The production of preconditioned freeze-dried Oenococcus oeni primes its metabolism to withstand environmental stresses encountered upon inoculation into wine
Bibliographic record
Abstract
Oenococcus oeni is the most resistant lactic acid bacteria species to the environmental stresses encountered in wine, particularly the acidity, presence of ethanol and phenolic compounds. Indigenous strains develop spontaneously following the yeast-driven alcoholic fermentation and may perform the malolactic fermentation whereby improving taste, aroma, and the microbial stability of wine. However, spontaneous fermentation is sometimes delayed, prolonged or incomplete. In order to better control its timing and quality, O. oeni strains are selected and developed to be used as malolactic starters. They are prepared under proprietary manufacturing processes to survive direct inoculation and are predominantly provided as freeze-dried preparations. In this study, we have investigated the physiological and molecular alterations occurring in O. oeni cells prepared by an industrial process that consists of preconditioning protocols and freeze-drying, and compared them to the same strain grown in a grape juice medium. We found that compared to cultured cells, the industrial production process improved survival under extreme conditions, i. e. at low pH or high tannin concentrations. In contrast, cultured cells resumed active growth more quickly and strongly than freeze-dried preparations in standard pH wines. A proteomic analysis showed that during the industrial production most non-essential metabolic processes are shut down and components of the general and the stringent stress response are upregulated. The presence of major components of the stress response facilitates protein homeostasis and physiological changes that further ensure the integrity of cells.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".