P2‐271: Presenilin 1 (PS1) is required for lysosome acidification: FAD mutations of PS1 cause loss of protein turnover by autophagy
Bibliographic record
Abstract
Autophagy pathology is exceptionally robust in AD, where AVs collect in massive numbers within grossly distended portions of axons and dendrites of affected neurons, likely reflecting defective AV clearance. This lysosome-related pathology, along with neuronal loss and amyloid deposition, are greatly accentuated in early-onset familial AD (FAD) due to mutations of PS1, the most common cause of FAD. However, underlying mechanisms are unknown. We used immunoblotting, immunocytochemistry, and ultrastructural (EM) approaches to define the role of presenilin 1 (PS1) in autophagic/lysosomal protein degradation in both cell culture and animal models. PS1 ablation specifically blocks substrate proteolysis and autophagosome clearance during macroautophagy by selectively preventing autolysosomes acidification and cathepsin activation. Neurons in mice hypomorphic for PS1 or conditionally depleted of PS1 display similar abnormalities. PS1 mutations causing early-onset AD produce a similar lysosomal/autophagy phenotype and partial loss of autophagy function as shown in fibroblasts from patients with familial AD. PS1 is essential for lysosome acidification and proteolysis during autophagy. Disruption of autophagy function by PS1 mutations promotes accumulation of pathogenic proteins and neuronal death in AD underscoring the significance of lysosomal system dysfunction in AD pathogenesis.
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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) | 0.001 | 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".