Single calcium channel domain gating of synaptic vesicle fusion at fast synapses; analysis by graphic modeling
Bibliographic record
Abstract
At fast-transmitting presynaptic terminals Ca(2+) enter through voltage gated calcium channels (CaVs) and bind to a synaptic vesicle (SV) -associated calcium sensor (SV-sensor) to gate fusion and discharge. An open CaV generates a high-concentration plume, or nanodomain of Ca(2+) that dissipates precipitously with distance from the pore. At most fast synapses, such as the frog neuromuscular junction (NMJ), the SV sensors are located sufficiently close to individual CaVs to be gated by single nanodomains. However, at others, such as the mature rodent calyx of Held (calyx of Held), the physiology is more complex with evidence that CaVs that are both close and distant from the SV sensor and it is argued that release is gated primarily by the overlapping Ca(2+) nanodomains from many CaVs. We devised a 'graphic modeling' method to sum Ca(2+) from individual CaVs located at varying distances from the SV-sensor to determine the SV release probability and also the fraction of that probability that can be attributed to single domain gating. This method was applied first to simplified, low and high CaV density model release sites and then to published data on the contrasting frog NMJ and the rodent calyx of Held native synapses. We report 3 main predictions: the SV-sensor is positioned very close to the point at which the SV fuses with the membrane; single domain-release gating predominates even at synapses where the SV abuts a large cluster of CaVs, and even relatively remote CaVs can contribute significantly to single domain-based gating.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".