Accumulation of APP C-terminal fragments causes endolysosomal dysfunction through the dysregulation of late endosome to lysosome-ER contact sites
Bibliographic record
Abstract
Neuronal endosomal and lysosomal abnormalities are among the early changes observed in Alzheimer’s disease (AD) before plaques appear. However, it is unclear whether distinct endolysosomal defects are temporally organized and how altered γ-secretase function or amyloid precursor protein (APP) metabolism contribute to these changes. Inhibiting γ-secretase chronically, in mouse embryonic fibroblast and hippocampal neurons, led to a gradual endolysosomal collapse initiated by decreased lysosomal calcium and increased cholesterol, causing downstream defects in endosomal recycling and maturation. This endolysosomal demise is γ-secretase dependent, requires membrane-tethered APP cytoplasmic domains, and is rescued by APP depletion. APP C-terminal fragments (CTFs) localized to late endosome/lysosome-endoplasmic reticulum contacts; an excess of APP-CTFs herein reduced lysosomal Ca2+ refilling from the endoplasmic reticulum, promoting cholesterol accretion. Tonic regulation by APP-CTFs provides a mechanistic explanation for their cellular toxicity: failure to timely degrade APP-CTFs sustains downstream signaling, instigating lysosomal dyshomeostasis, as observed in prodromal AD. This is the opposite of substrates such as Notch, which require intramembrane proteolysis to initiate signaling.
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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 | 0.000 |
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".