Increased NF-κB signalling up-regulates BACE1 expression and its therapeutic potential in Alzheimer's disease
Bibliographic record
Abstract
Elevated levels of β-site APP cleaving enzyme 1 (BACE1) were found in the brain of some sporadic Alzheimer's disease (AD) patients; however, the underlying mechanism is unknown. BACE1 cleaves β-amyloid precursor protein (APP) to generate amyloid β protein (Aβ), a central component of neuritic plaques in AD brains. Nuclear factor-kappa B (NF-κB) signalling plays an important role in gene regulation and is implicated in inflammation, oxidative stress and apoptosis. In this report we found that both BACE1 and NF-κB p65 levels were significantly increased in the brains of AD patients. Two functional NF-κB-binding elements were identified in the human BACE1 promoter region. We found that NF-κB p65 expression resulted in increased BACE1 promoter activity and BACE1 transcription, while disruption of NF-κB p65 decreased BACE1 gene expression in p65 knockout (RelA-knockout) cells. In addition, NF-κB p65 expression leads to up-regulated β-secretase cleavage and Aβ production, while non-steroidal anti-inflammatory drugs (NSAIDs) inhibited BACE1 transcriptional activation induced by strong NF-κB activator tumour necrosis factor-alpha (TNF-α). Taken together, our results clearly demonstrate that NF-κB signalling facilitates BACE1 gene expression and APP processing, and increased BACE1 expression mediated by NF-κB signalling in the brain could be one of the novel molecular mechanisms underlying the development of AD in some sporadic cases. Furthermore, NSAIDs could block the inflammation-induced BACE1 transcription and Aβ production. Our study suggests that inhibition of NF-κB-mediated BACE1 expression may be a valuable drug target for AD therapy.
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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 | 0.001 |
| 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".