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Genes2021; 12(12); doi: 10.3390/genes12121965

Circulating Transcriptional Profile Modulation in Response to Metabolic Unbalance Due to Long-Term Exercise in Equine Athletes: A Pilot Study.

Abstract: Physical exercise has been associated with the modulation of micro RNAs (miRNAs), actively released in body fluids and recognized as accurate biomarkers. The aim of this study was to measure serum miRNA profiles in 18 horses taking part in endurance competitions, which represents a good model to test metabolic responses to moderate intensity prolonged efforts. Serum levels of miRNAs of eight horses that were eliminated due to metabolic unbalance (Non Performer-NP) were compared to those of 10 horses that finished an endurance competition in excellent metabolic condition (Performer-P). Circulating miRNA (ci-miRNA) profiles in serum were analyzed through sequencing, and differential gene expression analysis was assessed comparing NP versus P groups. Target and pathway analysis revealed the up regulation of a set of miRNAs (of mir-211 mir-451, mir-106b, mir-15b, mir-101-1, mir-18a, mir-20a) involved in the modulation of myogenesis, cardiac and skeletal muscle remodeling, angiogenesis, ventricular contractility, and in the regulation of gene expression. Our preliminary data open new scenarios in the definition of metabolic adaptations to the establishment of efficient training programs and the validation of athletes' elimination from competitions.
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Publication Date: 2021-12-09 PubMed ID: 34946914PubMed Central: PMC8701225DOI: 10.3390/genes12121965Google Scholar: Lookup
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  • Journal Article
  • Research Support
  • Non-U.S. Gov't

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.

This study investigates how physical exercise influences the activity of micro RNAs (miRNAs) in horses. The researchers found that endurance exercise can alter the miRNAs, which may potentially help in optimizing training programs and determining whether horses should be withdrawn from competitions.

Objectives and Methodology

  • The primary objective of this research was to understand how metabolic imbalances due to long-term exercise could influence the transcriptional profile (miRNAs) of horse athletes.
  • Eighteen horses participating in endurance competitions were observed as part of the study.
  • These horses’ endurance competitions were chosen as the study model as they represent the metabolic responses to prolonged moderately intense efforts.
  • The researchers segregated the horses into two groups. The first group consisted of eight horses that were eliminated due to metabolic imbalance (Non Performer – NP), and the second had ten horses that finished the race in excellent metabolic conditions (Performer-P).
  • The circulating miRNA (ci-miRNA) profiles in the serum of these horses were then analyzed through sequencing.
  • A differential gene expression analysis was carried out by comparing the NP and P groups.

Findings

  • The study identified an upregulation in a set of miRNAs (including mir-211, mir-451, mir-106b, mir-15b, mir-101-1, mir-18a, mir-20a) in the horses.
  • These miRNAs are involved in the modulation of myogenesis (formation of muscle tissue), cardiac and skeletal muscle remodeling, angiogenesis (formation of new blood vessels), ventricular contractility (ability of heart muscle to contract), and the regulation of gene expression.

Implications

  • The findings from this pilot study open up new avenues in understanding the metabolic adaptations that occur due to endurance exercises.
  • These insights could potentially aid in the development of more efficient training programs and can provide significant criteria for deciding on the elimination of athletes from competitions due to metabolic imbalances.

Cite This Article

APA
Cappelli K, Mecocci S, Capomaccio S, Beccati F, Palumbo AR, Tognoloni A, Pepe M, Chiaradia E. (2021). Circulating Transcriptional Profile Modulation in Response to Metabolic Unbalance Due to Long-Term Exercise in Equine Athletes: A Pilot Study. Genes (Basel), 12(12). https://doi.org/10.3390/genes12121965

Publication

Genes
ISSN: 2073-4425
NlmUniqueID: 101551097
Country: Switzerland
Language: English
Volume: 12
Issue: 12

