Advances in the metabolic engineering of Yarrowia lipolytica for the production of terpenoids.

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Title: Advances in the metabolic engineering of Yarrowia lipolytica for the production of terpenoids.
Authors: Ma, Yi-Rong1 (AUTHOR), Wang, Kai-Feng1 (AUTHOR), Wang, Wei-Jian1 (AUTHOR), Ding, Ying1 (AUTHOR), Shi, Tian-Qiong1 (AUTHOR), Huang, He1 (AUTHOR), Ji, Xiao-Jun1 (AUTHOR) xiaojunji@njtech.edu.cn
Source: Bioresource Technology. Jun2019, Vol. 281, p449-456. 8p.
Subjects: Terpenes, Microbiological synthesis, Engineering
Abstract: Highlights • Y. lipolytica is a promising chassis for the production of plant derived terpenoids. • Metabolic engineering strategies for terpenoids synthesis in Y. lipolytica are summarized. • Prospective for terpenoids synthesis in Y. lipolytica based on latest technology are discussed. Abstract Terpenoids are a large class of natural compounds based on the C5 isoprene unit, with many biological effects such activity against cancer and allergies, while some also have an agreeable aroma. Consequently, they have received extensive attention in the food, pharmaceutical and cosmetic fields. With the identification and analysis of the underlying natural product synthesis pathways, current microbial-based metabolic engineering approaches have yielded new strategies for the production of highly valuable terpenoids. Yarrowia lipolytica is a non-conventional oleaginous yeast that is rapidly emerging as a valuable host for the production of terpenoids due to its own endogenous mevalonate pathway and high oil production capacity. This review aims to summarize the status and strategies of metabolic engineering for the heterologous synthesis of terpenoids in Y. lipolytica in recent years and proposes new methods aiming towards further improvement of terpenoid production. [ABSTRACT FROM AUTHOR]
Copyright of Bioresource Technology is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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DbLabel: Engineering Source
An: 135377360
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  Data: Highlights • Y. lipolytica is a promising chassis for the production of plant derived terpenoids. • Metabolic engineering strategies for terpenoids synthesis in Y. lipolytica are summarized. • Prospective for terpenoids synthesis in Y. lipolytica based on latest technology are discussed. Abstract Terpenoids are a large class of natural compounds based on the C5 isoprene unit, with many biological effects such activity against cancer and allergies, while some also have an agreeable aroma. Consequently, they have received extensive attention in the food, pharmaceutical and cosmetic fields. With the identification and analysis of the underlying natural product synthesis pathways, current microbial-based metabolic engineering approaches have yielded new strategies for the production of highly valuable terpenoids. Yarrowia lipolytica is a non-conventional oleaginous yeast that is rapidly emerging as a valuable host for the production of terpenoids due to its own endogenous mevalonate pathway and high oil production capacity. This review aims to summarize the status and strategies of metabolic engineering for the heterologous synthesis of terpenoids in Y. lipolytica in recent years and proposes new methods aiming towards further improvement of terpenoid production. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
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  Data: <i>Copyright of Bioresource Technology is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1016/j.biortech.2019.02.116
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        Text: English
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              Text: Jun2019
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