Structure Function and Splice Site Analysis of the Synaptogenic Activity of the Neurexin-1β LNS Domain
Bibliographic record
Abstract
Recent findings suggest that the neurexin-neuroligin link promotes both GABAergic and glutamatergic synaptogenesis, but the mechanism by which neurexins influence the clustering of appropriate neuroligins and postsynaptic differentiation remains unclear. Previous studies suggested that the presence or absence of alternatively spliced residues at splice site 4 (S4) in the neurexin LNS domain may regulate neurexin function. We demonstrate that addition of the S4 insert selectively reduces the ability of neurexin-1beta to cluster neuroligin-1/3/4 and glutamatergic postsynaptic proteins, although clustering of neuroligin-2 and GABAergic postsynaptic proteins remain strong. Furthermore, addition of the S4 insert decreases the binding affinity of neurexin-1beta to neuroligins-1 and -4 but has little effect on binding to neuroligins-2 and -3. Additional structure-function studies reveal the neurexin binding interface mediating synaptogenic activity to be composed primarily of residues in the beta2beta3, beta6beta7, and beta10beta11 loops on one rim of the LNS domain beta sandwich. Mutation of two predicted Ca(2+)-binding residues disrupts postsynaptic protein clustering and binding to neuroligins, consistent with previous findings that neurexin-neuroligin binding is Ca2+ dependent. Glutamatergic postsynaptic clustering was more readily disrupted by the mutagenesis than GABAergic postsynaptic protein clustering. Perhaps neurexins-neuroligins, or neurexin-1beta at least, is most important for GABA synapse formation or controlling the balance of GABA and glutamate synapses. These results suggest that differential neurexin-neuroligin binding affinities and splice variations may play an instructive role in postsynaptic differentiation.
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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 | 0.000 | 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".