SorCS1 inhibits amyloid-β binding to neurexin and rescues amyloid-β-induced synaptic pathology
Bibliographic record
Abstract
Abstract Amyloid-β oligomers (AβOs), toxic peptide aggregates found in Alzheimer’s disease (AD), cause synapse pathology. AβOs interact with Neurexins (NRXs), key synaptic organizers, and this interaction dampens normal trafficking and function of NRXs. Axonal trafficking of NRX is in part regulated by its interaction with SorCS1, a protein sorting receptor, but the impact of SorCS1 regulation of NRXs in Aβ pathology was previously unstudied. Here, we show competitive interaction of SorCS1 and AβOs with β-NRXs and rescue effects of SorCS1 on AβO-induced synaptic pathology. Like AβOs, SorCS1 binds to NRX1β through the histidine-rich-domain (HRD) of NRX1β, and SorCS1 and AβOs compete for NRX1β binding. In cultured hippocampal neurons, SorCS1 colocalizes with NRX1β on the axon surface, and axonal expression of SorCS1 rescues AβO-induced impairment of NRX-mediated presynaptic organization and presynaptic vesicle recycling as well as AβO-induced structural defects in excitatory synapses. Thus, we reveal a role of SorCS1 in the rescue of AβO-induced NRX dysfunction and synaptic pathology, providing the basis for a novel potential therapeutic strategy for 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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".