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Updated: Mar 7, 2026
Genetic Engineering of an Unconventional Yeast for Renewable Biofuel and Biochemical Production
Published on: September 20, 2016
Using a vector pool containing variable-strength promoters to optimize protein production in Yarrowia lipolytica
Rémi Dulermo1, François Brunel2, Thierry Dulermo2
1Micalis Institute, INRA-AgroParisTech, UMR1319, Team BIMLip: Integrative Metabolism of Microbial Lipids, Université Paris-Saclay, domaine de Vilvert, 78350, Jouy-en-Josas, France. remi.dulermo@ifremer.fr.
Background:
The yeast Yarrowia lipolytica is an increasingly common biofactory. To enhance protein expression, several promoters have been developed, including the constitutive TEF promoter, the inducible POX2 promotor, and the hybrid hp4d promoter. Recently, new hp4d-inspired promoters have been created that couple various numbers of UAS1 tandem elements with the minimal LEU2 promoter or the TEF promoter. Three different protein-secretion signaling sequences can be used: preLip2, preXpr2, and preSuc2.
Results:
To our knowledge, our study is the first to use a set of vectors with promoters of variable strength to produce proteins of industrial interest. We used the more conventional TEF and hp4d promoters along with five new hybrid promoters: 2UAS1-pTEF, 3UAS1-pTEF, 4UAS1-pTEF, 8UAS1-pTEF, and hp8d. We compared the production of RedStar2, glucoamylase, and xylanase C when strains were grown on three media. As expected, levels of RedStar2 and glucoamylase were greatest in the strain with the 8UAS1-pTEF promoter, which was stronger. However, surprisingly, the 2UAS1-pTEF promoter was associated with the greatest xylanase C production and activity. This finding underscored that stronger promoters are not always better when it comes to protein production. We therefore developed a method for easily identifying the best promoter for a given protein of interest. In this gateway method, genes for YFP and α-amylase were transferred into a pool of vectors containing different promoters and gene expression was then analyzed. We observed that, in most cases, protein production and activity were correlated with promoter strength, although this pattern was protein dependent.
Conclusions:
Protein expression depends on more than just promoter strength. Indeed, promoter suitability appears to be protein dependent; in some cases, optimal expression and activity was obtained using a weaker promoter. We showed that using a vector pool containing promoters of variable strength can be a powerful tool for rapidly identifying the best producer for a given protein of interest.
