Exercise-Induced Meat Quality Improvement Is Associated with an lncRNA-miRNA-mRNA Network in Tibetan Sheep
Simple Summary
Abstract
1. Introduction
2. Materials and Methods
2.1. Experimental Design and Sample Collection
2.2. Morphological Structure and Intramuscular Fat Content Analysis
2.3. Transcriptome Sequencing and Transcript Assembly
2.4. Differential Expression and Functional Enrichment Analysis
2.5. Constructing the ceRNA Network
3. Results
3.1. Phenotypic Differences in the Biceps Femoris
3.2. Summary of Sequencing Data
3.3. Differential Expression of mRNAs and lncRNAs
3.4. GO and KEGG Analysis of DEmRNAs
3.5. lncRNA-miRNA-mRNA Network Construction
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Abbreviations
| IMF | Intramuscular fat |
| mRNA | Messenger RNA |
| miRNA | MicroRNA |
| lncRNA | Long non-coding RNA |
| ceRNA | Competing endogenous RNA |
| LE | Low exercise |
| HE | High exercise |
| DE | Differentially expressed |
| rRNA | Ribosomal RNA |
| TPM | Transcripts per million reads |
| FC | Fold change |
| GO | Gene ontology |
| KEGG | Kyoto encyclopedia of genes and genomes |
| SCC | Spearman’s rank correlation coefficient |
| PCC | Pearson correlation coefficient |
References
- Yan, Z.; Yang, J.; Wei, W.; Zhou, M.; Mo, D.; Wan, X.; Ma, R.; Wu, M.; Huang, J.; Liu, Y.; et al. A time-resolved multi-omics atlas of transcriptional regulation in response to high-altitude hypoxia across whole-body tissues. Nat. Commun. 2024, 15, 3970. [Google Scholar] [CrossRef]
- Liang, X.; Duan, Q.; Li, B.; Wang, Y.; Bu, Y.; Zhang, Y.; Kuang, Z.; Mao, L.; An, X.; Wang, H.; et al. Genomic structural variation contributes to evolved changes in gene expression in high-altitude Tibetan sheep. Proc. Natl. Acad. Sci. USA 2024, 121, e2322291121. [Google Scholar] [CrossRef] [PubMed]
- Zhang, J.; Zhang, Y.; Wang, M.; Luobu, G.; Duoji, Z.; Pubu, C.; Zheng, J.; Miao, H.; Zhao, Y. Whole-genome sequencing resources of 301 indigenous Tibetan sheep from the Himalayan region. Sci. Data 2025, 12, 1351. [Google Scholar] [CrossRef] [PubMed]
- Realini, C.E.; Pavan, E.; Johnson, P.L.; Font-I-Furnols, M.; Jacob, N.; Agnew, M.; Craigie, C.R.; Moon, C.D. Consumer liking of M. longissimus lumborum from New Zealand pasture-finished lamb is influenced by intramuscular fat. Meat Sci. 2021, 173, 108380. [Google Scholar] [CrossRef] [PubMed]
- Matarneh, S.K.; Silva, S.L.; Gerrard, D.E. New Insights in Muscle Biology that Alter Meat Quality. Annu. Rev. Anim. Biosci. 2021, 9, 355–377. [Google Scholar] [CrossRef]
- Smith, J.A.B.; Murach, K.A.; Dyar, K.A.; Zierath, J.R. Exercise metabolism and adaptation in skeletal muscle. Nat. Rev. Mol. Cell Biol. 2023, 24, 607–632. [Google Scholar] [CrossRef]
- Yasuda, T.; Ishihara, T.; Ichimura, A.; Ishihara, N. Mitochondrial dynamics define muscle fiber type by modulating cellular metabolic pathways. Cell Rep. 2023, 42, 112434. [Google Scholar] [CrossRef]
