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Animals : an open access journal from MDPI2025; 15(3); doi: 10.3390/ani15030326

Blood-Based Whole-Genome Methylation Analysis of Yili Horses Pre- and Post-Racing.

Abstract: This study aims to analyze the whole-genome DNA methylation differences in Yili horses before and after racing, with the goal of identifying differentially methylated genes associated with racing performance and exploring the epigenetic mechanisms underlying exercise in horses. Blood samples were collected from the jugular veins of the top 3 Yili horses in a 5000 m race, which included 25 competitors, both prior to and within 5 min after the race. Genomic DNA was extracted, followed by sequencing using Whole-Genome Bisulfite Sequencing (WGBS) to assess DNA methylation levels, differentially methylated regions (DMRs), and differentially methylated genes (DMGs). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed on the identified DMGs to select candidate genes potentially associated with equine exercise. A total of 18,374 differentially methylated CG regions, 254 differentially methylated CHG regions, and 584 differentially methylated CHH regions were identified. A total of 4293 DMGs were anchored in gene bodies and 2187 DMGs in promoter regions. Functional analysis revealed that these DMGs were mainly enriched in terms related to binding and kinase activity, as well as pathways such as PI3K-Akt signaling and Kaposi sarcoma-associated herpesvirus infection. Further analysis indicated that genes such as IFNAR2, FGF4, and DGKH could be potential candidate genes associated with equine athletic performance. The findings of this study contribute to understanding the epigenetic regulatory mechanisms of equine athletic performance, providing a reference for further in-depth research on horse racing.
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Publication Date: 2025-01-24 PubMed ID: 39943096PubMed Central: PMC11815882DOI: 10.3390/ani15030326Google Scholar: Lookup
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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.

The study examines changes in genes (methylation) of Yili horses before and after they participate in a race, exploring the genetic mechanisms behind the physical performance of horses. It finds various regions and genes where changes occur and suggests possible candidate genes related to horse athletic performance.

Whole-Genome Methylation Analysis

The study investigates how the process of methylation in the DNA of Yili horses is affected before and after racing. Methylation is a chemical process that can modify the function of the genes, and changes in its patterns can influence physical performance:

  • Blood samples were taken from the top 3 Yili horses both before and after a 5000 m race.
  • The DNA was then extracted from these samples and sequenced using Whole-Genome Bisulfite Sequencing (WGBS), a technique that allows researchers to measure the level of DNA methylation.

Identifying Changes in Methylation

Significant differences in methylation were found in the horse DNA after the race, particularly within specific differentially methylated regions (DMRs) and genes (DMGs):

  • The study identified 18,374 differentially methylated CG regions, 254 differentially methylated CHG regions, and 584 differentially methylated CHH regions. These are areas of the DNA where methylation changes were observed.
  • It further discovered 4293 DMGs located within gene bodies, and 2187 DMGs in promoter regions of the DNA. These genes are areas of the DNA where the functionality was altered due to changes in methylation.

Functional Analysis and Candidate Genes

Based on the changes in methylation observed, the study then tried to identify potential genes that could be associated with horse athletic performance:

  • Utilizing Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses, they observed these DMGs were mainly enriched in terms related to binding and kinase activity. This signifies they potentially have an impact on horse physiology and performance.
  • Pathways such as PI3K-Akt signaling and Kaposi sarcoma-associated herpesvirus infection were especially noted. These pathways are of importance in cellular function and could likely impact the physical performance of the horses.
  • The study suggested possible candidate genes that could be associated with equine athletic performance, giving a baseline for further targeted research in the field.

The research contributes to our understanding of how genetics can influence the physical performance of horses. Such insights could be valuable in training or breeding programmes, helping to optimize athletic performance in horses.

