Localization of mutant ubiquitin in the brain of a transgenic mouse line with proteasomal inhibition and its validation at specific sites in Alzheimer's disease
Bibliographic record
Abstract
Loss of protein quality control by the ubiquitin-proteasome system (UPS) during aging is one of the processes putatively contributing to cellular stress and Alzheimer's disease (AD) pathogenesis. Recently, pooled Genome Wide Association Studies (GWAS), pathway analysis and proteomics identified protein ubiquitination as one of the key modulators of AD. Mutations in ubiquitin B mRNA that result in UBB(+1) dose-dependently cause an impaired UPS, subsequent accumulation of UBB(+1) and most probably depositions of other aberrant proteins present in plaques and neurofibrillary tangles. We used specific immunohistochemical probes for a comprehensive topographic mapping of the UBB(+1) distribution in the brains of transgenic mouse line 3413 overexpressing UBB(+1). We also mapped the expression of UBB(+1) in brain areas of AD patients selected based upon the distribution of UBB(+1) in line 3413. Therefore, we focused on the olfactory bulb, basal ganglia, nucleus basalis of Meynert, inferior colliculus and raphe nuclei. UBB(+1) distribution was compared with established probes for pre-tangles and tangles and Aβ plaques. UBB(+1) distribution found in line 3413 is partly mirrored in the AD brain. Specifically, nuclei with substantial accumulations of tangle-bearing neurons, such as the nucleus basalis of Meynert and raphe nuclei also present high densities of UBB(+1) positive tangles. Line 3413 is useful for studying the contribution of proteasomal dysfunction in AD. The findings are consistent with evidence that areas outside the forebrain are also affected in AD. Line 3413 may also be predictive for other conformational diseases, including related tauopathies and polyglutamine diseases, in which UBB(+1) accumulates in their cellular hallmarks.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.002 | 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".