Synaptic proteasome is inhibited in Alzheimer’s disease models and associates with memory impairment in mice
Bibliographic record
Abstract
BACKGROUND: The proteasome plays key roles in synaptic plasticity and memory by regulating protein turnover, quality control, and elimination of oxidized/misfolded proteins. Here, we investigate proteasome function and localization at synapses in Alzheimer's disease (AD) post-mortem brain tissue and in experimental models. METHOD: We used primary hippocampal cultures, amyloid-β oligomers (AβO)-injected or transgenic animal models, and human brain tissue to determine brain proteasome function and subcellular localization. We further induced proteasome inhibition in vitro and in vivo to determine AD-like phenotypes and memory in mice. RESULT: We found a marked increase in ubiquitinylated proteins in post-mortem AD hippocampi compared to controls. Using several experimental models, we show that AβO inhibit synaptic proteasome activity and trigger a reduction in synaptic proteasome content. We further show proteasome inhibition specifically in hippocampal synaptic fractions derived from APPswePS1ΔE9 mice. Reduced synaptic proteasome activity instigated by AβOs is corrected by treatment with rolipram, a phosphodiesterase-4 inhibitor, in mice. Results further show that dynein inhibition blocks AβO-induced reduction in dendritic proteasome content in hippocampal neurons. Finally, proteasome inhibition induces AD-like pathological features, including reactive oxygen species and dendritic spine loss in hippocampal neurons, inhibition of hippocampal mRNA translation, and memory impairment in mice. CONCLUSION: Results suggest that proteasome inhibition may contribute to synaptic and memory deficits 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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".