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Home / Equine Research Bank / Biology (Basel) / Molecular Mechanisms Underlying Differences in Athletic Abil...
Biology2025; 14(10); 1364; doi: 10.3390/biology14101364

Molecular Mechanisms Underlying Differences in Athletic Ability in Racehorses Based on Whole Transcriptome Sequencing.

Abstract: This study aimed to compare blood samples from Yili horses with outstanding and average performance in 5000 m races through transcriptome sequencing, identify key differentially expressed genes, lncRNAs, and circRNAs, as well as related enriched pathways, and elucidate their regulatory networks. This study used six healthy four-year-old Yili stallions as subjects, divided into an excellent group (E group, = 3) and an ordinary group (O group, = 3) based on their 5000-m race performance. Blood RNA-Seq technology was used to analyze differentially expressed mRNAs, lncRNAs, and circRNAs. A total of 2298 mRNAs, 264 lncRNAs, and 215 circRNAs were identified as differentially expressed. Key genes such as EGR1, FOSB, MRPL1, LOC100049811, SIRPB2, and CYTB regulate athletic performance. These genes and their associated RNAs synergistically participate in energy metabolism, protein homeostasis, and muscle remodeling processes, revealing the molecular mechanisms influencing athletic performance and providing important references for identifying candidate genes associated with equine athletic performance.
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Publication Date: 2025-10-05 PubMed ID: 41154767PubMed Central: PMC12561781DOI: 10.3390/biology14101364Google Scholar: Lookup
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  • Journal Article

Summary

This research summary has been generated with artificial intelligence and may contain errors and omissions. Refer to the original study to confirm details provided. Submit correction.

Overview

  • This study investigated differences in gene expression between high-performing and average-performing Yili racehorses using whole transcriptome sequencing of blood samples.
  • The research identified specific genes and RNA molecules linked to athletic ability, shedding light on molecular pathways influencing racehorse performance.

Study Objectives

  • Compare gene expression profiles between two groups of Yili stallions differentiated by their 5000 m racing performance: an excellent group and an ordinary group.
  • Identify differentially expressed messenger RNAs (mRNAs), long non-coding RNAs (lncRNAs), and circular RNAs (circRNAs) related to athletic ability.
  • Elucidate molecular pathways and regulatory networks that underlie variations in athletic performance.

Experimental Design and Methods

  • Subjects: Six healthy four-year-old Yili stallions were selected and divided equally into two performance groups:
    • Excellent group (E group, n = 3)
    • Ordinary group (O group, n = 3)
  • Sample Collection: Blood samples were collected from all horses for genetic analysis.
  • Transcriptome Sequencing: RNA-Seq technology was applied to sequence the whole transcriptome in the blood samples, enabling comprehensive profiling of gene expression.
  • Data Analysis:
    • Identification of differentially expressed RNAs, including mRNAs, lncRNAs, and circRNAs, between groups.
    • Bioinformatic analyses to investigate the biological pathways enriched by these differentially expressed RNAs.
    • Building regulatory interaction networks to understand how these RNAs work together to influence athletic traits.

Key Findings

  • A total of 2298 mRNAs showed differential expression between excellent and ordinary performers.
  • 264 lncRNAs and 215 circRNAs were also differentially expressed, highlighting the role of non-coding RNAs in athletic performance.
  • Several key genes were identified as crucial regulators of athletic ability:
    • EGR1 (Early Growth Response 1): Known to be involved in muscle adaptation and growth response to stimuli.
    • FOSB: Participates in gene regulation under physiological stress and muscle remodeling.
    • MRPL1: Mitochondrial ribosomal protein potentially linked to energy metabolism.
    • LOC100049811: A gene locus associated with performance differences, though its exact function may require further research.
    • SIRPB2: Involved in immune regulation, possibly influencing recovery or endurance.
    • CYTB (Cytochrome b): Critical component of mitochondrial electron transport chain and ATP production.
  • Identified genes and associated RNAs work synergistically in:
    • Energy metabolism — important for sustained physical exertion during racing.
    • Protein homeostasis — maintaining muscle protein balance and repair.
    • Muscle remodeling — adaptations required for enhanced muscular performance and endurance.

