Analyze Diet
BMC genomics2016; 17(1); 831; doi: 10.1186/s12864-016-3168-2

Novel equine tissue miRNAs and breed-related miRNA expressed in serum.

Abstract: MiRNAs regulate multiple genes at the post-transcriptional level and therefore play an important role in many biological processes. It has been suggested that miRNA exported outside the cells contribute to inter-cellular communication. Consequently, circulating miRNAs are of particular interest and are promising biomarkers for many diseases. The number of miRNAs annotated in the horse genome is much lower compared to model organisms like human and mouse. We therefore aimed to identify novel equine miRNAs for tissue types and breed in serum. We analysed 71 small RNA-seq libraries derived from nine tissues (gluteus medius, platysma, masseter muscle, heart, liver, cartilage, bone, total blood and serum) using miRDeep2 and miRdentify tools. Known miRNAs represented between 2.3 and 62.9 % of the reads in 71 libraries. A total of 683 novel miRNAs were identified. Breed and tissue type affected the number of miRNAs detected and interestingly, affected its average intensity. A total of 50 miRNAs in serum proved to be potential biomarkers to differentiate specific breed types, of which miR-122, miR-200, miR-483 were over-expressed and miR-328 was under-expressed in ponies compared to Warmbloods. The different miRNAs profiles, as well as the differences in their expression levels provide a foundation for more hypotheses based on the novel miRNAs discovered. We identified 683 novel equine miRNAs expressed in seven solid tissues, blood and serum. Additionally, our approach evidenced that such data supported identification of specific miRNAs as markers of functions related to breeds or disease tissues.
Publication Date: 2016-10-26 PubMed ID: 27782799PubMed Central: PMC5080802DOI: 10.1186/s12864-016-3168-2Google Scholar: Lookup
The Equine Research Bank provides access to a large database of publicly available scientific literature. Inclusion in the Research Bank does not imply endorsement of study methods or findings by Mad Barn.
  • 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 research aims to identify new microRNAs (miRNAs) in horse tissue and understand how they vary in serum between different horse breeds. The study also examined how these miRNAs might serve as biomarkers for various diseases.

MicroRNAs Role and Interest

  • MiRNAs are molecules that regulate gene expression at the post-transcriptional level, influencing various biological processes.
  • The theory that miRNAs can be exported out of cells to contribute to communication between cells makes circulating miRNAs a subject of interest in scientific research.
  • Such circulating miRNAs are seen as potential biomarkers for several diseases.

Background and Aim of the Study

  • The number of miRNAs recorded in the horse genome is far less than in more thoroughly studied organisms like humans and mice.
  • This research aimed to identify new horse-specific miRNAs in tissue types and breed in serum to narrow this knowledge gap.

Methodology

  • The team analyzed 71 small RNA-seq libraries extracted from nine different tissues using the tools miRDeep2 and miRdentify.
  • The tissues examined were the gluteus medius, platysma, masseter muscle, heart, liver, cartilage, bone, total blood, and serum.

Findings

  • From 2.3 to 62.9% of the reads in the 71 libraries were known miRNAs.
  • The researchers identified a total of 683 new miRNAs.
  • Both the breed and the tissue type influenced the number of miRNAs detected and their average intensity.
  • 50 miRNAs in serum were identified as potential biomarkers to differentiate specific horse breeds.
  • Among these, miR-122, miR-200, and miR-483 were found to be over-expressed, whereas miR-328 was found to be under-expressed in ponies compared to Warmbloods.

Conclusion and Implications of the Study

  • The discovery of new miRNAs and differences in their expression creates the foundation for forming new hypotheses based on these newly-discovered miRNAs.
  • These findings could contribute to identifying specific miRNAs as markers of functions related to animal breeds or disease tissues.

Cite This Article

APA
Pacholewska A, Mach N, Mata X, Vaiman A, Schibler L, Barrey E, Gerber V. (2016). Novel equine tissue miRNAs and breed-related miRNA expressed in serum. BMC Genomics, 17(1), 831. https://doi.org/10.1186/s12864-016-3168-2

Publication

ISSN: 1471-2164
NlmUniqueID: 100965258
Country: England
Language: English
Volume: 17
Issue: 1
Pages: 831
PII: 831

