Macropinocytosis of amyloid precursor protein requires the adaptor protein Fe65 and the recruitment and activity of Arf6 and the RhoGTPases Rac1, Cdc42 and RhoA
Bibliographic record
Abstract
Abstract Alzheimer’s disease (AD) is a progressive neurodegenerative disorder characterized by the buildup of the highly toxic peptide amyloid-beta (Aβ). Previously, we demonstrated that Aβ is generated from the cleavage of amyloid precursor protein (APP) after internalization to lysosomes via macropinocytosis. However, the regulation of APP micropinocytosis has remained uncharacterized. Evidence suggests that APP may function as a cell surface receptor which could contribute to this regulation. Arf6 and the RhoGTPases Rac1, Cdc42 and RhoA are known to regulate macropinocytosis in response to signaling of other receptors. An adaptor protein called Fe65, which can associate with both amyloid precursor protein and Arf6, could function as the link between APP and these known regulatory elements. Thus, we hypothesized that the binding and/or crosslinking of APP recruits Fe65, which then recruits and activates Arf6, which in turn activates Rac1, Cdc42 and RhoA, resulting in APP macropinocytosis. Rapid and transient recruitment of Fe65 and Arft6 was observed to APP 30 seconds following binding/crosslinking. Rac1, Cdc42 and RhoA all examined demonstrated more sustained recruitment to crosslinked APP. Prevention of Fe65 binding by APP mutation and Arf6 inhibition by NAV-2729 prevented the recruitment of all proteins. Together, these observations are the first to demonstrate that a network of regulatory proteins is recruited to bound/crosslinked APP which regulates its macropinocytosis. Targeting these regulatory proteins could be explored to modulate the membrane to lysosomal trafficking of APP and reducing the production of Aβ in AD.
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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) | 0.001 | 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".