The inhibitory effects of Remodelin on myoblasts differentiation
Bibliographic record
Abstract
Summary Myoblasts differentiation is a highly regulated and complex process leading to the formation of fused and aligned mature myotubes. Growing interest in the role of epigenetics in muscle differentiation has highlighted epi-modulators as crucial regulators of this process. Our in vitro study aimed to explore the potential effects of the inhibition of the acetyltransferase Nat10 on myoblasts differentiation, by using Remodelin, a Nat10 selective inhibitor. We cultivated and differentiated murine C2C12 myoblasts on ultra-compliant gelatin substrates for up to 16 days and treated them with Remodelin. A combination of morphological analyses, confocal microscopy, transcriptomic profiling (RNA-seq), quantitative proteomics and metabolomics analyses was employed to assess the impact of Nat10 inhibition on myotube formation and maturation. To evaluate the reproducibility of Remodelin effects across myogenic systems and species, L6 rat myoblasts were included as a secondary comparative model. Remodelin treatment impaired myotube organization, alignment, and structural maturation in both C2C12 and L6 cells compared to untreated controls. In C2C12 cultures, Remodelin also abolished spontaneous myotube contractility. Intersection of transcriptomics and proteomics analyses confirmed that Remodelin effectively slowed myotube formation. Overall, these results indicate that Remodelin broadly affects the regulatory networks involved in skeletal muscle differentiation.
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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) | 0.000 | 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".