Wine fermentation
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA727920 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR15009900.fastq.gz | Fastqsanger.gz | |||
| SRR15009901.fastq.gz | Fastqsanger.gz | |||
| SRR15009902.fastq.gz | Fastqsanger.gz | |||
| SRR15009903.fastq.gz | Fastqsanger.gz | |||
| SRR15009904.fastq.gz | Fastqsanger.gz |
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Project description:The yeast Dekkera bruxellensis is as ethanol tolerant as Saccharomyces cerevisiae and may be found in bottled wine. It causes the spoilage of wine, beer, cider and soft drinks. In wines, the metabolic products responsible for spoilage by Dekkera bruxellensis are mainly volatile phenols. These chemical compounds are responsible for the taints described as ‘‘medicinal’’ in white wines (due to vinyl phenols) and as ‘‘leather’’, ‘‘horse sweat’’ and ‘‘stable’’ in red wines (due to ethyl phenols mainly 4-ethylphenol). Apart from the negative aroma nuances imparted by these yeasts, positive aromas such as ‘smoky’, ‘spicy’ and ‘toffee’ are also cited. Our goal was to identify the impact that the wine spoilage yeast Dekkera bruxellensis has on fermenting S. cerevisiae cells, especially on its gene expression level. To this end we co-inoculated both yeast species at the start of fermentation in a synthetic wine must, using S. cerevisiae-only fermentations without Dekkera bruxellensis as a control. All fermentations were employed in special membrane reactors (1.2 um pore size cut-off) physically separating Dekkera bruxellensis from wine yeast S. cerevisiae. Biomass separation with this membrane was done to abolish the possibility of hybridizing also D. bruxellensis probes on Agilent V2 (8x15K format) G4813 DNA microarrays designed just for S. cerevisiae ORF targets. The 1.2 um pore membrane separating both yeasts allowed the exchange of ethanol, metabolites and sugars during the fermentation.
2012-06-13 | GSE38582 | GEO
Project description:The yeast Dekkera bruxellensis is as ethanol tolerant as Saccharomyces cerevisiae and may be found in bottled wine. It causes the spoilage of wine, beer, cider and soft drinks. In wines, the metabolic products responsible for spoilage by Dekkera bruxellensis are mainly volatile phenols. These chemical compounds are responsible for the taints described as M-bM-^@M-^XM-bM-^@M-^XmedicinalM-bM-^@M-^YM-bM-^@M-^Y in white wines (due to vinyl phenols) and as M-bM-^@M-^XM-bM-^@M-^XleatherM-bM-^@M-^YM-bM-^@M-^Y, M-bM-^@M-^XM-bM-^@M-^Xhorse sweatM-bM-^@M-^YM-bM-^@M-^Y and M-bM-^@M-^XM-bM-^@M-^XstableM-bM-^@M-^YM-bM-^@M-^Y in red wines (due to ethyl phenols mainly 4-ethylphenol). Apart from the negative aroma nuances imparted by these yeasts, positive aromas such as M-bM-^@M-^XsmokyM-bM-^@M-^Y, M-bM-^@M-^XspicyM-bM-^@M-^Y and M-bM-^@M-^XtoffeeM-bM-^@M-^Y are also cited. Our goal was to identify the impact that the wine spoilage yeast Dekkera bruxellensis has on fermenting S. cerevisiae cells, especially on its gene expression level. To this end we co-inoculated both yeast species at the start of fermentation in a synthetic wine must, using S. cerevisiae-only fermentations without Dekkera bruxellensis as a control. All fermentations were employed in special membrane reactors (50 KDa pore size cut-off) physically separating Dekkera bruxellensis from wine yeast S. cerevisiae. Biomass separation with this membrane was done to abolish the possibility of hybridizing also D. bruxellensis probes on Agilent V2 (8x15K format) G4813 DNA microarrays designed just for S. cerevisiae ORF targets. The 50 KDa pore membrane separating both yeasts allowed the exchange of ethanol, metabolites and sugars during the fermentation. Fermentations were carried out in synthetic wine must in duplicate for both the control S. cerevisiae (strain Lalvin EC1118) and mixed fermentation. Sampling of yeast S. cerevisiae for RNA extractions were performed at 22 h of fermentation, during the exponential growth phase of S. cerevisiae, at 92 h and 144 h of fermentation, during its early and late stationary growth phase and at 187 h of fermentation, during its phase of growth decline.
2012-06-12 | E-GEOD-38582 | biostudies-arrayexpress
Project description:<p>A set of 917 wines of Czech origin registered in a national competition were analysed using nuclear magnetic resonance spectroscopy (NMR) with the aim to build and evaluate multivariate statistical models and machine learning methods for the classification of type (5 types), variety (13 varieties) and location (4 locations) based on 1H NMR spectra. The predictive models afforded more than 93% of correctness for classification of dry and medium dry, medium, sweet white wines and dry red wines. The trained Random Forest (RF) method is able to classify Pinot noir with 96% of correctness, Blaufränkisch 96%, Riesling 92%, Cabernet Sauvignon 77%, Chardonnay 76%, Gewürtztraminer 60%, Hibernal 60%, Grüner Veltliner 52%, Pinot gris 48%, Sauvignon Blanc 45%, Pálava 40%. Pinot blanc and Chardonnay are varieties, which are often misplaced and were discriminated with 71% of correctness. Chemometrics represents prospective tool to predict important features in wine, potentially important in quality assessment and fraud detection.</p>
2020-08-28 | MTBLS1677 | MetaboLights
Project description:Comparative gene expression analysis of two wine yeast strains at three time points (days 2, 5 and 14) during fermentation of colombar must. In our study we conducted parallel fermentations with the VIN13 and BM45 wine yeast strains in two different media, namely MS300 (syntheticmust) and Colombar must. The intersection of transcriptome datasets from both MS300 (simulated wine must;GSE11651) and Colombar fermentations should help to delineate relevant and ‘noisy’ changes in gene expression in response to experimental factors such as fermentation stage and strain identity.
