Optimization of base editors for the functional correction of SMN2 as a treatment for spinal muscular atrophy
Bibliographic record
Abstract
Spinal muscular atrophy (SMA) is caused by mutations in SMN1. SMN2 is a paralogous gene with a C•G-to-T•A transition in exon 7, which causes this exon to be skipped in most SMN2 transcripts, and results in low levels of the protein survival motor neuron (SMN). Here we show, in fibroblasts derived from patients with SMA and in a mouse model of SMA that, irrespective of the mutations in SMN1, adenosine base editors can be optimized to target the SMN2 exon-7 mutation or nearby regulatory elements to restore the normal expression of SMN. After optimizing and testing more than 100 guide RNAs and base editors, and leveraging Cas9 variants with high editing fidelity that are tolerant of different protospacer-adjacent motifs, we achieved the reversion of the exon-7 mutation via an A•T-to-G•C edit in up to 99% of fibroblasts, with concomitant increases in the levels of the SMN2 exon-7 transcript and of SMN. Targeting the SMN2 exon-7 mutation via base editing or other CRISPR-based methods may provide long-lasting outcomes to patients with SMA. Optimized base editors targeting the exon-7 mutation in SMN2 restore expression of the survival motor neuron (SMN) protein to normal levels, as shown in mice with spinal muscular atrophy and in fibroblasts from patients with this genetic disease.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 |
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".