Scientific and Practical Implications

  • The study provides insight into the molecular basis of athletic performance differences in racehorses, emphasizing the roles of both coding and non-coding RNAs.
  • Understanding these molecular mechanisms helps identify candidate genetic markers for breeding programs aimed at improving racehorse performance.
  • Findings may contribute to the development of targeted interventions or training regimens that enhance energy metabolism and muscle adaptation based on an individual horse’s genetic profile.
  • The regulatory networks uncovered can guide future research on how gene expression modulation impacts athletics in horses and potentially other animals.

Summary

  • This research leveraged whole transcriptome sequencing of blood samples from Yili racehorses with varying athletic performance to pinpoint key genes and RNA molecules linked to superior racing ability.
  • The study highlights complex interactions involving energy production, protein dynamics, and muscular remodeling as fundamental biological processes underpinning athletic differences.
  • The identification of these molecular signatures paves the way for genetic and genomic approaches to optimizing equine athletic performance.

Cite This Article

APA
Huang Q, Ren W, Shan D, Su Y, Li Z, Li L, Wang R, Ma S, Wang J. (2025). Molecular Mechanisms Underlying Differences in Athletic Ability in Racehorses Based on Whole Transcriptome Sequencing. Biology (Basel), 14(10), 1364. https://doi.org/10.3390/biology14101364

Publication

Biology
ISSN: 2079-7737
NlmUniqueID: 101587988
Country: Switzerland
Language: English
Volume: 14
Issue: 10
PII: 1364

Researcher Affiliations

Huang, Qiuping
  • College of Animal Science, Xinjiang Agricultural University, Urumqi 830052, China.
Ren, Wanlu
  • College of Animal Science, Xinjiang Agricultural University, Urumqi 830052, China.
  • Xinjiang Key Laboratory of Equine Breeding and Exercise Physiology, Urumqi 830052, China.
Shan, Dehaxi
  • College of Animal Science, Xinjiang Agricultural University, Urumqi 830052, China.
Su, Yi
  • College of Animal Science, Xinjiang Agricultural University, Urumqi 830052, China.
Li, Zexu
  • College of Animal Science, Xinjiang Agricultural University, Urumqi 830052, China.
Li, Luling
  • College of Animal Science, Xinjiang Agricultural University, Urumqi 830052, China.
Wang, Ran
  • College of Animal Science, Xinjiang Agricultural University, Urumqi 830052, China.
Ma, Shikun
  • College of Animal Science, Xinjiang Agricultural University, Urumqi 830052, China.
Wang, Jianwen
  • College of Animal Science, Xinjiang Agricultural University, Urumqi 830052, China.
  • Xinjiang Key Laboratory of Equine Breeding and Exercise Physiology, Urumqi 830052, China.

Grant Funding

  • 32302735 / National Natural Science Foundation of China
  • 2022A02013-1 / Major Science and Technology Special Project of Xinjiang Uygur Autonomous Region
  • ZYYD2025JD02 / Central Guidance for Local Science and Technology Development Fund
  • XJEDU2025J057 / Basic Research Funding Projects for Scientific Research in Xinjiang Universities

Conflict of Interest Statement

The authors declare no conflicts of interest.

