<i>lncMGPF</i> is a novel positive regulator of muscle growth and regeneration
Bibliographic record
Abstract
BACKGROUND: Long non-coding RNAs (lncRNAs) play critical regulatory roles in diverse biological processes and diseases. While a large number of lncRNAs have been identified in skeletal muscles until now, their function and underlying mechanisms in skeletal myogenesis remain largely unclear. METHODS: We characterized a novel functional lncRNA designated lncMGPF (lncRNA muscle growth promoting factor) using RACE, Northern blot, fluorescence in situ hybridization and quantitative real-time PCR. Its function was determined by gene overexpression, interference, and knockout experiments in C2C12 myoblasts, myogenic progenitor cells, and an animal model. The molecular mechanism by which lncMGPF regulates muscle differentiation was mainly examined by cotransfection experiments, luciferase reporter assay, RNA immunoprecipitation, RNA pull-down, and RNA stability analyses. RESULTS: We report that lncMGPF, which is highly expressed in muscles and positively regulated by myoblast determination factor (MyoD), promotes myogenic differentiation of muscle cells in vivo and in vitro. lncMGPF knockout in mice substantially decreases growth rate, reduces muscle mass, and impairs muscle regeneration. Overexpression of lncMGPF in muscles can rescue the muscle phenotype of knockout mice and promote muscle growth of wild-type mice. Mechanistically, lncMGPF promotes muscle differentiation by acting as a molecular sponge of miR-135a-5p and thus increasing the expression of myocyte enhancer factor 2C (MEF2C), as well as by enhancing human antigen R-mediated messenger RNA stabilization of myogenic regulatory genes such as MyoD and myogenin (MyoG). We confirm that pig lncRNA AK394747 and human lncRNA MT510647 are homologous to mouse lncMGPF, with conserved function and mechanism during myogenesis. CONCLUSIONS: Our data reveal that lncMGPF is a novel positive regulator of myogenic differentiation, muscle growth and regeneration in mice, pigs, and humans.
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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 | 0.000 |
| 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".