Researcher Affiliations

Cappelli, Katia
  • Department of Veterinary Medicine, University of Perugia, 06126 Perugia, Italy.
  • Sports Horse Research Center, University of Perugia, 06126 Perugia, Italy.
Mecocci, Samanta
  • Department of Veterinary Medicine, University of Perugia, 06126 Perugia, Italy.
Capomaccio, Stefano
  • Department of Veterinary Medicine, University of Perugia, 06126 Perugia, Italy.
  • Sports Horse Research Center, University of Perugia, 06126 Perugia, Italy.
Beccati, Francesca
  • Department of Veterinary Medicine, University of Perugia, 06126 Perugia, Italy.
  • Sports Horse Research Center, University of Perugia, 06126 Perugia, Italy.
Palumbo, Andrea Rosario
  • Department of Veterinary Medicine, University of Perugia, 06126 Perugia, Italy.
Tognoloni, Alessia
  • Department of Veterinary Medicine, University of Perugia, 06126 Perugia, Italy.
Pepe, Marco
  • Department of Veterinary Medicine, University of Perugia, 06126 Perugia, Italy.
  • Sports Horse Research Center, University of Perugia, 06126 Perugia, Italy.
Chiaradia, Elisabetta
  • Department of Veterinary Medicine, University of Perugia, 06126 Perugia, Italy.
  • Sports Horse Research Center, University of Perugia, 06126 Perugia, Italy.

MeSH Terms

  • Animals
  • Biomarkers / metabolism
  • Circulating MicroRNA / genetics
  • Female
  • Gene Expression Regulation
  • Horses / physiology
  • Male
  • Metabolic Diseases / physiopathology
  • Physical Conditioning, Animal
  • Physical Endurance
  • Pilot Projects
  • Transcriptome

Conflict of Interest Statement

The authors declare no conflict of interest. The funders had no role in the design of the study, in the collection, analyses, or interpretation of data, in the writing of the manuscript, or in the decision to publish the results.