References

This article includes 66 references
  1. Kirsch K, Düe M, Holzhausen H, Sandersen C. Correlation of competition performance with heart rate and blood lactate response during interval training sessions in eventing horses.. Comp. Exerc. Physiol. 2019;15:187–198.
    doi: 10.3920/CEP180050google scholar: lookup
  2. Zuluaga Cabrera AM, Casas Soto MJ, Martínez Aranzales JR, Correa Valencia NM, Arias Gutiérrez MP. Blood lactate concentrations and heart rates of Colombian Paso horses during a field exercise test.. Veter-Anim. Sci. 2021;13:100185.
    doi: 10.1016/j.vas.2021.100185pmc: PMC8219982pubmed: 34189341google scholar: lookup
  3. McGivney BA, Browne JA, Fonseca RG, Katz LM, MacHugh DE, Whiston R, Hill EW. MSTN genotypes in Thoroughbred horses influence skeletal muscle gene expression and racetrack performance.. Anim. Genet. 2012;43:810–812.
    doi: 10.1111/j.1365-2052.2012.02329.xpubmed: 22497477google scholar: lookup
  4. Pira E, Vacca GM, Dettori ML, Piras G, Moro M, Paschino P, Pazzola M. Polymorphisms at myostatin gene (MSTN) and the associations with sport performances in Anglo-Arabian racehorses.. Animals 2021;11:964.
    doi: 10.3390/ani11040964pmc: PMC8065447pubmed: 33808485google scholar: lookup
  5. Eivers S, McGivney B, Gu J, MacHugh D, Katz L, Hill E. PGC-1α encoded by the PPARGC1A gene regulates oxidative energy metabolism in equine skeletal muscle during exercise.. Anim. Genet. 2012;43:153–162.
    doi: 10.1111/j.1365-2052.2011.02238.xpubmed: 22404351google scholar: lookup
  6. Pereira GL, de Matteis R, Meira CT, Regitano LC, Silva JAIV, Chardulo LAL, Curi RA. Comparison of sequence variants in the PDK4 and COX4I2 genes between racing and cutting lines of quarter horses and associations with the speed index.. J. Equine Vet. Sci. 2016;39:1–6.
    doi: 10.1016/j.jevs.2015.07.001google scholar: lookup
  7. Yang L, Li P, Huang X, Wang C, Zeng Y, Wang J, Yao X, Meng J. Effects of combined transcriptome and metabolome analysis training on athletic performance of 2-year-old trot-type Yili horses.. Genes 2025;16:197.
    doi: 10.3390/genes16020197pmc: PMC11855102pubmed: 40004526google scholar: lookup
  8. McGee SL, Hargreaves M. Epigenetics and exercise.. Trends Endocrinol. Metab. 2019;30:636–645.
    doi: 10.1016/j.tem.2019.06.002pubmed: 31279665google scholar: lookup
  9. Bou T, Ding W, Ren X, Liu H, Gong W, Jia Z, Zhang X, Dugarjaviin M, Bai D. Muscle fibre transition and transcriptional changes of horse skeletal muscles during traditional Mongolian endurance training.. Equine Vet. J. 2024;56:178–192.
    doi: 10.1111/evj.13968pubmed: 37345447google scholar: lookup
  10. Wang J, Ren W, Li Z, Li L, Wang R, Ma S, Zeng Y, Meng J, Yao X. Regulatory Mechanisms of Yili Horses During an 80 km Race Based on Transcriptomics and Metabolomics Analyses.. Int. J. Mol. Sci. 2025;26:2426.
    doi: 10.3390/ijms26062426pmc: PMC11942362pubmed: 40141070google scholar: lookup
  11. Ma B, Wang S, Wu W, Shan P, Chen Y, Meng J, Xing L, Yun J, Hao L, Wang X. Mechanisms of circRNA/lncRNA-miRNA interactions and applications in disease and drug research.. Biomed. Pharmacother. 2023;162:114672.
    doi: 10.1016/j.biopha.2023.114672pubmed: 37060662google scholar: lookup
  12. Li R, Li B, Jiang A, Cao Y, Hou L, Zhang Z, Zhang X, Liu H, Kim K-H, Wu W. Exploring the lncRNAs related to skeletal muscle fiber types and meat quality traits in pigs.. Genes 2020;11:883.
    doi: 10.3390/genes11080883pmc: PMC7465969pubmed: 32759632google scholar: lookup