- Mishra, P.; Varuzhanyan, G.; Pham, A.H.; Chan, D.C. Mitochondrial Dynamics is a Distinguishing Feature of Skeletal Muscle Fiber Types and Regulates Organellar Compartmentalization. Cell Metab. 2015, 22, 1033–1044. [Google Scholar] [CrossRef]
- Alasnier, C.; Rémignon, H.; Gandemer, G. Lipid characteristics associated with oxidative and glycolytic fibres in rabbit muscles. Meat Sci. 1996, 43, 213–224. [Google Scholar] [CrossRef]
- Renand, G.; Picard, B.; Touraille, C.; Berge, P.; Lepetit, J. Relationships between muscle characteristics and meat quality traits of young Charolais bulls. Meat Sci. 2001, 59, 49–60. [Google Scholar] [CrossRef]
- Crouse, J.D.; Koohmaraie, M.; Seideman, S.D. The relationship of muscle fibre size to tenderness of beef. Meat Sci. 1991, 30, 295–302. [Google Scholar] [CrossRef]
- Mallett, G. The effect of exercise and physical activity on skeletal muscle epigenetics and metabolic adaptations. Eur. J. Appl. Physiol. 2025, 125, 611–627. [Google Scholar] [CrossRef]
- Wilson, J.M.; Loenneke, J.P.; Jo, E.; Wilson, G.J.; Zourdos, M.C.; Kim, J.S. The effects of endurance, strength, and power training on muscle fiber type shifting. J. Strength Cond. Res. 2012, 26, 1724–1729. [Google Scholar] [CrossRef]
- Li, J.; Zhang, S.; Li, C.; Zhang, X.; Shan, Y.; Zhang, Z.; Bo, H.; Zhang, Y. Endurance exercise-induced histone methylation modification involved in skeletal muscle fiber type transition and mitochondrial biogenesis. Sci. Rep. 2024, 14, 21154. [Google Scholar] [CrossRef] [PubMed]
- Li, J.; Zhang, Z.; Bo, H.; Zhang, Y. Exercise couples mitochondrial function with skeletal muscle fiber type via ROS-mediated epigenetic modification. Free Radic. Biol. Med. 2024, 213, 409–425. [Google Scholar] [CrossRef] [PubMed]
- Luo, X.; Zhang, H.; Cao, X.; Yang, D.; Yan, Y.; Lu, J.; Wang, X.; Wang, H. Endurance Exercise-Induced Fgf21 Promotes Skeletal Muscle Fiber Conversion Through TGF-β1 and p38 MAPK Signaling Pathway. Int. J. Mol. Sci. 2023, 24, 11401. [Google Scholar] [CrossRef]
- Feng, Y.; Rao, Z.; Tian, X.; Hu, Y.; Yue, L.; Meng, Y.; Zhong, Q.; Chen, W.; Xu, W.; Li, H.; et al. Endurance training enhances skeletal muscle mitochondrial respiration by promoting MOTS-c secretion. Free Radic. Biol. Med. 2025, 227, 619–628. [Google Scholar] [CrossRef]
- Iacono, G.; Massoni-Badosa, R.; Heyn, H. Single-cell transcriptomics unveils gene regulatory network plasticity. Genome Biol. 2019, 20, 110. [Google Scholar] [CrossRef]
- Nguyen, T.C.; Zaleta-Rivera, K.; Huang, X.; Dai, X.; Zhong, S. RNA, Action Through Interactions. Trends Genet. 2018, 34, 867–882. [Google Scholar] [CrossRef]
- Schwanhäusser, B.; Busse, D.; Li, N.; Dittmar, G.; Schuchhardt, J.; Wolf, J.; Chen, W.; Selbach, M. Corrigendum: Global quantification of mammalian gene expression control. Nature 2013, 495, 126–127. [Google Scholar] [CrossRef]
