Therapeutic effects of remediating autophagy failure in a mouse model of Alzheimer disease by enhancing lysosomal proteolysis
Bibliographic record
Abstract
The extensive autophagic-lysosomal pathology in Alzheimer disease (AD) brain has revealed a major defect: in the proteolytic clearance of autophagy substrates. Autophagy failure contributes on several levels to AD pathogenesis and has become an important therapeutic target for AD and other neurodegenerative diseases. We recently observed broad therapeutic effects of stimulating autophagic-lysosomal proteolysis in the TgCRND8 mouse model of AD that exhibits defective proteolytic clearance of autophagic substrates, robust intralysosomal amyloid-β peptide (Aβ) accumulation, extracellular β-amyloid deposition and cognitive deficits. By genetically deleting the lysosomal cysteine protease inhibitor, cystatin B (CstB), to selectively restore depressed cathepsin activities, we substantially cleared Aβ, ubiquitinated proteins and other autophagic substrates from autolysosomes/lysosomes and rescued autophagic-lysosomal pathology, as well as reduced total Aβ40/42 levels and extracellular amyloid deposition, highlighting the underappreciated importance of the lysosomal system for Aβ clearance. Most importantly, lysosomal remediation prevented the marked learning and memory deficits in TgCRND8 mice. Our findings underscore the pathogenic significance of autophagic-lysosomal dysfunction in AD and demonstrate the value of reversing this dysfunction as an innovative therapeautic strategy for 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".