The effect of PACAP administration on LPS-induced cytokine expression in the Atlantic salmon SHK-1 cell line.
Bibliographic record
Abstract
Recent work has identified pituitary adenylate cyclase activating polypeptide (PACAP) as a potential antimicrobial and immune stimulating agent which may be suitable for use in aquaculture. However, its effects on teleost immunity are not well studied and may be significantly different than what has been observed in mammals. In this study we examined the effects of PACAP on the Atlantic salmon macrophage cell line SHK-1. PACAP was able to increase the expression of LPS-induced il-1β in at concentrations of 1 uM when administered 24h prior to LPS stimulation. Furthermore, concentrations as low as 40nM had an effect when administered both 24h prior and in tandem with LPS. PACAP was also capable of increasing the expression of il-1β and tnf-α in SHK-1 cells challenged with a low dose of heat-killed Flavobacterium columnare. We attempted to get a better understanding of the mechanism underlying this enhancement of il-1β expression by manipulating downstream signaling of PACAP with inhibitors of phosphodiesterase and phospholipase C activity. We found that inducing cAMP accumulation with phosphodiesterase inhibitors failed to recapitulate the effect of PACAP administration on LPS-mediated il-1β expression by PACAP, while use of a phospholipase C inhibitor caused a PACAP-like enhancement in LPS-mediated il-1β expression. Interestingly, the VPAC1 receptor inhibitor PG97-269, but not the PAC1 inhibitor max.d.4, also was capable of causing a PACAP-like enhancement in LPS-mediated il-1β expression. This suggests that fish do not utilize the PACAP receptors in the same manner as mammals, but that it still exerts an immunostimulatory effect that make it a good immunostimulant for use in aquaculture.
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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) | 0.001 | 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".