Differential induction of muscle atrophy pathways in two mouse models of spinal muscular atrophy
Bibliographic record
Abstract
Motor neuron loss and neurogenic atrophy are hallmarks of spinal muscular atrophy (SMA), a leading genetic cause of infant deaths. Previous studies have focused on deciphering disease pathogenesis in motor neurons. However, a systematic evaluation of atrophy pathways in muscles is lacking. Here, we show that these pathways are differentially activated depending on severity of disease in two different SMA model mice. Although proteasomal degradation is induced in skeletal muscle of both models, autophagosomal degradation is present only in Smn(2B/-) mice but not in the more severe Smn(-/-); SMN2 mice. Expression of FoxO transcription factors, which regulate both proteasomal and autophagosomal degradation, is elevated in Smn(2B/-) muscle. Remarkably, administration of trichostatin A reversed all molecular changes associated with atrophy. Cardiac muscle also exhibits differential induction of atrophy between Smn(2B/-) and Smn(-/-); SMN2 mice, albeit in the opposite direction to that of skeletal muscle. Altogether, our work highlights the importance of cautious analysis of different mouse models of SMA as distinct patterns of atrophy induction are at play depending on disease severity. We also revealed that one of the beneficial impacts of trichostatin A on SMA model mice is via attenuation of muscle atrophy through reduction of FoxO expression to normal levels.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".