PepBiotics, novel cathelicidin-inspired antimicrobials to fight pulmonary bacterial infections
Bibliographic record
Abstract
Antimicrobial peptides are considered potential alternatives to antibiotics. Here we describe the antibacterial properties of a family of novel cathelicidin-related (CR-) peptides, which we named PepBiotics, against bacteria typically present in cystic fibrosis (CF) patients. Broth dilution assays were used to determine antibacterial activity of PepBiotics under physiological conditions, as well as development of bacterial resistance against these peptides. Toxicity was tested in mice and cell cultures while molecular interactions of PepBiotics with bacterial membrane components was determined using CD, ITC and LPS/LTA induced macrophage studies. A relatively small number of PepBiotics remained highly antibacterial against CF-related respiratory pathogens Pseudomonas aeruginosa and Staphylococcus aureus, at high ionic strength and low pH. Interestingly, these PepBiotics also prevented LPS/LTA induced activation of macrophages and was shown to be non-toxic to primary human nasal epithelial cells. Furthermore, both P. aeruginosa and S. aureus were unable to induce resistance against CR-163 and CR-172, two PepBiotics selected for their excellent antimicrobial and immunomodulatory properties. Toxicity studies in mice indicated that intratracheal administration of CR-163 was well tolerated in vivo. Finally, interaction of CR-163 with bacterial-type anionic membranes but not with mammalian-type (zwitterionic lipid) membranes was confirmed using ITC and 31P solid state NMR. PepBiotics are a promising novel class of highly active antimicrobial peptides, of which CR-163 showed the most potential for treatment of clinically relevant (CF-) pathogens in physiological conditions. These observations emphasize the therapeutic potential of PepBiotics against CF-related bacterial respiratory infections.
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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".