DJK-5, an anti-biofilm peptide, increases Staphylococcus aureus sensitivity to colistin killing in co-biofilms with Pseudomonas aeruginosa
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Chronic infections represent a significant global health and economic challenge. Biofilms, which are bacterial communities encased in an extracellular polysaccharide matrix, contribute to approximately 80% of these infections. In particular, pathogens such as Pseudomonas aeruginosa and Staphylococcus aureus are frequently co-isolated from the sputum of patients with cystic fibrosis and are commonly found in chronic wound infections. Within biofilms, bacteria demonstrate a remarkable increase in resistance and tolerance to antimicrobial treatment. We investigated the efficacy of combining the last-line antibiotic colistin with a membrane- and stringent stress response-targeting anti-biofilm peptide DJK-5 against co-biofilms comprised of multidrug-resistant P. aeruginosa and methicillin-resistant S. aureus (MRSA). Colistin lacks canonical activity against S. aureus. However, our study revealed that under co-biofilm conditions, the antibiofilm peptide DJK-5 synergized with colistin against S. aureus. Similar enhancement was observed when daptomycin, a cyclic lipopeptide against Gram-positive bacteria, was combined with DJK-5, resulting in increased activity against P. aeruginosa. The combinatorial treatment induced morphological changes in both P. aeruginosa and S. aureus cell shape and size within co-biofilms. Importantly, our findings also demonstrate synergistic activity against both P. aeruginosa and S. aureus in a murine subcutaneous biofilm-like abscess model. In conclusion, combinatorial treatments with colistin or daptomycin and the anti-biofilm peptide DJK-5 show significant potential for targeting co-biofilm infections. These findings offer promising avenues for developing new therapeutic approaches to combat complex chronic infections.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it