ArticleBiology2026
Exercise-Induced Meat Quality Improvement Is Associated with an lncRNA-miRNA-mRNA Network in Tibetan Sheep.
Article in Biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
- Integrated Lipidomic and Amino Acid Metabolomic Analyses Reveal Muscle Metabolic Differences in Tibetan Sheep Under Grazing and House-Feeding Systems.Animals : an open access journal from MDPI · 2026Article
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Authors and funding
6 authors.
Funding
Abstract
Tibetan sheep, a unique breed indigenous to the Qinghai-Tibet Plateau, exhibit remarkable adaptations to high-altitude hypoxia, and their muscle quality is a key economic determinant. However, the molecular mechanisms by which exercise regulates meat quality in this breed remain poorly understood. This study aimed to systematically investigate the effects of different exercise volumes on the biceps femoris muscle of Tibetan sheep, integrating histological analysis with high-throughput transcriptome sequencing. We compared a low-exercise group with a high-exercise group and found that long-term endurance exercise resulted in phenotypic changes suggestive of a shift toward oxidative muscle fiber characteristics. This adaptation was characterized by significantly reduced muscle fiber diameter and cross-sectional area, alongside a crucial increase in intramuscular fat content, collectively enhancing meat tenderness, flavor, and juiciness. Transcriptomic analysis revealed extensive gene expression reprogramming, identifying 208 mRNAs and 490 lncRNAs that were differentially expressed and primarily associated with muscle fiber transition and energy metabolism. Furthermore, we constructed a putative lncRNA-miRNA-mRNA competing endogenous RNA network based on expression correlations and bioinformatic predictions, highlighting potential key regulatory axes such as LOC105603384/miR-16-z/
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