References

This article includes 57 references
  1. Dhabhar F.S.. Effects of stress on immune function: The good, the bad, and the beautiful.. Immunol. Res. 2014;58:193–210.
    doi: 10.1007/s12026-014-8517-0pubmed: 24798553google scholar: lookup
  2. Morton J.P., Kayani A.C., McArdle A., Drust B.. The Exercise-Induced stress response of skeletal muscle, with specific emphasis on humans.. Sports Med. 2009;39:643–662.
    doi: 10.2165/00007256-200939080-00003pubmed: 19769414google scholar: lookup
  3. Cappelli K., Mecocci S., Gioiosa S., Giontella A., Silvestrelli M., Cherchi R., Valentini A., Chillemi G., Capomaccio S.. Gallop racing shifts mature mRNA towards introns: Does exercise-induced stress enhance genome plasticity?. Genes 2020;11:410.
    doi: 10.3390/genes11040410pmc: PMC7230505pubmed: 32283859google scholar: lookup
  4. Cappelli K., Amadori M., Mecocci S., Miglio A., Antognoni M.T., Razzuoli E.. Immune response in young thoroughbred racehorses under training.. Animals 2020;10:1809.
    doi: 10.3390/ani10101809pmc: PMC7600081pubmed: 33027949google scholar: lookup
  5. Cappelli K., Felicetti M., Capomaccio S., Nocelli C., Silvestrelli M., Verini-Supplizi A.. Effect of training status on immune defence related gene expression in Thoroughbred: Are genes ready for the sprint?. Vet. J. 2013;195:373–376.
    doi: 10.1016/j.tvjl.2012.07.021pubmed: 22990119google scholar: lookup
  6. Noakes T.D.. Physiological models to understand exercise fatigue and the adaptations that predict or enhance athletic performance.. Scand. J. Med. Sci. Sports. 2000;10:123–145.
    doi: 10.1034/j.1600-0838.2000.010003123.xpubmed: 10843507google scholar: lookup
  7. Nagy A., Dyson S.J., Murray J.K.. A veterinary review of endurance riding as an international competitive sport.. Vet. J. 2012;194:288–293.
    doi: 10.1016/j.tvjl.2012.06.022pubmed: 22819800google scholar: lookup
  8. Amory H., Votion D.M., Fraipont A., Goachet A.G., Robert C., Farnir F., Van Erck E.. Altered systolic left ventricular function in horses completing a long distance endurance race.. Equine Vet. J. 2010;42:216–219.
    doi: 10.1111/j.2042-3306.2010.00253.xpubmed: 21059009google scholar: lookup
  9. Scoppetta F., Tartaglia M., Renzone G., Avellini L., Gaiti A., Scaloni A., Chiaradia E.. Plasma protein changes in horse after prolonged physical exercise: A proteomic study.. J. Proteom. 2012;75:4494–4504.
    doi: 10.1016/j.jprot.2012.04.014pubmed: 22546489google scholar: lookup
  10. Mooren F.C., Viereck J., Krüger K., Thum T.. Circulating micrornas as potential biomarkers of aerobic exercise capacity.. Am. J. Physiol. Heart Circ. Physiol. 2014;306:H557–H563.
    doi: 10.1152/ajpheart.00711.2013pmc: PMC3920240pubmed: 24363306google scholar: lookup
  11. Polakovičová M., Musil P., Laczo E., Hamar D., Kyselovič J.. Circulating MicroRNAs as potential biomarkers of exercise response.. Int. J. Mol. Sci. 2016;17:1553.
    doi: 10.3390/ijms17101553pmc: PMC5085619pubmed: 27782053google scholar: lookup
  12. Xu T., Liu Q., Yao J., Dai Y., Wang H., Xiao J.. Circulating microRNAs in response to exercise.. Scand. J. Med. Sci. Sports. 2015;25:e149–e154.
    doi: 10.1111/sms.12421pubmed: 25648616google scholar: lookup
  13. Lombardi G., Perego S., Sansoni V., Banfi G.. Circulating miRNA as fine regulators of the physiological responses to physical activity: Pre-analytical warnings for a novel class of biomarkers.. Clin. Biochem. 2016;49:1331–1339.
    doi: 10.1016/j.clinbiochem.2016.09.017pubmed: 27693050google scholar: lookup
  14. Turchinovich A., Weiz L., Langheinz A., Burwinkel B.. Characterization of extracellular circulating microRNA.. Nucleic Acids Res. 2011;39:7223–7233.
    doi: 10.1093/nar/gkr254pmc: PMC3167594pubmed: 21609964google scholar: lookup
  15. Valadi H., Ekström K., Bossios A., Sjöstrand M., Lee J.J., Lötvall J.O.. Exosome-mediated transfer of mRNAs and microRNAs is a novel mechanism of genetic exchange between cells.. Nat. Cell Biol. 2007;9:654–659.