Researcher Affiliations

Pacholewska, Alicja
  • Department of Clinical Veterinary Medicine, Swiss Institute of Equine Medicine, Vetsuisse Faculty, University of Bern, and Agroscope, Länggassstrasse 124, 3012, Bern, Switzerland. alicja.pacholewska@vetsuisse.unibe.ch.
  • Department of Clinical Research and Veterinary Public Health, Institute of Genetics, Vetsuisse Faculty, University of Bern, Bremgartenstrasse 109A, 3012, Bern, Switzerland. alicja.pacholewska@vetsuisse.unibe.ch.
Mach, Núria
  • Animal Genetics and Integrative Biology unit (GABI), INRA, AgroParis Tech, University of Paris-Saclay, 78350, Jouy-en-Josas, France.
Mata, Xavier
  • Animal Genetics and Integrative Biology unit (GABI), INRA, AgroParis Tech, University of Paris-Saclay, 78350, Jouy-en-Josas, France.
Vaiman, Anne
  • Animal Genetics and Integrative Biology unit (GABI), INRA, AgroParis Tech, University of Paris-Saclay, 78350, Jouy-en-Josas, France.
Schibler, Laurent
  • Animal Genetics and Integrative Biology unit (GABI), INRA, AgroParis Tech, University of Paris-Saclay, 78350, Jouy-en-Josas, France.
Barrey, Eric
  • Animal Genetics and Integrative Biology unit (GABI), INRA, AgroParis Tech, University of Paris-Saclay, 78350, Jouy-en-Josas, France.
Gerber, Vincent
  • Department of Clinical Veterinary Medicine, Swiss Institute of Equine Medicine, Vetsuisse Faculty, University of Bern, and Agroscope, Länggassstrasse 124, 3012, Bern, Switzerland.

MeSH Terms

  • Animals
  • Base Sequence
  • Biomarkers
  • Breeding
  • Chromosome Mapping
  • Gene Expression Profiling / methods
  • Gene Expression Regulation
  • High-Throughput Nucleotide Sequencing
  • Horses / blood
  • Horses / genetics
  • MicroRNAs / blood
  • MicroRNAs / genetics
  • Nucleic Acid Conformation
  • Organ Specificity / genetics
  • Workflow

