Controlling heterologous gene expression in yeast cell factories on different carbon substrates and across the diauxic shift: a comparison of yeast promoter activities

BACKGROUND Predictable control of gene expression is necessary for the rational design and optimization of cell factories. In the yeast Saccharomyces cerevisiae, the promoter is one of the most important tools available for controlling gene expression. However, the complex expression patterns of yeast promoters have not been fully characterised and compared on different carbon sources (glucose, sucrose, galactose and ethanol) and across the diauxic shift in glucose batch cultivation. These conditions are of importance to yeast cell factory design because they are commonly used and encountered in industrial processes. Here, the activities of a series of "constitutive" and inducible promoters were characterised in single cells throughout the fermentation using green fluorescent protein (GFP) as a reporter. RESULTS The "constitutive" promoters, including glycolytic promoters, transcription elongation factor promoters and ribosomal promoters, differed in their response patterns to different carbon sources; however, in glucose batch cultivation, expression driven by these promoters decreased sharply as glucose was depleted and cells moved towards the diauxic shift. Promoters induced at low-glucose levels (P(HXT7), P(SSA1) and P(ADH2)) varied in induction strength on non-glucose carbon sources (sucrose, galactose and ethanol); in contrast to the "constitutive" promoters, GFP expression increased as glucose decreased and cells moved towards the diauxic shift. While lower than several "constitutive" promoters during the exponential phase, expression from the SSA1 promoter was higher in the post-diauxic phase than the commonly-used TEF1 promoter. The galactose-inducible GAL1 promoter provided the highest GFP expression on galactose, and the copper-inducible CUP1 promoter provided the highest induced GFP expression following the diauxic shift. CONCLUSIONS The data provides a foundation for predictable and optimised control of gene expression levels on different carbon sources and throughout batch fermentation, including during and after the diauxic shift. This information can be applied for designing expression approaches to improve yields, rates and titres in yeast cell factories.