Overexpression of the cell adhesion protein neuroligin‐1 induces learning deficits and impairs synaptic plasticity by altering the ratio of excitation to inhibition in the hippocampus
Bibliographic record
Abstract
Trans-synaptic cell-adhesion molecules have been implicated in regulating CNS synaptogenesis. Among these, the Neuroligin (NL) family (NLs 1-4) of postsynaptic adhesion proteins has been shown to promote the development and specification of excitatory versus inhibitory synapses. NLs form a heterophilic complex with the presynaptic transmembrane protein Neurexin (NRX). A differential association of NLs with postsynaptic scaffolding proteins and NRX isoforms has been suggested to regulate the ratio of excitatory to inhibitory synapses (E/I ratio). Using transgenic mice, we have tested this hypothesis by overexpressing NL1 in vivo to determine whether the relative levels of these cell adhesion molecules may influence synapse maturation, long-term potentiation (LTP), and/or learning. We found that NL1-overexpressing mice show significant deficits in memory acquisition, but not in memory retrieval. Golgi and electron microscopy analysis revealed changes in synapse morphology indicative of increased maturation of excitatory synapses. In parallel, electrophysiological examination indicated a shift in the synaptic activity toward increased excitation as well as impairment in LTP induction. Our results demonstrate that altered balance in the expression of molecules necessary for synapse specification and development (such as NL1) can lead to defects in memory formation and synaptic plasticity and outline the importance of rigidly controlled synaptic maturation processes.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".