3-Hydroxypropionic acid contributes to the antibacterial activity of glycerol metabolism by the food microbe Limosilactobacillus reuteri
Bibliographic record
Abstract
Strains of Limosilactobacillus reuteri are used as starter and bioprotective cultures and contribute to the preservation of food through the production of fermentation metabolites lactic and acetic acid, and of the antimicrobial reuterin. Reuterin consists of acrolein and 3-hydroxypropionaldehyde (3-HPA), which can be further metabolized to 1,3-propanediol and 3-hydroxypropionic acid (3-HP). While reuterin has been the focus of many investigations, the contribution of 3-HP to the antimicrobial activity of food related reuterin-producers is unknown. We show that the antibacterial activity of 3-HP was stronger at pH 4.8 compared to pH 5.5 and 6.6. Gram-positive bacteria were in general more resistant against 3-HP and propionic acid than Gram-negative indicator strains including common food pathogens, while spoilage yeast and molds were not inhibited by ≤ 640 mM 3-HP. The presence of acrolein decreased the minimal inhibitory activity of 3-HP against E. coli indicating synergistic antibacterial activity. 3-HP was formed during the growth of the reuterin-producers, and by resting cells of L. reuteri DSM 20016. Taken together, this study shows that food-related reuterin producers strains synthesize a second antibacterial compound, which might be of relevance when strains are added as starter or bioprotective cultures to food products.
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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) | 0.000 | 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".