  13. Mao C, You W, Yang Y, Cheng H, Hu X, Lan X, Song E. Comprehensive characterization of lncRNA N6-methyladenosine modification dynamics throughout bovine skeletal muscle development.. J. Anim. Sci. Biotechnol. 2025;16:36.
    doi: 10.1186/s40104-025-01164-2pmc: PMC11884139pubmed: 40045371google scholar: lookup
  14. Ma M, Chen M, Wu X, Sooranna SR, Liu Q, Shi D, Wang J, Li H. A newly identified lncRNA lnc000100 regulates proliferation and differentiation of cattle skeletal muscle cells.. Epigenetics 2023;18:2270864.
    doi: 10.1080/15592294.2023.2270864pmc: PMC10768731pubmed: 37910666google scholar: lookup
  15. Stefaniuk-Szmukier M, Szmatoła T, Ropka-Molik K. Molecular Signatures of Exercise Adaptation in Arabian Racing Horses: Transcriptomic Insights from Blood and Muscle.. Genes 2025;16:431.
    doi: 10.3390/genes16040431pmc: PMC12027288pubmed: 40282391google scholar: lookup
  16. Xu B, Yang G, Jiao B, Zhu H. Analysis of ancient and modern horse genomes reveals the critical impact of lncRNA-mediated epigenetic regulation on horse domestication.. Front. Genet. 2022;13:944933.
    doi: 10.3389/fgene.2022.944933pmc: PMC9579347pubmed: 36276948google scholar: lookup
  17. Su Y, Ren W, Ma S, Meng J, Yao X, Zeng Y, Li Z, Li L, Wang R, Wang J. Comparative analysis of blood whole transcriptome profiles in Yili horses pre-and post-5000-meter racing.. Front. Genet. 2025;16:1651628.
    doi: 10.3389/fgene.2025.1651628pmc: PMC12425706pubmed: 40949872google scholar: lookup
  18. Robinson M.D, McCarthy D.J, Smyth G.K. edgeR: A Bioconductor package for differential expression analysis of digital gene expression data.. Bioinformatics 2010;26:139–140.
    doi: 10.1093/bioinformatics/btp616pmc: PMC2796818pubmed: 19910308google scholar: lookup
  19. Memczak S, Jens M, Elefsinioti A, Torti F, Krueger J, Rybak A, Maier L, Mackowiak S.D, Gregersen L.H, Munschauer M. Circular RNAs are a large class of animal RNAs with regulatory potency.. Nature 2013;495:333–338.
    doi: 10.1038/nature11928pubmed: 23446348google scholar: lookup
  20. Gao Y, Wang J, Zhao F. CIRI: An efficient and unbiased algorithm for de novo circular RNA identification.. Genome Biol. 2015;16:4.
    doi: 10.1186/s13059-014-0571-3pmc: PMC4316645pubmed: 25583365google scholar: lookup
  21. 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.
    doi: 10.1038/nbt.3122pmc: PMC4643835pubmed: 25690850google scholar: lookup
  22. Zhou L, Chen J, Li Z, Li X, Hu X, Huang Y, Zhao X, Liang C, Wang Y, Sun L. Integrated profiling of microRNAs and mRNAs: microRNAs located on Xq27. 3 associate with clear cell renal cell carcinoma.. PLoS ONE 2010;5:e15224.
    doi: 10.1371/journal.pone.0015224pmc: PMC3013074pubmed: 21253009google scholar: lookup
  23. Han H, McGivney B.A, Allen L, Bai D, Corduff L.R, Davaakhuu G, Davaasambuu J, Dorjgotov D, Hall T.J, Hemmings A.J. Common protein-coding variants influence the racing phenotype in galloping racehorse breeds.. Commun. Biol. 2022;5:1320.
    doi: 10.1038/s42003-022-04206-xpmc: PMC9748125pubmed: 36513809google scholar: lookup
  24. Ropka-Molik K, Stefaniuk-Szmukier M, Musiał A, Velie B. The genetics of racing performance in Arabian horses.. Int. J. Genom. 2019;2019:9013239.
    doi: 10.1155/2019/9013239pmc: PMC6745119pubmed: 31565654google scholar: lookup
  25. Allen J, Ramsden M, Nisar S. Skeletal muscle structure, function and pathology.. Orthop. Trauma. 2024;38:137–144.
    doi: 10.1016/j.mporth.2024.03.004google scholar: lookup
  26. Yang Y, Fan X, Yan J, Chen M, Zhu M, Tang Y, Liu S, Tang Z. A comprehensive epigenome atlas reveals DNA methylation regulating skeletal muscle development.. Nucleic Acids Res. 2021;49:1313–1329.
