Unlocking the Secrets of Peptide Transport in Wine Yeast: Insights into Oligopeptide Transporter Functions and Nitrogen Source Preferences
Bibliographic record
Abstract
Data files and R script for analysis included in the article "Unlocking the Secrets of Peptide Transport in Wine Yeast: Insights into Oligopeptide Transporter Functions and Nitrogen Source Preferences". Abstract: Oligopeptides from grape must represent a secondary source of nitrogen for yeasts to grow and carry out fermentation. Saccharomyces cerevisiae takes up oligopeptides from the environment through multiple oligopeptide transporters with different peptide length specificities. However, due to difficulties associated with the qualitative and quantitative measurement of peptides in natural matrices, peptide transporter specificities have been mostly researched in single peptide environments. Using a peptide mapping method, we monitored the relative consumption of peptides derived from a protein hydrolysate by a set of CRISPR-Cas9-engineered S. cerevisiae wine strains to study oligopeptide transporters from the Opt and Fot families. Results show that Opt2 can import peptides containing three to -at least- seven amino acid residues, which is a broader peptide length specificity than previously reported, while Opt1 was not functional as a peptide transporter in these strains. Fot1, Fot2 and Fot3, previously referred to as di-tripeptide transporters in S. cerevisiae wine strains, could also import tetrapeptides. The consumption order of peptides was determined by the peptide length as higher chain length peptides were taken up by Opt2 only after most di-tetrapeptides were depleted from the media. Altogether, Fot and Opt2 activity assured completion of the fermentation process without necessarily requiring ammonia or free amino acids. Analysis of peptide transporter gene expression during fermentation showed an effect of SO42- not only on OPT1, but also on FOT, and supported the assumption of a possible interplay between Fot and Opt2 activities.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.119 | 0.043 |
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".