Phosphorylation of tau antagonizes apoptosis by stabilizing β-catenin, a mechanism involved in Alzheimer's neurodegeneration
Bibliographic record
Abstract
Hyperphosphorylated tau is the major protein subunit of neurofibrillary tangles in Alzheimer's disease (AD) and related tauopathies. It is not understood, however, why the neurofibrillary tangle-containing neurons seen in the AD brains do not die of apoptosis but rather degeneration even though they are constantly awash in a proapoptotic environment. Here, we show that cells overexpressing tau exhibit marked resistance to apoptosis induced by various apoptotic stimuli, which also causes correlated tau hyperphosphorylation and glycogen synthase kinase 3 (GSK-3) activation. GSK-3 overexpression did not potentiate apoptotic stimulus-induced cell apoptosis in the presence of high levels of tau. The resistance of neuronal cells bearing hyperphosphorylated tau to apoptosis was also evident by the inverse staining pattern of PHF-1-positive tau and activated caspase-3 or fragmented nuclei in cells and the brains of rats or tau-transgenic mice. Tau hyperphosphorylation was accompanied by decreases in beta-catenin phosphorylation and increases in nuclear translocation of beta-catenin. Reduced levels of beta-catenin antagonized the antiapoptotic effect of tau, whereas overexpressing beta-catenin conferred resistance to apoptosis. These results reveal an antiapoptotic function of tau hyperphosphorylation, which likely inhibits competitively phosphorylation of beta-catenin by GSK-3beta and hence facilitates the function of beta-catenin. Our findings suggest that tau phosphorylation may lead the neurons to escape from an acute apoptotic death, implying the essence of neurodegeneration seen in the AD brains and related tauopathies.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".