    doi: 10.1093/nar/gkaa1203pmc: PMC7897484pubmed: 33434283google scholar: lookup
  27. Smith J.A, 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.
    doi: 10.1038/s41580-023-00606-xpmc: PMC10527431pubmed: 37225892google scholar: lookup
  28. Ji F, Lee H.S, Kim J.-H. Resistance exercise and skeletal muscle: Protein synthesis, degradation, and controversies.. Eur. J. Appl. Physiol. 2025;125:2353–2382.
    doi: 10.1007/s00421-025-05832-zpubmed: 40512179google scholar: lookup
  29. Pardo P.S, Mohamed J.S, Lopez M.A, Boriek A.M. Induction of Sirt1 by mechanical stretch of skeletal muscle through the early response factor EGR1 triggers an antioxidative response.. J. Biol. Chem. 2011;286:2559–2566.
    doi: 10.1074/jbc.M110.149153pmc: PMC3024751pubmed: 20971845google scholar: lookup
  30. Pardo P.S, Boriek A.M. An autoregulatory loop reverts the mechanosensitive Sirt1 induction by EGR1 in skeletal muscle cells.. Aging 2012;4:456.
    doi: 10.18632/aging.100470pmc: PMC3433932pubmed: 22820707google scholar: lookup
  31. Haasper C, Jagodzinski M, Drescher M, Meller R, Wehmeier M, Krettek C, Hesse E. Cyclic strain induces FosB and initiates osteogenic differentiation of mesenchymal cells.. Exp. Toxicol. Pathol. 2008;59:355–363.
    doi: 10.1016/j.etp.2007.11.013pubmed: 18222075google scholar: lookup
  32. Inoue D, Kido S, Matsumoto T. Transcriptional induction of FosB/ΔFosB gene by mechanical stress in osteoblasts.. J. Biol. Chem. 2004;279:49795–49803.
    doi: 10.1074/jbc.M404096200pubmed: 15383527google scholar: lookup
  33. Wei X, Li H, Zhao G, Yang J, Li L, Huang Y, Lan X, Ma Y, Hu L, Zheng H. ΔFosB regulates rosiglitazone-induced milk fat synthesis and cell survival.. J. Cell. Physiol. 2018;233:9284–9298.
    doi: 10.1002/jcp.26218pubmed: 29154466google scholar: lookup
  34. Xiong G, Xie Y, Tan Y, Ye Y, Tan X, Jiang L, Qin E, Wei X, Li J, Liang T. HMGB1-Mediated Pyroptosis Promotes Inflammation and Contributes to Skeletal Muscle Atrophy induced by Cigarette Smoke.. Am. J. Physiol.-Cell Physiol. 2025;329:C325–C340.
    doi: 10.1152/ajpcell.01014.2024pubmed: 40445389google scholar: lookup
  35. Huang H, Lu G, Xiao L, Tan B, Yang Y, Hong L, Li Z, Cai G, Gu T. Effect of m6A Recognition Protein YTHDC1 on Skeletal Muscle Growth.. Animals 2025;15:1978.
    doi: 10.3390/ani15131978pmc: PMC12248823pubmed: 40646877google scholar: lookup
  36. Zhou L, Mudianto T, Ma X, Riley R, Uppaluri R. Targeting EZH2 enhances antigen presentation, antitumor immunity, and circumvents anti–PD-1 resistance in head and neck cancer.. Clin. Cancer Res. 2020;26:290–300.
    doi: 10.1158/1078-0432.CCR-19-1351pmc: PMC6942613pubmed: 31562203google scholar: lookup
  37. Galmiche L, Serre V, Beinat M, Assouline Z, Lebre A.S., Chretien D, Nietschke P, Benes V, Boddaert N, Sidi D. Exome sequencing identifies MRPL3 mutation in mitochondrial cardiomyopathy.. Hum. Mutat. 2011;32:1225–1231.
    doi: 10.1002/humu.21562pubmed: 21786366google scholar: lookup
  38. Chatterjee S.D., Zhou J, Dasgupta R, Cramer-Blok A, Timmer M, van der Stelt M, Ubbink M. Protein dynamics influence the enzymatic activity of phospholipase A/Acyltransferases 3 and 4.. Biochemistry 2021;60:1178–1190.
    doi: 10.1021/acs.biochem.0c00974pmc: PMC8154263pubmed: 33749246google scholar: lookup
  39. Agarwal A.K., Garg A. Phospholipid biosynthetic pathways and lipodystrophies: A novel syndrome due to PLAAT3 deficiency.. Nat. Rev. Endocrinol. 2024;20:128–129.
    doi: 10.1038/s41574-023-00950-0pubmed: 38191657google scholar: lookup
