Arginine and citrulline do not stimulate growth of two<i>Oenococcus oeni</i>strains in wine
Bibliographic record
Abstract
Arginine metabolism by wine lactic acid bacteria (LAB) may lead to wine quality degradation. While arginine is essential for growth of the wine relevant LAB Oenococcus oeni, it remains unclear whether it also stimulates its growth. This study evaluated the effect of arginine and citrulline, the partially metabolized intermediate of the arginine deiminase pathway, on the growth of two commercial O. oeni strains in comparison with a Lactobacillus buchneri strain in wine and at wine pH values. Neither arginine nor citrulline increased growth of both O. oeni strains in comparison with the L. buchneri strain. However, arginine and citrulline were partially degraded in all incubations. The extent of citrulline degradation correlated with lower pH values in oenococcal cultivations but with higher pH values in those of the L. buchneri strain. The degradation kinetics of O. oeni and L. buchneri for malic acid and arginine differed and the latter grew in sterile filtered post-malolactic fermentation wine. This study shows that arginine and citrulline did not stimulate growth of the two O. oeni strains studied, and that their physiological role differed among the wine LAB considered. While arginine may play a role in wine microbiological stability, other nutrients should be investigated for their suitability to create a selective ecological advantage for O. oeni strains in wine.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".