Myostatin-induced inhibition of the long noncoding RNA Malat1 is associated with decreased myogenesis
Bibliographic record
Abstract
Myostatin, a member of the transforming growth factor-β (TGF-β) superfamily of secreted proteins, is a potent negative regulator of myogenesis. Free myostatin induces the phosphorylation of the Smad family of transcription factors, which, in turn, regulates gene expression, via the canonical TGF-β signaling pathway. There is, however, emerging evidence that myostatin can regulate gene expression independent of Smad signaling. As such, we acquired global gene expression data from the gastrocnemius muscle of C57BL/6 mice following a 6-day treatment with recombinant myostatin compared with vehicle-treated animals. Of the many differentially expressed genes, the myostatin-associated decrease (-11.20-fold; P < 0.05) in the noncoding metastasis-associated lung adenocarcinoma transcript 1 (Malat1) was the most significant and the most intriguing because of numerous reports describing its novel role in regulating cell growth. We therefore sought to further characterize the role of Malat1 expression in skeletal muscle myogenesis. RT-PCR-based quantification of C2C12 and primary human skeletal muscle cells revealed a significant and persistent upregulation (4- to 7-fold; P < 0.05) of Malat1 mRNA during the differentiation of myoblasts into myotubes. Conversely, targeted knockdown of Malat1 using siRNA suppressed myoblast proliferation by arresting cell growth in the G(0)/G(1) phase. These results reveal Malat1 as novel downstream target of myostatin with a considerable ability to regulate myogenesis. The identification of new targets of myostatin will have important repercussions for regenerative biology through inhibition and/or reversal of muscle atrophy and wasting diseases.
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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 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".