Scribble Interacts with β-Catenin to Localize Synaptic Vesicles to Synapses
Bibliographic record
Abstract
An understanding of how synaptic vesicles are recruited to and maintained at presynaptic compartments is required to discern the molecular mechanisms underlying presynaptic assembly and plasticity. We have previously demonstrated that cadherin-beta-catenin complexes cluster synaptic vesicles at presynaptic sites. Here we show that scribble interacts with the cadherin-beta-catenin complex to coordinate vesicle localization. Scribble and beta-catenin are colocalized at synapses and can be coimmunoprecipitated from neuronal lysates, indicating an interaction between scribble and beta-catenin at the synapse. Using an RNA interference approach, we demonstrate that scribble is important for the clustering of synaptic vesicles at synapses. Indeed, in scribble knockdown cells, there is a diffuse distribution of synaptic vesicles along the axon, and a deficit in vesicle recycling. Despite this, synapse number and the distribution of the presynaptic active zone protein, bassoon, remain unchanged. These effects largely phenocopy those observed after ablation of beta-catenin. In addition, we show that loss of beta-catenin disrupts scribble localization in primary neurons but that the localization of beta-catenin is not dependent on scribble. Our data supports a model by which scribble functions downstream of beta-catenin to cluster synaptic vesicles at developing synapses.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".