References

This article includes 67 references
  1. Cortez MA, Bueso-Ramos C, Ferdin J, Lopez-Berestein G, Sood AK, Calin GA. MicroRNAs in body fluids--the mix of hormones and biomarkers.. Nat Rev Clin Oncol 2011 Jun 7;8(8):467-77.
    doi: 10.1038/nrclinonc.2011.76pmc: PMC3423224pubmed: 21647195google scholar: lookup
  2. Berezikov E. Evolution of microRNA diversity and regulation in animals.. Nat Rev Genet 2011 Nov 18;12(12):846-60.
    doi: 10.1038/nrg3079pubmed: 22094948google scholar: lookup
  3. Chen X, Ba Y, Ma L, Cai X, Yin Y, Wang K, Guo J, Zhang Y, Chen J, Guo X, Li Q, Li X, Wang W, Zhang Y, Wang J, Jiang X, Xiang Y, Xu C, Zheng P, Zhang J, Li R, Zhang H, Shang X, Gong T, Ning G, Wang J, Zen K, Zhang J, Zhang CY. Characterization of microRNAs in serum: a novel class of biomarkers for diagnosis of cancer and other diseases.. Cell Res 2008 Oct;18(10):997-1006.
    doi: 10.1038/cr.2008.282pubmed: 18766170google scholar: lookup
  4. van der Kolk JH, Pacholewska A, Gerber V. The role of microRNAs in equine medicine: a review.. Vet Q 2015 Jun;35(2):88-96.
    doi: 10.1080/01652176.2015.1021186pubmed: 25695624google scholar: lookup
  5. Liang H, Gong F, Zhang S, Zhang CY, Zen K, Chen X. The origin, function, and diagnostic potential of extracellular microRNAs in human body fluids.. Wiley Interdiscip Rev RNA 2014 Mar-Apr;5(2):285-300.
    doi: 10.1002/wrna.1208pubmed: 24259376google scholar: lookup
  6. Brase JC, Wuttig D, Kuner R, Sültmann H. Serum microRNAs as non-invasive biomarkers for cancer.. Mol Cancer 2010 Nov 26;9:306.
    doi: 10.1186/1476-4598-9-306pmc: PMC3002336pubmed: 21110877google scholar: lookup
  7. Mar-Aguilar F, Mendoza-Ramírez JA, Malagón-Santiago I, Espino-Silva PK, Santuario-Facio SK, Ruiz-Flores P, Rodríguez-Padilla C, Reséndez-Pérez D. Serum circulating microRNA profiling for identification of potential breast cancer biomarkers.. Dis Markers 2013;34(3):163-9.
    doi: 10.1155/2013/259454pmc: PMC3810231pubmed: 23334650google scholar: lookup
  8. Alevizos I, Illei GG. MicroRNAs as biomarkers in rheumatic diseases.. Nat Rev Rheumatol 2010 Jul;6(7):391-8.
    doi: 10.1038/nrrheum.2010.81pmc: PMC3041596pubmed: 20517293google scholar: lookup
  9. Kim MC, Lee SW, Ryu DY, Cui FJ, Bhak J, Kim Y. Identification and characterization of microRNAs in normal equine tissues by Next Generation Sequencing.. PLoS One 2014;9(4):e93662.
  10. Desjardin C, Vaiman A, Mata X, Legendre R, Laubier J, Kennedy SP, Laloe D, Barrey E, Jacques C, Cribiu EP, Schibler L. Next-generation sequencing identifies equine cartilage and subchondral bone miRNAs and suggests their involvement in osteochondrosis physiopathology.. BMC Genomics 2014 Sep 17;15(1):798.
    doi: 10.1186/1471-2164-15-798pmc: PMC4190437pubmed: 25227120google scholar: lookup
  11. Pacholewska A, Drögemüller M, Klukowska-Rötzler J, Lanz S, Hamza E, Dermitzakis ET, Marti E, Gerber V, Leeb T, Jagannathan V. The transcriptome of equine peripheral blood mononuclear cells.. PLoS One 2015;10(3):e0122011.
  12. Buza T, Arick M 2nd, Wang H, Peterson DG. Computational prediction of disease microRNAs in domestic animals.. BMC Res Notes 2014 Jun 27;7:403.
    doi: 10.1186/1756-0500-7-403pmc: PMC4091757pubmed: 24970281google scholar: lookup
  13. Griffiths-Jones S, Grocock RJ, van Dongen S, Bateman A, Enright AJ. miRBase: microRNA sequences, targets and gene nomenclature.. Nucleic Acids Res 2006 Jan 1;34(Database issue):D140-4.
    doi: 10.1093/nar/gkj112pmc: PMC1347474pubmed: 16381832google scholar: lookup