  40. Uyama T, Tsuboi K, Ueda N. An involvement of phospholipase A/acyltransferase family proteins in peroxisome regulation and plasmalogen metabolism.. FEBS Lett. 2017;591:2745–2760.
    doi: 10.1002/1873-3468.12787pubmed: 28796890google scholar: lookup
  41. Koeberle A, Shindou H, Harayama T, Yuki K, Shimizu T. Polyunsaturated fatty acids are incorporated into maturating male mouse germ cells by lysophosphatidic acid acyltransferase 3.. FASEB J. 2012;26:169–180.
    doi: 10.1096/fj.11-184879pubmed: 21968070google scholar: lookup
  42. Blaauw R, Calder P.C., Martindale R.G., Berger M.M. Combining proteins with n-3 PUFAs (EPA+ DHA) and their inflammation pro-resolution mediators for preservation of skeletal muscle mass.. Crit. Care. 2024;28:38.
    doi: 10.1186/s13054-024-04803-8pmc: PMC10835849pubmed: 38302945google scholar: lookup
  43. Valentine W.J., Tokuoka S.M., Hishikawa D, Kita Y, Shindou H, Shimizu T. LPAAT3 incorporates docosahexaenoic acid into skeletal muscle cell membranes and is upregulated by PPARδ activation.. J. Lipid Res. 2018;59:184–194.
    doi: 10.1194/jlr.M077321pmc: PMC5794415pubmed: 29284664google scholar: lookup
  44. Martin R.A., Viggars M.R., Esser K.A. Metabolism and exercise: The skeletal muscle clock takes centre stage.. Nat. Rev. Endocrinol. 2023;19:272–284.
    doi: 10.1038/s41574-023-00805-8pmc: PMC11783692pubmed: 36726017google scholar: lookup
  45. Visser N, Nelemans L.C., He Y, Lourens H.J., Corrales M.G., Huls G, Wiersma V.R., Schuringa J.J., Bremer E. Signal regulatory protein beta 2 is a novel positive regulator of innate anticancer immunity.. Front. Immunol. 2023;14:1287256.
    doi: 10.3389/fimmu.2023.1287256pmc: PMC10729450pubmed: 38116002google scholar: lookup
  46. Nakatsu Y, Matsunaga Y, Nakanishi M, Yamamotoya T, Sano T, Kanematsu T, Asano T. Prolyl isomerase Pin1 in skeletal muscles contributes to systemic energy metabolism and exercise capacity through regulating SERCA activity.. Biochem. Biophys. Res. Commun. 2024;715:150001.
    doi: 10.1016/j.bbrc.2024.150001pubmed: 38676996google scholar: lookup
  47. Barclay A.N., Brown M.H. The SIRP family of receptors and immune regulation.. Nat. Rev. Immunol. 2006;6:457–464.
    doi: 10.1038/nri1859pubmed: 16691243google scholar: lookup
  48. Londino J.D., Gulick D, Isenberg J.S., Mallampalli R.K. Cleavage of signal regulatory protein α (SIRPα) enhances inflammatory signaling.. J. Biol. Chem. 2015;290:31113–31125.
    doi: 10.1074/jbc.M115.682914pmc: PMC4692235pubmed: 26534964google scholar: lookup
  49. Wu J.-I., Lin Y.-P., Tseng C.-W., Chen H.-J., Wang L.-H. Crabp2 promotes metastasis of lung cancer cells via HuR and integrin β1/FAK/ERK signaling.. Sci. Rep. 2019;9:845.
    doi: 10.1038/s41598-018-37443-4pmc: PMC6351595pubmed: 30696915google scholar: lookup
  50. Du C, Chen B, Huang Y, Liang W, Su Y.H., Liu X. Roads diverged in a wood: Proteins and RNAs encoded by cytochrome b (CYTB) gene in health and disease.. Clin. Transl. Discov. 2025;5:e70026.
    doi: 10.1002/ctd2.70026google scholar: lookup
  51. Massie R, Wong L.J.C., Milone M. Exercise intolerance due to cytochrome b mutation.. Muscle Nerve. 2010;42:136–140.
    doi: 10.1002/mus.21649pubmed: 20544923google scholar: lookup
  52. Islam R, Rahman M, Fatema K. A case report of methylmalonic acidemia associated with CYBT gene mutation.. Paediatr. Nephrol. J. Bangladesh. 2022;7:81–83.
    doi: 10.4103/pnjb.pnjb_12_22google scholar: lookup
  53. Miyamoto-Mikami E, Fuku N. Variation of Mitochondrial DNA and elite athletic performance. Elsevier; Amsterdam, The Netherlands 2019;pp. 129–145.
