SORCS1 alters amyloid precursor protein processing and variants may increase Alzheimer's disease risk
Bibliographic record
Abstract
OBJECTIVE: Sorting mechanisms that cause the amyloid precursor protein (APP) and the β-secretases and γ-secretases to colocalize in the same compartment play an important role in the regulation of Aβ production in Alzheimer's disease (AD). We and others have reported that genetic variants in the Sortilin-related receptor (SORL1) increased the risk of AD, that SORL1 is involved in trafficking of APP, and that underexpression of SORL1 leads to overproduction of Aβ. Here we explored the role of one of its homologs, the sortilin-related VPS10 domain containing receptor 1 (SORCS1), in AD. METHODS: We analyzed the genetic associations between AD and 16 SORCS1-single nucleotide polymorphisms (SNPs) in 6 independent data sets (2,809 cases and 3,482 controls). In addition, we compared SorCS1 expression levels of affected and unaffected brain regions in AD and control brains in microarray gene expression and real-time polymerase chain reaction (RT-PCR) sets, explored the effects of significant SORCS1-SNPs on SorCS1 brain expression levels, and explored the effect of suppression and overexpression of the common SorCS1 isoforms on APP processing and Aβ generation. RESULTS: Inherited variants in SORCS1 were associated with AD in all datasets (0.001 < p < 0.049). In addition, SorCS1 influenced APP processing. While overexpression of SorCS1 reduced γ-secretase activity and Aβ levels, the suppression of SorCS1 increased γ-secretase processing of APP and the levels of Aβ. INTERPRETATIONS: These data suggest that inherited or acquired changes in SORCS1 expression or function may play a role in the pathogenesis of AD.
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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".