  14. Li J, Chen Z, Zhao J, Fang L, Fang R, Xiao J, Chen X, Zhou A, Zhang Y, Ren L, Hu X, Zhao Y, Zhang S, Li N. Difference in microRNA expression and editing profile of lung tissues from different pig breeds related to immune responses to HP-PRRSV.. Sci Rep 2015 Apr 9;5:9549.
    doi: 10.1038/srep09549pmc: PMC5381705pubmed: 25856272google scholar: lookup
  15. Friedländer MR, Chen W, Adamidi C, Maaskola J, Einspanier R, Knespel S, Rajewsky N. Discovering microRNAs from deep sequencing data using miRDeep.. Nat Biotechnol 2008 Apr;26(4):407-15.
    doi: 10.1038/nbt1394pubmed: 18392026google scholar: lookup
  16. Friedländer MR, Mackowiak SD, Li N, Chen W, Rajewsky N. miRDeep2 accurately identifies known and hundreds of novel microRNA genes in seven animal clades.. Nucleic Acids Res 2012 Jan;40(1):37-52.
    doi: 10.1093/nar/gkr688pmc: PMC3245920pubmed: 21911355google scholar: lookup
  17. Hansen TB, Venø MT, Kjems J, Damgaard CK. miRdentify: high stringency miRNA predictor identifies several novel animal miRNAs.. Nucleic Acids Res 2014;42(16):e124.
    doi: 10.1093/nar/gku598pmc: PMC4176371pubmed: 25053842google scholar: lookup
  18. Ewing B, Green P. Base-calling of automated sequencer traces using phred. II. Error probabilities.. Genome Res 1998 Mar;8(3):186-94.
    doi: 10.1101/gr.8.3.186pubmed: 9521922google scholar: lookup
  19. Spornraft M, Kirchner B, Haase B, Benes V, Pfaffl MW, Riedmaier I. Optimization of extraction of circulating RNAs from plasma--enabling small RNA sequencing.. PLoS One 2014;9(9):e107259.
  20. Unger L, Fouché N, Leeb T, Gerber V, Pacholewska A. Optimized methods for extracting circulating small RNAs from long-term stored equine samples.. Acta Vet Scand 2016 Jun 29;58(1):44.
    pmc: PMC4928274pubmed: 27356979doi: 10.1186/s13028-016-0224-5google scholar: lookup
  21. Langmead B, Trapnell C, Pop M, Salzberg SL. Ultrafast and memory-efficient alignment of short DNA sequences to the human genome.. Genome Biol 2009;10(3):R25.
    doi: 10.1186/gb-2009-10-3-r25pmc: PMC2690996pubmed: 19261174google scholar: lookup
  22. Altuvia Y, Landgraf P, Lithwick G, Elefant N, Pfeffer S, Aravin A, Brownstein MJ, Tuschl T, Margalit H. Clustering and conservation patterns of human microRNAs.. Nucleic Acids Res 2005;33(8):2697-706.
    doi: 10.1093/nar/gki567pmc: PMC1110742pubmed: 15891114google scholar: lookup
  23. Hébert SS, Nelson PT. Studying microRNAs in the brain: technical lessons learned from the first ten years.. Exp Neurol 2012 Jun;235(2):397-401.
  24. Romao JM, Jin W, He M, McAllister T, Guan le L. MicroRNAs in bovine adipogenesis: genomic context, expression and function.. BMC Genomics 2014 Feb 18;15:137.
    doi: 10.1186/1471-2164-15-137pmc: PMC3930007pubmed: 24548287google scholar: lookup
  25. Wang Y, Jiang F, Wang H, Song T, Wei Y, Yang M, Zhang J, Kang L. Evidence for the expression of abundant microRNAs in the locust genome.. Sci Rep 2015 Sep 2;5:13608.
    doi: 10.1038/srep13608pmc: PMC4556993pubmed: 26329925google scholar: lookup
  26. Robinson MD, McCarthy DJ, Smyth GK. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data.. Bioinformatics 2010 Jan 1;26(1):139-40.
  27. Shkurnikov MY, Knyazev EN, Fomicheva KA, Mikhailenko DS, Nyushko KM, Saribekyan EK, Samatov TR, Alekseev BY. Analysis of Plasma microRNA Associated with Hemolysis.. Bull Exp Biol Med 2016 Apr;160(6):748-50.
    doi: 10.1007/s10517-016-3300-ypubmed: 27165077google scholar: lookup
  28. Lewis BP, Shih IH, Jones-Rhoades MW, Bartel DP, Burge CB. Prediction of mammalian microRNA targets.. Cell 2003 Dec 26;115(7):787-98.
    doi: 10.1016/S0092-8674(03)01018-3pubmed: 14697198google scholar: lookup
