PACAP binds conserved receptors and modulates cytokine gene expression and protein secretion in trout cell lines
Bibliographic record
Abstract
Antimicrobial peptides (AMPs) are an alternative to antibiotics for treatment and prevention of infections with a lower risk of bacterial resistance. Pituitary adenylate cyclase activating polypeptide (PACAP) is an outstanding AMP with versatile effects including antimicrobial activity and modulation of immune responses. The objective of this research was to study PACAP immunomodulatory effect on rainbow trout cell lines infected with Aeromonas salmonicida. PACAP from Clarias gariepinus (PACAP1) and a modified PACAP (PACAP5) were tested. RT-qPCR results showed that il1b and il8 expression in RTgutGC was significantly downregulated while tgfb expression was upregulated after PACAP treatment. Importantly, the concentration of IL-1β and IFN-γ increased in the conditioned media of RTS11 cells incubated with PACAP1 and exposed to A. salmonicida. There was a poor correlation between gene expression and protein concentration, suggesting a stimulation of the translation of IL-1β protein from previously accumulated transcripts or the cleavage of accumulated IL-1β precursor. In-silico studies of PACAP-receptor interactions showed a turn of the peptide characteristic of PACAP-PAC1 interaction, correlated with the higher number of interactions observed with this specific receptor, which is also in agreement with the higher PACAP specificity described for PAC1 compared to VPAC1 and VPACA2. Finally, the in silico analysis revealed nine amino acids related to the PACAP receptor-associated functionality.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".