Segregated localization of target-SNARE proteins within presynaptic terminals of Munc18-1 deficient photoreceptors
Bibliographic record
Abstract
Abstract Sec1/Munc18 family proteins are essential for SNARE-mediated vesicular exocytosis. However, where SNARE proteins are localized in Munc18-1 deficient presynaptic terminals remains unclear due to the rapid degeneration of neurons lacking Munc18-1. Here, we found that removing Munc18-1 from photoreceptor cells did not result in major cellular loss until postnatal day 14, which allowed us to investigate the role of Munc18-1 in endogenous presynaptic terminals. In the absence of Munc18-1, even before major photoreceptor cell degeneration, functional impairments were present. While Munc18-1 was not required for the pre-synaptic enrichment of the t-SNARE proteins syntaxin-3 and SNAP-25, it played a critical role in their proper localization. In wild-type conditions, t-SNAREs are highly colocalized. However, in the absence of Munc18-1, their distribution becomes strikingly segregated. Immuno-electron microscopy revealed that without Munc18-1, syntaxin-3 is retained within various organelle membranes rather than being targeted to synaptic plasma membranes. These findings provide the first evidence that Munc18-1 is important to prevent segregation of syntaxin-3 and SNAP-25 within presynaptic terminals.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
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".