2009-05-01 | GSE13695 | GEO
Project description:In wine fermentation, the blending of non-Saccharomyces yeast with Saccharomyces cerevisiae to improve the complexity of wine has become common practice, but data regarding the impact on yeast physiology and on genetic and metabolic regulation remain limited. Here we describe a transcriptomic analysis of single species and mixed species fermentations.
2019-02-01 | GSE112581 | GEO
Project description:Our study involves a transcriptomic approach to the analysis of industrial yeast metabolism. Historically, among the hundreds of yeast species, Saccharomyces cerevisiae has played an important role in scientific investigations and industrial applications, and it is universally acknowledged as one of the model systems for eukaryotic organisms. Yeast is also an important component of the wine fermentation process and determines various attributes of the final product. Our research takes a holistic approach to the improvement of industrial yeast strains by integrating large data sets from various yeast strains during fermentation. This means that analysis can be done in such a way as to co-evaluate several parameters simultaneously to identify points of interest and target genes for metabolic engineering. Eventually we hope to construct an accurate information matrix and a more complete cellular map for the fermenting yeast. This will enable accurate model-building for industrial yeast and facilitated the design of intelligent yeast improvement strategies which can be applied via traditional avenues of molecular biology.
2008-07-01 | GSE11651 | GEO
Project description:Background: The composition of grapevine berry at harvest is a major determinant of wine quality. Optimal oenological maturity of berries is characterized by a high sugar/acidity ratio, high anthocyanin content in the skin, and low astringency. However, harvest time is still mostly determined empirically, based on crude biochemical composition and berry tasting. In this context, it is interesting to identify genes that are expressed/repressed specifically at the late stages of ripening and which may be used as indicators of maturity. Results: Whole bunches and berries sorted by density were collected in vineyard on Chardonnay (white cultivar) grapevines for two consecutive years at three stages of ripening (7-days before harvest (TH-7),harvest (TH), and 10-days after harvest (TH+10)). Microvinification and sensory analysis indicate that the quality of the wines made from the whole bunches collected at TH-7, TH and TH+10 differed, TH providing the highest quality wines. In parallel, gene expression was studied with Qiagen/Operon microarrays using two types of samples, i.e. whole bunches and berries sorted by density. Only 12 genes were consistently up- or down-regulated in whole bunches and density sorted berries for the two years studied in Chardonnay. 52 genes were differentially expressed between the TH-7 and TH samples. In order to determine whether these genes followed a similar pattern of expression during the late stagesof berry ripening in a red cultivar, nine genes were selected for RT-PCR analysis with Cabernet Sauvignon grown under two different temperature regimes affecting the precocity of ripening. The expression profiles and their relationship to ripening were confirmed in Cabernet Sauvignon for seven genes, encoding a carotenoid cleavage dioxygenase, a galactinol synthase, a late embryogenesis abundant protein, a dirigent-like protein, a histidine kinase receptor, avalencene synthase and a putative S-adenosyl-L-methionine:salicylic acid carboxyl methyltransferase. Conclusions: This set of up- and down-regulated genes characterize the late stages of berry ripening in the two cultivars studied, and are indirectly linked to wine quality. They might be used directly or indirectly to design immunological, biochemical or molecular tools aimed at the determination of optimal ripening in these cultivars.
2011-11-08 | E-MTAB-481 | biostudies-arrayexpress
Project description:Comparative gene expression analysis of two wine yeast strains at three time points (days 2, 5 and 14) during fermentation of colombar must. In our study we conducted parallel fermentations with the VIN13 and BM45 wine yeast strains in two different media, namely MS300 (syntheticmust) and Colombar must. The intersection of transcriptome datasets from both MS300 (simulated wine must;GSE11651) and Colombar fermentations should help to delineate relevant and ânoisyâ changes in gene expression in response to experimental factors such as fermentation stage and strain identity. Experiment Overall Design: Two industrial wine yeast strains (BM45 and VIN13) grown micro-aerobically in Colombar must. Microarray analysis was performed at three time points during fermentation (days 2, 5 and 14), representing the exponential, early and late stationary growth phases respectively.
2009-05-14 | E-GEOD-13695 | biostudies-arrayexpress
Project description:We used genome-wide expression analyses to study the response of Saccharomyces cerevisiae to stress throughout a 15-day wine fermentation. Forty percent of the yeast genome significantly changed expression levels to mediate long-term adaptation to an environment in which ethanol is both a stressor and a carbon source. Within this set, we identify a group of 223 genes, designated as the Fermentation Stress Response (FSR), that are dramatically and permanently induced; FSR genes exhibited changes ranging from four-to eighty-fold. The FSR is novel; 62% of the genes involved have not been implicated in global stress responses and 28% of the genes have no functional annotation. Genes involved in respiratory metabolism and gluconeogenesis were expressed during fermentation despite the presence of high concentrations of glucose. Ethanol, rather than nutrient depletion, was responsible for entry of yeast cells into stationary phase. Ethanol seems to regulate yeast metabolism through hitherto undiscovered regulatory networks during wine fermentation. Keywords: time course, stress response, fermentation
2008-04-17 | GSE8536 | GEO
Project description:Comparison between two commercial wine yeast strains (UCD522 and P29) differing in their production of H2S during wine fermentation.
2009-12-03 | GSE16215 | GEO