- Mauger, D.M.; Cabral, B.J.; Presnyak, V.; Su, S.V.; Reid, D.W.; Goodman, B.; Link, K.; Khatwani, N.; Reynders, J.; Moore, M.J.; et al. mRNA structure regulates protein expression through changes in functional half-life. Proc. Natl. Acad. Sci. USA 2019, 116, 24075–24083. [Google Scholar] [CrossRef]
- Wu, L.; Ran, L.; Lang, H.; Zhou, M.; Yu, L.; Yi, L.; Zhu, J.; Liu, L.; Mi, M. Myricetin improves endurance capacity by inducing muscle fiber type conversion via miR-499. Nutr. Metab. 2019, 16, 27. [Google Scholar] [CrossRef] [PubMed]
- Zhang, D.; Wang, X.; Li, Y.; Zhao, L.; Lu, M.; Yao, X.; Xia, H.; Wang, Y.; Liu, M.; Jiang, J.; et al. Thyroid hormone regulates muscle fiber type conversion via miR-133a1. J. Cell Biol. 2014, 207, 753–766. [Google Scholar] [CrossRef] [PubMed]
- Yu, J.A.; Wang, Z.; Yang, X.; Ma, M.; Li, Z.; Nie, Q. LncRNA-FKBP1C regulates muscle fiber type switching by affecting the stability of MYH1B. Cell Death Discov. 2021, 7, 73. [Google Scholar] [CrossRef] [PubMed]
- Cesana, M.; Cacchiarelli, D.; Legnini, I.; Santini, T.; Sthandier, O.; Chinappi, M.; Tramontano, A.; Bozzoni, I. A long noncoding RNA controls muscle differentiation by functioning as a competing endogenous RNA. Cell 2011, 147, 358–369. [Google Scholar] [CrossRef]
- Tay, Y.; Rinn, J.; Pandolfi, P.P. The multilayered complexity of ceRNA crosstalk and competition. Nature 2014, 505, 344–352. [Google Scholar] [CrossRef]
- Yu, Z.; Xu, X.; Ai, N.; Wang, K.; Zhang, P.; Li, X.; LiuFu, S.; Liu, X.; Jiang, J.; Gu, J.; et al. Integrated analysis of circRNA, lncRNA, miRNA and mRNA to reveal the ceRNA regulatory network of postnatal skeletal muscle development in Ningxiang pig. Front. Cell Dev. Biol. 2023, 11, 1185823. [Google Scholar] [CrossRef]
- Cui, R.; Kang, X.; Liu, Y.; Liu, X.; Chan, S.; Wang, Y.; Li, Z.; Ling, Y.; Feng, D.; Li, M.; et al. Integrated analysis of the whole transcriptome of skeletal muscle reveals the ceRNA regulatory network related to the formation of muscle fibers in Tan sheep. Front. Genet. 2022, 13, 991606. [Google Scholar] [CrossRef]
- Shen, J.; Hao, Z.; Wang, J.; Hu, J.; Liu, X.; Li, S.; Ke, N.; Song, Y.; Lu, Y.; Hu, L.; et al. Comparative Transcriptome Profile Analysis of Longissimus dorsi Muscle Tissues from Two Goat Breeds with Different Meat Production Performance Using RNA-Seq. Front. Genet. 2021, 11, 619399. [Google Scholar] [CrossRef]
- Ai, Y.; Zhu, Y.; Wang, L.; Zhang, X.; Zhang, J.; Long, X.; Gu, Q.; Han, H. Dynamic Changes in the Global Transcriptome of Postnatal Skeletal Muscle in Different Sheep. Genes 2023, 14, 1298. [Google Scholar] [CrossRef]
- Zhao, L.; Li, F.; Zhang, X.; Yuan, L.; Tian, H.; Xu, D.; Zhang, D.; Zhang, Y.; Zhao, Y.; Huang, K.; et al. Integrating genome-wide association and transcriptome analysis to provide molecular insights into growth rates in sheep. J. Integr. Agric. 2024, in press. [Google Scholar] [CrossRef]
