Comparative 'omic' profiling of industrial wine yeast strains

• Main Author: Rossouw, Debra
• Other Authors: Bauer, Florian F.
• Language: en
• Published: Stellenbosch : University of Stellenbosch 2009
• Subjects: Wine yeast; Transcriptomics; Agriculture; Proteomics; Wine and wine making > Microbiology; Yeast > Genetics; Systems biology; Fermentation
• Online Access: http://hdl.handle.net/10019.1/1454

Thesis (PhD(Agric) Viticulture and Oenology. Wine Biotechnology))--University of Stellenbosch, 2009. === The main goal of this project was to elucidate the underlying genetic factors responsible for the different fermentation phenotypes and physiological adaptations of industrial wine yeast strains. To address this problem an ‘omic’ approach was pursued: Five industrial wine yeast strains, namely VIN13, EC1118, BM45, 285 and DV10, were subjected to transcriptional, proteomic and exometabolomic profiling during alcoholic fermentation in simulated wine-making conditions. The aim was to evaluate and integrate the various layers of data in order to obtain a clearer picture of the genetic regulation and metabolism of wine yeast strains under anaerobic fermentative conditions. The five strains were also characterized in terms of their adhesion/flocculation phenotypes, tolerance to various stresses and survival under conditions of nutrient starvation. Transcriptional profiles for the entire yeast genome were obtained for three crucial stages during fermentation, namely the exponential growth phase (day 2), early stationary phase (day 5) and late stationary phase (day 14). Analysis of changes in gene expression profiles during the course of fermentation provided valuable insights into the genetic changes that occur as the yeast adapt to changing conditions during fermentation. Comparison of differentially expressed transcripts between strains also enabled the identification of genetic factors responsible for differences in the metabolism of these strains, and paved the way for genetic engineering of strains with directed modifications in key areas. In particular, the integration of exo-metabolite profiles and gene expression data for the strains enabled the construction of statistical models with a strong predictive capability which was validated experimentally. Proteomic analysis enabled correlations to be made between relative transcript abundance and protein levels for approximately 450 gene and protein pairs per analysis. The alignment of transcriptome and proteome data was very accurate for interstrain comparisons. For intrastrain comparisons, there was almost no correlation between trends in protein and transcript levels, except in certain functional categories such as metabolism. The data also provide interesting insights into molecular evolutionary mechanisms that underlie the phenotypic diversity of wine yeast strains. Overall, the systems biology approach to the study of yeast metabolism during alcoholic fermentation opened up new avenues for hypothesis-driven research and targeted engineering strategies for the genetic enhancement/ modification of wine yeast for commercial applications.