Cite This Article

APA
Wang J, Ren W, Li Z, Ma S, Li L, Wang R, Zeng Y, Meng J, Yao X. (2025). Blood-Based Whole-Genome Methylation Analysis of Yili Horses Pre- and Post-Racing. Animals (Basel), 15(3). https://doi.org/10.3390/ani15030326

Publication

Animals : an open access journal from MDPI
ISSN: 2076-2615
NlmUniqueID: 101635614
Country: Switzerland
Language: English
Volume: 15
Issue: 3

Researcher Affiliations

Wang, Jianwen
  • College of Animal Science, Xinjiang Agricultural University, Urumqi 830052, China.
  • Xinjiang Key Laboratory of Equine Breeding and Exercise Physiology, 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.
Li, Zexu
  • College of Animal Science, Xinjiang Agricultural University, Urumqi 830052, China.
Ma, Shikun
  • 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.
Zeng, Yaqi
  • College of Animal Science, Xinjiang Agricultural University, Urumqi 830052, China.
  • Xinjiang Key Laboratory of Equine Breeding and Exercise Physiology, Urumqi 830052, China.
Meng, Jun
  • College of Animal Science, Xinjiang Agricultural University, Urumqi 830052, China.
  • Xinjiang Key Laboratory of Equine Breeding and Exercise Physiology, Urumqi 830052, China.
Yao, Xinkui
  • 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 / The Autonomous Region Major Science and Technology Project
  • PT2311 / The Autonomous Region Innovation Environment (Talent and Base) Construction Project

Conflict of Interest Statement

The authors declare no conflicts of interest.

