A Novel Cell-based β-secretase Enzymatic Assay for Alzheimer’s Disease
Bibliographic record
Abstract
BACKGROUND: Deposition of the amyloid β protein (Aβ) into neuritic plaques is the neuropathological hallmark of Alzheimer's Disease (AD). Aβ is generated through the cleavage of the Amyloid Precursor Protein (APP) by β-secretase and γ-secretase. Currently, the evaluation of APP cleavage by β-secretase in experimental settings has largely depended on models that do not replicate the physiological conditions of this process. OBJECTIVE: To establish a novel live cell-based β-secretase enzymatic assay utilizing a novel chimeric protein that incorporates the natural sequence of APP and more closely replicates its cleavage by β-secretase under physiological conditions. METHODS: We have developed a chimeric protein construct, ASGβ, incorporating the β-site cleavage sequence of APP targeted by β-secretase and its intracellular trafficking signal into a Phosphatase-eGFP secreted reporter system. Upon cleavage by β-secretase, ASGβ releases a phosphatase-containing portion that can be measured in the culture medium, and an intracellular fraction that can be detected through Western Blot. Subsequently, we have generated a cell line stably expressing ASGβ that can be utilized to assay β-secretase in real time. RESULTS: ASGβ is specifically targeted by β-secretase, being cleaved exclusively at the site responsible for the generation of Aβ. Dosage response to β-secretase inhibitors shows that β-secretase activity can be positively correlated to phosphatase activity in culture media. CONCLUSION: Our findings suggest this system could be a high-throughput tool to screen compounds that aim to modulate β-secretase activity and Aβ production under physiological conditions, as well as evaluating factors that regulate this cleavage.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".