Multi-omics methods to unravel microbial diversity in fermentation of Riesling wines
Abstract
Wine aroma is shaped by the wine’s chemical compositions, in which both grape constituents and microbes play crucial roles. Although wine quality is influenced by the microbial communities, less is known about their population interactions. Previous studies linking the effect of native microbial communities to sensory relevant aroma compounds with their interactive properties have been vastly unsuccessful to date. Partially because studies relied on relatively few isolated strains or chemical compounds, which may be not sufficient to fully understand this complex picture.
Native microbial communities from different Riesling vineyards were studied over multiple experiments during vinification as well as over a two-year to reveal their effects on chemical and sensory composition of spontaneously fermented Riesling wines.
We demonstrate that by combining modern untargeted high-throughput omics technologies and statistical approaches, it is possible to look into samples in situ in the actual natural environment. Our results indicate that both vineyard and winery microbial communities are found to play significant roles in wine. Microbial communities within the fermenting were strongly influenced by vineyard of origin.
These population dynamics are consequently translated into diverse sensory properties through sensory relevant chemical interactions. We found that both sensory and chemical compositions were heavily influenced by the microbial community composition during the vinification as well as the vineyard and the year. Such methodologies allow to find novel microbial and chemical patterns which could be further tested with targeted studies. In addition to deconstructing the microbial community composition in complex natural environment, we leverage on shotgun metagenomic data to undertake the functional analysis of the microbial community during wine fermentation. In the future, multiomics approaches will be essential for fully discovering the complexity of biological networks, where microbes, host and chemical compounds interact with human sensory perceptions. These developed approaches benefit any industry that works with complex biological interactions.
DOI:
Issue: OENO IVAS 2019
Type: Article
Authors
Section for Evolutionary Genomics, Natural History Museum of Denmark, University of Copenhagen, Co-penhagen, Denmark
Institute for Viticulture & Oenology, DLR Rheinpfalz, Neustadt/Wstr.,Germany
Contact the author
Keywords
Metagenomics, Metabarcoding, Chemical interactions, Machine learning