References

This article includes 52 references
  1. Mihelic K, Vrbanac Z, Bojanic K, Kostanjsak T, Ljubic BB, Gotic J, Vnuk D, Bottegaro NB. Changes in acute phase response biomarkers in racing endurance horses.. Animals 2022;12:2993.
    doi: 10.3390/ani12212993pmc: PMC9657625pubmed: 36359117google scholar: lookup
  2. Djokovic S, Markovic L, Djermanovic V, Trailovic R. Indicators of exhaustion and stress markers in endurance horses.. Med. Weter. 2021;77:331–336.
    doi: 10.21521/mw.6545google scholar: lookup
  3. Manso HC, Trindade KLG, Silva CJFL, Cruz RKS, Vilela CF, Coelho CS, Ribeiro JD, Manso HECCC. The welfare of horses competing in three-barrel race events is shown to be not inhibited by short intervals between starts.. Animals 2024;14:583.
    doi: 10.3390/ani14040583pmc: PMC10886278pubmed: 38396551google scholar: lookup
  4. Arfuso F, Rizzo M, Giannetto C, Giudice E, Cirincione R, Cassata G, Cicero L, Piccione G. Oxidant and antioxidant parameters’ assessment together with homocysteine and muscle enzymes in racehorses: Evaluation of positive effects of exercise.. Antioxidants 2022;11:1176.
    doi: 10.3390/antiox11061176pmc: PMC9220350pubmed: 35740073google scholar: lookup
  5. Rossi R, Lo Feudo CM, Zucca E, Vizzarri F, Corino C, Ferrucci F. Innovative blood antioxidant test in Standardbred Trotter Horses.. Antioxidants 2022;10:2013.
    doi: 10.3390/antiox10122013pmc: PMC8698842pubmed: 34943116google scholar: lookup
  6. Seaborne RA, Sharples AP. The interplay between exercise metabolism, epigenetics, and skeletal muscle remodeling.. Exerc. Sport. Sci. Rev. 2020;48:188–200.
    doi: 10.1249/JES.0000000000000227pubmed: 32658040google scholar: lookup
  7. McGivney BA, Eivers SS, MacHugh DE, MacLeod JN, O’Gorman GM, Park SDE, Katz LM, Hill EW. Transcriptional adaptations following exercise in thoroughbred horse skeletal muscle highlights molecular mechanisms that lead to muscle hypertrophy.. BMC Genom. 2009;10:638.
    doi: 10.1186/1471-2164-10-638pmc: PMC2812474pubmed: 20042072google scholar: lookup
  8. Bryan K, McGivney BA, Farries G, McGettigan PA, McGivney CL, Gough KF, MacHugh DE, Katz LM, Hill EW. Equine skeletal muscle adaptations to exercise and training: Evidence of differential regulation of autophagosomal and mitochondrial components.. BMC Genom. 2017;18:595.
    doi: 10.1186/s12864-017-4007-9pmc: PMC5551008pubmed: 28793853google scholar: lookup
  9. Barrès R, Yan J, Egan B, Treebak JT, Rasmussen M, Fritz T, Caidahl K, Krook A, O’Gorman DJ, Zierath JR. Acute exercise remodels promoter methylation in human skeletal muscle.. Cell Metab. 2012;15:405–411.
    doi: 10.1016/j.cmet.2012.01.001pubmed: 22405075google scholar: lookup
  10. Cappelli K, Capomaccio S, Viglino A, Silvestrelli M, Beccati F, Moscati L, Chiaradia E. Circulating miRNAs as putative biomarkers of exercise adaptation in endurance horses.. Front. Physiol. 2018;9:429.
    doi: 10.3389/fphys.2018.00429pmc: PMC5928201pubmed: 29740341google scholar: lookup
  11. Denham J, O’Brien BJ, Marques FZ, Charchar FJ. Changes in the leukocyte methylome and its effect on cardiovascular-related genes after exercise.. J. Appl. Physiol. 2015;118:475–488.
    doi: 10.1152/japplphysiol.00878.2014pubmed: 25539938google scholar: lookup
  12. Stefaniuk-Szmukier M, Ropka-Molik K, Piórkowska K, Zukowski K, Bugno-Poniewierska M. Transcriptomic hallmarks of bone remodeling revealed by RNA-Seq profiling in blood of Arabian horses during racing training regime.. Gene 2018;676:256–262.
    doi: 10.1016/j.gene.2018.07.040pubmed: 30021131google scholar: lookup
  13. Krueger F, Andrews SR. Bismark: A flexible aligner and methylation caller for Bisulfite-Seq applications.. Bioinformatics 2011;27:1571–1572.
    doi: 10.1093/bioinformatics/btr167pmc: PMC3102221pubmed: 21493656google scholar: lookup
  14. Zhou SY, Zhang YM, Wang L, Zhang ZZ, Cai BY, Liu KT, Zhang H, Dai MH, Sun LL, Xu XM. CDKN2B methylation is associated with carotid artery calcification in ischemic stroke patients.. J. Transl. Med. 2016;14:333.
