Antibacterial properties of the pituitary adenylate cyclase-activating polypeptide: A new human antimicrobial peptide
Bibliographic record
Abstract
The Pituitary Adenylate Cyclase-Activating Polypeptide (PACAP), a polycationic, amphiphilic and helical neuropeptide, is well known for its neuroprotective actions and cell penetrating properties. In the present study, we evaluated the potent antibacterial property of PACAP38 and related analogs against various bacterial strains. Interestingly, PACAP38 and related analogs can inhibit the growth of various bacteria including Escherichia coli (JM109), Bacillus subtilis (PY79), and the pathogenic Burkholderia cenocepacia (J2315). Investigation of the mechanism of action suggested that a PACAP metabolite, identified as PACAP(9-38), might indeed be responsible for the observed PACAP38 antibacterial action. Surprisingly, PACAP(9-38), which does not induce haemolysis, exhibits an increased specificity toward Burkholderia cenocepacia J2315 compared to other tested bacteria. Finally, the predisposition of PACAP(9-38) to adopt a π-helix conformation rather than an α-helical conformation like PACAP38 could explain this gain in specificity. Overall, this study has revealed a new function for PACAP38 and related derivatives that can be added to its pleiotropic biological activities. This innovative study could therefore pave the way toward the development of new therapeutic agents against multiresistant bacteria, and more specifically the Burkholderia cenocepacia complex.
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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".