Human Host Defense Peptide LL-37 Prevents Bacterial Biofilm Formation
Bibliographic record
Abstract
The ability to form biofilms is a critical factor in chronic infections by Pseudomonas aeruginosa and has made this bacterium a model organism with respect to biofilm formation. This study describes a new, previously unrecognized role for the human cationic host defense peptide LL-37. In addition to its key role in modulating the innate immune response and weak antimicrobial activity, LL-37 potently inhibited the formation of bacterial biofilms in vitro. This occurred at the very low and physiologically meaningful concentration of 0.5 microg/ml, far below that required to kill or inhibit growth (MIC = 64 microg/ml). LL-37 also affected existing, pregrown P. aeruginosa biofilms. Similar results were obtained using the bovine neutrophil peptide indolicidin, but no inhibitory effect on biofilm formation was detected using subinhibitory concentrations of the mouse peptide CRAMP, which shares 67% identity with LL-37, polymyxin B, or the bovine bactenecin homolog Bac2A. Using microarrays and follow-up studies, we were able to demonstrate that LL-37 affected biofilm formation by decreasing the attachment of bacterial cells, stimulating twitching motility, and influencing two major quorum sensing systems (Las and Rhl), leading to the downregulation of genes essential for biofilm development.
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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".