Abstract
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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Donadeu FX, Schauer SN, Sontakke SD (2012) Involvement of miRNAs in ovarian follicular and luteal development. J Endocrinol 215(3):323–334. https://doi.org/10.1530/JOE-12-0252
Abernathy DG, Yoo AS (2015) MicroRNA-dependent genetic networks during neural development. Cell Tissue Res 359(1):179–185. https://doi.org/10.1007/s00441-014-1899-4
Vienberg S, Geiger J, Madsen S, Dalgaard LT (2016) MicroRNAs in metabolism. Acta Physiol (Oxf) 219:346. https://doi.org/10.1111/apha.12681
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. https://doi.org/10.1373/clinchem.2010.147405
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. https://doi.org/10.1373/clinchem.2013.214213
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. https://doi.org/10.1016/j.metabol.2014.12.003
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. https://doi.org/10.3390/jcm5030030
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. https://doi.org/10.1371/journal.pone.0126049
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. https://doi.org/10.1038/nmeth.3014
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. https://doi.org/10.1073/pnas.1019055108
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. https://doi.org/10.1371/journal.pone.0114598
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. https://doi.org/10.1016/j.vetpar.2016.03.025
Muroya S, Ogasawara H, Hojito M (2015) Grazing affects Exosomal circulating MicroRNAs in cattle. PLoS One 10(8):e0136475. https://doi.org/10.1371/journal.pone.0136475
Donadeu FX, Sontakke SD, Ioannidis J MicroRNA indicators of follicular steroidogenesis. Reprod Fertil Dev 2016:906. https://doi.org/10.1071/RD15282
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. https://doi.org/10.1186/s13048-015-0208-5.
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. https://doi.org/10.1095/biolreprod.111.093252.
R Core Team (2013) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria
RStudio Team (2015) RStudio: integrated development for R. RStudio Inc., Boston, MA
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. https://doi.org/10.1093/nar/gkv555
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. https://doi.org/10.1186/gb-2009-10-3-r25
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. https://doi.org/10.1093/bioinformatics/btp616
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. https://doi.org/10.1158/1940-6207.CAPR-11-0370
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. https://doi.org/10.1371/journal.pone.0153200
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. https://doi.org/10.1371/journal.pone.0122554
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. https://doi.org/10.1371/journal.pone.0127443
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. https://doi.org/10.1158/0008-5472.CAN-04-0496
Acknowledgments
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.
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Science+Business Media, LLC
About this protocol
Cite this protocol
Ioannidis, J., Risse, J., Donadeu, F.X. (2018). Profiling of MicroRNAs in the Biofluids of Livestock Species. In: Ying, SY. (eds) MicroRNA Protocols . Methods in Molecular Biology, vol 1733. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-7601-0_5
Download citation
DOI: https://doi.org/10.1007/978-1-4939-7601-0_5
Published:
Publisher Name: Humana Press, New York, NY
Print ISBN: 978-1-4939-7600-3
Online ISBN: 978-1-4939-7601-0
eBook Packages: Springer Protocols