Lnc-MEG8 regulates yak myoblast differentiation via the miR-22-3p/RTL1 axis
Bibliographic record
Abstract
BACKGROUND: The yak (Bos grunniens) is essential to the livelihoods of Tibetan people on the Qinghai-Tibet Plateau; however, its growth and productivity are constrained by the region's harsh climate and high altitude. Yak skeletal muscle myoblasts, which have evolved to thrive under these challenging conditions, offer a valuable model for investigating muscle development. In this study, we performed transcriptome profiling of yak longissimus dorsi muscle at different growth stages, identifying a key long non-coding RNA, LncRNA-XR_314844 (Lnc-MEG8), with a potential role in muscle development. RESULTS: We developed a novel technique to isolate high-quality yak myoblasts, enabling detailed analysis of Lnc-MEG8. Our results indicated that Lnc-MEG8's subcellular localization varies during muscle cell growth: it is found in both the nucleus and cytoplasm during proliferation but shifts mainly to the cytoplasm during differentiation. Functional experiments showed that Lnc-MEG8 promotes cell proliferation and inhibits differentiation, while its silencing had the opposite effect. Further analysis revealed that both Lnc-MEG8 and the gene RTL1 share miR-22-3p as a common target. Dual-luciferase assays confirmed miR-22-3p directly targets both Lnc-MEG8 and RTL1 mRNA. Co-transfection of Lnc-MEG8 and a miR-22-3p mimic restored RTL1 expression, highlighting Lnc-MEG8's regulatory role. Lnc-MEG8 also counteracts miR-22-3p's suppression of key muscle genes such as MyF5 and MyoG, facilitating myotube formation. CONCLUSION: These findings demonstrate that the Lnc-MEG8-miR-22-3p-RTL1 axis plays a crucial role in yak muscle development, providing insights that could advance muscle tissue engineering and enhance yak meat quality.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.002 | 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".