Distinct roles for Ste20-like kinase SLK in muscle function and regeneration
Bibliographic record
Abstract
BACKGROUND: Cell growth and terminal differentiation are controlled by complex signaling systems that regulate the tissue-specific expression of genes controlling cell fate and morphogenesis. We have previously reported that the Ste20-like kinase SLK is expressed in muscle tissue and is required for cell motility. However, the specific function of SLK in muscle tissue is still poorly understood. METHODS: To gain further insights into the role of SLK in differentiated muscles, we expressed a kinase-inactive SLK from the human skeletal muscle actin promoter. Transgenic muscles were surveyed for potential defects. Standard histological procedures and cardiotoxin-induced regeneration assays we used to investigate the role of SLK in myogenesis and muscle repair. RESULTS: High levels of kinase-inactive SLK in muscle tissue produced an overall decrease in SLK activity in muscle tissue, resulting in altered muscle organization, reduced litter sizes, and reduced breeding capacity. The transgenic mice did not show any differences in fiber-type distribution but displayed enhanced regeneration capacity in vivo and more robust differentiation in vitro. CONCLUSIONS: Our results show that SLK activity is required for optimal muscle development in the embryo and muscle physiology in the adult. However, reduced kinase activity during muscle repair enhances regeneration and differentiation. Together, these results suggest complex and distinct roles for SLK in muscle development and function.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".