Inhibition of Bacterial Biofilm Formation and Swarming Motility by a Small Synthetic Cationic Peptide
Bibliographic record
Abstract
Biofilms cause up to 80% of infections and are difficult to treat due to their substantial multidrug resistance compared to their planktonic counterparts. Based on the observation that human peptide LL-37 is able to block biofilm formation at concentrations below its MIC, we screened for small peptides with antibiofilm activity and identified novel synthetic cationic peptide 1037 of only 9 amino acids in length. Peptide 1037 had very weak antimicrobial activity, but at 1/30th the MIC the peptide was able to effectively prevent biofilm formation (>50% reduction in cell biomass) by the Gram-negative pathogens Pseudomonas aeruginosa and Burkholderia cenocepacia and Gram-positive Listeria monocytogenes. Using a flow cell system and a widefield fluorescence microscope, 1037 was shown to significantly reduce biofilm formation and lead to cell death in biofilms. Microarray and follow-up studies showed that, in P. aeruginosa, 1037 directly inhibited biofilms by reducing swimming and swarming motilities, stimulating twitching motility, and suppressing the expression of a variety of genes involved in biofilm formation (e.g., PA2204). Comparison of microarray data from cells treated with peptides LL-37 and 1037 enabled the identification of 11 common P. aeruginosa genes that have a role in biofilm formation and are proposed to represent functional targets of these peptides. Peptide 1037 shows promise as a potential therapeutic agent against chronic, recurrent biofilm infections caused by a variety of 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".