Proportional Downscaling of Glutamatergic Release Sites by the General Anesthetic Propofol at<i>Drosophila</i>Motor Nerve Terminals
Bibliographic record
Abstract
Propofol is the most common general anesthetic used for surgery in humans, yet its complete mechanism of action remains elusive. In addition to potentiating inhibitory synapses in the brain, propofol also impairs excitatory neurotransmission. We use electrophysiological recordings from individual glutamatergic boutons in male and female larvalDrosophila melanogastermotor nerve terminals to characterize this effect. We recorded from two bouton types, which have distinct presynaptic physiology and different average numbers of release sites or active zones. We show that a clinically relevant dose of propofol (3 μm) impairs neurotransmitter release similarly at both bouton types by decreasing the number of active release sites by half, without affecting release probability. In contrast, an analog of propofol has no effect on glutamate release. Coexpressing a truncated syntaxin1A protein in presynaptic boutons completely blocked this effect of propofol. Overexpressing wild-type syntaxin1A in boutons also conferred a level of resistance by increasing the number of active release sites to a physiological ceiling set by the number of active zones or T-bars, and in this way counteracting the effect of propofol. These results point to the presynaptic release machinery as a target for the general anesthetic. Proportionally equivalent effects of propofol on the number of active release sites across the different bouton types suggests that glutamatergic circuits that involve smaller boutons with fewer release sites may be more vulnerable to the presynaptic effects of the drug.
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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".