Base editing strategy allows insertion of the A673T mutation in APP gene to prevent the development of Alzheimer’s disease
Bibliographic record
Abstract
ABSTRACT Amyloid precursor protein (APP), a membrane protein mostly found in neurons, is preferentially cut by the α-secretase enzyme, however, abnormal cleavage by β-secretase leads to the formation of β-amyloid peptide plaque in the brains of Alzheimer’s patients. Genome analysis of an Icelandic population that did not appear to show symptoms of Alzheimer’s at advanced age led to the discovery of the A673T mutation, reducing β-secretase cleavage by 40%. We hypothesized that the insertion of this mutation in a patient’s genome could be an effective and sustainable method to slow down or prevent the progression of familial and sporadic forms of Alzheimer’s disease. We have thus modified the APP gene in HEK293T cells and in SH-SY5Y neuroblastoma using a Cas9n-deaminase enzyme, which changes a cytosine into a thymine, thus converting the alanine codon to a threonine. Several Cas9n-deaminase variants were tested to compare their efficiency of conversion. The results were characterized and quantified by deep sequencing. We successfully modified the APP gene in up to 56.7% of the HEK293T cells. Our approach aimed to attest to the efficiency of base editing in the development of treatments against genetic diseases as well as provide a new strategy for the treatment of Alzheimer’s.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 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".