Control of APP processing and Aβ generation level by BACE1 enzymatic activity and transcription
Bibliographic record
Abstract
Deposition of amyloid beta protein (Abeta) is one of the characteristic features of Alzheimer's disease (AD) neuropathology. Beta-secretase, a beta-site APP cleaving enzyme 1 (BACE1), is essential for Abeta biosynthesis. Although inhibition of BACE1 is considered a valid therapeutic target for AD, the enzymatic dynamics of BACE1 in regulating APP processing and Abeta generation has not yet been fully defined. To examine this issue, tightly controlled inducible BACE1 gene expression was established in the neuronal cell line N2ABP1 and the non-neuronal cell line E2BP1 using an ecdysone-inducible system. The BACE1 protein level was increased in a time- and dosage-dependent manner in the inducible BACE1 stable cells by treatment with inducer ponasterone A. The generation of APP CTFbeta, the beta-secretase product, increased proportionally with the level of BACE1 protein expression. However, Abeta40/42 production sharply increased to the plateau level with a relatively small increase in BACE1 expression. Although further increasing BACE1 expression increased beta-secretase activity, it had no additional effect on Abeta production. Furthermore, we found that BACE1 mRNA levels and BACE1 promoter activity were significantly lower than APP mRNA levels and APP promoter activity. Our data demonstrate that lower BACE transcription is responsible for the minority of APP undergoing the amyloidogenic pathway and relatively lower Abeta production in the normal conditions, and that a slight increase in BACE1 can induce a dramatic elevation in Abeta production, indicating that the increase in BACE1 can potentially increase neuritic plaque formation in the pathological condition.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".