Antibiofilm activities of lactoferricin-related Trp- and Arg-rich antimicrobial hexapeptides against pathogenic <i>Staphylococcus aureus</i> and <i>Pseudomonas aeruginosa</i> strains
Bibliographic record
Abstract
Recently, several antimicrobial peptides (AMPs), varying in length from 12 to 37 residues, have been shown to act as antibiofilm agents. Here, we report a study of 23 hexapeptides modeled after four different Trp- and Arg-rich AMPs, including the RRWQWR-NH2 peptide, derived from bovine lactoferrin. They were tested against the pathogenic Gram-negative Pseudomonas aeruginosa PAO1 strain and a Gram-positive Staphylococcus aureus MRSA strain. Both strains were engineered to express the green fluorescent protein (GFP) protein, and fluorescence detection was used to measure the ability of the peptides to prevent biofilm formation (minimum biofilm inhibitory concentration (MBIC)) or to cause the breakdown of established biofilms (minimum biofilm eradication concentration (MBEC)). Similar antibiofilm activities were obtained with the standard crystal violet dye assay. Most Trp- and Arg-rich hexapeptides displayed a potent antibiofilm activity against the Gram-positive S. aureus MRSA strain. In particular, hexapeptides with 3 Arg and 3 Trp were very effective, especially when they contained the three Trp in sequence. Somewhat unexpectedly, the antimicrobial (MIC) values correlated with the MBIC and MBEC values, which has not been seen for several other AMP/antibiofilm peptides. Our results demonstrate that short Trp- and Arg-rich peptides merit further studies as antibiofilm agents that could be deployed to address part of the antimicrobial resistance problem.
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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) | 0.000 | 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".