Evaluation of Exon Inclusion Induced by Splice Switching Antisense Oligonucleotides in SMA Patient Fibroblasts
Bibliographic record
Abstract
Spinal muscular atrophy (SMA), a lethal neurological disease caused by the loss of SMN1, presents a unique case in the field of antisense oligonucleotide (AON)-mediated therapy. While SMN1 mutations are responsible for the disease, AONs targeting intronic splice silencer (ISS) sites in SMN2, including FDA-approved nusinersen, have been shown to restore SMN expression and ameliorate the symptoms. Currently, many studies involving AON therapy for SMA focus on investigating novel AON chemistries targeting SMN2 that may be more effective and less toxic than nusinersen. Here, we describe a protocol for in vitro evaluation of exon inclusion using lipotransfection of AONs followed by reverse transcription polymerase chain reaction (RT-PCR), quantitative polymerase chain reaction (qPCR), and Western blotting. This method can be employed for various types of AON chemistries. Using this method, we demonstrate that AONs composed of alternating locked nucleic acids (LNAs) and DNA nucleotides (LNA/DNA mixmers) lead to efficient SMN2 exon inclusion and restoration of SMN protein at a very low concentration, and therefore, LNA/DNA mixmer-based antisense oligonucleotides may be an attractive therapeutic strategy to treat splicing defects caused by genetic diseases. The in vitro evaluation method described here is fast, easy, and sensitive enough for the testing of various novel AONs.
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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) | 0.000 | 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 | 0.000 | 0.000 |
| 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".