JOURNAL ARTICLE

Tailor‐made production of mannosylerythritol lipids: From genetic modification to chemical synthesis.

  • Published In: Journal of the American Oil Chemists' Society (JAOCS), 2025, v. 102, n. 2. P. 413 1 of 3

  • Database: Business Source Ultimate 2 of 3

  • Authored By: Saika, Azusa; Fukuoka, Tokuma 3 of 3

Abstract

Mannosylerythritol lipids (MELs) are glycolipid biosurfactants produced by basidiomycetous yeasts. Mannosylerythritol lipids have received increasing attention because they not only have excellent interfacial activity but also have anti‐inflammatory and antimicrobial activity, and against cancer cell line, repair damaged skin, as carriers for drug delivery, and to interact with proteins. These properties are dependent on their structures; therefore, the tailored production of targeted MELs is required to obtain the desired properties. Over the past two decades, the genomes of MEL producers have been analyzed, revealing the MELs synthesis pathway and related genes. Using this information, it has become possible to tailor‐produce MELs by modifying the synthesis pathway. Furthermore, in recent years, novel chemical synthesis methods for MELs have been developed, enabling precise control over the fatty acid chain lengths of chemically synthesized MELs. This review compiles examples of the tailored production of MELs using methods ranging from the genetic modification of MELs producers to chemical synthesis methods. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Journal of the American Oil Chemists' Society (JAOCS). 2025/02, Vol. 102, Issue 2, p413
  • Document Type:Literature Review
  • Subject Area:Chemistry
  • Publication Date:2025
  • ISSN:0003-021X
  • DOI:10.1002/aocs.12898
  • Accession Number:183991842
  • Copyright Statement:Copyright of Journal of the American Oil Chemists' Society (JAOCS) is the property of Wiley-Blackwell 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.)

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