Pituitary adenylate cyclase‐activating polypeptide (PACAP) stimulates the expression and the release of tissue plasminogen activator (tPA) in neuronal cells: involvement of tPA in the neuroprotective effect of PACAP
Bibliographic record
Abstract
Pituitary adenylate cyclase-activating polypeptide (PACAP) and tissue plasminogen activator (tPA) play important roles in neuronal migration and survival. However, a direct link between the neurotrophic effects of PACAP and tPA has never been investigated. In this study, we show that, in PC12 cells, PACAP induced a 9.85-fold increase in tPA gene expression through activation of the protein kinase A- and protein kinase C-dependent signaling pathways. In immature cerebellar granule neurons (CGN), PACAP stimulated tPA mRNA expression and release of proteolytically active tPA. Immunocytochemical labeling revealed the presence of tPA in the cytoplasm and processes of cultured CGN. The inhibitory effect of PACAP on CGN motility was not affected by the tPA substrate plasminogen or the tPA inhibitor plasminogen activator inhibitor-1. In contrast, plasminogen activator inhibitor-1 significantly reduced the stimulatory effect of PACAP on CGN survival. Altogether, these data indicate that tPA gene expression is activated by PACAP in both tumoral and normal neuronal cells. The present study also demonstrates that PACAP stimulates the release of tPA which promotes CGN survival by a mechanism dependent of its proteolytic activity.
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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) | 0.001 | 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".