Nerve Growth Factor Enhances Tau Isoform Expression and Transcription in IMR32 Cells
Bibliographic record
Abstract
The present study characterized the nerve growth factor (NGF)-mediated regulation of tau protein expression and transcription in IMR32 human neuroblastoma cells. Treatment of IMR32 cells with 50 ng/mL NGF resulted in increased levels of specific tau protein isoforms. A 550 bp fragment of the tau promoter was cloned and treatment of transfected IMR32 and PC12 cells with NGF also resulted in increased promoter activation, suggesting that the NGF-mediated increase in tau isoforms is regulated, at least in part, at the level of transcription. Pretreatment with the MAP kinase inhibitor U0126 or the PKC inhibitor bisindolylmaleimide 1 (BIS-1) attenuated the NGF-mediated increase in tau transcription, indicating that the NGF-mediated activation of the MAP kinase and PKC signaling pathways modulate tau transcription. Pre-treatment of cells with the Akt inhibitor, LY294002 or with NOS inhibitors Nω-nitro-L-arginine methylester (L-NAME) or s-methylisothiourea (S-MIU) had no effect on the NGF-mediated increase in tau promoter activation, suggesting that NO and the NGF-Akt signaling pathway do not modulate tau transcription. Taken together, these data demonstrate that NGF increases the levels of multiple human tau isoforms in IMR32 cells which may result, at least in part, from NGF-mediated PKC and MAP kinase-induced tau transcription.
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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 | 0.001 |
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".