    doi: 10.1186/s12967-016-1093-4pmc: PMC5134267pubmed: 27905995google scholar: lookup
  15. Feng H, Conneely KN, Wu H. A Bayesian hierarchical model to detect differentially methylated loci from single nucleotide resolution sequencing data.. Nucleic Acids Res. 2014;42:e69.
    doi: 10.1093/nar/gku154pmc: PMC4005660pubmed: 24561809google scholar: lookup
  16. Wu H, Xu T, Feng H, Chen L, Li B, Yao B, Qin Z, Jin P, Conneely KN. Detection of differentially methylated regions from whole-genome bisulfite sequencing data without replicates.. Nucleic Acids Res. 2015;43:e141.
    doi: 10.1093/nar/gkv715pmc: PMC4666378pubmed: 26184873google scholar: lookup
  17. Han HG, McGivney BA, Allen L, Bai DY, Corduff LR, Davaakhuu G, Davaasambuu J, Dorjgotov D, Hall TJ, Hemmings AJ. Common protein-coding variants influence the racing phenotype in galloping racehorse breeds.. Commun. Biol. 2023;5:1320.
    doi: 10.1038/s42003-022-04206-xpmc: PMC9748125pubmed: 36513809google scholar: lookup
  18. Lee JR, Hong CP, Moon JW, Jung YD, Kim DS, Kim TH, Gim JA, Bae JH, Choi Y, Eo J. Genome-wide analysis of DNA methylation patterns in horse.. BMC Genom. 2014;15:598.
    doi: 10.1186/1471-2164-15-598pmc: PMC4117963pubmed: 25027854google scholar: lookup
  19. Gim JA, Hong CP, Kim DS, Moon JW, Choi Y, Eo J, Kwon YJ, Lee JR, Jung YD, Bae JH. Genome-wide analysis of DNA methylation before-and after exercise in the Thoroughbred horse with MeDIP-Seq.. Mol. Cells. 2015;38:210–220.
    doi: 10.14348/molcells.2015.2138pmc: PMC4363720pubmed: 25666347google scholar: lookup
  20. Yue CJ, Wang JK, Shen YF, Zhang JL, Liu J, Xiao AP, Liu YS, Eer H, Zhang QE. Whole-genome DNA methylation profiling reveals epigenetic signatures in developing muscle in Tan and Hu sheep and their offspring.. Front. Vet. Sci. 2023;10:1186040.
    doi: 10.3389/fvets.2023.1186040pmc: PMC10301830pubmed: 37388464google scholar: lookup
  21. Caiye Z, Song SZ, Li MN, Huang XOY, Luo Y, Fang SL. Genome-wide DNA methylation analysis reveals different methylation patterns in Chinese indigenous sheep with different type of tail.. Front. Vet. Sci. 2023;10:1125262.
    doi: 10.3389/fvets.2023.1125262pmc: PMC10196035pubmed: 37215474google scholar: lookup
  22. Dechow CD, Liu WS. DNA methylation patterns in peripheral blood mononuclear cells from Holstein cattle with variable milk yield.. BMC Genom. 2018;19:744.
    doi: 10.1186/s12864-018-5124-9pmc: PMC6182825pubmed: 30309336google scholar: lookup
  23. Cao H, Fang C, Liu LL, Farnir F, Liu WJ. Identification of susceptibility genes underlying bovine respiratory disease in Xinjiang brown cattle based on DNA methylation.. Int. J. Mol. Sci. 2024;25:4928.
    doi: 10.3390/ijms25094928pmc: PMC11084705pubmed: 38732144google scholar: lookup
  24. Wang XR, Ren W, Peng YD, Khan MZ, Liang HL, Zhang YG, Liu XT, Chen YH, Kou XY, Wang LY. Elucidating the role of transcriptomic networks and DNA methylation in collagen deposition of Dezhou Donkey skin.. Animals 2024;14:1222.
    doi: 10.3390/ani14081222pmc: PMC11047689pubmed: 38672366google scholar: lookup
  25. Mayr C. What are 3′UTRs doing?. Cold Spring Harbor Perspect. Biol. 2019;11:a034728.
    doi: 10.1101/cshperspect.a034728pmc: PMC6771366pubmed: 30181377google scholar: lookup
  26. Mayr C. Alternative 3′UTRs act as scaffolds to regulate membrane protein localization.. FEBS J. 2015;282:37.
    pmc: PMC4697748pubmed: 25896326
  27. Anna P, Manel E. Epigenetic modifications and human disease.. Nat. Biotechnol. 2010;28:1057–1068.
    pubmed: 20944598
  28. Zhang SH, Shen LY, Xia YD, Yang Q, Li XW, Tang GQ, Jiang YZ, Wang JY, Li MZ, Zhu L. DNA methylation landscape of fat deposits and fatty acid composition in obese and lean pigs.. Sci. Rep. 2016;6:35063.
    doi: 10.1038/srep35063pmc: PMC5056348pubmed: 27721392google scholar: lookup
