Interaction of Acetylcholinesterase with Neurexin-1β regulates Glutamatergic Synaptic stability in Hippocampal neurons
Bibliographic record
Abstract
BACKGROUND: Excess expression of acetylcholinesterase (AChE) in the cortex and hippocampus causes a decrease in the number of glutamatergic synapses and alters the expression of neurexin and neuroligin, trans-synaptic proteins that control synaptic stability. The molecular sequence and three-dimensional structure of AChE are homologous to the corresponding aspects of the ectodomain of neuroligin. This study investigated whether excess AChE interacts physically with neurexin to destabilize glutamatergic synapses. RESULTS: The results showed that AChE clusters colocalized with neurexin assemblies in the neurites of hippocampal neurons and that AChE co-immunoprecipitated with neurexin from the lysate of these neurons. Moreover, when expressed in human embryonic kidney 293 cells, N-glycosylated AChE co-immunoprecipitated with non-O-glycosylated neurexin-1β, with N-glycosylation of the AChE being required for this co-precipitation to occur. Increasing extracellular AChE decreased the association of neurexin with neuroligin and inhibited neuroligin-induced synaptogenesis. The number and activity of excitatory synapses in cultured hippocampal neurons were reduced by extracellular catalytically inactive AChE. CONCLUSIONS: Excessive glycosylated AChE could competitively disrupt a subset of the neurexin-neuroligin junctions consequently impairing the integrity of glutamatergic synapses. This might serve a molecular mechanism of excessive AChE induced neurodegeneration.
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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".