  29. Tabas-Madrid D, Nogales-Cadenas R, Pascual-Montano A. GeneCodis3: a non-redundant and modular enrichment analysis tool for functional genomics.. Nucleic Acids Res 2012 Jul;40(Web Server issue):W478-83.
    doi: 10.1093/nar/gks402pmc: PMC3394297pubmed: 22573175google scholar: lookup
  30. Kanehisa M, Goto S. KEGG: kyoto encyclopedia of genes and genomes.. Nucleic Acids Res 2000 Jan 1;28(1):27-30.
    doi: 10.1093/nar/28.1.27pmc: PMC102409pubmed: 10592173google scholar: lookup
  31. Valen E, Preker P, Andersen PR, Zhao X, Chen Y, Ender C, Dueck A, Meister G, Sandelin A, Jensen TH. Biogenic mechanisms and utilization of small RNAs derived from human protein-coding genes.. Nat Struct Mol Biol 2011 Aug 7;18(9):1075-82.
    doi: 10.1038/nsmb.2091pubmed: 21822281google scholar: lookup
  32. Mach N, Plancade S, Pacholewska A, Lecardonnel J, Rivière J, Moroldo M, Vaiman A, Morgenthaler C, Beinat M, Nevot A, Robert C, Barrey E. Integrated mRNA and miRNA expression profiling in blood reveals candidate biomarkers associated with endurance exercise in the horse.. Sci Rep 2016 Mar 10;6:22932.
    doi: 10.1038/srep22932pmc: PMC4785432pubmed: 26960911google scholar: lookup
  33. Levine M, Tjian R. Transcription regulation and animal diversity.. Nature 2003 Jul 10;424(6945):147-51.
    doi: 10.1038/nature01763pubmed: 12853946google scholar: lookup
  34. Small EM, Olson EN. Pervasive roles of microRNAs in cardiovascular biology.. Nature 2011 Jan 20;469(7330):336-42.
    doi: 10.1038/nature09783pmc: PMC3073349pubmed: 21248840google scholar: lookup
  35. Olson EN. MicroRNAs as therapeutic targets and biomarkers of cardiovascular disease.. Sci Transl Med 2014 Jun 4;6(239):239ps3.
  36. Guo Z, Maki M, Ding R, Yang Y, Zhang B, Xiong L. Genome-wide survey of tissue-specific microRNA and transcription factor regulatory networks in 12 tissues.. Sci Rep 2014 Jun 3;4:5150.
    pmc: PMC5381490pubmed: 24889152doi: 10.1038/srep05150google scholar: lookup
  37. Lee S, Hwang S, Yu HJ, Oh D, Choi YJ, Kim MC, Kim Y, Ryu DY. Expression of microRNAs in Horse Plasma and Their Characteristic Nucleotide Composition.. PLoS One 2016;11(1):e0146374.
  38. Ameres SL, Zamore PD. Diversifying microRNA sequence and function.. Nat Rev Mol Cell Biol 2013 Aug;14(8):475-88.
    doi: 10.1038/nrm3611pubmed: 23800994google scholar: lookup
  39. Cloonan N, Wani S, Xu Q, Gu J, Lea K, Heater S, Barbacioru C, Steptoe AL, Martin HC, Nourbakhsh E, Krishnan K, Gardiner B, Wang X, Nones K, Steen JA, Matigian NA, Wood DL, Kassahn KS, Waddell N, Shepherd J, Lee C, Ichikawa J, McKernan K, Bramlett K, Kuersten S, Grimmond SM. MicroRNAs and their isomiRs function cooperatively to target common biological pathways.. Genome Biol 2011 Dec 30;12(12):R126.
    doi: 10.1186/gb-2011-12-12-r126pmc: PMC3334621pubmed: 22208850google scholar: lookup
  40. Pfaffl MW, Kirchner B. Limitations and Challenges in MicroGenomics. What we can learn from single-cell and exosome expression profiling?. International Symposium on Microgenomics 2016.
  41. Eisenberg I, Alexander MS, Kunkel LM. miRNAS in normal and diseased skeletal muscle.. J Cell Mol Med 2009 Jan;13(1):2-11.
  42. Petersen JL, Mickelson JR, Cothran EG, Andersson LS, Axelsson J, Bailey E, Bannasch D, Binns MM, Borges AS, Brama P, da Câmara Machado A, Distl O, Felicetti M, Fox-Clipsham L, Graves KT, Guérin G, Haase B, Hasegawa T, Hemmann K, Hill EW, Leeb T, Lindgren G, Lohi H, Lopes MS, McGivney BA, Mikko S, Orr N, Penedo MC, Piercy RJ, Raekallio M, Rieder S, Røed KH, Silvestrelli M, Swinburne J, Tozaki T, Vaudin M, M Wade C, McCue ME. Genetic diversity in the modern horse illustrated from genome-wide SNP data.. PLoS One 2013;8(1):e54997.
