Neurotrophin-3 Enhances the Synaptic Organizing Function of TrkC-Protein Tyrosine Phosphatase in Rat Hippocampal Neurons
Bibliographic record
Abstract
Neurotrophin-3 (NT-3) and its high-affinity receptor TrkC play crucial trophic roles in neuronal differentiation, axon outgrowth, and synapse development and plasticity in the nervous system. We demonstrated previously that postsynaptic TrkC functions as a glutamatergic synapse-inducing (synaptogenic) cell adhesion molecule trans-interacting with presynaptic protein tyrosine phosphatase σ (PTPσ). Given that NT-3 and PTPσ bind distinct domains of the TrkC extracellular region, here we tested the hypothesis that NT-3 modulates TrkC/PTPσ binding and synaptogenic activity. NT-3 enhanced PTPσ binding to cell surface-expressed TrkC and facilitated the presynapse-inducing activity of TrkC in rat hippocampal neurons. Imaging of recycling presynaptic vesicles combined with TrkC knockdown and rescue approaches demonstrated that NT-3 rapidly potentiates presynaptic function via binding endogenous postsynaptic TrkC in a tyrosine kinase-independent manner. Thus, NT-3 positively modulates the TrkC-PTPσ complex for glutamatergic presynaptic assembly and function independently from TrkC kinase activation. Our findings provide new insight into synaptic roles of neurotrophin signaling and mechanisms controlling synaptic organizing complexes. Significance statement: Although many synaptogenic adhesion complexes have been identified in recent years, little is known about modulatory mechanisms. Here, we demonstrate a novel role of neurotrophin-3 in synaptic assembly and function as a positive modulator of the TrkC-protein tyrosine phosphatase σ complex. This study provides new insight into the involvement of neurotrophin signaling in synapse development and plasticity, presenting a molecular mechanism that may underlie previous observations of short- and long-term enhancement of presynaptic function by neurotrophin. Given the links of synaptogenic adhesion molecules to autism and schizophrenia, this study might also contribute to a better understanding of the pathogenesis of these disorders and provide a new direction for ameliorating imbalances in synaptic signaling networks.
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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".