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Systematic Integration Of “Omics” Data To Improve Innovation In Beer Crafting

Reputable Mentor II
Reputable Mentor II
Dunn B, Kvitek D, Carrera M, Jiang X, Tan G, Lopez-Ferrer D, Huhmer A.
ASMS 2015 Poster Note 64486
Purpose: This study represents a detailed analysis of individual biological components for two yeast strains: WLP001 (California Ale) and WLP300 (Hefe Weizen). The datasets are comprised of quantitative proteomics and metabolomic data collected during the fermentation process. Integration of these datasets will allow better understanding of flavor and taste differences as part of the evolutionary endeavor of both strains. Results: This work describes the process of explaining the differences among two popular beer strains. Proteomics data was used to compare the fermentation process among both samples. Metabolomics data was then used to further explain the sensorial differences between both strains and to explain the expression differences between the two enzymatic machineries. Protein expression points out that these two ecologically similar strains are significantly different. 60 enzymes including proteolytic enzymes as well as others that are responsible for the flavor and taste of beer are statistically different. We also noticed the different profiles of molecular functions and processes for each strain when ontology enrichment was performed using Thermo ScientificTM ProteinCenterTM software. These findings were reinforced by the metabolomic data that shows very different molecular profiles. Over 2000 compounds were monitored and ~600 compounds were significantly different. This work demonstrates that complex relationships between different layers of biological information that give rise to cellular functions can only be capture by combining global measurements across these different levels.

Stanford University, Invitae, Spanish National research Council, and Thermo Fisher Scientific
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Last update:
‎10-15-2021 11:34 AM
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