PACAP sequence modifications modulate the peptide antimicrobial activity against bacterial pathogens affecting aquaculture
Bibliographic record
Abstract
The global aquaculture industry has significant losses each year due to disease outbreaks. Antibiotics are one of the common methods to treat fish infections, but prolonged use can lead to the emergence of resistant strains. Aeromonas spp. Infections are a common and problematic disease in fish, and members of this genera can produce antibiotic resistant strains. Antimicrobial peptides (AMPs) have emerged as an alternative method to treat and prevent infections and pituitary adenylate cyclase activating polypeptide (PACAP) is a prominent member of this family. The objective of this research was to study PACAP's direct antimicrobial activity and its toxicity in fish cells. Four synthetic variants of the natural PACAP from Clarias gariepinus were tested in addition to the natural variant. The experimental results show a different antimicrobial activity against A. salmonicida and A. hydrophila of each PACAP variant, and for the first time show dependence on the culture broth used. Furthermore, the results suggest that the underlying mechanism of PACAP antimicrobial activity includes a bacterial membrane permeabilizing effect, classifying PACAP as a membrane disruptive AMP. This study also demonstrated that the five PACAP variants evaluated showed low toxicity in vitro, at concentrations relevant for in vivo applications. Therefore, PACAP could be a promising alternative to antibiotics in the aquaculture sector.
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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".