ProBDNF Induces Neuronal Apoptosis via Activation of a Receptor Complex of p75<sup>NTR</sup>and Sortilin
Bibliographic record
Abstract
Brain-derived neurotrophic factor (BDNF) is best characterized for critical roles in neuronal survival, differentiation, and synaptic modulation mediated by the TrkB receptor tyrosine kinase. Developmentally regulated death signaling by BDNF has also been demonstrated via activation of p75NTR. Because recent studies suggest that proNGF, the precursor form of NGF, is more active than mature NGF in inducing apoptosis after binding to p75NTR and a coreceptor, sortilin, we asked whether the precursor of BDNF (proBDNF) is also a proapoptotic ligand in the nervous system. proBDNF is secreted by cultured neurons, and recombinant proBDNF binds to sortilin. In sympathetic neurons coexpressing sortilin and p75NTR, we found that proBDNF is an apoptotic ligand that induces death at subnanomolar concentrations. In contrast, mature BDNF, but not proBDNF, is effective in inducing TrkB phosphorylation. proBDNF effects are dependent on cellular coexpression of both p75NTR and sortilin, because neurons deficient in p75NTR are resistant to proBDNF-induced apoptosis, and competitive antagonists of sortilin block sympathetic neuron death. Moreover, addition of preformed complexes of soluble sortilin and proBDNF failed to induce apoptosis of cells coexpressing both sortilin and p75NTR, suggesting that interaction of proBDNF with both receptors on the cell surface is required to initiate cell death. Together with our past findings, these data suggest that the neurotrophin family is capable of modulating diverse biological processes via differential processing of the proneurotrophins.
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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".