The Intracellular Domain of Sortilin Interacts with Amyloid Precursor Protein and Regulates Its Lysosomal and Lipid Raft Trafficking
Bibliographic record
Abstract
The processing of Amyloid precursor protein (APP) is multifaceted, comprising of protein transport, internalization and sequential proteolysis. However, the exact mechanism of APP intracellular trafficking and distribution remains unclear. To determine the interaction between sortilin and APP and the effect of sortilin on APP trafficking and processing, we studied the binding site and its function by mapping experiments, colocalization, coimmunoprecipitation and sucrose gradient fractionation. We identified for the first time that sortilin interacts with APP at both N- and C-terminal regions. The sortilin-FLVHRY (residues 787-792) and APP-NPTYKFFE (residues 759-766) motifs are crucial for the C-terminal interaction. We also found that lack of the FLVHRY motif reduces APP lysosomal targeting and increases APP distribution in lipid rafts in co-transfected HEK293 cells. These results are consistent with our in vivo data where sortilin knockout mice showed a decrease of APP lysosomal distribution and an increase of APP in lipid rafts. We further confirmed that overexpression of sortilin-FLVHRY mutants failed to rescue the lysosomal degradation of APP. Thus, our data suggests that sortilin is implicated in APP lysosomal and lipid raft targeting via its carboxyl-terminal F/YXXXXF/Y motif. Our study provides new molecular insights into APP trafficking and processing.
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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) | 0.000 | 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 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".