Early Growth Response 1 (Egr-1) Is a Transcriptional Activator of β-Secretase 1 (BACE-1) in the Brain
Bibliographic record
Abstract
Accumulation of amyloid-β peptide (Aβ) in the brain is regarded as central to Alzheimer's disease (AD) pathogenesis. Aβ is generated by a sequential cleavage of amyloid precursor protein (APP) by β-secretase 1 (BACE-1) followed by γ-secretase. BACE-1 cleavage of APP is the committed step in Aβ synthesis. Understanding the mechanism by which BACE-1 is activated leading to Aβ synthesis in the brain can provide better understanding of AD pathology and help to develop novel therapies. In this study, we found that the levels of Aβ and BACE-1 are significantly reduced in the brains of mice lacking transcription factor early growth response 1 (Egr-1) when compared with the WT. We demonstrate that in COS-7 cells, Egr-1 binds to the BACE-1 promoter and activates BACE-1 transcription. In rat hippocampal primary neurons, overexpression of Egr-1 induces BACE-1 expression, activates BACE-1, promotes amyloidogenic APP processing, and enhances Aβ synthesis. In mouse hippocampal primary neurons, knockdown of BACE-1 almost completely blocks Egr-1-induced amyloidogenic APP processing and Aβ synthesis. Our data indicate that Egr-1 promotes Aβ synthesis via transcriptional activation of BACE-1 and suggest that Egr-1 plays role in activation of BACE-1 and acceleration of Aβ synthesis in AD brain. Egr-1 is a potential therapeutic target for AD.
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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 | 0.001 |
| 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 | 0.001 |
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".