    doi: 10.1038/ncb1596pubmed: 17486113google scholar: lookup
  16. Makarova J.A., Maltseva D.V., Galatenko V.V., Abbasi A., Maximenko D.G., Grigoriev A.I., Tonevitsky A.G., Northoff H.. Exercise immunology meets MiRNAs.. Exerc. Immunol. Meets MiRNAs. 2014;20:135–164.
    pubmed: 24974725
  17. 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
  18. Head S.R., Kiyomi Komori H., LaMere S.A., Whisenant T., Van Nieuwerburgh F., Salomon D.R., Ordoukhanian P.. Library construction for next-generation sequencing: Overviews and challenges.. Biotechniques 2014;56:61–77.
    doi: 10.2144/000114133pmc: PMC4351865pubmed: 24502796google scholar: lookup
  19. Langmead B., Salzberg S.L.. Fast gapped-read alignment with Bowtie 2.. Nat. Methods. 2012;9:357–359.
    doi: 10.1038/nmeth.1923pmc: PMC3322381pubmed: 22388286google scholar: lookup
  20. Kalbfleisch T.S., Rice E.S., DePriest M.S., Walenz B.P., Hestand M.S., Vermeesch J.R., O’Connell B.L., Fiddes I.T., Vershinina A.O., Saremi N.F.. Improved reference genome for the domestic horse increases assembly contiguity and composition.. Commun. Biol. 2018;1:1–8.
    doi: 10.1038/s42003-018-0199-zpmc: PMC6240028pubmed: 30456315google scholar: lookup
  21. Risso D., Ngai J., Speed T.P., Dudoit S.. Normalization of RNA-seq data using factor analysis of control genes or samples.. Nat. Biotechnol. 2014;32:896–902.
    doi: 10.1038/nbt.2931pmc: PMC4404308pubmed: 25150836google scholar: lookup
  22. Robinson M.D., McCarthy D.J., Smyth G.K.. edgeR: A Bioconductor package for differential expression analysis of digital gene expression data.. Bioinformatics 2009;26:139–140.
    doi: 10.1093/bioinformatics/btp616pmc: PMC2796818pubmed: 19910308google scholar: lookup
  23. Shannon P., Markiel A., Ozier O., Baliga N.S., Wang J.T., Ramage D., Amin N., Schwikowski B., Ideker T.. Cytoscape: A software Environment for integrated models of biomolecular interaction networks.. Genome Res. 2003;13:2498–2504.
    doi: 10.1101/gr.1239303pmc: PMC403769pubmed: 14597658google scholar: lookup
  24. Morris J.H., Apeltsin L., Newman A.M., Baumbach J., Wittkop T., Su G., Bader G.D., Ferrin T.E.. ClusterMaker: A multi-algorithm clustering plugin for Cytoscape.. BMC Bioinform. 2011;12:436.
    doi: 10.1186/1471-2105-12-436pmc: PMC3262844pubmed: 22070249google scholar: lookup
  25. Maere S., Heymans K., Kuiper M.. BiNGO: A Cytoscape plugin to assess overrepresentation of Gene Ontology categories in Biological Networks.. Bioinformatics 2005;21:3448–3449.
    doi: 10.1093/bioinformatics/bti551pubmed: 15972284google scholar: lookup
  26. Fielding C.L., Magdesian K.G., Rhodes D.M., Meier C.A., Higgins J.C.. Clinical and biochemical abnormalities in endurance horses eliminated from competition for medical complications and requiring emergency medical treatment: 30 cases (2005–2006): Retrospective study.. J. Vet. Emerg. Crit. Care. 2009;19:473–478.
    doi: 10.1111/j.1476-4431.2009.00441.xpubmed: 19821889google scholar: lookup
  27. Vega R.B., Konhilas J.P., Kelly D.P., Leinwand L.A.. Molecular Mechanisms Underlying Cardiac Adaptation to Exercise.. Cell Metab. 2017;25:1012–1026.
    doi: 10.1016/j.cmet.2017.04.025pmc: PMC5512429pubmed: 28467921google scholar: lookup
  28. Garciarena C.D., Pinilla O.A., Nolly M.B., Laguens R.P., Escudero E.M., Cingolani H.E., Ennis I.L.. Endurance training in the spontaneously hypertensive rat conversion of pathological into physiological cardiac hypertrophy.. Hypertension 2009;53:708–714.
    doi: 10.1161/HYPERTENSIONAHA.108.126805pubmed: 19221208google scholar: lookup
  29. Feng H.J., Ouyang W., Liu J.H., Sun Y.G., Hu R., Huang L.H., Xian J.L., Jing C.F., Zhou M.J.. Global microRNA profiles and signaling pathways in the development of cardiac hypertrophy.. Braz. J. Med. Biol. Res. 2014;47:361–368.
    doi: 10.1590/1414-431X20142937pmc: PMC4075303pubmed: 24728214google scholar: lookup
