Molecular, cellular, and functional characterizations of pituitary adenylate cyclase‐activating polypeptide and its receptors in the cerebellum of New and Old World monkeys
Bibliographic record
Abstract
The neuropeptide pituitary adenylate cyclase-activating polypeptide (PACAP) exerts trophic activities during cerebellar development, and a neuroprotective effect of PACAP has been demonstrated in pathological conditions such as stroke. However, all these data have been obtained in rodents, and neuroprotective effects of PACAP in primates remain unknown. Because of their evolutionary relationships with humans, monkeys represent powerful models for validating the therapeutic interest in PACAP. The objective of the present study was to characterize PACAP and its receptors in the cerebellum of two nonhuman primates. RT-PCR and in situ hybridization experiments revealed that PACAP is expressed in the cerebellum by Purkinje cells. Via immunohistochemistry, PACAP was detected in Purkinje cells and radial glial fibers. With regard to PACAP receptors, PAC1-R and VPAC1-R were detected by RT-PCR. In situ hybridization revealed a strong expression of PAC1-R and VPAC1-R in the granule cell layer (GCL), and VPAC1-R was also expressed in the Purkinje cell layer. A high density of PACAP binding sites was visualized in the GCL and the Purkinje cell layer. Competition studies indicated that, in the GCL, PACAP induced complete displacement of [(125)I]PACAP27 binding, whereas vasoactive intestinal polypeptide (VIP) was a weak competitor. In contrast, in the Purkinje cell layer, both PACAP and VIP displaced [(125)I]PACAP27 binding. Measurement of cAMP levels showed that PACAP is a powerful activator of adenylyl cyclase, whereas VIP is about 100-fold less potent. Altogether, these observations constitute the first demonstration of a functional PACAPergic system in monkey cerebellum. They strongly suggest that neuroprotective effects of PACAP can be transposed to primates, including human.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".