Development of a Gene Therapy Strategy for the Restoration of Survival Motor Neuron Protein Expression: Implications for Spinal Muscular Atrophy Therapy
Bibliographic record
Abstract
Spinal muscular atrophy (SMA) is a motor neuron degeneration disorder, and manifests itself in patients as muscle weakness and paralysis that ultimately leads to death. Currently, there is no effective treatment for this disease. As a first step in developing a treatment for SMA, we are examining whether delivery of the gene encoding survival motor neuron (SMN) protein to primary fibroblast cell lines derived from SMA patients can lead to restoration of nuclear-staining foci, called gems, which are absent in patients with severe SMA. Using adenovirus-mediated gene delivery, we show that SMN can be efficiently expressed in patient fibroblasts, and leads to restoration of nuclear gems, which are thought to be important for the functional rescue of the SMA phenotype. The number of gems per cell is equal to or greater than those found in fibroblasts of normal individuals. Furthermore, ectopic expression of SMN also caused relocalization of Gemin2, an SMN-interacting protein, to gems. Overall, this work is the first demonstration of the feasibility of virus-based delivery of the SMN-coding gene to restore the normal SMN expression pattern in SMA patient-derived cells, and holds promise for gene therapy of SMA, as a potential long-term therapy for this devastating childhood 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 | 0.000 | 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".