  30. Faraldi M., Gomarasca M., Sansoni V., Perego S., Banfi G., Lombardi G.. Normalization strategies differently affect circulating miRNA profile associated with the training status.. Sci. Rep. 2019;9:1584.
    doi: 10.1038/s41598-019-38505-xpmc: PMC6367481pubmed: 30733582google scholar: lookup
  31. Das A., Samidurai A., Salloum F.N.. Deciphering Non-coding RNAs in Cardiovascular Health and Disease.. Front. Cardiovasc. Med. 2018;5:73.
    doi: 10.3389/fcvm.2018.00073pmc: PMC6036139pubmed: 30013975google scholar: lookup
  32. Keller P., Vollaard N.B.J., Gustafsson T., Gallagher I.J., Sundberg C.J., Rankinen T., Britton S.L., Bouchard C., Koch L.G., Timmons J.A.. A transcriptional map of the impact of endurance exercise training on skeletal muscle phenotype.. J. Appl. Physiol. 2011;110:46–59.
    doi: 10.1152/japplphysiol.00634.2010pmc: PMC3253010pubmed: 20930125google scholar: lookup
  33. Solich J., Kuśmider M., Faron-Górecka A., Pabian P., Kolasa M., Zemła B., Dziedzicka-Wasylewska M.. Serum Level of miR-1 and miR-155 as Potential Biomarkers of Stress-Resilience of NET-KO and SWR/J Mice.. Cells 2020;9:917.
    doi: 10.3390/cells9040917pmc: PMC7226811pubmed: 32283635google scholar: lookup
  34. Håkansson K.E.J., Sollie O., Simons K.H., Quax P.H.A., Jensen J., Nossent A.Y.. Circulating Small Non-coding RNAs as Biomarkers for Recovery After Exhaustive or Repetitive Exercise.. Front. Physiol. 2018;9:1136.
    doi: 10.3389/fphys.2018.01136pmc: PMC6113669pubmed: 30246800google scholar: lookup
  35. Nielsen S., Åkerström T., Rinnov A., Yfanti C., Scheele C., Pedersen B.K., Laye M.J.. The miRNA plasma signature in response to acute aerobic exercise and endurance training.. PLoS ONE 2014;9:e87308.
    doi: 10.1371/journal.pone.0087308pmc: PMC3929352pubmed: 24586268google scholar: lookup
  36. Brown M.D., Hudlicka O.. Modulation of physiological angiogenesis in skeletal muscle by mechanical forces: Involvement of VEGF and metalloproteinases.. Angiogenesis 2003;6:1–14.
    doi: 10.1023/A:1025809808697pubmed: 14517399google scholar: lookup
  37. Baggish A.L., Hale A., Weiner R.B., Lewis G.D., Systrom D., Wang F., Wang T.J., Chan S.Y.. Dynamic regulation of circulating microRNA during acute exhaustive exercise and sustained aerobic exercise training.. J. Physiol. 2011;589:3983–3994.
    doi: 10.1113/jphysiol.2011.213363pmc: PMC3179997pubmed: 21690193google scholar: lookup
  38. Wang D., Wang Y., Ma J., Wang W., Sun B., Zheng T., Wei M., Sun Y.. MicroRNA-20a participates in the aerobic exercise-based prevention of coronary artery disease by targeting PTEN.. Biomed. Pharmacother. 2017;95:756–763.
    doi: 10.1016/j.biopha.2017.08.086pubmed: 28888922google scholar: lookup
  39. Dickinson B.A., Semus H.M., Montgomery R.L., Stack C., Latimer P.A., Lewton S.M., Lynch J.M., Hullinger T.G., Seto A.G., Van Rooij E.. Plasma microRNAs serve as biomarkers of therapeutic efficacy and disease progression in hypertension-induced heart failure.. Eur. J. Heart Fail. 2013;15:650–659.
    doi: 10.1093/eurjhf/hft018pubmed: 23388090google scholar: lookup
  40. Hua Z., Lv Q., Ye W., Wong C.K.A., Cai G., Gu D., Ji Y., Zhao C., Wang J., Yang B.B.. Mirna-directed regulation of VEGF and other angiogenic under hypoxia.. PLoS ONE 2006;1:e116.
    doi: 10.1371/journal.pone.0000116pmc: PMC1762435pubmed: 17205120google scholar: lookup
  41. Triozzi P.L., Achberger S., Aldrich W., Singh A.D., Grane R., Borden E.C.. The association of blood angioregulatory microRNA levels with circulating endothelial cells and angiogenic proteins in patients receiving dacarbazine and interferon.. J. Transl. Med. 2012;10:241.
    doi: 10.1186/1479-5876-10-241pmc: PMC3573971pubmed: 23217102google scholar: lookup
  42. Caporali A., Emanueli C.. MicroRNA-503 and the Extended MicroRNA-16 Family in Angiogenesis.. Trends Cardiovasc. Med. 2011;21:162–166.
    doi: 10.1016/j.tcm.2012.05.003pmc: PMC3407871pubmed: 22814423google scholar: lookup
  43. Tijsen A.J., Van der Made I., Van den Hoogenhof M.M., Wijnen W.J., Van Deel E.D., de Groot N.E., Alekseev S., Fluiter K., Schroen B., Goumans M.-J.. The microRNA-15 family inhibits the TGFβ-pathway in the heart.. Cardiovasc. Res. 2014;104:61–71.
