Synergy between Human Peptide LL-37 and Polymyxin B against Planktonic and Biofilm Cells of Escherichia coli and Pseudomonas aeruginosa
Bibliographic record
Abstract
The rise in antimicrobial resistant bacteria is limiting the number of effective treatments for bacterial infections. Escherichia coli and Pseudomonas aeruginosa are two of the pathogens with the highest prevalence of resistance, and with the greatest need for new antimicrobial agents. Combinations of antimicrobial peptides (AMPs) and antibiotics that display synergistic effects have been shown to be an effective strategy in the development of novel therapeutic agents. In this study, we investigated the synergy between the AMP LL-37 and various classes of antibiotics against E. coli and P. aeruginosa strains. Of the six antibiotics tested (ampicillin, tetracycline, ciprofloxacin, gentamicin, aztreonam, and polymyxin B (PMB)), LL-37 displayed the strongest synergy against E. coli MG1655 and P. aeruginosa PAO1 laboratory strains when combined with PMB. Given the strong synergy, the PMB + LL-37 combination was chosen for further examination where it demonstrated synergy against multidrug-resistant and clinical E. coli isolates. Synergy of PMB + LL-37 towards clinical isolates of P. aeruginosa varied and showed synergistic, additive, or indifferent effects. The PMB + LL-37 combination treatment showed significant prevention of biofilm formation as well as eradication of pre-grown E. coli and P. aeruginosa biofilms. Using the Galleria mellonella wax worm model, we showed that the PMB + LL-37 combination treatment retained its antibacterial capacities in vivo. Flow analyses were performed to characterize the mode of action. The results of the present study provide proof of principle for the synergistic response between LL-37 and PMB and give novel insights into a promising new antimicrobial combination against gram-negative planktonic and biofilm cells.
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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".