Characterization and Profiling of Liver microRNAs by RNA-sequencing in Cattle Divergently Selected for Residual Feed Intake

  • Al-Husseini, Wijdan (The Centre for Genetics Analysis and Applications, School of Environmental and Rural Science, University of New England) ;
  • Chen, Yizhou (NSW Department of Primary Industries, Elizabeth Macarthur Agricultural Institute) ;
  • Gondro, Cedric (The Centre for Genetics Analysis and Applications, School of Environmental and Rural Science, University of New England) ;
  • Herd, Robert M. (NSW Department of Primary Industries, Beef Industry Centre) ;
  • Gibson, John P. (The Centre for Genetics Analysis and Applications, School of Environmental and Rural Science, University of New England) ;
  • Arthur, Paul F. (NSW Department of Primary Industries, Elizabeth Macarthur Agricultural Institute)
  • Received : 2015.07.19
  • Accepted : 2015.12.14
  • Published : 2016.10.01


MicroRNAs (miRNAs) are short non-coding RNAs that post-transcriptionally regulate expression of mRNAs in many biological pathways. Liver plays an important role in the feed efficiency of animals and high and low efficient cattle demonstrated different gene expression profiles by microarray. Here we report comprehensive miRNAs profiles by next-gen deep sequencing in Angus cattle divergently selected for residual feed intake (RFI) and identify miRNAs related to feed efficiency in beef cattle. Two microRNA libraries were constructed from pooled RNA extracted from livers of low and high RFI cattle, and sequenced by Illumina genome analyser. In total, 23,628,103 high quality short sequence reads were obtained and more than half of these reads were matched to the bovine genome (UMD 3.1). We identified 305 known bovine miRNAs. Bta-miR-143, bta-miR-30, bta-miR-122, bta-miR-378, and bta-let-7 were the top five most abundant miRNAs families expressed in liver, representing more than 63% of expressed miRNAs. We also identified 52 homologous miRNAs and 10 novel putative bovine-specific miRNAs, based on precursor sequence and the secondary structure and utilizing the miRBase (v. 21). We compared the miRNAs profile between high and low RFI animals and ranked the most differentially expressed bovine known miRNAs. Bovine miR-143 was the most abundant miRNA in the bovine liver and comprised 20% of total expressed mapped miRNAs. The most highly expressed miRNA in liver of mice and humans, miR-122, was the third most abundant in our cattle liver samples. We also identified 10 putative novel bovine-specific miRNA candidates. Differentially expressed miRNAs between high and low RFI cattle were identified with 18 miRNAs being up-regulated and 7 other miRNAs down-regulated in low RFI cattle. Our study has identified comprehensive miRNAs expressed in bovine liver. Some of the expressed miRNAs are novel in cattle. The differentially expressed miRNAs between high and low RFI give some insights into liver miRNAs regulating physiological pathways underlying variation in this measure of feed efficiency in bovines.


Bovine;Feed Efficiency;Gene Expression;miRNAs;Next Generation Sequencing


Supported by : Cooperative Research Centre for Beef Genetic Technologies, University of New England


