Influence of Peptide Supply and Cosubstrates on Phenylalanine Metabolism of <i>Lactobacillus sanfranciscensis</i> DSM20451<sup>T</sup> and <i>Lactobacillus plantarum</i> TMW1.468
Bibliographic record
Abstract
Bread spoilage is mainly due to the growth of filamentous fungi, and metabolites produced during sourdough fermentation by lactobacilli can inhibit fungal growth. One of these metabolites is phenyllactic acid (PLA), which is a catabolite from phenylalanine. In this work, the influence of peptide supply and cosubstrates was determined on PLA formation from phenylalanine by Lactobacillus plantarum TMW1.468 and Lactobacillus sanfranciscensis DSM20451(T). Transport of single amino acids is not efficient in lactobacilli, and only 1% of the offered phenylalanine was converted to PLA. PLA yields were increased 2-4-fold when peptides instead of single amino acids were used as a substrate. The accumulation of phenylalanine after peptide addition indicated that, after transport, transamination was the second limiting factor. In L. plantarum TMW1.468, PLA yields were increased from 5 to >30% upon the addition of alpha-ketoglutarate. In L. sanfranciscensis DSM20451, a combination of both citric acid and alpha-ketoglutarate increased PLA formation. The combined effect of citric acid and alpha-ketoglutarate can be attributed to changes in the NAD/NADH ratio.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".