  43. Chang J, Nicolas E, Marks D, Sander C, Lerro A, Buendia MA, Xu C, Mason WS, Moloshok T, Bort R, Zaret KS, Taylor JM. miR-122, a mammalian liver-specific microRNA, is processed from hcr mRNA and may downregulate the high affinity cationic amino acid transporter CAT-1.. RNA Biol 2004 Jul;1(2):106-13.
    doi: 10.4161/rna.1.2.1066pubmed: 17179747google scholar: lookup
  44. Lagos-Quintana M, Rauhut R, Yalcin A, Meyer J, Lendeckel W, Tuschl T. Identification of tissue-specific microRNAs from mouse.. Curr Biol 2002 Apr 30;12(9):735-9.
    doi: 10.1016/S0960-9822(02)00809-6pubmed: 12007417google scholar: lookup
  45. Fong MY, Zhou W, Liu L, Alontaga AY, Chandra M, Ashby J, Chow A, O'Connor ST, Li S, Chin AR, Somlo G, Palomares M, Li Z, Tremblay JR, Tsuyada A, Sun G, Reid MA, Wu X, Swiderski P, Ren X, Shi Y, Kong M, Zhong W, Chen Y, Wang SE. Breast-cancer-secreted miR-122 reprograms glucose metabolism in premetastatic niche to promote metastasis.. Nat Cell Biol 2015 Feb;17(2):183-94.
    doi: 10.1038/ncb3094pmc: PMC4380143pubmed: 25621950google scholar: lookup
  46. Esau C, Davis S, Murray SF, Yu XX, Pandey SK, Pear M, Watts L, Booten SL, Graham M, McKay R, Subramaniam A, Propp S, Lollo BA, Freier S, Bennett CF, Bhanot S, Monia BP. miR-122 regulation of lipid metabolism revealed by in vivo antisense targeting.. Cell Metab 2006 Feb;3(2):87-98.
    doi: 10.1016/j.cmet.2006.01.005pubmed: 16459310google scholar: lookup
  47. Bamford NJ, Potter SJ, Harris PA, Bailey SR. Breed differences in insulin sensitivity and insulinemic responses to oral glucose in horses and ponies of moderate body condition score.. Domest Anim Endocrinol 2014 Apr;47:101-7.
  48. Patel D, Boufraqech M, Jain M, Zhang L, He M, Gesuwan K, Gulati N, Nilubol N, Fojo T, Kebebew E. MiR-34a and miR-483-5p are candidate serum biomarkers for adrenocortical tumors.. Surgery 2013 Dec;154(6):1224-8; discussion 1229.
    pmc: PMC3874721pubmed: 24238045doi: 10.1016/j.surg.2013.06.022google scholar: lookup
  49. Chabre O, Libé R, Assie G, Barreau O, Bertherat J, Bertagna X, Feige JJ, Cherradi N. Serum miR-483-5p and miR-195 are predictive of recurrence risk in adrenocortical cancer patients.. Endocr Relat Cancer 2013 Aug;20(4):579-94.
    pubmed: 23756429doi: 10.1530/erc-13-0051google scholar: lookup
  50. Frischknecht M, Jagannathan V, Plattet P, Neuditschko M, Signer-Hasler H, Bachmann I, Pacholewska A, Drögemüller C, Dietschi E, Flury C, Rieder S, Leeb T. A Non-Synonymous HMGA2 Variant Decreases Height in Shetland Ponies and Other Small Horses.. PLoS One 2015;10(10):e0140749.
  51. Lin Y, Liu AY, Fan C, Zheng H, Li Y, Zhang C, Wu S, Yu D, Huang Z, Liu F, Luo Q, Yang CJ, Ouyang G. MicroRNA-33b Inhibits Breast Cancer Metastasis by Targeting HMGA2, SALL4 and Twist1.. Sci Rep 2015 Apr 28;5:9995.
    doi: 10.1038/srep09995pmc: PMC4412117pubmed: 25919570google scholar: lookup
  52. Barrey E, Mucher E, Jeansoule N, Larcher T, Guigand L, Herszberg B, Chaffaux S, Guérin G, Mata X, Benech P, Canale M, Alibert O, Maltere P, Gidrol X. Gene expression profiling in equine polysaccharide storage myopathy revealed inflammation, glycogenesis inhibition, hypoxia and mitochondrial dysfunctions.. BMC Vet Res 2009 Aug 7;5:29.
    doi: 10.1186/1746-6148-5-29pmc: PMC2741442pubmed: 19664222google scholar: lookup
  53. Barrey E, Bonnamy B, Barrey EJ, Mata X, Chaffaux S, Guerin G. Muscular microRNA expressions in healthy and myopathic horses suffering from polysaccharide storage myopathy or recurrent exertional rhabdomyolysis.. Equine Vet J Suppl 2010 Nov;(38):303-10.