    doi: 10.1093/cvr/cvu184pubmed: 25103110google scholar: lookup
  44. Kirschner M.B., Edelman J.J.B., Kao S.C.H., Vallely M.P., Van Zandwijk N., Reid G.. The impact of hemolysis on cell-free microRNA biomarkers.. Front. Genet. 2013;4:94.
    doi: 10.3389/fgene.2013.00094pmc: PMC3663194pubmed: 23745127google scholar: lookup
  45. Doss J.F., Corcoran D.L., Jima D.D., Telen M.J., Dave S.S., Chi J.T.. A comprehensive joint analysis of the long and short RNA transcriptomes of human erythrocytes.. BMC Genom. 2015;16:952.
    doi: 10.1186/s12864-015-2156-2pmc: PMC4647483pubmed: 26573221google scholar: lookup
  46. Davidsen P.K., Gallagher I.J., Hartman J.W., Tarnopolsky M.A., Dela F., Helge J.W., Timmons J.A., Phillips S.M.. High responders to resistance exercise training demonstrate differential regulation of skeletal muscle microRNA expression.. J. Appl. Physiol. 2011;110:309–317.
    doi: 10.1152/japplphysiol.00901.2010pubmed: 21030674google scholar: lookup
  47. Ren J., Zhang J., Xu N., Han G., Geng Q., Song J., Li S., Zhao J., Chen H.. Signature of circulating MicroRNAs As potential biomarkers in vulnerable coronary artery disease.. PLoS ONE 2013;8:e80738.
    doi: 10.1371/journal.pone.0080738pmc: PMC3855151pubmed: 24339880google scholar: lookup
  48. Henderson C.A., Gomez C.G., Novak S.M., Mi-Mi L., Gregorio C.C.. Overview of the muscle cytoskeleton.. Compreh. Physiol. 2017;7:891–944.
    pmc: PMC5890934pubmed: 28640448
  49. Anderson B.R., Granzier H.L.. Titin-based tension in the cardiac sarcomere: Molecular origin and physiological adaptations.. Prog. Biophys. Mol. Biol. 2012;110:204–217.
    doi: 10.1016/j.pbiomolbio.2012.08.003pmc: PMC3484226pubmed: 22910434google scholar: lookup
  50. Lewinter M.M., Granzier H.L.. Cardiac titin and heart disease.. J. Cardiovasc. Pharmacol. 2014;63:207–212.
    doi: 10.1097/FJC.0000000000000007pmc: PMC4268868pubmed: 24072177google scholar: lookup
  51. Fassett J.T., Xu X., Kwak D., Wang H., Liu X., Hu X., Bache R.J., Chen Y.. Microtubule Actin Cross-Linking Factor 1 Regulates Cardiomyocyte Microtubule Distribution and Adaptation to Hemodynamic Overload.. PLoS ONE 2013;8:e73887.
    doi: 10.1371/journal.pone.0073887pmc: PMC3784444pubmed: 24086300google scholar: lookup
  52. Allen D.G., Lamb G.D., Westerblad H.. Impaired calcium release during fatigue.. J. Appl. Physiol. 2008;104:296–305.
    doi: 10.1152/japplphysiol.00908.2007pubmed: 17962573google scholar: lookup
  53. Chu B., Hong Z., Zheng X.. Acylglycerol Kinase-Targeted Therapies in Oncology.. Front. Cell Dev. Biol. 2021;9:1948.
    doi: 10.3389/fcell.2021.659158pmc: PMC8339474pubmed: 34368119google scholar: lookup
  54. Zhang Z., Jones A., Joo H.Y., Zhou D., Cao Y., Chen S., Erdjument-Bromage H., Renfrow M., He H., Tempst P.. USP49 deubiquitinates histone H2B and regulates cotranscriptional pre-mRNA splicing.. Genes Dev. 2013;27:1581–1595.
    doi: 10.1101/gad.211037.112pmc: PMC3731547pubmed: 23824326google scholar: lookup
  55. Todd C.D., Deniz Ö., Taylor D., Branco M.R.. Functional evaluation of transposable elements as enhancers in mouse embryonic and trophoblast stem cells.. eLife 2019;8:e44344.
    doi: 10.7554/eLife.44344pmc: PMC6544436pubmed: 31012843google scholar: lookup
  56. Capomaccio S., Verini-Supplizi A., Galla G., Vitulo N., Barcaccia G., Felicetti M., Silvestrelli M., Cappelli K.. Transcription of LINE-derived sequences in exercise-induced stress in horses.. Anim. Genet. 2010;41:23–27.
    doi: 10.1111/j.1365-2052.2010.02094.xpubmed: 21070272google scholar: lookup
  57. Choi S., Liu X., Li P., Akimoto T., Lee S.Y., Zhang M., Yan Z.. Transcriptional profiling in mouse skeletal muscle following a single bout of voluntary running: Evidence of increased cell proliferation.. J. Appl. Physiol. 2005;99:2406–2415.
    doi: 10.1152/japplphysiol.00545.2005pubmed: 16081620google scholar: lookup

