BDNF Enhances Quantal Neurotransmitter Release and Increases the Number of Docked Vesicles at the Active Zones of Hippocampal Excitatory Synapses
Bibliographic record
Abstract
Brain-derived neurotrophic factor (BDNF) is emerging as a key mediator of activity-dependent modifications of synaptic strength in the CNS. We investigated the hypothesis that BDNF enhances quantal neurotransmitter release by modulating the distribution of synaptic vesicles within presynaptic terminals using organotypic slice cultures of postnatal rat hippocampus. BDNF specifically increased the number of docked vesicles at the active zone of excitatory synapses on CA1 dendritic spines, with only a small increase in active zone size. In agreement with the hypothesis that an increased docked vesicle density enhances quantal neurotransmitter release, BDNF increased the frequency, but not the amplitude, of AMPA receptor-mediated miniature EPSCs (mEPSCs) recorded from CA1 pyramidal neurons in hippocampal slices. Synapse number, independently estimated from dendritic spine density and electron microscopy measurements, was also increased after BDNF treatment, indicating that the actions of BNDF on mEPSC frequency can be partially attributed to an increased synaptic density. Our results further suggest that all these actions were mediated via tyrosine kinase B (TrkB) receptor activation, established by inhibition of plasma membrane tyrosine kinases with K-252a. These results provide additional evidence of a fundamental role of the BDNF-TrkB signaling cascade in synaptic transmission, as well as in cellular models of hippocampus-dependent learning and memory.
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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".