P1‐002: BASE EDITING STRATEGY ALLOWS HIGH FREQUENCY INSERTION OF THE PROTECTIVE A673T MUTATION IN THE APP GENE TO PREVENT THE DEVELOPMENT OF ALZHEIMER'S DISEASE
Bibliographic record
Abstract
There are currently 47.5 million cases of Alzheimer's disease (AD) in the world and there will be 75.6 million cases in 2030 according to the World Health Organization. Amyloid precursor protein (APP) is usually cut by the alpha-secretase, however an abnormal cut by beta-secretase leads to the accumulation of beta-amyloid peptides, which form plaques in Alzheimer patient brain. Numerous APP gene mutations favour the accumulation of plaques. However, it was discovered that a variant of the APP gene (A673T) in Icelanders reduces by 40 % beta-secretase cutting and prevents the development of AD in older person (more than 95 years). We hypothesized that the insertion of this mutation in the patient genome would be an effective and sustainable treatment to slow down the progression of sporadic and familial Alzheimer's disease forms (FAD). The objective of our project was in a first time to show the protective effect of A673T in a FAD APP gene and determine against which mutation the treatment was the most effective. Secondly, we wanted to achieve a permanent correction by base editing to insert the A673T mutation and obtain evidence of the reduced formation of amyloid plaque. A Plasmid containing the APP gene mutated for a FAD was transfected in neuroblastoma SH-SY5Y and the supernatant was harvested 72 hours later. Another plasmid containing this mutation in addition of the A673T mutation was transfected in parallel. Every known FAD mutation in exon 16 and 17 of APP were tested. The concentrations of Aβ peptides were quantified with double antibody sandwich Elisa. Next, we introduced A673T mutation by base editing with the CRISPR/Cas9 system in HEK 293T cells and SH-SY5Y neuroblastoma. The results were characterized and quantified by Deep Sequencing. The Aβ peptides concentration was decreased in most of the cases when the A673T mutations was inserted up to 80%. We also succeeded to edit the A673T mutation in up to 57% of the APP genes. Our approach aims to attest the protective effect of A673T and the efficiency of base editing in the development of an Alzheimer's disease treatment.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".