Structure-function relationships of the antibacterial activity of phenolic acids and their metabolism by lactic acid bacteria
Bibliographic record
Abstract
AIMS: To determine structure-function relationships of antibacterial phenolic acids and their metabolites produced by lactic acid bacteria (LAB). METHODS AND RESULTS: Minimum inhibitory concentrations (MICs) of 6 hydroxybenzoic and 6 hydroxycinnamic acids were determined with Lactobacillus plantarum, Lactobacillus hammesii, Escherichia coli and Bacillus subtilis as indicator strains. The antibacterial activity of phenolic acids increased at lower pH. A decreasing number of hydroxyl groups enhanced the activity of hydroxybenzoic acids, but had minor effects on hydroxycinnamic acids. Substitution of hydroxyl groups with methoxy groups increased the activity of hydroxybenzoic, but not of hydroxycinnamic, acid. Metabolism of chlorogenic, caffeic, p-coumaric, ferulic, protocatechuic or p-hydroxybenzoic acids by L. plantarum, L. hammesii, Lactobacillus fermentum and Lactobacillus reuteri was analysed by LC-DAD-MS. Furthermore, MICs of substrates and metabolites were compared. Decarboxylated and/or reduced metabolites of phenolic acids had a lower activity than the substrates. Strain-specific metabolism of phenolic acids generally corresponded to resistance. CONCLUSIONS: The influence of lipophilicity on the antibacterial activity of hydroxybenzoic acids is stronger than that of hydroxycinnamic acids. Metabolism of phenolic acids by LAB detoxifies phenolic acids. SIGNIFICANCE AND IMPACT OF THE STUDY: Results allow the targeted selection of plant extracts for food preservation, and selection of starter cultures for fermented 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) | 0.000 | 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".