Regulation of Synaptic Vesicle Docking by Different Classes of Macromolecules in Active Zone Material
Bibliographic record
Abstract
The docking of synaptic vesicles at active zones on the presynaptic plasma membrane of axon terminals is essential for their fusion with the membrane and exocytosis of their neurotransmitter to mediate synaptic impulse transmission. Dense networks of macromolecules, called active zone material, (AZM) are attached to the presynaptic membrane next to docked vesicles. Electron tomography has shown that some AZM macromolecules are connected to docked vesicles, leading to the suggestion that AZM is somehow involved in the docking process. We used electron tomography on the simply arranged active zones at frog neuromuscular junctions to characterize the connections of AZM to docked synaptic vesicles and to search for the establishment of such connections during vesicle docking. We show that each docked vesicle is connected to 10-15 AZM macromolecules, which fall into four classes based on several criteria including their position relative to the presynaptic membrane. In activated axon terminals fixed during replacement of docked vesicles by previously undocked vesicles, undocked vesicles near vacated docking sites on the presynaptic membrane have connections to the same classes of AZM macromolecules that are connected to docked vesicles in resting terminals. The number of classes and the total number of macromolecules to which the undocked vesicles are connected are inversely proportional to the vesicles' distance from the presynaptic membrane. We conclude that vesicle movement toward and maintenance at docking sites on the presynaptic membrane are directed by an orderly succession of stable interactions between the vesicles and distinct classes of AZM macromolecules positioned at different distances from the membrane. Establishing the number, arrangement and sequence of association of AZM macromolecules involved in vesicle docking provides an anatomical basis for testing and extending concepts of docking mechanisms provided by biochemistry.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".