Development of a therapeutic approach for hereditary diseases with prime editing: A study on Alzheimer's disease
Bibliographic record
Abstract
Background The amyloid precursor protein (APP) plays a role in the pathophysiology of Alzheimer’s disease. Excess cleavage by β-secretase enzyme leads to the accumulation of β-amyloid peptides. In contrast, a rare variant of the APP gene (A673T) decreases cleavage by β-secretase. However, inserting this mutation with gene editing technology is a challenge. Our hypothesis is that prime editing allows a significant editing rate while inducing few off-target mutations in the cell leading to a significant reduction in the amount of pathological peptides produced. The objective of this study is to achieve a permanent insertion of the A673T mutation by prime editing and obtain evidence of the reduced Aβ peptides accumulation in vitro. Method Prime editing guide RNAs (pegRNA) were constructed according to different protospacer adjacent motifs (PAM) around the A673T mutation by varying the length of the primer binding site (PBS) and the reverse transcriptase template (RTT) sequence. Initially, one guide was used on the target gene (PE2). Afterwards, additional guides were used to favor DNA editing (PE3, PE3b). Different techniques were used to optimize editing, such as adding valproic acid, repeated transfections, modifying the PAM and the structure of the prime editor. The mutation was introduced in HEK 293T cells and later in neurons derived from fibroblasts of patients with Alzheimer’s disease. The results were quantified by Sanger and deep sequencing. Result Prime editing demonstrated precise editing in up to 64% in HEK 293T cells (Figure 1) with no notable off-target mutations within the target window. In future experiments, the level of Aβ40-42 peptides will be measured in the derived neurons. Conclusion Our approach aims to demonstrate the potential of prime editing in the development of a treatment for Alzheimer's disease based on the protective effect of A673T. Our project also aims to evaluate optimization methods for prime editing.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".