LDLR-related protein 10 (LRP10) regulates amyloid precursor protein (APP) trafficking and processing: evidence for a role in Alzheimer’s disease
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Bibliographic record
Abstract
BACKGROUND: The Aβ peptide that accumulates in Alzheimer's disease (AD) is derived from amyloid precursor protein (APP) following proteolysis by β- and γ-secretases. Substantial evidence indicates that alterations in APP trafficking within the secretory and endocytic pathways directly impact the interaction of APP with these secretases and subsequent Aβ production. Various members of the low-density lipoprotein receptor (LDLR) family have been reported to play a role in APP trafficking and processing and are important risk factors in AD. We recently characterized a distinct member of the LDLR family called LDLR-related protein 10 (LRP10) that shuttles between the trans-Golgi Network (TGN), plasma membrane (PM), and endosomes. Here we investigated whether LRP10 participates in APP intracellular trafficking and Aβ production. RESULTS: In this report, we provide evidence that LRP10 is a functional APP receptor involved in APP trafficking and processing. LRP10 interacts directly with the ectodomain of APP and colocalizes with APP at the TGN. Increased expression of LRP10 in human neuroblastoma SH-SY5Y cells induces the accumulation of mature APP in the Golgi and reduces its presence at the cell surface and its processing into Aβ, while knockdown of LRP10 expression increases Aβ production. Mutations of key motifs responsible for the recycling of LRP10 to the TGN results in the aberrant redistribution of APP with LRP10 to early endosomes and a concomitant increase in APP β-cleavage into Aβ. Furthermore, expression of LRP10 is significantly lower in the post-mortem brain tissues of AD patients, supporting a possible role for LRP10 in AD. CONCLUSIONS: The present study identified LRP10 as a novel APP sorting receptor that protects APP from amyloidogenic processing, suggesting that a decrease in LRP10 function may contribute to the pathogenesis of Alzheimer's disease.
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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 it