Paradoxical effects of mutant ubiquitin on Aβ plaque formation in an Alzheimer mouse model
Bibliographic record
Abstract
Amyloid-β (Aβ) plaques are a prominent pathological hallmark of Alzheimer's disease (AD). They consist of aggregated Aβ peptides, which are generated through sequential proteolytic processing of the transmembrane protein amyloid precursor protein (APP) and several Aβ-associated factors. Efficient clearance of Aβ from the brain is thought to be important to prevent the development and progression of AD. The ubiquitin-proteasome system (UPS) is one of the major pathways for protein breakdown in cells and it has been suggested that impaired UPS-mediated removal of protein aggregates could play an important role in the pathogenesis of AD. To study the effects of an impaired UPS on Aβ pathology in vivo, transgenic APPSwe/PS1ΔE9 mice (APPPS1) were crossed with transgenic mice expressing mutant ubiquitin (UBB+1), a protein-based inhibitor of the UPS. Surprisingly, the APPPS1/UBB+1 crossbreed showed a remarkable decrease in Aβ plaque load during aging. Further analysis showed that UBB+1 expression transiently restored PS1-NTF expression and γ-secretase activity in APPPS1 mice. Concurrently, UBB+1 decreased levels of β-APP-CTF, which is a γ-secretase substrate. Although UBB+1 reduced Aβ pathology in APPPS1 mice, it did not improve the behavioral deficits in these animals.
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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 | 0.000 |
| 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".