An Over Expression APP Model for Anti-Alzheimer Disease Drug Screening Created by Zinc Finger Nuclease Technology
Bibliographic record
Abstract
Zinc Finger Nucleases (ZFNs), famous for their ability to precisely and efficiently modify specific genomic loci, have been employed in numerous transgenic model organism and cell constructions. Here we employ the ZFNs technology, with homologous recombination (HR), to construct sequence-specific Amyloid Precursor Protein (APP) knock-in cells. With the use of ZFNs, we established APP knock in cell lines with gene-modification efficiencies of about 7%. We electroporated DNA fragment containing the promoter and the protein coding regions of the zinc finger nucleases into cells, instead of the plasmids, to avoid problems associated with off target homologous recombination, and adopted a pair of mutated FokI cleavage domains to reduce the toxic effects of the ZFNs on cell growth. Since over-expression of APP, or a subdomain of it, might lead to an immediately lethal effect, we used the Cre-LoxP System to regulate APP expression. Our genetically transformed cell lines, w5c1 and s12c8, showed detectable APP and Amyloid β (Aβ) production. The Swedish double mutation in the APP coding sequence enhanced APP and Aβ abundance. What is more, the activity of the three key secretases in Aβ formation could be modulated, indicating that these transgenic cells have potential for drug screening to modify amyloid metabolism in cells. Our transformed cells could readily be propagated in culture and should provide an excellent experimental medium for elucidating aspects of the molecular pathogenesis of Alzheimer's disease, especially those concerning the amyloidogenic pathways involving mutations in the APP coding sequence. The cellular models may also serve as a tool for deriving potentially useful therapeutic agents.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".