  29. Gough DJ, Messina NL, Clarke CJP, Johnstone RW, Levy DE. Constitutive type I interferon modulates homeostatic balance through tonic signaling.. Immunity 2012;36:166–174.
    doi: 10.1016/j.immuni.2012.01.011pmc: PMC3294371pubmed: 22365663google scholar: lookup
  30. Dunn GP, Bruce AT, Sheehan KCF, Shankaran V, Uppaluri R, Bui JD, Diamond MS, Koebel CM, Arthur C, White JM. A critical function for type I interferons in cancer immunoediting.. Nat. Immunol. 2005;6:722–729.
    doi: 10.1038/ni1213pubmed: 15951814google scholar: lookup
  31. Lobo LF, de Morais MG, Marcucci-Barbosa LS, Martins FD, Avelar LM, Vieira ELM, Aidar FJ, Wanner SP, Silva LS, Noman MC. A single bout of fatiguing aerobic exercise induces similar pronounced immunological responses in both sexes.. Front. Physiol. 2022;13:833580.
    doi: 10.3389/fphys.2022.833580pmc: PMC9213785pubmed: 35755444google scholar: lookup
  32. Amatori S, Sisti D, Bertuccioli A, Rocchi MBL, Luchetti F, Nasoni MG, Papa S, Citarella R, Perroni F, Benedetti S. Are pre-race serum blood biomarkers associated with the 24-h ultramarathon race performance?. Eur. J. Sport Sci. 2024;24:431–439.
    doi: 10.1002/ejsc.12073google scholar: lookup
  33. Gavrielatos A, Ratkevica I, Stenfors N, Hanstock HG. Influence of exercise duration on respiratory function and systemic immunity among healthy, endurance-trained participants exercising in sub-zero conditions.. Respir. Res. 2022;23:121.
    doi: 10.1186/s12931-022-02029-2pmc: PMC9103459pubmed: 35550109google scholar: lookup
  34. Meleiro MNCZ, de Carvalho HJC, Ribeiro RR, da Silva MD, Gomes CMS, Miglino MA, Prada ILD. Immune functions alterations due to racing stress in Thoroughbred Horses.. Animals 2022;12:1203.
    doi: 10.3390/ani12091203pmc: PMC9104563pubmed: 35565629google scholar: lookup
  35. Chikhaoui M, Fadhéla S, Belalia CZA, Ayad N. Pre- and post-exercise variation of blood parameters on performance endurance horses: A first race study from Algeria.. J. Hell. Vet. Med. Soc. 2023;73:4987–4996.
    doi: 10.12681/jhvms.28752google scholar: lookup
  36. Platanias LC. Mechanisms of type-I- and type-II- interferon-mediated signalling.. Nat. Rev. Immunol. 2005;5:375–386.
    doi: 10.1038/nri1604pubmed: 15864272google scholar: lookup
  37. Li TT, Gao SJ. KSHV hijacks FoxO1 to promote cell proliferation and cellular transformation by antagonizing oxidative stress.. J. Med. Virol. 2023;95:e28676.
    doi: 10.1002/jmv.28676pmc: PMC10285692pubmed: 36929740google scholar: lookup
  38. Lin J, Chen DZ, Chen YP. Fgf4 alleviates EAH inflammation by regulating liver macrophage M1/M2 status in mice.. Hepatology 2022;76:S586.
  39. Song LT, Wang LY, Hou YS, Zhou J, Chen CC, Ye XX, Dong WLY, Gao H, Liu Y, Qiao GT. FGF4 protects the liver from nonalcoholic fatty liver disease by activating the AMP-activated protein kinase-Caspase 6 signal axis.. Hepatology 2022;76:1105–1120.
    doi: 10.1002/hep.32404pubmed: 35152446google scholar: lookup
  40. Flick M, Vinther AML, Jacobsen S, Berg LC, Gimeno M, Verwilghen D, Howden W, Averay K, van Galen G. Effect of exercise on serum neutrophil gelatinase-associated lipocalin concentration in racehorses.. Vet. Clin. Pathol. 2021;50:551–554.
    doi: 10.1111/vcp.13027pubmed: 34779025google scholar: lookup
  41. Wilson J, De Donato M, Appelbaum B, Garcia CT, Peters S. Differential expression of innate and adaptive immune genes during acute physical exercise in American Quarter Horses.. Animals 2023;13:308.
    doi: 10.3390/ani13020308pmc: PMC9854435pubmed: 36670847google scholar: lookup
  42. Liang ZH, Pan NF, Lin SS, Qiu ZY, Liang P, Wang J, Zhang Z, Pan YC. Exosomes from mmu_circ_0001052-modified adipose-derived stem cells promote angiogenesis of DFU via miR-106a-5p and FGF4/p38MAPK pathway.. Stem Cell Res. Ther. 2022;13:336.
    doi: 10.1186/s13287-022-03015-7pmc: PMC9308214pubmed: 35870977google scholar: lookup
  43. Ying L, Wang LY, Guo KW, Hou YS, Li N, Wang SY, Liu XF, Zhao QJ, Zhou J, Zhao LW. Paracrine FGFs target skeletal muscle to exert potent anti-hyperglycemic effects.. Nat. Commun. 2021;21:7256.
