Ubiquitin–proteasome pathway mediates degradation of APH‐1
Bibliographic record
Abstract
Gamma-secretase catalyzes intramembraneous proteolysis of several type I transmembrane proteins, including beta-amyloid precursor protein (APP), to generate amyloid beta protein (Abeta), a key player in the pathogenesis of Alzheimer's disease (AD). The critical components of the gamma-secretase complex include presenilin (PS), nicastrin (NCT), presenilin enhancer-2 (PEN-2) and anterior pharynx defective-1 (APH-1). Abnormalities of the ubiquitin-proteasome pathway have been implicated in the pathogenesis of AD; while PS and PEN-2 turnover is regulated by this pathway, it is unknown whether the ubiquitin-proteasome pathway is also involved in the degradation of APH-1 protein. In this study, we found that the expression of endogenous and exogenous APH-1 significantly increased in cells treated with proteasome-specific inhibitors. The effect of the proteasome inhibitors on APH-1 was dose- and time-dependent. APH-1 protein was ubiquitinated. Pulse-chase metabolic labeling experiments showed that the degradation of newly synthesized radiolabeled APH-1 proteins was inhibited by lactacystin. Disruption of the PS1 and PS2 genes did not affect the degradation of APH-1 by the ubiquitin-proteasome pathway. Furthermore, over-expression of APH-1 and inhibition of proteasomal APH-1 degradation facilitated gamma-secretase cleavage of APP to generate Abeta. These results demonstrate that the degradation of APH-1 protein is mediated by the ubiquitin-proteasome pathway.
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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) | 0.002 | 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".