P2‐258: Live cell imaging reveals multiple mechanisms for rapid Amyloid Precursor Protein transport to the lysosome
Bibliographic record
Abstract
Many lines of evidence suggest that the cleavage of the Amyloid Precursor Protein (APP) into beta-amyloid occurs after transport into the endosomal/ lysosomal system and that this transport may regulate beta-amyloid production. We have previously demonstrated gamma-secretase proteins and activity in the lysosome, along with unexpectedly rapid transport of APP to the lysosome, suggesting that the lysosome might be a site of amyloid production. Here we attempt to identify the regulatory proteins controlling this pathway. We used constructs encoding APP fused to a C-terminal fluorescent (or photoactivatable) protein tag and an N-terminal HA epitope tag. Compartments were identified using fluorescent-tagged marker proteins including LAMP (lysosome) and GalT (Golgi). In addition, cells were co-transfected with siRNAs or dominant-negative mutant forms or regulatory trafficking proteins. Transport from the cell surface of neuronal SN56 cells was followed by surface labeling these cells with fluorescently labeled anti-HA antibody. Transport from the Golgi to the lysosome is followed after first activating photactivatable GFP(paGFP)-tagged APP in the Golgi using 405 nm laser light. Live cell movies were then generated using Zeiss LSM510 Meta confocal microscope. We find that surface-labeled APP traffics rapidly to the lysosome from the cells surface by first forming large (1 micron) vesicles at the cell surface which then fuse with LAMP1-mRFP labeled lysosomes. This process is disrupted by dominant negative mutants of Arf6, suggesting that it is a form of macropinocytosis. In addition, rapid APP transport from the Golgi to the lysosome is disrupted by siRNAs encoding the adaptor protein AP-3. These rapid direct transport pathways for APP to the lysosome suggests that the lysosome might be an important site for APP processing. Future work will attempt to assess the relationship between APP transport and beta-amyloid production.
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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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".