Reversal of autophagy dysfunction in the TgCRND8 mouse model of Alzheimer's disease ameliorates amyloid pathologies and memory deficits
Bibliographic record
Abstract
Autophagy, a major degradative pathway for proteins and organelles, is essential for survival of mature neurons. Extensive autophagic-lysosomal pathology in Alzheimer's disease brain contributes to Alzheimer's disease pathogenesis, although the underlying mechanisms are not well understood. Here, we identified and characterized marked intraneuronal amyloid-β peptide/amyloid and lysosomal system pathology in the Alzheimer's disease mouse model TgCRND8 similar to that previously described in Alzheimer's disease brains. We further establish that the basis for these pathologies involves defective proteolytic clearance of neuronal autophagic substrates including amyloid-β peptide. To establish the pathogenic significance of these abnormalities, we enhanced lysosomal cathepsin activities and rates of autophagic protein turnover in TgCRND8 mice by genetically deleting cystatin B, an endogenous inhibitor of lysosomal cysteine proteases. Cystatin B deletion rescued autophagic-lysosomal pathology, reduced abnormal accumulations of amyloid-β peptide, ubiquitinated proteins and other autophagic substrates within autolysosomes/lysosomes and reduced intraneuronal amyloid-β peptide. The amelioration of lysosomal function in TgCRND8 markedly decreased extracellular amyloid deposition and total brain amyloid-β peptide 40 and 42 levels, and prevented the development of deficits of learning and memory in fear conditioning and olfactory habituation tests. Our findings support the pathogenic significance of autophagic-lysosomal dysfunction in Alzheimer's disease and indicate the potential value of restoring normal autophagy as an innovative therapeutic strategy for Alzheimer's disease.
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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 | 0.000 |
| 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 | 0.001 |
| 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".