Nerve growth factor activation of the TrkA receptor induces cell death, by macropinocytosis, in medulloblastoma Daoy cells
Bibliographic record
Abstract
Ectopic expression of the TrkA receptor tyrosine kinase in tumors of the nervous system can mediate nerve growth factor (NGF)-dependent cell death by apoptosis and /or autophagy. Herein, we demonstrate that TrkA can also induce cell death in medulloblastoma Daoy cells by a caspase-independent mechanism that involves the hyperstimulation of macropinocytosis. Specifically, NGF-stimulates the uptake of AlexaFluor546-dextran into lysosome-associated membrane protein-1 positive vacuoles which fuse with microtubule associated protein light chain 3 (LC3) positive autophagosomes, to form large intracellular vacuoles (> 1 mum), which then fuse with lysotracker positive lysosomes. While LC3 cleavage and the appearance of LC3 positive vacuoles suggest the induction of autophagy, siRNA reduced expression of four proteins essential to autophagy (beclin-1, Atg5, LC3 and Atg9) neither blocks NGF-induced vacuole formation nor cell death. TrkA activated cell death does not require p38, JNK or Erk1/2 kinases but does require activation of class III PI-3 kinase and is blocked by the casein kinase 1 (CK1) inhibitor, D4476. This inhibitor does not interfere with TrkA activation but does block NGF-dependent AlexaFluor546-dextran uptake and CK1 dependent phosphorylation of beta-catenin. Collectively, these data demonstrate that TrkA stimulates cell death by a novel mechanism involving CK1-dependent hyperstimulation of macropinocytosis.
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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 | 0.000 |
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".