Broad-Spectrum Anti-biofilm Peptide That Targets a Cellular Stress Response
Bibliographic record
Abstract
Bacteria form multicellular communities known as biofilms that cause two thirds of all infections and demonstrate a 10 to 1000 fold increase in adaptive resistance to conventional antibiotics. Currently, there are no approved drugs that specifically target bacterial biofilms. Here we identified a potent anti-biofilm peptide 1018 that worked by blocking (p)ppGpp, an important signal in biofilm development. At concentrations that did not affect planktonic growth, peptide treatment completely prevented biofilm formation and led to the eradication of mature biofilms in representative strains of both Gram-negative and Gram-positive bacterial pathogens including Pseudomonas aeruginosa, Escherichia coli, Acinetobacter baumannii, Klebsiella pneumoniae, methicillin resistant Staphylococcus aureus, Salmonella Typhimurium and Burkholderia cenocepacia. Low levels of the peptide led to biofilm dispersal, while higher doses triggered biofilm cell death. We hypothesized that the peptide acted to inhibit a common stress response in target species, and that the stringent response, mediating (p)ppGpp synthesis through the enzymes RelA and SpoT, was targeted. Consistent with this, increasing (p)ppGpp synthesis by addition of serine hydroxamate or over-expression of relA led to reduced susceptibility to the peptide. Furthermore, relA and spoT mutations blocking production of (p)ppGpp replicated the effects of the peptide, leading to a reduction of biofilm formation in the four tested target species. Also, eliminating (p)ppGpp expression after two days of biofilm growth by removal of arabinose from a strain expressing relA behind an arabinose-inducible promoter, reciprocated the effect of peptide added at the same time, leading to loss of biofilm. NMR and chromatography studies showed that the peptide acted on cells to cause degradation of (p)ppGpp within 30 minutes, and in vitro directly interacted with ppGpp. We thus propose that 1018 targets (p)ppGpp and marks it for degradation in cells. Targeting (p)ppGpp represents a new approach against biofilm-related drug resistance.
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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 | 0.001 |
| 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".