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Charting Shifts in Saccharomyces cerevisiae Gene Expression across Asynchronous Time Trajectories with Diffusion Maps
Taylor Reiter1,2,3, Rachel Montpetit2, Ron Runnebaum2,4
1Food Science Graduate Group, University of California-Davis, Davis, California, USA.
Mbio
|October 5, 2021
Summary
A new method, DMap-DE, analyzes Saccharomyces cerevisiae gene expression during wine fermentation. It reveals how grape origin and other yeasts impact fermentation, linking gene expression to wine
Area of Science:
- * Microbiology and Fermentation Science
- * Genomics and Bioinformatics
- * Enology and Viticulture
Background:
- * Saccharomyces cerevisiae is crucial for fermentation, adapting its gene expression to environmental cues.
- * Comparing gene expression across different fermentation batches is challenging due to asynchronous rates.
- * Understanding site-specific variations in grape musts is key to consistent fermentation outcomes.
Purpose of the Study:
- * To develop a novel computational method for analyzing asynchronous time-series gene expression data.
- * To identify site-specific gene expression differences in Saccharomyces cerevisiae during Pinot noir fermentation.
- * To correlate these gene expression patterns with environmental factors and fermentation outcomes.
Main Methods:
- * Development of a novel analysis approach combining diffusion mapping with continuous differential expression analysis (DMap-DE).
- * Time-course gene expression profiling of Saccharomyces cerevisiae across fermentations of Pinot noir grapes from 15 distinct sites.
- * Analysis of gene expression data to identify site-specific deviations and correlations with environmental factors.
Main Results:
- * DMap-DE successfully identified site-specific gene expression deviations in Saccharomyces cerevisiae.
- * Gene expression changes correlated with the presence of non-Saccharomyces yeasts (Hanseniaspora uvarum) and initial nitrogen levels in grape must.
- * The method demonstrated effectiveness in analyzing other gene expression datasets, such as hypoxic responses.
Conclusions:
- * DMap-DE is a robust tool for investigating asynchronous time-series gene expression data, outperforming other dimensionality reduction methods.
- * Site-specific factors, including non-Saccharomyces yeast populations and nitrogen content, significantly influence Saccharomyces cerevisiae gene expression during fermentation.
- * These findings provide insights into the molecular basis of fermentation variability and the origin of unique sensory characteristics in wines.