  54. Soci U.P.R., Melo S.F.S., Gomes J.L.P., Silveira A.C., Nóbrega C., de Oliveira E.M. Exercise training and epigenetic regulation: Multilevel modification and regulation of gene expression. Springer; Singapore 2017;pp. 281–322.
    pubmed: 29098627
  55. Furrer R, Handschin C. Molecular aspects of the exercise response and training adaptation in skeletal muscle.. Free Radic. Biol. Med. 2024;223:53–68.
    doi: 10.1016/j.freeradbiomed.2024.07.026pmc: PMC7617583pubmed: 39059515google scholar: lookup
  56. Zhou Y, Zhang X, Baker J.S., Davison G.W., Yan X. Redox signaling and skeletal muscle adaptation during aerobic exercise.. Iscience 2024;27:109643.
    doi: 10.1016/j.isci.2024.109643pmc: PMC11033207pubmed: 38650987google scholar: lookup
  57. McGivney B, Herdan C, Gough K, Katz L, Hill E. Effect of Training on PPARGC1A and FNDC5 Gene Expression in Thoroughbred Horses.. Equine Vet. J. 2014;46:35.
    doi: 10.1111/evj.12267_106google scholar: lookup
  58. Ropka-Molik K, Stefaniuk-Szmukier M, Ukowski K.Z., Piórkowska K, Bugno-Poniewierska M. Exercise-induced modification of the skeletal muscle transcriptome in Arabian horses.. Physiol. Genom. 2017;49:318–326.
    doi: 10.1152/physiolgenomics.00130.2016pubmed: 28455310google scholar: lookup
  59. Godet A.-C., Roussel E, Laugero N, Morfoisse F, Lacazette E, Garmy-Susini B, Prats A.-C. Translational control by long non-coding RNAs.. Biochimie 2024;217:42–53.
    doi: 10.1016/j.biochi.2023.08.015pubmed: 37640229google scholar: lookup
  60. Lissek T. Enhancement of physiology via adaptive transcription.. Pflügers Arch.-Eur. J. Physiol. 2025;477:187–199.
    doi: 10.1007/s00424-024-03037-5pmc: PMC11761519pubmed: 39482558google scholar: lookup
  61. Jaiswal A, Kaushik N, Choi E.H., Kaushik N.K. Functional impact of non-coding RNAs in high-grade breast carcinoma: Moving from resistance to clinical applications: A comprehensive review.. Biochim. Biophys. Acta (BBA)-Rev. Cancer. 2023;1878:188915.
    doi: 10.1016/j.bbcan.2023.188915pubmed: 37196783google scholar: lookup
  62. Liu D, Dong Z, Wang J, Tao Y, Sun X, Yao X. The existence and function of mitochondrial component in extracellular vesicles.. Mitochondrion 2020;54:122–127.
    doi: 10.1016/j.mito.2020.08.005pubmed: 32861876google scholar: lookup
  63. Koshinaka K, Honda A, Masuda H, Sato A. Effect of quercetin treatment on mitochondrial biogenesis and exercise-induced AMP-activated protein kinase activation in rat skeletal muscle.. Nutrients 2020;12:729.
    doi: 10.3390/nu12030729pmc: PMC7146161pubmed: 32164219google scholar: lookup
  64. Liang Y, Zhang Y, Xu L, Zhou D, Jin Z, Zhou H, Lin S, Cao J, Huang L. CircRNA expression pattern and ceRNA and miRNA–mRNA networks involved in anther development in the CMS line of Brassica campestris.. Int. J. Mol. Sci. 2019;20:4808.
    doi: 10.3390/ijms20194808pmc: PMC6801457pubmed: 31569708google scholar: lookup
  65. Mach N, Plancade S, Pacholewska A, Lecardonnel J, Rivière J, Moroldo M, Vaiman A, Morgenthaler C, Beinat M, Nevot A. Integrated mRNA and miRNA expression profiling in blood reveals candidate biomarkers associated with endurance exercise in the horse.. Sci. Rep. 2016;6:22932.
    doi: 10.1038/srep22932pmc: PMC4785432pubmed: 26960911google scholar: lookup
  66. Zhao H, Tan Z, Zhou J, Wu Y, Hu Q, Ling Q, Ling J, Liu M, Ma J, Zhang D. The regulation of circRNA and lncRNAprotein binding in cardiovascular diseases: Emerging therapeutic targets.. Biomed. Pharmacother. 2023;165:115067.
    doi: 10.1016/j.biopha.2023.115067pubmed: 37392655google scholar: lookup

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