- Folch, J.; Lees, M.; Stanley, G.H.S. A simple method for the isolation and purification of total lipides from animal tissues. J. Biol. Chem. 1957, 226, 497–509. [Google Scholar] [CrossRef]
- Chen, S.; Zhou, Y.; Chen, Y.; Gu, J. fastp: An ultra-fast all-in-one FASTQ preprocessor. Bioinformatics 2018, 34, i884–i890. [Google Scholar] [CrossRef] [PubMed]
- Langmead, B.; Salzberg, S.L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 2012, 9, 357–359. [Google Scholar] [CrossRef] [PubMed]
- Kim, D.; Langmead, B.; Salzberg, S.L. HISAT: A fast spliced aligner with low memory requirements. Nat. Methods 2015, 12, 357–360. [Google Scholar] [CrossRef]
- Trapnell, C.; Williams, B.A.; Pertea, G.; Mortazavi, A.; Kwan, G.; van Baren, M.J.; Salzberg, S.L.; Wold, B.J.; Pachter, L. Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation. Nat. Biotechnol. 2010, 28, 511–515. [Google Scholar] [CrossRef]
- Pertea, M.; Pertea, G.M.; Antonescu, C.M.; Chang, T.C.; Mendell, J.T.; Salzberg, S.L. StringTie enables improved reconstruction of a transcriptome from RNA-seq reads. Nat. Biotechnol. 2015, 33, 290–295. [Google Scholar] [CrossRef]
- Kang, Y.J.; Yang, D.C.; Kong, L.; Hou, M.; Meng, Y.Q.; Wei, L.; Gao, G. CPC2: A fast and accurate coding potential calculator based on sequence intrinsic features. Nucleic Acids Res. 2017, 45, W12–W16. [Google Scholar] [CrossRef]
- Sun, L.; Luo, H.; Bu, D.; Zhao, G.; Yu, K.; Zhang, C.; Liu, Y.; Chen, R.; Zhao, Y. Utilizing sequence intrinsic composition to classify protein-coding and long non-coding transcripts. Nucleic Acids Res. 2013, 41, e166. [Google Scholar] [CrossRef]
- Wucher, V.; Legeai, F.; Hédan, B.; Rizk, G.; Lagoutte, L.; Leeb, T.; Jagannathan, V.; Cadieu, E.; David, A.; Lohi, H.; et al. FEELnc: A tool for long non-coding RNA annotation and its application to the dog transcriptome. Nucleic Acids Res. 2017, 45, e57. [Google Scholar] [CrossRef]
- Li, B.; Dewey, C.N. RSEM: Accurate transcript quantification from RNA-Seq data with or without a reference genome. BMC Bioinform. 2011, 12, 323. [Google Scholar] [CrossRef]
- Love, M.I.; Huber, W.; Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014, 15, 550. [Google Scholar] [CrossRef]
- Conesa, A.; Madrigal, P.; Tarazona, S.; Gomez-Cabrero, D.; Cervera, A.; McPherson, A.; Szcześniak, M.W.; Gaffney, D.J.; Elo, L.L.; Zhang, X.; et al. A survey of best practices for RNA-seq data analysis. Genome Biol. 2016, 17, 13. [Google Scholar] [CrossRef]
- Naseer, Q.A.; Liu, L.; Xue, X.; Chen, S.; Chen, J.; Qu, J.; Cui, L.; Wang, X.; Dang, S. Expression profile of lncRNAs and mRNAs in intestinal macrophages. Mol. Med. Rep. 2020, 22, 3735–3746. [Google Scholar] [CrossRef]
- Klopfenstein, D.V.; Zhang, L.; Pedersen, B.S.; Ramírez, F.; Warwick Vesztrocy, A.; Naldi, A.; Mungall, C.J.; Yunes, J.M.; Botvinnik, O.; Weigel, M.; et al. GOATOOLS: A Python library for Gene Ontology analyses. Sci. Rep. 2018, 8, 10872. [Google Scholar] [CrossRef] [PubMed]
