Profiling of MicroRNAs in the Biofluids of Livestock Species

  • Jason IoannidisEmail author
  • Judith Risse
  • F. Xavier DonadeuEmail author
Part of the Methods in Molecular Biology book series (MIMB, volume 1733)


The value of circulating microRNAs (miRNAs) as noninvasive biomarkers of human disease has been extensively demonstrated. Significant potential also exists in other species, particularly in relation to control of veterinary diseases and selection/monitoring of production traits in livestock. Although robust protocols have been developed for miRNA profiling of human biofluids, significant optimization may be required before these can be applied to other species. In this chapter, we describe protocols for small-RNA sequencing and RT-qPCR analyses of plasma samples from livestock species. In addition, we provide brief data analysis protocols for small-RNA sequencing and RT-qPCR data. Finally, we highlight important considerations for these protocols such as low RNA yield, platform-specific biases, and optimal normalization approaches.

Key words

Cow miRNA microRNA Biomarker Plasma Follicular fluid Sequencing 



We would like to thank Bushra Mohammed, Stephanie Schauer, and Sadanand Sontakke for assistance with developing protocols. This work was funded by Zoetis Inc. and BBSRC.


  1. 1.
    Donadeu FX, Schauer SN, Sontakke SD (2012) Involvement of miRNAs in ovarian follicular and luteal development. J Endocrinol 215(3):323–334. CrossRefPubMedGoogle Scholar
  2. 2.
    Abernathy DG, Yoo AS (2015) MicroRNA-dependent genetic networks during neural development. Cell Tissue Res 359(1):179–185. CrossRefPubMedGoogle Scholar
  3. 3.
    Vienberg S, Geiger J, Madsen S, Dalgaard LT (2016) MicroRNAs in metabolism. Acta Physiol (Oxf) 219:346. CrossRefGoogle Scholar
  4. 4.
    Weber JA, Baxter DH, Zhang S, Huang DY, Huang KH, Lee MJ et al (2010) The microRNA spectrum in 12 body fluids. Clin Chem 56(11):1733–1741. CrossRefPubMedPubMedCentralGoogle Scholar
  5. 5.
    Wang HY, Yan LX, Shao Q, Fu S, Zhang ZC, Ye W et al (2014) Profiling plasma MicroRNA in nasopharyngeal carcinoma with deep sequencing. Clin Chem 60:773. CrossRefPubMedGoogle Scholar
  6. 6.
    Higuchi C, Nakatsuka A, Eguchi J, Teshigawara S, Kanzaki M, Katayama A et al (2015) Identification of circulating miR-101, miR-375 and miR-802 as biomarkers for type 2 diabetes. Metabolism 64(4):489–497. CrossRefPubMedGoogle Scholar
  7. 7.
    Afonso MB, Rodrigues PM, Simao AL, Castro RE (2016) Circulating microRNAs as potential biomarkers in non-alcoholic fatty liver disease and hepatocellular carcinoma. J Clin Med 5(3):30. CrossRefPubMedCentralGoogle Scholar
  8. 8.
    Fuchs RT, Sun Z, Zhuang F, Robb GB (2015) Bias in ligation-based small RNA sequencing library construction is determined by adaptor and RNA structure. PLoS One 10(5):e0126049. CrossRefPubMedPubMedCentralGoogle Scholar
  9. 9.
    Mestdagh P, Hartmann N, Baeriswyl L, Andreasen D, Bernard N, Chen C et al (2014) Evaluation of quantitative miRNA expression platforms in the microRNA quality control (miRQC) study. Nat Methods 11(8):809–815. CrossRefPubMedGoogle Scholar
  10. 10.
    Arroyo JD, Chevillet JR, Kroh EM, Ruf IK, Pritchard CC, Gibson DF et al (2011) Argonaute2 complexes carry a population of circulating microRNAs independent of vesicles in human plasma. Proc Natl Acad Sci U S A 108(12):5003–5008. CrossRefPubMedPubMedCentralGoogle Scholar
  11. 11.
    Ahanda ML, Zerjal T, Dhorne-Pollet S, Rau A, Cooksey A, Giuffra E (2014) Impact of the genetic background on the composition of the chicken plasma MiRNome in response to a stress. PLoS One 9(12):e114598. CrossRefPubMedPubMedCentralGoogle Scholar