  54. Lanz S, Gerber V, Marti E, Rettmer H, Klukowska-Rötzler J, Gottstein B, Matthews JB, Pirie S, Hamza E. Effect of hay dust extract and cyathostomin antigen stimulation on cytokine expression by PBMC in horses with recurrent airway obstruction.. Vet Immunol Immunopathol 2013 Oct 1;155(4):229-37.
    doi: 10.1016/j.vetimm.2013.07.005pubmed: 23972861google scholar: lookup
  55. Kirschner MB, Edelman JJ, Kao SC, Vallely MP, van Zandwijk N, Reid G. The Impact of Hemolysis on Cell-Free microRNA Biomarkers.. Front Genet 2013;4:94.
    pmc: PMC3663194pubmed: 23745127doi: 10.3389/fgene.2013.00094google scholar: lookup
  56. Kirschner MB, Kao SC, Edelman JJ, Armstrong NJ, Vallely MP, van Zandwijk N, Reid G. Haemolysis during sample preparation alters microRNA content of plasma.. PLoS One 2011;6(9):e24145.
  57. Andrews S. FastQC: a quality control tool for high throughput sequence data. 2010.
  58. Martin M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet J 2011;17:10.
    doi: 10.14806/ej.17.1.200google scholar: lookup
  59. Wade CM, Giulotto E, Sigurdsson S, Zoli M, Gnerre S, Imsland F, Lear TL, Adelson DL, Bailey E, Bellone RR, Blöcker H, Distl O, Edgar RC, Garber M, Leeb T, Mauceli E, MacLeod JN, Penedo MC, Raison JM, Sharpe T, Vogel J, Andersson L, Antczak DF, Biagi T, Binns MM, Chowdhary BP, Coleman SJ, Della Valle G, Fryc S, Guérin G, Hasegawa T, Hill EW, Jurka J, Kiialainen A, Lindgren G, Liu J, Magnani E, Mickelson JR, Murray J, Nergadze SG, Onofrio R, Pedroni S, Piras MF, Raudsepp T, Rocchi M, Røed KH, Ryder OA, Searle S, Skow L, Swinburne JE, Syvänen AC, Tozaki T, Valberg SJ, Vaudin M, White JR, Zody MC, Lander ES, Lindblad-Toh K. Genome sequence, comparative analysis, and population genetics of the domestic horse.. Science 2009 Nov 6;326(5954):865-7.
    doi: 10.1126/science.1178158pmc: PMC3785132pubmed: 19892987google scholar: lookup
  60. Tam S, Tsao MS, McPherson JD. Optimization of miRNA-seq data preprocessing.. Brief Bioinform 2015 Nov;16(6):950-63.
    doi: 10.1093/bib/bbv019pmc: PMC4652620pubmed: 25888698google scholar: lookup
  61. Wang L, Wang S, Li W. RSeQC: quality control of RNA-seq experiments.. Bioinformatics 2012 Aug 15;28(16):2184-5.
    doi: 10.1093/bioinformatics/bts356pubmed: 22743226google scholar: lookup
  62. Smit A, Hubley R, Green P. RepeatMasker Open-3.0. 1996-2010.
  63. Jurka J. Repbase update: a database and an electronic journal of repetitive elements.. Trends Genet 2000 Sep;16(9):418-20.
    doi: 10.1016/S0168-9525(00)02093-Xpubmed: 10973072google scholar: lookup
  64. Griffiths-Jones S, Saini HK, van Dongen S, Enright AJ. miRBase: tools for microRNA genomics.. Nucleic Acids Res 2008 Jan;36(Database issue):D154-8.
    doi: 10.1093/nar/gkm952pmc: PMC2238936pubmed: 17991681google scholar: lookup
  65. Gruber AR, Lorenz R, Bernhart SH, Neuböck R, Hofacker IL. The Vienna RNA websuite.. Nucleic Acids Res 2008 Jul 1;36(Web Server issue):W70-4.
    doi: 10.1093/nar/gkn188pmc: PMC2447809pubmed: 18424795google scholar: lookup
  66. Robinson MD, Oshlack A. A scaling normalization method for differential expression analysis of RNA-seq data.. Genome Biol 2010;11(3):R25.
    doi: 10.1186/gb-2010-11-3-r25pmc: PMC2864565pubmed: 20196867google scholar: lookup
  67. Robinson MD, Smyth GK. Moderated statistical tests for assessing differences in tag abundance.. Bioinformatics 2007 Nov 1;23(21):2881-7.
    doi: 10.1093/bioinformatics/btm453pubmed: 17881408google scholar: lookup

