The activity of bacteriocins from Carnobacterium maltaromaticum UAL307 against Gram-negative bacteria in combination with EDTA treatment
Bibliographic record
Abstract
Bacteriocins from gram-positive bacteria are potent antimicrobial peptides that inhibit pathogenic and food-spoilage bacteria. They are usually ineffective against gram-negative bacteria because they cannot penetrate the outer membrane (OM). Disruption of the OM of some gram-negative bacteria was reported to sensitize them to certain bacteriocins. This study evaluates the activity of three purified bacteriocins [carnocyclin A (CclA), carnobacteriocin BM1 (CbnBM1) and piscicolin 126 (PisA)] produced by Carnobacterium maltaromaticum UAL307, which has been approved for preservation of food in United States and Canada, against three gram-negative bacteria (Escherichia coli DH5α, Pseudomonas aeruginosa ATCC 14207 and Salmonella Typhimurium ATCC 23564). Their efficacy is compared with bacteriocins of other classes: the lantibiotics nisin A (positive control) and gallidermin, and the cyclic peptide subtilosin A (SubA). In combination with EDTA, CclA inhibited both E. coli and Pseudomonas. PisA inhibited Pseudomonas, but CbnBM1 showed weak activity toward Pseudomonas. In comparison, nisin and gallidermin inhibited the growth of all three strains, whereas SubA was active against E. coli and Pseudomonas only at high concentrations. The results reveal that UAL307 bacteriocins can inhibit gram-negative bacteria if the OM is weakened, and that the different classes of bacteriocins in this study exert unique modes of action toward such bacteria.
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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".