Surface SV2A-Syt1 nanoclusters act as a sequestration hub that limits dynamin-1 recruitment and targeting to recycling synaptic vesicles
Bibliographic record
Abstract
Abstract Following exocytosis, the recapture of plasma membrane-stranded vesicular proteins into recycling synaptic vesicles (SVs) is essential for sustaining neurotransmission. Surface clustering of vesicular proteins has been postulated as a ‘pre-assembly’ mechanism for endocytosis – ensuring high-fidelity retrieval. Here, we used single-molecule imaging to examine the nanoclustering of synaptotagmin-1 (Syt1) and synaptic vesicle protein 2A (SV2A) in hippocampal neurons. Syt1 forms surface nanoclusters through interaction of its C2B domain with SV2A, that are sensitive to mutations in this domain (Syt1 K326A/K328A ) and knocking down SV2A. SV2A co-cluster with Syt1 and blocking SV2A’s cognate interaction with Syt1 (SV2A T84A ) also decreased SV2A clustering. Surprisingly, impairing SV2A-Syt1 nanoclustering enhanced plasma membrane recruitment of key endocytic protein dynamin-1, leading to accelerated Syt1 endocytosis, altered intracellular sorting and decreased trafficking of Syt1 to Rab5-positive endocytic compartments. SV2A-Syt1 surface nanoclusters therefore negatively regulate the rate of their own re-entry into recycling SVs by controlling the recruitment of the endocytic machinery.
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 | 0.000 |
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".