Suppression of the necroptotic cell death pathways improves survival in Smn2B/− mice
Bibliographic record
Abstract
Spinal muscular atrophy (SMA) is a monogenic neuromuscular disease caused by low levels of the Survival Motor Neuron (SMN) protein. Motor neuron degeneration is the central hallmark of the disease. However, the SMN protein is ubiquitously expressed and depletion of the protein in peripheral tissues results in intrinsic disease manifestations, including muscle defects, independent of neurodegeneration. The approved SMN-restoring therapies have led to remarkable clinical improvements in SMA patients. Yet, the presence of a significant number of non-responders stresses the need for complementary therapeutic strategies targeting processes which do not rely solely on restoring SMN. Dysregulated cell death pathways are candidates for SMN-independent pathomechanisms in SMA. Receptor-interacting protein kinase 1 (RIPK1) and RIPK3 have been widely recognized as critical therapeutic targets of necroptosis, an important form of programmed cell death. In addition, Caspase-1 plays a fundamental role in inflammation and cell death. In this study, we evaluate the role of necroptosis, particularly RIPK3 and Caspase-1, in the Smn2B/− mouse model of SMA. We have generated a triple mutant (TKO), the Smn2B/−; Ripk3−/−; Casp1−/− mouse. TKO mice displayed a robust increase in survival and improved motor function compared to Smn2B/− mice. While there was no protection against motor neuron loss or neuromuscular junction pathology, larger muscle fibers were observed in TKO mice compared to Smn2B/− mice. Our study shows that necroptosis modulates survival, motor behavior and muscle fiber size independent of SMN levels and independent of neurodegeneration. Thus, small-molecule inhibitors of necroptosis as a combinatorial approach together with SMN-restoring drugs could be a future strategy for the treatment of 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) | 0.001 | 0.001 |
| 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 | 0.002 |
| 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".