Citations

This article has been cited 16 times.
  1. Felekkis K, Pieri M, Papaneophytou C. Exploring the Feasibility of Circulating miRNAs as Diagnostic and Prognostic Biomarkers in Osteoarthritis: Challenges and Opportunities.. Int J Mol Sci 2023 Aug 24;24(17).
    doi: 10.3390/ijms241713144pubmed: 37685951google scholar: lookup
  2. Yassin AM, AbuBakr HO, Abdelgalil AI, Farid OA, El-Behairy AM, Gouda EM. Circulating miR-146b and miR-27b are efficient biomarkers for early diagnosis of Equidae osteoarthritis.. Sci Rep 2023 May 17;13(1):7966.
    doi: 10.1038/s41598-023-35207-3pubmed: 37198318google scholar: lookup
  3. Perera TRW, Skerrett-Byrne DA, Gibb Z, Nixon B, Swegen A. The Future of Biomarkers in Veterinary Medicine: Emerging Approaches and Associated Challenges.. Animals (Basel) 2022 Aug 26;12(17).
    doi: 10.3390/ani12172194pubmed: 36077913google scholar: lookup
  4. Hamza E, Cosandey J, Gerber V, Koch C, Unger L. The potential of three whole blood microRNAs to predict outcome and monitor treatment response in sarcoid-bearing equids.. Vet Res Commun 2023 Jan;47(1):87-98.
    doi: 10.1007/s11259-022-09930-7pubmed: 35484337google scholar: lookup
  5. Cosandey J, Hamza E, Gerber V, Ramseyer A, Leeb T, Jagannathan V, Blaszczyk K, Unger L. Diagnostic and prognostic potential of eight whole blood microRNAs for equine sarcoid disease.. PLoS One 2021;16(12):e0261076.
    doi: 10.1371/journal.pone.0261076pubmed: 34941894google scholar: lookup
  6. Sarwalia P, Raza M, Soni A, Dubey P, Chandel R, Kumar R, Kumaresan A, Onteru SK, Pal A, Singh K, Iquebal MA, Jaiswal S, Kumar D, Datta TK. Establishment of Repertoire of Placentome-Associated MicroRNAs and Their Appearance in Blood Plasma Could Identify Early Establishment of Pregnancy in Buffalo (Bubalus bubalis).. Front Cell Dev Biol 2021;9:673765.
    doi: 10.3389/fcell.2021.673765pubmed: 34513824google scholar: lookup
  7. Lee S, Baker ME, Clinton M, Taylor SE. Use of Omics Data in Fracture Prediction; a Scoping and Systematic Review in Horses and Humans.. Animals (Basel) 2021 Mar 30;11(4).
    doi: 10.3390/ani11040959pubmed: 33808497google scholar: lookup
  8. Zarski LM, Giessler KS, Jacob SI, Weber PSD, McCauley AG, Lee Y, Soboll Hussey G. Identification of Host Factors Associated with the Development of Equine Herpesvirus Myeloencephalopathy by Transcriptomic Analysis of Peripheral Blood Mononuclear Cells from Horses.. Viruses 2021 Feb 24;13(3).
    doi: 10.3390/v13030356pubmed: 33668216google scholar: lookup
  9. Castanheira C, Balaskas P, Falls C, Ashraf-Kharaz Y, Clegg P, Burke K, Fang Y, Dyer P, Welting TJM, Peffers MJ. Equine synovial fluid small non-coding RNA signatures in early osteoarthritis.. BMC Vet Res 2021 Jan 9;17(1):26.
    doi: 10.1186/s12917-020-02707-7pubmed: 33422071google scholar: lookup
  10. Unger L, Abril C, Gerber V, Jagannathan V, Koch C, Hamza E. Diagnostic potential of three serum microRNAs as biomarkers for equine sarcoid disease in horses and donkeys.. J Vet Intern Med 2021 Jan;35(1):610-619.
    doi: 10.1111/jvim.16027pubmed: 33415768google scholar: lookup
  11. Billa PA, Faulconnier Y, Ye T, Chervet M, Le Provost F, Pires JAA, Leroux C. Deep RNA-Seq reveals miRNome differences in mammary tissue of lactating Holstein and Montbéliarde cows.. BMC Genomics 2019 Jul 30;20(1):621.
    doi: 10.1186/s12864-019-5987-4pubmed: 31362707google scholar: lookup
  12. Soula A, Valere M, López-González MJ, Ury-Thiery V, Groppi A, Landry M, Nikolski M, Favereaux A. Small RNA-Seq reveals novel miRNAs shaping the transcriptomic identity of rat brain structures.. Life Sci Alliance 2018 Oct;1(5):e201800018.
    doi: 10.26508/lsa.201800018pubmed: 30456375google scholar: lookup
  13. Pacholewska A, Kraft MF, Gerber V, Jagannathan V. Differential Expression of Serum MicroRNAs Supports CD4⁺ T Cell Differentiation into Th2/Th17 Cells in Severe Equine Asthma.. Genes (Basel) 2017 Dec 12;8(12).
    doi: 10.3390/genes8120383pubmed: 29231896google scholar: lookup
  14. Lawson C, Kovacs D, Finding E, Ulfelder E, Luis-Fuentes V. Extracellular Vesicles: Evolutionarily Conserved Mediators of Intercellular Communication.. Yale J Biol Med 2017 Sep;90(3):481-491.
    pubmed: 28955186
  15. Scott EY, Mansour T, Bellone RR, Brown CT, Mienaltowski MJ, Penedo MC, Ross PJ, Valberg SJ, Murray JD, Finno CJ. Identification of long non-coding RNA in the horse transcriptome.. BMC Genomics 2017 Jul 4;18(1):511.
    doi: 10.1186/s12864-017-3884-2pubmed: 28676104google scholar: lookup
  16. Mach N, Ramayo-Caldas Y, Clark A, Moroldo M, Robert C, Barrey E, López JM, Le Moyec L. Understanding the response to endurance exercise using a systems biology approach: combining blood metabolomics, transcriptomics and miRNomics in horses.. BMC Genomics 2017 Feb 17;18(1):187.
    doi: 10.1186/s12864-017-3571-3pubmed: 28212624google scholar: lookup