Citations

This article has been cited 8 times.
  1. Mecocci S, Porzio E, Chiaradia E, Pepe M, Paris A, Bergagna S, Pietrucci D, Chillemi G, Beccati F, Cappelli K. Omic technology to monitoring resilience and adaptation to exercise and heat stress in endurance horses. Front Vet Sci 2025;12:1734969.
    doi: 10.3389/fvets.2025.1734969pubmed: 41585523google scholar: lookup
  2. Castanheira CIGD, Taylor S, Skiöldebrand E, Rubio-Martinez LM, Hackl M, Clegg PD, Peffers MJ. Synovial Fluid and Serum MicroRNA Signatures in Equine Osteoarthritis. Int J Mol Sci 2025 Nov 19;26(22).
    doi: 10.3390/ijms262211190pubmed: 41303673google scholar: lookup
  3. Cullen JN, Cieslak J, Petersen JL, Bellone RR, Finno CJ, Kalbfleisch TS, Calloe K, Capomaccio S, Cappelli K, Coleman SJ, Distl O, Durward-Akhurst SA, Giulotto E, Hamilton NA, Hill EW, Katz LM, Klaerke DA, Lindgren G, MacHugh DE, Mackowski M, MacLeod JN, Metzger J, Murphy BA, Orlando L, Raudsepp T, Silvestrelli M, Strand E, Tozaki T, Trachsel DS, Valderrama Figueroa LS, Velie BD, Wade CM, Waud B, Mickelson JR, McCue ME. Charting the equine miRNA landscape: An integrated pipeline and browser for annotating, quantifying, and visualizing expression. PLoS Genet 2025 Sep;21(9):e1011835.
    doi: 10.1371/journal.pgen.1011835pubmed: 40911641google scholar: lookup
  4. Reynolds DE, Vallapureddy P, Morales RT, Oh D, Pan M, Chintapula U, Linardi RL, Gaesser AM, Ortved K, Ko J. Equine mesenchymal stem cell derived extracellular vesicle immunopathology biomarker discovery. J Extracell Biol 2023 Jun;2(6):e89.
    doi: 10.1002/jex2.89pubmed: 38938916google scholar: lookup
  5. Reißmann M, Rajavel A, Kokov ZA, Schmitt AO. Identification of Differentially Expressed Genes after Endurance Runs in Karbadian Horses to Determine Candidates for Stress Indicators and Performance Capability. Genes (Basel) 2023 Oct 24;14(11).
    doi: 10.3390/genes14111982pubmed: 38002925google scholar: lookup
  6. Torres-Aguilera I, Pinto-Hernandez P, Iglesias-Gutierrez E, Terrados N, Fernandez-Sanjurjo M. Circulating plasma levels of miR-106b-5p predicts maximal performance in female and male elite kayakers. Front Sports Act Living 2023;5:1040955.
    doi: 10.3389/fspor.2023.1040955pubmed: 36866085google scholar: lookup
  7. Iacomino G. miRNAs: The Road from Bench to Bedside. Genes (Basel) 2023 Jan 25;14(2).
    doi: 10.3390/genes14020314pubmed: 36833241google scholar: lookup
  8. Mecocci S, Trabalza-Marinucci M, Cappelli K. Extracellular Vesicles from Animal Milk: Great Potentialities and Critical Issues. Animals (Basel) 2022 Nov 22;12(23).
    doi: 10.3390/ani12233231pubmed: 36496752google scholar: lookup
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