Integrating transcriptomics and metabolomics for the analysis of the aroma profiles of Saccharomyces cerevisiae strains from diverse origins
Bibliographic record
Abstract
During must fermentation thousands of volatile aroma compounds are formed, with higher alcohols, acetate esters and ethyl esters being the main aromatic compounds contributing to floral and fruity aromas. The action of yeast, in particular Saccharomyces cerevisiae , on the must components will build the architecture of the wine flavour and its fermentation bouquet. The objective of the present work was to better understand the molecular and metabolic bases of aroma production during a fermentation process. For such, comparative transcriptomic and metabolic analysis was performed at two time points (5 and 50 g/L of CO 2 released) in fermentations conducted by four yeast strains from different origins and/or technological applications (cachaça, sake, wine, and laboratory), and multivariate factorial analyses were used to rationally identify new targets for improving aroma production. Results showed that strains from cachaça, sake and wine produced higher amounts of acetate esters, ethyl esters, acids and higher alcohols, in comparison with the laboratory strain. At fermentation time T1 (5 g/L CO 2 released), comparative transcriptomics of the three S. cerevisiae strains from different fermentative environments in comparison with the laboratory yeast S288c, showed an increased expression of genes related with tetracyclic and pentacyclic triterpenes metabolism, involved in sterol synthesis. Sake strain also showed upregulation of genes ADH7 and AAD6 , involved in the formation of higher alcohols in the Ehrlich pathway. For fermentation time point T2 (50 g/L CO 2 released), again sake strain, but also VL1 strain, showed an increased expression of genes involved in formation of higher alcohols in the Ehrlich pathway, namely ADH7 , ADH6 and AAD6 , which is in accordance with the higher levels of methionol, isobutanol, isoamyl alcohol and phenylethanol observed. Our approach revealed successful to integrate data from several technologies (HPLC, GC-MS, microarrays) and using different data analysis methods (PCA, MFA). The results obtained increased our knowledge on the production of wine aroma and flavour, identifying new gene in association to the formation of flavour active compounds, mainly in the production of fatty acids, and ethyl and acetate esters.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".