The p75 Neurotrophin Receptor Activates Akt (Protein Kinase B) through a Phosphatidylinositol 3-Kinase-dependent Pathway
Bibliographic record
Abstract
The Akt kinase plays a crucial role in supporting Trk-dependent cell survival, whereas the p75 neurotrophin receptor (p75NTR) facilitates cellular apoptosis. The precise mechanism that p75NTR uses to promote cell death is not certain, but one possibility is that p75NTR-dependent ceramide accumulation inhibits phosphatidylinositol 3-kinase-mediated Akt activation. To test this hypothesis, we developed a system for examining p75NTR-dependent apoptosis and determined the effect of p75NTR on Akt activation. Surprisingly, p75NTR increased, rather than decreased, Akt phosphorylation in a variety of cell types, including human Niemann-Pick fibroblasts, which lack acidic sphingomyelinase activity. The p75NTR expression level required to elicit Akt phosphorylation was much lower than that required to activate the JNK pathway or to mediate apoptosis. We show that p75NTR-dependent Akt phosphorylation was independent of TrkA signaling, required active phosphatidylinositol 3-kinase, and was associated with increased tyrosine phosphorylation of p85 and Shc and with reduced cytosolic tyrosine phosphatase activity. Finally, we show that p75NTR expression increased survival in cells exposed to staurosporine or subjected to serum withdrawal. These findings indicate that p75NTR facilitates cell survival through novel signaling cascades that result in Akt activation.
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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 | 0.000 |
| 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".