The aims of the first years of this proposal were to acquire, develop and optimize technologies for the analysis of problem fermentations. The goal of this work is to develop better fermentation management strategies to reduce and hopefully eliminate the incidence of slow and incomplete fermentations. In this first phase of the research we have successfully adapted functional genomic analysis to Saccharomyces grown under enological conditions. We identified several key differences in the physiology of yeast grown under nutrient sufficient versus nitrogen-limited conditions. We have begun identifying molecular markers associated with healthy or robust fermentations and those associated with nutritional or environmental stress. The analysis of the effect of acetic acid addition over the time course of fermentation on the yeast transcriptome has been completed, as has the analysis of the effect of DAP addition across the duration of the fermentation. The analysis of the impact of temperature of fermentation and of temperature shift has been or soon will be completed. We have definitively demonstrated that ethanol significantly changes the structure of lipid bilayers (model yeast cell membranes) indicating a likely physical component to the yeast physiological response that complements the gene-regulated response.During this grant period, we have developed neural network and mechanistic modeling tools for predicting fermentation performance. Decision Tree Analysis and other data mining tools have been employed to identify factors most impacting fermentation rate and success. Using the models developed, we have identified loss of cell activity as potentially more important than loss of cell viability and demonstrated a relationship between initial sugar level and necessary maximum cell concentration (measured by optical density) for early prediction of fermentation completion. In addition, we have also used experimentation, along with modeling, to identify windows of effectiveness for nitrogen and cell additions for resolving problem fermentations effectively.We have adapted direct molecular technologies for the analysis of the microbial complement of wine samples. This capability now allows us to detect the presence of all common wine microbes in a juice, must or wine sample without the need for cultivation and permits a more statistically robust sampling of fermentations. Additional development of rapid methods for quantitation of these populations will provide data of sufficient quality to be useful in the development of neural networks for the prediction of fermentation behavior. This research program has already shown that dietary phenolic antioxidants have an effect on the metabolic activity of yeast cells. It will now be possible to determine the nature of that effect. One of the main criticisms of the body of work demonstrating the benefits of dietary antioxidants to human health is that the mechanism of this effect has not been demonstrated. This research proposal will determine the mechanism of the stimulation of yeast cells by phenolic compounds. Much of this information will be directly applicable to mammalian cells due to the high conservation of function among the eukaryotes.
/wp-content/uploads/2017/09/AFV-Header-Logo.png 0 0 AVF /wp-content/uploads/2017/09/AFV-Header-Logo.png AVF2003-10-17 09:28:472017-10-17 09:29:16Analysis of Saccharomyces During Normal and Problem Fermentations