An integrated analysis of mRNA and miRNA in skeletal muscle from myostatin-edited Meishan pigs
Bibliographic record
Abstract
Myostatin (MSTN) is a key muscle factor that negatively regulates skeletal muscle growth and development. Our laboratory recently produced genetically engineered Meishan pigs containing a ZFN-edited MSTN loss-of-function mutation (MSTN−/−, MKO) that led to the hypertrophy of skeletal muscles. In this study, we performed transcriptome sequencing and miRNA sequencing in skeletal muscle samples from MKO and wildtype Meishan (MWT) pigs to investigate the effect of MSTN−/− on expression of mRNA and miRNA. Our results indicated that, compared to MWT pigs, there were 200 genes and 4 miRNAs being significantly up-regulated, and 238 genes and 5 miRNAs being significantly down-regulated in MKO pigs. Analysis by GO and KEGG pathways revealed that differentially expressed miRNAs and their target genes of those differentially expressed miRNAs were involved in the signal pathways of skeletal muscle growth and development such as AMPK, mTOR, and TGF-beta. An integrated analysis of the correlation between miRNA-mRNA and transcriptome predicated that XK and METTL8 were target genes for miR-499-5p, while LRP4 was a target gene for miR-490-3p. Our results provide important clues to help us further investigate MSTN′s regulatory mechanisms during skeletal muscle growth and development.
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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 | 0.000 |
| 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".