LNA/DNA mixmer-based antisense oligonucleotides correct alternative splicing of the SMN2 gene and restore SMN protein expression in type 1 SMA fibroblasts
Bibliographic record
Abstract
Spinal muscular atrophy (SMA) is an autosomal recessive disorder affecting motor neurons, and is currently the most frequent genetic cause of infant mortality. SMA is caused by a loss-of-function mutation in the survival motor neuron 1 (SMN1) gene. SMN2 is an SMN1 paralogue, but cannot compensate for the loss of SMN1 since exon 7 in SMN2 mRNA is excluded (spliced out) due to a single C-to-T nucleotide transition in the exon 7. One of the most promising strategies to treat SMA is antisense oligonucleotide (AON)-mediated therapy. AONs are utilized to block intronic splicing silencer number 1 (ISS-N1) on intron 7 of SMN2, which causes exon 7 inclusion of the mRNA and the recovery of the expression of functional SMN protein from the endogenous SMN2 gene. We developed novel locked nucleic acid (LNA)-based antisense oligonucleotides (LNA/DNA mixmers), which efficiently induce exon 7 inclusion in SMN2 and restore the SMN protein production in SMA patient fibroblasts. The mixmers are highly specific to the targeted sequence, and showed significantly higher efficacy than an all-LNA oligonucleotide with the equivalent sequence. These data suggest that use of LNA/DNA mixmer-based AONs may be an attractive therapeutic strategy to treat SMA.
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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) | 0.001 | 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 | 0.000 | 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".