  12. 12.
    Hansen EP, Kringel H, Thamsborg SM, Jex A, Nejsum P (2016) Profiling circulating miRNAs in serum from pigs infected with the porcine whipworm, Trichuris suis. Vet Parasitol 223:30–33. CrossRefPubMedGoogle Scholar
  13. 13.
    Muroya S, Ogasawara H, Hojito M (2015) Grazing affects Exosomal circulating MicroRNAs in cattle. PLoS One 10(8):e0136475. CrossRefPubMedPubMedCentralGoogle Scholar
  14. 14.
    Donadeu FX, Sontakke SD, Ioannidis J MicroRNA indicators of follicular steroidogenesis. Reprod Fertil Dev 2016:906.
  15. 15.
    Noferesti SS, Sohel MM, Hoelker M, Salilew-Wondim D, Tholen E, Looft C et al (2015) Controlled ovarian hyperstimulation induced changes in the expression of circulatory miRNA in bovine follicular fluid and blood plasma. J Ovarian Res 8(1):81. CrossRefPubMedPubMedCentralGoogle Scholar
  16. 16.
    da Silveira JC, Veeramachaneni DN, Winger QA, Carnevale EM, Bouma GJ (2012) Cell-secreted vesicles in equine ovarian follicular fluid contain miRNAs and proteins: a possible new form of cell communication within the ovarian follicle. Biol Reprod 86(3):71. CrossRefPubMedGoogle Scholar
  17. 17.
    R Core Team (2013) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, AustriaGoogle Scholar
  18. 18.
    RStudio Team (2015) RStudio: integrated development for R. RStudio Inc., Boston, MAGoogle Scholar
  19. 19.
    Rueda A, Barturen G, Lebron R, Gomez-Martin C, Alganza A, Oliver JL et al (2015) sRNAtoolbox: an integrated collection of small RNA research tools. Nucleic Acids Res 43(W1):W467–W473. CrossRefPubMedPubMedCentralGoogle Scholar
  20. 20.
    Langmead B, Trapnell C, Pop M, Salzberg SL (2009) Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol 10(3):R25. CrossRefPubMedPubMedCentralGoogle Scholar
  21. 21.
    Robinson MD, McCarthy DJ, Smyth GK (2010) edgeR: a bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26(1):139–140. CrossRefPubMedGoogle Scholar
  22. 22.
    Pritchard CC, Kroh E, Wood B, Arroyo JD, Dougherty KJ, Miyaji MM et al (2012) Blood cell origin of circulating microRNAs: a cautionary note for cancer biomarker studies. Cancer Prev Res (Phila) 5(3):492–497. CrossRefGoogle Scholar
  23. 23.
    Shah JS, Soon PS, Marsh DJ (2016) Comparison of methodologies to detect low levels of hemolysis in serum for accurate assessment of serum microRNAs. PLoS One 11(4):e0153200. CrossRefPubMedPubMedCentralGoogle Scholar
  24. 24.
    Bae IS, Chung KY, Yi J, Kim TI, Choi HS, Cho YM et al (2015) Identification of reference genes for relative quantification of circulating MicroRNAs in bovine serum. PLoS One 10(3):e0122554. CrossRefPubMedPubMedCentralGoogle Scholar
  25. 25.
    Schlosser K, McIntyre LA, White RJ, Stewart DJ (2015) Customized internal reference controls for improved assessment of circulating MicroRNAs in disease. PLoS One 10(5):e0127443. CrossRefPubMedPubMedCentralGoogle Scholar
  26. 26.
    Andersen CL, Jensen JL, Orntoft TF (2004) Normalization of real-time quantitative reverse transcription-PCR data: a model-based variance estimation approach to identify genes suited for normalization, applied to bladder and colon cancer data sets. Cancer Res 64(15):5245–5250. CrossRefPubMedGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2018

Authors and Affiliations

  1. 1.The Roslin Institute and Royal (Dick) School of Veterinary StudiesUniversity of EdinburghMidlothianUK
  2. 2.Edinburgh GenomicsUniversity of EdinburghEdinburghUK
  3. 3.Bioinformatics GroupWageningen UniversityWageningenUSA

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