Peroxisome Proliferator-activated Receptor γ Is a Novel Target of the Nerve Growth Factor Signaling Pathway in PC12 Cells
Bibliographic record
Abstract
Peroxisome proliferator-activated receptor gamma (PPARgamma), a member of the nuclear receptor superfamily, is subject to considerable interest because of its role in adipocyte differentiation, metabolic control, and anti-inflammatory action. PPARgamma research in brain cells is presently focused on glial PPARgamma because of its potential as a pharmacological target in the treatment of neurodegenerative diseases with an inflammatory component. In neurons PPARgamma function is far from clear, and PPARgamma agonist-dependent and -independent effects on cell survival or differentiation have been reported. We used PC12 cells, widely used to study neuronal signaling, such as nerve growth factor (NGF)-induced differentiation and survival or epidermal growth factor-dependent cell proliferation to dissect the possible involvement of PPARgamma in these pathways. We show that NGF but not epidermal growth factor increases the transcriptional activity of PPARgamma, and modulates the expression of this transcription factor. Because NGF signals through the tyrosine kinase (TrkA) NGF receptor and/or the p75NTR receptor, we used rescue experiments with a PC12 cell mutant lacking TrkA to show that NGF-induced PPARgamma activation is dependent on TrkA activation. Our results point out PPARgamma as a novel target of the TrkA-mediated neuronal cell survival and differentiating pathway and suggest a potential new inflammatory-independent therapeutic approach for pharmacological intervention in neurological disorders.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".