Agrin Regulates Rapsyn Interaction with Surface Acetylcholine Receptors, and This Underlies Cytoskeletal Anchoring and Clustering
Bibliographic record
Abstract
The acetylcholine receptor (AChR)-associated protein rapsyn is essential for neuromuscular synapse formation and clustering of AChRs, but its mode of action remains unclear. We have investigated whether agrin, a key nerve-derived synaptogenic factor, influences rapsyn-AChR interactions and how this affects clustering and cytoskeletal linkage of AChRs. By precipitating AChRs and probing for associated rapsyn, we found that in denervated diaphragm rapsyn associates with synaptic as well as with extrasynaptic AChRs showing that rapsyn interacts with unclustered AChRs in vivo. Interestingly, synaptic AChRs are associated with more rapsyn suggesting that clustering of AChRs may require increased interaction with rapsyn. In similar experiments in cultured myotubes, rapsyn interacted with intracellular AChRs and with unclustered AChRs at the cell surface, although surface interactions are much more prominent. Remarkably, agrin induces recruitment of additional rapsyn to surface AChRs and clustering of AChRs independently of the secretory pathway. This agrin-induced increase in rapsyn-AChR interaction strongly correlates with clustering, because staurosporine and herbimycin blocked both the increase and clustering. Conversely, laminin and calcium induced both increased rapsyn-AChR interaction and AChR clustering. Finally, time course experiments revealed that the agrin-induced increase occurs with AChRs that become cytoskeletally linked, and that this precedes receptor clustering. Thus, we propose that neural agrin controls postsynaptic aggregation of the AChR by enhancing rapsyn interaction with surface AChRs and inducing cytoskeletal anchoring and that this is an important precursor step for AChR clustering.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".