Interaction of amyloid-β (Aβ) oligomers with neurexin 2α and neuroligin 1 mediates synapse damage and memory loss in mice
Bibliographic record
Abstract
Brain accumulation of the amyloid-β protein (Aβ) and synapse loss are neuropathological hallmarks of Alzheimer disease (AD). Aβ oligomers (AβOs) are synaptotoxins that build up in the brains of patients and are thought to contribute to memory impairment in AD. Thus, identification of novel synaptic components that are targeted by AβOs may contribute to the elucidation of disease-relevant mechanisms. Trans-synaptic interactions between neurexins (Nrxs) and neuroligins (NLs) are essential for synapse structure, stability, and function, and reduced NL levels have been associated recently with AD. Here we investigated whether the interaction of AβOs with Nrxs or NLs mediates synapse damage and cognitive impairment in AD models. We found that AβOs interact with different isoforms of Nrx and NL, including Nrx2α and NL1. Anti-Nrx2α and anti-NL1 antibodies reduced AβO binding to hippocampal neurons and prevented AβO-induced neuronal oxidative stress and synapse loss. Anti-Nrx2α and anti-NL1 antibodies further blocked memory impairment induced by AβOs in mice. The results indicate that Nrx2α and NL1 are targets of AβOs and that prevention of this interaction reduces the deleterious impact of AβOs on synapses and cognition. Identification of Nrx2α and NL1 as synaptic components that interact with AβOs may pave the way for development of novel approaches aimed at halting synapse failure and cognitive loss in AD.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".