Integrative  Functional Genomic Resource Development in  Vitis vinifera: Abiotic Stress and Wine Quality.

Abiotic stress in the form of drought, salinity, and cold has a major impact on grape production and quality. Several studies have shown that water-deficit-stressed grapevines produce superior quality wine. The molecular genetic and biochemical basis for this correlation remains poorly understood. An integrative and quantitative analysis of mRNA, protein, and metabolite changes following abiotic stress imposition is required to enhance production efficiency under stress conditions and to understand the plant-derived contribution to constituents of wine quality.  It will also require the customization and application of comprehensive bioinformatics systems to track and analyze changes that arise in response to abiotic stress and potentially related to aroma, flavor, and color characteristics of grape juice and wine. One long-term goal of our research is to develop comprehensive genomic tools to facilitate the genetic engineering of improved abiotic stress tolerance traits in V. vinifera. The specific objectives for accomplishing this goal include: 1) extensive gene discovery through large-scale expressed sequence tag (EST) sequencing and mRNA expression profiling using oligonucleotide-based microarray (i.e. Affymetrix GeneChip®) expression monitoring in roots, leaves, flowers, and fruits of grapevines exposed to multiple abiotic stresses; 2) global mRNA expression profile data will be complemented by protein expression analyses using state-of-the-art proteomics methodologies; and 3) identification of specific metabolites and metabolite profiles in grapevines and fruit following abiotic stress that confer desirable aroma, flavor and color quality characteristics and improved health benefits. Metabolite profiles from grape juice of well-watered and water-deficit-treated vines will be compared with quantitative data from mRNA and protein expression patterns using comprehensive bioinformatics systems to store and analyze data sets. Ultimately, these data sets will be integrated into a reliable prediction model for wine characteristics. The proposed research will greatly facilitate future gene discovery and enable improvements to be made in both production efficiency and wine quality under environmentally adverse growing conditions.

This research is a collaborative effort among investigators at the University of Nevada, Reno and the Virginia Bioinformatics Institute.