CRISPR/Cas9 Mediated Disruption of the Swedish APP Allele as a Therapeutic Approach for Early-Onset Alzheimer’s Disease
Bibliographic record
Abstract
The APPswe ( Swedish ) mutation in the amyloid precursor protein ( APP ) gene causes dominantly inherited Alzheimer's disease (AD) as a result of increased β-secretase cleavage of the amyloid-β (Aβ) precursor protein. This leads to abnormally high Aβ levels, not only in brain but also in peripheral tissues of mutation carriers. Here, we selectively disrupted the human mutant APP SW allele using CRISPR. By applying CRISPR/Cas9 from Streptococcus pyogenes , we generated allele-specific deletions of either APP SW or APP WT . As measured by ELISA, conditioned media of targeted patient-derived fibroblasts displayed an approximate 60% reduction in secreted Aβ. Next, coding sequences for the APP SW -specific guide RNA (gRNA) and Cas9 were packaged into separate adeno-associated viral (AAV) vectors. Site-specific indel formation was achieved both in primary neurons isolated from APP SW transgenic mouse embryos (Tg2576) and after co-injection of these vectors into hippocampus of adult mice. Taken together, we here present proof-of-concept data that CRISPR/Cas9 can selectively disrupt the APP SW allele both ex vivo and in vivo —and thereby decrease pathogenic Aβ. Hence, this system may have the potential to be developed as a tool for gene therapy against AD caused by APPswe and other point mutations associated with increased Aβ.
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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) | 0.002 | 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".