Antibiofilm activity of lactoferrin-derived synthetic peptides against <i>Pseudomonas aeruginosa</i> PAO1
Bibliographic record
Abstract
Many pathogenic bacteria can protect themselves from the effects of antibiotics and the host immune response system by forming biofilms. Biofilms are polymer-entrapped bacterial cells, which adhere to each other and are often attached to a surface. Eradication of bacterial biofilms typically requires much higher concentrations of antibiotics than are normally needed to kill cultured planktonic cells, raising serious clinical concerns. In an attempt to prevent the formation of biofilms or to break up existing biofilms of pathogenic bacteria, herein we have used the standard crystal violet assay as well as the Calgary biofilm device to test several lactoferrin- and lactoferricin-derived antimicrobial peptides for their antibiofilm activity against Pseudomonas aeruginosa PAO1. Our results revealed that the short bovine lactoferricin-derived RRWQWR-NH2 (20–25) hexapeptide has no activity against P. aeruginosa PAO1. Moreover, the longer human lactoferricin-derived peptide GRRRRSVQWCA (1–11) and the bovine lactoferrampin (268–284) peptide were also almost devoid of activity. However, several different “mix-and-match” dimeric versions of the two lactoferricin-derived peptides proved quite effective in preventing the formation of biofilms at low concentrations, and in some cases, could even eradicate an existing biofilm. Moreover, the full-length bovine lactoferricinB (17–41) peptide also displayed considerable antimicrobial activity. Some of the longer lactoferricin-derived dimeric peptides acted through a bactericidal mechanism, whereas others seemed to interfere in cell-signalling processes. Taken together, our results indicate that synthetic dimeric peptides comprising short naturally occurring human and bovine lactoferricin constructs could be further developed as antibiofilm agents.
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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".