Myostatin inhibits proliferation and insulin-stimulated glucose uptake in mouse liver cells
Bibliographic record
Abstract
Although myostatin functions primarily as a negative regulator of skeletal muscle growth and development, accumulating biological and epidemiological evidence indicates an important contributing role in liver disease. In this study, we demonstrate that myostatin suppresses the proliferation of mouse Hepa-1c1c7 murine-derived liver cells (50%; p < 0.001) in part by reducing the expression of the cyclins and cyclin-dependent kinases that elicit G1-S phase transition of the cell cycle (p < 0.001). Furthermore, real-time PCR-based quantification of the long noncoding RNA metastasis associated lung adenocarcinoma transcript 1 (Malat1), recently identified as a myostatin-responsive transcript in skeletal muscle, revealed a significant downregulation (25% and 50%, respectively; p < 0.05) in the livers of myostatin-treated mice and liver cells. The importance of Malat1 in liver cell proliferation was confirmed via arrested liver cell proliferation (p < 0.05) in response to partial Malat1 siRNA-mediated knockdown. Myostatin also significantly blunted insulin-stimulated glucose uptake and Akt phosphorylation in liver cells while increasing the phosphorylation of myristoylated alanine-rich C-kinase substrate (MARCKS), a protein that is essential for cancer cell proliferation and insulin-stimulated glucose transport. Together, these findings reveal a plausible mechanism by which circulating myostatin contributes to the diminished regenerative capacity of the liver and diseases characterized by liver insulin resistance.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".