The biological activity of proNGF is determined by the relative levels of TrkA and p75NTR receptors
Bibliographic record
Abstract
Nerve growth factor (NGF) regulates neuronal survival, differentiation and function and is found in CNS as the NGF precursor, proNGF. Whether proNGF is neurotrophic or apoptotic is controversial. NGF and proNGF bind the TrkA receptor which is responsible for cell survival and differentiation, and p75NTR, which can mediate apoptosis. ProNGF has a higher affinity for p75NTR while NGF has a higher affinity for TrkA. It is known that the ratio of TrkA to p75NTR can determine whether mature NGF signals cell survival or death. We show here that the ratio of TrkA to p75NTR also determines the fate of cells treated with proNGF. We used growth conditions, siRNA and knockouts to modulate TrkA levels in PC12 cells expressing both TrkA and p75NTR. We found that although proNGF is neurotrophic for cells expressing high levels of TrkA and moderate or low levels of p75NTR, proNGF induces apoptosis in cells expressing lower levels of TrkA and higher levels of p75NTR, while NGF is neurotrophic unless TrkA is eliminated. Thus, proNGF is more sensitive to relative TrkA/p75NTR levels than NGF. In conclusion, the biological activity of proNGF can be altered by modulating relative levels of TrkA and p75NTR, suggesting that proNGF is neurotrophic under normal circumstances, but that a shift in the balance between its two receptors, as occurs in neurodegenerative disease or injury, may alter proNGF signaling from cell survival to cell death. Support: Canadian Institutes of Health Research (CIHR) Grants MOP‐64382 and IAO‐94360 to MF, NSERC Scholarship to MSI, Ministry of Science of Iran Scholarship to RM.
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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 | 0.001 |
| 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".