Bronchial epithelium repair by Esculentin-1a-derived antimicrobial peptides: involvement of metalloproteinase-9 and interleukin-8, and evaluation of peptides’ immunogenicity
Bibliographic record
Abstract
Abstract The airway epithelium is seriously damaged upon pulmonary Pseudomonas aeruginosa infection, especially in cystic fibrosis (CF) sufferers. Therefore, the discovery of novel anti-infective agents accelerating healing of infected injured tissues is crucial. The antipseudomonal peptides esculentin-1a(1–21)NH 2 and its diastereomer Esc(1–21)-1c (Esc peptides) hold promise in this respect. In fact, they stimulate airway epithelial wound repair, but no mechanistic insights are available. Here we demonstrated that this process occurs through promotion of cell migration by an indirect activation of epidermal growth factor receptor mediated by metalloproteinases. Furthermore, we showed an increased expression of metalloproteinase 9, at both gene and protein levels, in peptide-treated bronchial epithelial cells with a functional or mutated form of CF transmembrane conductance regulator. In addition, the two peptides counteracted the inhibitory effect of Pseudomonas lipopolysaccharide (mimicking an infection condition) on the wound healing activity of the airway epithelium, and they enhanced the production of interleukin-8 from both types of cells. Finally, no immunogenicity was discovered for Esc peptides, suggesting their potential safety for clinical usage. Besides representing a step forward in understanding the molecular mechanism underlying the peptide-induced wound healing activity, these studies have contributed to highlight Esc peptides as valuable therapeutics with multiple functions.
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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".