Analysis of Saccharomyces During Normal and Problem Fermentations
The goal of this first year of the Long-Term Research Project was to compare and develop methodologies in three key areas: analysis of global gene expression in Saccharomyces in its native habitat of grape juice; refinement of the neural network technology for prediction of problem fermentations; development of a rapid method allowing profiling of the microbial composition of industrial samples. The RNA-based microarray methodologies worked well for samples of cells grown in juice-like synthetic media, but did not work well for samples prepared from cells grown in actual juices. Further, there does not appear to be a strong correlation between actual mRNA levels and protein content in the cells. Thus the proteome analysis seems to be most useful for profiling gene expression in industrial samples. However, the microarray data provided a wealth of information on which proteins to examine in the proteome gels and has given new insights into the physiological activities of the cells under stressful conditions leading to arrest of fermentation. Neural network training methods have been established for using historical fermentation kinetics data to predict sugar utilization rates based on juice characteristics and intended processing. Small-scale fermentations have been completed to find which critical inputs to use for the neural network prediction, as well as to validate a physical and mathematical model for cell growth and sugar utilization that is likely to direct future experimentation. Temperature gradient gel electrophoresis (TGGE) works well to differentiate yeast genera using the primers that we developed and can be used to assess the microbial purity of industrial samples to be used in the proteome analyses.