ROCK inhibition as a therapy for spinal muscular atrophy: understanding the repercussions on multiple cellular targets
Bibliographic record
Abstract
Spinal muscular atrophy (SMA) is the most common genetic disease causing infant death, due to an extended loss of motoneurons. This neuromuscular disorder results from deletions and/or mutations within the Survival Motor Neuron 1 (SMN1) gene, leading to a pathological decreased expression of functional full-length SMN protein. Emerging studies suggest that the small GTPase RhoA and its major downstream effector Rho kinase (ROCK), which both play an instrumental role in cytoskeleton organization, contribute to the pathology of motoneuron diseases. Indeed, an enhanced activation of RhoA and ROCK has been reported in the spinal cord of an SMA mouse model. Moreover, the treatment of SMA mice with ROCK inhibitors leads to an increased lifespan as well as improved skeletal muscle and neuromuscular junction pathology, without preventing motoneuron degeneration. Although motoneurons are the primary target in SMA, an increasing number of reports show that other cell types inside and outside the central nervous system contribute to SMA pathogenesis. As administration of ROCK inhibitors to SMA mice was systemic, the improvement in survival and phenotype could therefore be attributed to specific effects on motoneurons and/or on other non-neuronal cell types. In the present review, we will present the various roles of the RhoA/ROCK pathway in several SMA cellular targets including neurons, myoblasts, glial cells, cardiomyocytes and pancreatic cells as well as discuss how ROCK inhibition may ameliorate their health and function. It is most likely a concerted influence of ROCK modulation on all these cell types that ultimately lead to the observed benefits of pharmacological ROCK inhibition in SMA mice.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".