Microarray and Suppression Subtractive Hybridization Analyses of Gene Expression in Pheochromocytoma Cells Reveal Pleiotropic Effects of Pituitary Adenylate Cyclase-Activating Polypeptide on Cell Proliferation, Survival, and Adhesion
Bibliographic record
Abstract
Pituitary adenylate cyclase-activating polypeptide (PACAP) exerts trophic effects on several neuronal, neuroendocrine, and endocrine cells. To gain insight into the pattern of the transcriptional modifications induced by PACAP during cell differentiation, we studied the effects of this neuropeptide on rat pheochromocytoma PC12 cells. We first analyzed the transcriptome of PC12 cells in comparison to that of terminally differentiated rat adrenomedullary chromaffin cells, using a high-density microarray, to identify genes associated with the proliferative phenotype that are possible targets of PACAP during differentiation of sympathoadrenal normal and tumoral cells. We then studied global gene expression in PC12 cells after 48 h of exposure to PACAP, using both cDNA microarray and suppression subtractive hybridization technologies. These complementary approaches resulted in the identification of 75 up-regulated and 70 down-regulated genes in PACAP-treated PC12 cells. Among the genes whose expression is modified in differentiated cells, a vast majority are involved in cell proliferation, survival, and adhesion/motility. Expression changes of most of these genes have been associated with progression of several neoplasms. A kinetic study of the effects of PACAP on some of the identified genes showed that the neuropeptide likely exerts early as well as late actions to achieve the gene expression program necessary for cell differentiation. In conclusion, the results of the present study underscore the pleiotropic role of PACAP in cell differentiation and provide important information on novel targets that could mediate the effects of this neuropeptide in normal and tumoral neuroendocrine cells.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".