Analysis of Saccharomyces During Normal and Problem Fermentations

This is the second year of a three year proposal. In this past year, the comparative analysis of the microarray and proteome technologies was completed. For several reasons, the proteome analysis was found to be ideal for profiling the physiological status of yeast during enological fermentations. The microarray analysis will not allow comparison of expression patterns across yeast strains and growth conditions as most of the genes and proteins of interest can not be quantified by this technique. However, the microarray analysis of the French White strain under nitrogen sufficient and limiting conditions yielded valuable insights into the metabolic state of the strains. Contrary to our initial hypothesis, the low nitrogen culture does not readily enter a non-proliferative phase and rather shows a failure to adapt to grape juice conditions. In contrast, metabolic pathways associated with ethanol tolerance, are expressed in the nitrogen sufficient culture before the onset of maximal fermentation rate. During this period, the proteome gel protocols have been optimized. Comparison of the proteomes of French White and Montrachet under nutrient sufficient conditions has revealed many differences in protein patterns. For the second objective, we have experimentally verified the mechanistic model that we developed in the previous year. We have also examined the effects of juice, yeast, and initial oxygen content on maximum cell concentration attained and on fermentation rate. In all cases, cell concentration was critical. In fact, we have built a correlation between maximum viable cell concentrations (mvcc) (reached at 24 – 48 hours) and fermentation rate. Under a certain threshold of mvcc, all of our fermentations stuck, indicating that this parameter may be useful in the early prediction of sluggish or stuck fermentations. The technology for using artificial neural networks to predict kinetics has also been largely developed. We have established that neural networks show promise for prediction of wine fermentation kinetics, except in cases of extrapolation to conditions outside of the training data set when the accuracy of prediction fails. Much progress was made in the last year on methods development for direct analysis of microorganisms in wine and musts. An evaluation of the 26S ribosomal RNA gene demonstrated this locus to be more discriminatory than the 18S gene for differentiation of various wine yeasts. In addition, denaturing gradient gel electrophoresis using chemical denaturants instead of temperature (“TGGE”) was shown to provide better separation of PCR products amplified from wine yeasts. A significant achievement this year was the optimization of methods for isolation of microbial DNA directly from fermenting wine. This accomplishment made it possible to use PCR-DGGE to profile the yeast successions that occurred during a fermentation at a local winery. Moreover, sequence analysis of the DGGE bands obtained in this profile allowed identification of the yeasts involved to the genus and/or species level. Additional progress was made in laboratory fermentations where DGGE profiling was demonstrated to accurately depict the presence or absence of specific yeast strains above a threshold level (~104 cells per mL) from within a mixed culture. In addition, methods were developed for isolation of microbes from the grape surface. Initial PCR-DGGE analysis revealed different populations of fungal species present on grapes which had undergone different viticultural treatments. Methods were also optimized for discrimination of bacterial strains by PCR-DGGE and work is underway to optimize bacterial DNA purification from musts and wine. Finally methods are being developed to selectively identify and differentiate Saccharomyces species from within wine fermentations.