P1‐303: Regulation of BACE1 and APP processing by Zinc signaling
Bibliographic record
Abstract
Alzheimer's disease (AD) is the most common neurodegenerative disorders leading to dementia. Senile neuritic plaques, neurofibrillary tangles, and neuronal losses in the brain are the hallmarks of AD pathology. A central component of the neuritic plaque consists of the 40-42 amino acid residue amyloid beta protein (Aβ). Aβ is derived from cleavage of β-amyloid precursor protein (APP) by β-secretase and γ-secretase. Zinc is an essential mineral and is involved in many aspects of cell metabolism. Zinc plays an important role in DNA and protein synthesis, immune function, and cell division. Disturbance of Zinc homeostasis has been implicated in neurodegeneration. This study aims to determine the role of Zinc signaling in AD pathogenesis. Promoter assay, Western blot analysis and gene disruption strategy were used to examine the effect of Zinc signaling pathway on BACE1 gene expression and APP processing. To determine whether zinc affects APP processing and Alzheimer's disease pathogenesis, cells have been treated with different dosage of zinc sulfate and cadmium chloride. The expression level of BACE1 was measured by RT-PCR. We found that low dosage of zinc and cadmium treatment altered the BACE1 expression. To further examine whether Zinc affects BACE1 gene expression via its effect on BACE1 gene transcription, BACE1 promoter reporter plasmids were transfected into cells and then treated with Zinc sulfate and cadmium chloride and the promoter activity was measured by a dual luciferase assay. Our study showed that the promoter activity was modulated by Zinc treatment. Furthermore, BACE1 expression and APP processing were altered in MTF-KO cells. These results indicate that heavy metal ions like zinc and cadmium may play an important role in regulating BACE1 gene expression and Aβ production.
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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) | 0.003 | 0.002 |
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".