Translation-specific disruption of Col1a1 expression in multiple models of Spinal Muscular Atrophy can be rescued by Risdiplam.
Bibliographic record
Abstract
Spinal muscular atrophy (SMA) is a monogenic neurodegenerative disorder caused by decreased levels of Survival of Motor Neuron (SMN) protein. If left untreated, SMA patients have a poor prognosis, marked by the degeneration of motor neurons, progressive muscle weakness and atrophy. The approval of SMN-restoring therapies that improve symptoms and lifespan in patients with SMA has created emerging, non-neuronal phenotypes and an urgent need for deepening our understanding of disease pathogenesis. Leveraging the knowledge that SMN loss drives alterations in translation, we used multiple tissues from a mouse model of SMA to uncover early translational alterations in key mRNAs and proteins, which act as contributors to pathogenesis and hallmarks of the disease. Among hundreds of differentially translated mRNAs, Col1a1 emerged as a translation-specific manifestation of early defects in the mouse model. These findings were confirmed in fibroblasts derived from patients with varying levels of disease severity. Notably, treatment with SMN-restoring therapies rescued COL1A1 protein levels, particularly in fibroblasts from patients with the most severe forms of the disease. Overall, our study identifies COL1A1 as an indicator of disease severity in SMA, which captures early molecular alterations and respond to SMN-modifying therapies.
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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 | 0.001 | 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) | 0.003 | 0.002 |
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".