    doi: 10.1038/s41467-021-27584-ypmc: PMC8671394pubmed: 34907199google scholar: lookup
  44. Engel L, Becker D, Nissen T, Russ I, Thaller G, Krattenmacher N. Mitochondrial DNA variation contributes to the aptitude for dressage and show jumping ability in the Holstein Horse breed.. Animals 2022;12:704.
    doi: 10.3390/ani12060704pmc: PMC8944467pubmed: 35327102google scholar: lookup
  45. Massie S, Bayly W, Ohmura H, Takahashi Y, Mukai K, Léguillette R. Field-training in young two-year-old Thoroughbreds: Investigating cardiorespiratory adaptations and the presence of exercise induced pulmonary hemorrhage.. BMC Vet. Res. 2024;20:159.
    doi: 10.1186/s12917-024-03997-xpmc: PMC11046817pubmed: 38671428google scholar: lookup
  46. Bora P, Gahurova L, Masek T, Hauserova A, Potesil D, Jansova D, Susor A, Zdráhal Z, Ajduk A, Pospísek M. p38-MAPK-mediated translation regulation during early blastocyst development is required for primitive endoderm differentiation in mice.. Commun. Biol. 2021;4:788.
    doi: 10.1038/s42003-021-02290-zpmc: PMC8233355pubmed: 34172827google scholar: lookup
  47. Yi CH, Chen LR, Lin ZF, Liu L, Shao WQ, Zhang R, Lin J, Zhang JB, Zhu WW, Jia HL. Lenvatinib targets FGF receptor 4 to enhance antitumor immune response of anti-programmed cell death-1 in HCC.. Hepatology 2021;74:2544–2560.
    doi: 10.1002/hep.31921pubmed: 34036623google scholar: lookup
  48. Zhang HJ, Hu JG, Zhu JF, Li QL, Fang L. Machine learning-based metabolism-related genes signature and immune infiltration landscape in diabetic nephropathy.. Front. Endocrinol. 2022;13:1026938.
    doi: 10.3389/fendo.2022.1026938pmc: PMC9722730pubmed: 36482994google scholar: lookup
  49. Peng H, Li JY, Xu HT, Wang X, He LY, McCauley N, Zhang KK, Xie LL. Offspring NAFLD liver phospholipid profiles are differentially programmed by maternal high-fat diet and maternal one carbon supplement.. J. Nutr. Biochem. 2023;111:109187.
    doi: 10.1016/j.jnutbio.2022.109187pubmed: 36270572google scholar: lookup
  50. Qu ML, Zuo LH, Zhang MR, Cheng P, Guo ZJ, Yang JY, Li CJ, Wu J. High glucose induces tau hyperphosphorylation in hippocampal neurons via inhibition of ALKBH5-mediated Dgkh m6A demethylation: A potential mechanism for diabetic cognitive dysfunction.. Cell Death Dis. 2023;14:385.
    doi: 10.1038/s41419-023-05909-7pmc: PMC10310746pubmed: 37385994google scholar: lookup
  51. Çolak-Genis E, Erdogan MÖ, Çam FS, Aydemir Ö, Gerik-Celebi HB, Solak M. Investigation of genetic changes in three families with bipolar disease.. Mol. Syndromol. 2024;15:464–473.
    doi: 10.1159/000539115pmc: PMC11614438pubmed: 39634238google scholar: lookup
  52. Sharma P, Yadav SK, Shah SD, Javed E, Lim JM, Pan S, Nayak AP, Panettieri RA, Penn RB, Kambayashi T. Diacylglycerol kinase inhibition reduces airway contraction by negative feedback regulation of Gq-signaling.. Am. J. Respir. Cell Mol. Biol. 2021;65:658–671.
    doi: 10.1165/rcmb.2021-0106OCpmc: PMC8641804pubmed: 34293268google scholar: lookup

Citations

This article has been cited 3 times.
  1. Shan D, Yao X, Ren W, Huang Q, Su Y, Li Z, Li L, Wang R, Ma S, Wang J. Genome-Wide DNA Methylation Analysis of Performance Variation in the 5000-m Speed Race of Yili Horses. Animals (Basel) 2026 Jan 19;16(2).
    doi: 10.3390/ani16020302pubmed: 41594492google scholar: lookup
  2. Yuan X, Yao X, Zeng Y, Wang J, Ren W, Wang T, Li X, Yang L, Yang X, Meng J. The Effect of Training on the Expression of Protein and Metabolites in the Plasma Exosomes of the Yili Horse. Animals (Basel) 2026 Jan 6;16(2).
    doi: 10.3390/ani16020158pubmed: 41594348google scholar: lookup
  3. Wang J, Ren W, Li Z, Li L, Wang R, Ma S, Zeng Y, Meng J, Yao X. Plasma Lipidomics and Proteomics Analyses Pre- and Post-5000 m Race in Yili Horses. Animals (Basel) 2025 Mar 30;15(7).
    doi: 10.3390/ani15070994pubmed: 40218387google scholar: lookup
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