboxylic acid production in Yarrowia lipolytica |
title_full | Genome-scale model-driven strain design for dicarboxylic acid production in Yarrowia lipolytica |
title_fullStr | Genome-scale model-driven strain design for dicarboxylic acid production in Yarrowia lipolytica |
title_full_unstemmed | Genome-scale model-driven strain design for dicarboxylic acid production in Yarrowia lipolytica |
title_short | Genome-scale model-driven strain design for dicarboxylic acid production in Yarrowia lipolytica |
title_sort | genome-scale model-driven strain design for dicarboxylic acid production in yarrowia lipolytica |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5861505/ https://www.ncbi.nlm.nih.gov/pubmed/29560822 http://dx.doi.org/10.1186/s12918-018-0542-5 |
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