Effects of Nerve Growth Factor and Nitric Oxide Synthase Inhibitors on Amyloid Precursor Protein mRNA Levels and Protein Stability
Bibliographic record
Abstract
We determined previously that nitric oxide (NO) modulates the nerve growth factor (NGF)-mediated increases in amyloid precursor protein (APP) levels in PC12 cells. To elucidate potential mechanisms, the effects of NGF and NO synthase (NOS) inhibitors on APP mRNA levels and protein stability were evaluated. Surprisingly, treatment of PC12 cells with NGF resulted in decreased levels of APP695 and APP751/770 mRNA. Therefore, the effect of NGF on APP protein stability was examined using the translation inhibitor, cycloheximide. Under these conditions, NGF did not alter the rate of APP degradation, suggesting that NGF may be enhancing the translation rate of APP. Since NOS inhibitors attenuate the NGF-mediated increase in APP levels, their effect on APP mRNA levels and protein stability was also assessed. S-methylisothiourea (S-MIU), selective for inducible NOS, decreased both APP695 and APP751/770 mRNA levels while the non-selective NOS inhibitor, N(ω)-nitro-L-arginine methylester (L-NAME) had no effect. In both control and NGF-treated PC12 cells, S-MIU increased the half-life of APP, with the greatest effect observed with the APP695 isoform. Based on these data we propose that in PC12 cells, NGF increases APP levels through enhanced translation rate and that NO, which modulates the NGF-induced increase in APP protein, also regulates APP mRNA levels and could play a role in APP processing.
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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 | 0.000 |
| 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".