The neuronal protein Neurexin directly interacts with the Scribble–Pix complex to stimulate F-actin assembly for synaptic vesicle clustering
Bibliographic record
Abstract
Synaptic vesicles (SVs) form distinct pools at synaptic terminals, and this well-regulated separation is necessary for normal neurotransmission. However, how the SV cluster, in particular synaptic compartments, maintains normal neurotransmitter release remains a mystery. The presynaptic protein Neurexin (NRX) plays a significant role in synaptic architecture and function, and some evidence suggests that NRX is associated with neurological disorders, including autism spectrum disorders. However, the role of NRX in SV clustering is unclear. Here, using the neuromuscular junction at the 2–3 instar stages of Drosophila larvae as a model and biochemical imaging and electrophysiology techniques, we demonstrate that Drosophila NRX (DNRX) plays critical roles in regulating synaptic terminal clustering and release of SVs. We found that DNRX controls the terminal clustering and release of SVs by stimulating presynaptic F-actin. Furthermore, our results indicate that DNRX functions through the scaffold protein Scribble and the GEF protein DPix to activate the small GTPase Ras-related C3 Botulinum toxin substrate 1 (Rac1). We observed a direct interaction between the C-terminal PDZ-binding motif of DNRX and the PDZ domains of Scribble and that Scribble bridges DNRX to DPix, forming a DNRX–Scribble–DPix complex that activates Rac1 and subsequently stimulates presynaptic F-actin assembly and SV clustering. Taken together, our work provides important insights into the function of DNRX in regulating SV clustering, which could help inform further research into pathological neurexin -mediated mechanisms in neurological disorders such as autism.
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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 | 0.001 | 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) | 0.003 | 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".