miR-155 Inhibits Expression of the MEF2A Protein to Repress Skeletal Muscle Differentiation
Bibliographic record
Abstract
microRNAs (miRNAs) are 21–23-nucleotide non-coding RNAs. It has become more and more evident that this class of small RNAs plays critical roles in the regulation of gene expression at the post-transcriptional level. MEF2A is a member of the MEF2 (myogenic enhancer factor 2) family of transcription factors. Prior report showed that the 3′-untranslated region (3′-UTR) of the Mef2A gene mediated its repression; however, the molecular mechanism underlying this intriguing observation was unknown. Here, we report that MEF2A is repressed by miRNAs. We identify miR-155 as one of the primary miRNAs that significantly represses the expression of MEF2A. We show that knockdown of the Mef2A gene by siRNA impairs myoblast differentiation. Similarly, overexpression of miR-155 leads to the repression of endogenous MEF2A expression and the inhibition of myoblast differentiation. Most importantly, reintroduction of MEF2A in miR-155 overexpressed myoblasts was able to partially rescue the miR-155-induced myoblast differentiation defect. Our data therefore establish miR-155 as an important regulator of MEF2A expression and uncover its function in muscle gene expression and myogenic 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".