  1. Anders, S. and W. Huber. 2010. Differential expression analysis for sequence count data. Genome Biol. 11:R106.
  2. Arthur, P. F., J. A. Archer, D. J. Johnston, R. M. Herd, E. C. Richardson, and P. F. Parnell. 2001. Genetic and phenotypic variance and covariance components for feed intake, feed efficiency, and other postweaning traits in Angus cattle. J. Anim. Sci. 79:2805-2811.
  3. Chen, Y., C. Gondro, K. Quinn, R. M. Herd, P. F. Parnell, and B. Vanselow. 2011. Global gene expression profiling reveals genes expressed differentially in cattle with high and low residual feed intake. Anim. Genet. 42:475-490.
  4. Donoghue, K. A., P. F. Arthur, J. F. Wilkins, and R. M. Herd. 2011. Onset of puberty and early-life reproduction in Angus females divergently selected for post-weaning residual feed intake. Anim. Prod. Sci. 51:183-190.
  5. Fatima, A., D. J. Lynn, P. O'Boyle, C. Seoighe, and D. Morris. 2014. The miRNAome of the postpartum dairy cow liver in negative energy balance. BMC Genomics 15:279.
  6. Gardner, P. P., J. Daub, J. G. Tate, E. P. Nawrocki, D. L. Kolbe, S. Lindgreen, A. C. Wilkinson, R. D. Finn, S. Griffiths-Jones, S. R. Eddy, and A. Bateman. 2009. Rfam: updates to the RNA families database. Nucl. Acids Res. 37:D136-D140.
  7. Kozomara, A. and S. Griffiths-Jones. 2014. miRBase: annotating high confidence microRNAs using deep sequencing data. Nucl. Acids. Res. 42:D68-D73.
  8. Gu, Z., S. Eleswarapu, and H. Jiang. 2007. Identification and characterization of microRNAs from the bovine adipose tissue and mammary gland. FEBS Lett. 581:981-988.
  9. Hackenberg, M., N. Rodriguez-Ezpeleta, and A. M. Aransay. 2011. miRanalyzer: An update on the detection and analysis of microRNAs in high-throughput sequencing experiments. Nucl. Acids Res. 39:W132-W138.
  10. Hu, J., Y. Xu, J. Hao, S. Wang, C. Li, and S. Meng. 2012. MiR-122 in hepatic function and liver diseases. Protein Cell. 3:364-371.
  11. Jin, W., J. R. Grant, P. Stothard, S. S. Moore, and L. L. Guan. 2009. Characterization of bovine miRNAs by sequencing and bioinformatics analysis. BMC Mol. Biol. 10:90.
  12. Jordan, S. D., M. Kruger, D. M. Willmes, N. Redemann, F. T. Wunderlich, H. S. Bronneke, C. Merkwirth, H. Kashkar, V. M. Olkkonen, T. Bottger, T. Braun, J. Seibler, and J. C. Bruning. 2011. Obesity-induced overexpression of miRNA-143 inhibits insulin-stimulated AKT activation and impairs glucose metabolism. Nat. Cell Biol. 13:434-446.
  13. Koch, R. M., L. A. Swiger, D. Chambers, and K. E. Gregory. 1963. Efficiency of feed use in beef cattle. J. Anim. Sci. 22:486-494.
  14. Kornfeld, J. W., C. Baitzel, A. C. Konner, H. T. Nicholls, M. C. Vogt, K. Herrmanns, L. Scheja, C. Haumaitre, A. M. Wolf, U. Knippschild, J. Seibler, S. Cereghini, J. Heeren, M. Stoffel, and J. C. Bruning. 2013. Obesity-induced overexpression of miR-802 impairs glucose metabolism through silencing of Hnf1b. Nature 494:111-115.
  15. Kozomara, A. and S. Griffiths-Jones. 2011. miRBase: integrating microRNA annotation and deep-sequencing data. Nucl. Acids Res. 39:D152-D157.
  16. Langmead, B., C. Trapnell, M. Pop, and S. L. Salzberg. 2009. Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol. 10:R25.
  17. Lawless, N., A. B. Foroushani, M. S. McCabe, C. O'Farrelly, and D. J. Lynn. 2013. Next Generation sequencing reveals the expression of a unique miRNA profile in response to a grampositive bacterial infection. PLoS One. 8:e57543.
  18. Lewis, A. P. and C. L. Jopling. 2010. Regulation and biological function of the liver-specific miR-122. Biochem. Soc. Trans. 38:1553-1557.
  19. Liu, H. C., J. A. Hicks, N. Trakooljul, and S. H. Zhao. 2010. Current knowledge of microRNA characterization in agricultural animals. Anim. Genet. 41:225-231.
  20. Lu, J., G. Getz, E. A. Miska, E. Alvarez-Saavedra, J. Lamb, D. Peck, A. Sweet-Cordero, B. L. Ebert, R. H. Mak, A. A. Ferrando, J. R. Downing, T. Jacks, H. R. Horvitz, and T. R. Golub. 2005. MicroRNA expression profiles classify human cancers. Nature 435:834-838.
  21. Miles J. R., T. G. McDaneld, R. T. Wiedmann, R. A. Cushman, S. E. Echternkamp, J. L. Vallet, and T. P. L. Smith. 2012. MicroRNA expression profile in bovine cumulus-oocyte complexes: Possible role of let-7 and miR-106a in the development of bovine oocytes. Anim. Reprod. Sci. 130:16-26.
  22. Pandey, A. K., G. Verma, S. Vig, S. Srivastava, A. K. Srivastava, and M. Datta. 2011. miR-29a levels are elevated in the db/db mice liver and its overexpression leads to attenuation of insulin action on PEPCK gene expression in HepG2 cells. Mol. Cell. Endocrinol. 332:125-133.
  23. Romao, J. M., W. Jin, M. He, T. McAllister, and L. L. Guan. 2012. Altered MicroRNA expression in bovine subcutaneous and visceral adipose tissues from cattle under different diet. PLoS One. 7:e40605.
  24. Rottiers, V. and A. M. Naar. 2012. MicroRNAs in metabolism and metabolic disorders. Nat. Rev. Mol. Cell Biol. 13:239-250.
  25. Sherman, E. L., J. D. Nkrumah, C. Li, R. Bartusiak, B. Murdoch, and S. S. Moore. 2009. Fine mapping quantitative trait loci for feed intake and feed efficiency in beef cattle. J. Anim. Sci. 87:37-45.
  26. Tripurani, S. K., C. Xiao, M. Salem, and J. Yao. 2010. Cloning and analysis of fetal ovary microRNAs in cattle. Anim. Reprod. Sci. 120:16-22.
  27. Vejnar, C. E. and E. M. Zdobnov. 2012. miRmap: Comprehensive prediction of microRNA target repression strength. Nucl. Acids Res. 40:11673-11683.
  28. Wen, J. and J. R. Friedman. 2012. miR-122 regulates hepatic lipid metabolism and tumor suppression. J. Clin. Invest. 122:2773-2776.
  29. Yang, J. S., M. D. Phillips, D. Betel, P. Mu, A. Ventura, A. C. Siepel, K. C. Chen, and E. C. Lai. 2011. Widespread regulatory activity of vertebrate microRNA* species. RNA. 17:312-326.
  30. Yu, Z., Z. Jian, S. H. Shen, E. Purisima, and E. Wang. 2007. Global analysis of microRNA target gene expression reveals that miRNA targets are lower expressed in mature mouse and Drosophila tissues than in the embryos. Nucl. Acids Res. 35:152-164.

Cited by

  1. Proof-of-concept study: profile of circulating microRNAs in Bovine serum harvested during acute and persistent FMDV infection vol.14, pp.1, 2017,
  2. A transcriptome multi-tissue analysis identifies biological pathways and genes associated with variations in feed efficiency of growing pigs vol.18, pp.1, 2017,
  3. An integrative transcriptome analysis indicates regulatory mRNA-miRNA networks for residual feed intake in Nelore cattle vol.8, pp.1, 2018,
  4. GWAS and eQTL analysis identifies a SNP associated with both residual feed intake and GFRA2 expression in beef cattle vol.8, pp.1, 2018,
  5. MicroRNA-guided prioritization of genome-wide association signals reveals the importance of microRNA-target gene networks for complex traits in cattle vol.8, pp.1, 2018,