- Ashburner, M.; Ball, C.A.; Blake, J.A.; Botstein, D.; Butler, H.; Cherry, J.M.; Davis, A.P.; Dolinski, K.; Dwight, S.S.; Eppig, J.T.; et al. Gene ontology: Tool for the unification of biology. The Gene Ontology Consortium. Nat. Genet. 2000, 25, 25–29. [Google Scholar] [CrossRef] [PubMed]
- Xie, C.; Mao, X.; Huang, J.; Ding, Y.; Wu, J.; Dong, S.; Kong, L.; Gao, G.; Li, C.; Wei, L. KOBAS 2.0: A web server for annotation and identification of enriched pathways and diseases. Nucleic Acids Res. 2011, 39, W316–W322. [Google Scholar] [CrossRef] [PubMed]
- Ogata, H.; Goto, S.; Sato, K.; Fujibuchi, W.; Bono, H.; Kanehisa, M. KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucleic Acids Res. 1999, 27, 29–34. [Google Scholar] [CrossRef]
- Huang, D.W.; Sherman, B.T.; Zheng, X.; Yang, J.; Imamichi, T.; Stephens, R.; Lempicki, R.A. Extracting biological meaning from large gene lists with DAVID. Curr. Protoc. Bioinform. 2009, 27, 1–13. [Google Scholar] [CrossRef]
- Ye, W.; Shi, M.; Ren, K.; Liu, Y.; Duan, Y.; Cheng, Y.; Zhang, W.; Xia, X. Profiling the Spatial Expression Pattern and ceRNA Network of lncRNA, miRNA, and mRNA Associated with the Development of Intermuscular Bones in Zebrafish. Biology 2022, 12, 75. [Google Scholar] [CrossRef]
- Esbjörnsson, M.E.; Dahlström, M.S.; Gierup, J.W.; Jansson, E.C. Muscle fiber size in healthy children and adults in relation to sex and fiber types. Muscle Nerve 2021, 63, 586–592. [Google Scholar] [CrossRef] [PubMed]
- Lefaucheur, L. A second look into fibre typing—Relation to meat quality. Meat Sci. 2010, 84, 257–270. [Google Scholar] [CrossRef] [PubMed]
- de Morree, A.; Klein, J.D.D.; Gan, Q.; Farup, J.; Urtasun, A.; Kanugovi, A.; Bilen, B.; van Velthoven, C.T.J.; Quarta, M.; Rando, T.A. Alternative polyadenylation of Pax3 controls muscle stem cell fate and muscle function. Science 2019, 366, 734–738. [Google Scholar] [CrossRef] [PubMed]
- Roberston, M.J.; Raghunathan, S.; Potaman, V.N.; Zhang, F.; Stewart, M.D.; McConnell, B.K.; Schwartz, R.J. CRISPR-Cas9-induced IGF1 gene activation as a tool for enhancing muscle differentiation via multiple isoform expression. FASEB J. 2020, 34, 555–570. [Google Scholar] [CrossRef]
- Tajbakhsh, S. lncRNA-Encoded Polypeptide SPAR(s) with mTORC1 to Regulate Skeletal Muscle Regeneration. Cell Stem Cell 2017, 20, 428–430. [Google Scholar] [CrossRef]
- Somlyo, A.V.; Wang, H.; Choudhury, N.; Khromov, A.S.; Majesky, M.; Owens, G.K.; Somlyo, A.P. Myosin light chain kinase knockout. J. Muscle Res. Cell Motil. 2004, 25, 241–242. [Google Scholar] [CrossRef]
- Stull, J.T.; Kamm, K.E.; Vandenboom, R. Myosin light chain kinase and the role of myosin light chain phosphorylation in skeletal muscle. Arch. Biochem. Biophys. 2011, 510, 120–128. [Google Scholar] [CrossRef]
