Deletion of the Ste20-like kinase SLK in skeletal muscle results in a progressive myopathy and muscle weakness
Bibliographic record
Abstract
BACKGROUND: The Ste20-like kinase, SLK, plays an important role in cell proliferation and cytoskeletal remodeling. In fibroblasts, SLK has been shown to respond to FAK/Src signaling and regulate focal adhesion turnover through Paxillin phosphorylation. Full-length SLK has also been shown to be essential for embryonic development. In myoblasts, the overexpression of a dominant negative SLK is sufficient to block myoblast fusion. METHODS: In this study, we crossed the Myf5-Cre mouse model with our conditional SLK knockout model to delete SLK in skeletal muscle. A thorough analysis of skeletal muscle tissue was undertaken in order to identify defects in muscle development caused by the lack of SLK. Isometric force analysis was performed on adult knockout mice and compared to age-matched wild-type mice. Furthermore, cardiotoxin injections were performed followed by immunohistochemistry for myogenic markers to assess the efficiency muscle regeneration following SLK deletion. RESULTS: We show here that early deletion of SLK from the myogenic lineage does not markedly impair skeletal muscle development but delays the regenerative process. Interestingly, adult mice (~6 months) display an increase in the proportion of central nuclei and increased p38 activation. Furthermore, mice as young as 3 months old present with decreased force generation, suggesting that the loss of SLK impairs myofiber stability and function. Assessment of structural components revealed aberrant localization of focal adhesion proteins, such as FAK and paxillin. Our data show that the loss of SLK results in unstable myofibers resulting in a progressive myopathy. Additionally, the loss of SLK resulted in a delay in muscle regeneration following cardiotoxin injections. CONCLUSIONS: Our results show that SLK is dispensable for muscle development and regeneration but is required for myofiber stability and optimal force generation.
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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 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".