In Vivo Turnover of Tau and APP Metabolites in the Brains of Wild-Type and Tg2576 Mice: Greater Stability of sAPP in the β-Amyloid Depositing Mice
Bibliographic record
Abstract
The metabolism of the amyloid precursor protein (APP) and tau are central to the pathobiology of Alzheimer's disease (AD). We have examined the in vivo turnover of APP, secreted APP (sAPP), Abeta and tau in the wild-type and Tg2576 mouse brain using cycloheximide to block protein synthesis. In spite of overexpression of APP in the Tg2576 mouse, APP is rapidly degraded, similar to the rapid turnover of the endogenous protein in the wild-type mouse. sAPP is cleared from the brain more slowly, particularly in the Tg2576 model where the half-life of both the endogenous murine and transgene-derived human sAPP is nearly doubled compared to wild-type mice. The important Abeta degrading enzymes neprilysin and IDE were found to be highly stable in the brain, and soluble Abeta40 and Abeta42 levels in both wild-type and Tg2576 mice rapidly declined following the depletion of APP. The cytoskeletal-associated protein tau was found to be highly stable in both wild-type and Tg2576 mice. Our findings unexpectedly show that of these various AD-relevant protein metabolites, sAPP turnover in the brain is the most different when comparing a wild-type mouse and a beta-amyloid depositing, APP overexpressing transgenic model. Given the neurotrophic roles attributed to sAPP, the enhanced stability of sAPP in the beta-amyloid depositing Tg2576 mice may represent a neuroprotective response.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".