- Youm, T.H.; Woo, S.H.; Kwon, E.S.; Park, S.S. NADPH Oxidase 4 Contributes to Myoblast Fusion and Skeletal Muscle Regeneration. Oxid. Med. Cell Longev. 2019, 2019, 3585390. [Google Scholar] [CrossRef]
- Specht, K.S.; Kant, S.; Addington, A.K.; McMillan, R.P.; Hulver, M.W.; Learnard, H.; Campbell, M.; Donnelly, S.R.; Caliz, A.D.; Pei, Y.; et al. Nox4 mediates skeletal muscle metabolic responses to exercise. Mol. Metab. 2021, 45, 101160. [Google Scholar] [CrossRef]
- Sun, Q.; Hess, D.T.; Nogueira, L.; Yong, S.; Bowles, D.E.; Eu, J.; Laurita, K.R.; Meissner, G.; Stamler, J.S. Oxygen-coupled redox regulation of the skeletal muscle ryanodine receptor-Ca2+ release channel by NADPH oxidase 4. Proc. Natl. Acad. Sci. USA 2011, 108, 16098–16103. [Google Scholar] [CrossRef]
- Kossler, N.; Stricker, S.; Rödelsperger, C.; Robinson, P.N.; Kim, J.; Dietrich, C.; Osswald, M.; Kühnisch, J.; Stevenson, D.A.; Braun, T.; et al. Neurofibromin (Nf1) is required for skeletal muscle development. Hum. Mol. Genet. 2011, 20, 2697–2709. [Google Scholar] [CrossRef]
- Fay, C.X.; Zunica, E.R.M.; Awad, E.; Bradley, W.; Church, C.; Liu, J.; Liu, H.; Crossman, D.K.; Mobley, J.A.; Kirwan, J.P.; et al. Global proteomics and affinity mass spectrometry analysis of human Schwann cells indicates that variation in and loss of neurofibromin (NF1) alters protein expression and cellular and mitochondrial metabolism. Sci. Rep. 2025, 15, 3883. [Google Scholar] [CrossRef]





| Altitude | Average Raw Reads | Average Clean Reads | Average Remaining Clean Reads | Average Mapped Reads (%) | Unique Mapped (%) |
|---|---|---|---|---|---|
| LE | 81,866,550 | 81,478,946 | 79,869,272 | 70,187,050 (87.88%) | 61,536,710 (77.05%) |
| HE | 89,725,502 | 89,316,973 | 88,336,808 | 79,813,459 (90.35%) | 73,662,081 (83.39%) |
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© 2026 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license.
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Zhao, P.; Jiang, Z.; He, X.; Tian, T.; He, F.; Ma, X. Exercise-Induced Meat Quality Improvement Is Associated with an lncRNA-miRNA-mRNA Network in Tibetan Sheep. Biology 2026, 15, 158. https://doi.org/10.3390/biology15020158
Zhao P, Jiang Z, He X, Tian T, He F, Ma X. Exercise-Induced Meat Quality Improvement Is Associated with an lncRNA-miRNA-mRNA Network in Tibetan Sheep. Biology. 2026; 15(2):158. https://doi.org/10.3390/biology15020158
Chicago/Turabian StyleZhao, Pengfei, Zhiyong Jiang, Xin He, Ting Tian, Fang He, and Xiong Ma. 2026. "Exercise-Induced Meat Quality Improvement Is Associated with an lncRNA-miRNA-mRNA Network in Tibetan Sheep" Biology 15, no. 2: 158. https://doi.org/10.3390/biology15020158
APA StyleZhao, P., Jiang, Z., He, X., Tian, T., He, F., & Ma, X. (2026). Exercise-Induced Meat Quality Improvement Is Associated with an lncRNA-miRNA-mRNA Network in Tibetan Sheep. Biology, 15(2), 158. https://doi.org/10.3390/biology15020158
