Macropinocytosis of APP is controlled by a cascade of regulatory proteins
Bibliographic record
Abstract
Abstract Background The internalization of the Amyloid Precursor Protein (APP) is recognized as an important step in beta‐amyloid (Aβ) production. We have discovered that macropinocytosis of cell surface APP leads to rapid transport directly to lysosomes, bypassing early and late endosomes. This process leads to APP cleavage to Aβ and is regulated in part by Arf6. Here, we hypothesize that the binding/cross‐linking of APP at the cell surface recruits Fe65 and results in the recruitment and activation of Arf6. Arf6 then activates downstream small GTPases that are known to regulate macropinocytosis in non‐neuronal cells (including Rac1, Cdc42 and RhoA) leading to APP internalization by macropinocytosis. Method Neuro2a neuronal cells are transfected with fluorescent‐tagged proteins including Fe65, Rac1, Cdc42 and RhoA. Membrane PI(4,5)P2, a marker for membrane ruffling and the initiation of macropinocytosis, is visualized using fluorescent‐tagged marker known as PLCdelta‐PH. Cells are cultured in 37°C confocal dishes on the stage of a Leica SP8 confocal microscope. Fluorescent‐labelled anti‐APP antibodies are added to bind/cross‐link APP at the plasma membrane. We then use confocal microscopy/live cell imaging to visualize the recruitment of the regulatory proteins in real time. Result Upon anti‐APP antibody binding, we can see APP recruited to sites on the plasma membrane enriched with PI(4,5)P2 and the plasma membrane begins to ruffle. We see Fe65 rapidly recruited to the membrane, along with active Arf6 at these sites of membrane ruffling. We also observe Rac1, Cdc42 and RhoA recruited to these regions. APP at these sites is then observed to rapidly internalize to lysosomes, demonstrated by its colocalization with the lysosomal marker LAMP1. Conclusion These results demonstrate the active recruitment of GTPase proteins known to regulate macropinocytosis to the membrane at sites of APP binding/cross‐linking. These regulators could be targeted to modulate APP trafficking and reduce the production of Aβ.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".