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Identification and Function Prediction of Novel MicroRNAs in Laoshan Dairy Goats

  • Ji, Zhibin (College of Animal Science and Veterinary Medicine, Shandong Agricultural University) ;
  • Wang, Guizhi (College of Animal Science and Veterinary Medicine, Shandong Agricultural University) ;
  • Zhang, Chunlan (College of Animal Science and Veterinary Medicine, Shandong Agricultural University) ;
  • Xie, Zhijing (College of Animal Science and Veterinary Medicine, Shandong Agricultural University) ;
  • Liu, Zhaohua (College of Animal Science and Veterinary Medicine, Shandong Agricultural University) ;
  • Wang, Jianmin (College of Animal Science and Veterinary Medicine, Shandong Agricultural University)
  • Received : 2012.08.06
  • Accepted : 2012.10.31
  • Published : 2013.03.01

Abstract

MicroRNAs are a class of endogenous small RNAs that play important roles in post-transcriptional gene regulation by directing degradation of mRNAs or facilitating repression of target gene translation. In this study, three small RNA cDNA libraries from the mammary gland tissues of Laoshan dairy goats (Capra hircus) were constructed and sequenced, individually. Through Solexa high-throughput sequencing and bioinformatics analysis, we obtained 50 presumptive novel miRNAs candidates, and 55,448 putative target genes were predicted. GO annotations and KEGG pathway analyses showed the majority of target genes were involved in various biological processes and metabolic pathways. Our results discovered more information about the regulation network between miRNAs and mRNAs and paved a foundation for the molecular genetics of mammary gland development in goats.

Keywords

MicroRNA;Mammary Gland;Goat;Solexa High-throughput Sequencing

References

  1. Bartel, D. P. 2004. MicroRNAs: Genomics biogenesis, mechanism, and function. Cell 116:281-297. https://doi.org/10.1016/S0092-8674(04)00045-5
  2. Berezikov, E., V. Guryev, J. van de Belt, E. Wienholds, R. H. Plasterk and E. Cuppen. 2005. Phylogenetic shadowing and computational identification of human microRNA genes. Cell 120:21-24. https://doi.org/10.1016/j.cell.2004.12.031
  3. Brown, J. R. and P. Sanseau. 2005. A computational view of microRNAs and their targets. Drug Discov. Today 10:595-601. https://doi.org/10.1016/S1359-6446(05)03399-4
  4. Allen, E., Z. Xie, A. M. Gustafson and J. C. Carrington. 2005. microRNA-directed phasing during trans-acting siRNA biogenesis in plants. Cell 121:207-221. https://doi.org/10.1016/j.cell.2005.04.004
  5. Carbon, S., A. Ireland, C. J. Mungall, S. Shu, B. Marshall, S. Lewis, AmiGO Hub and Web Presence Working Group. 2009. AmiGO: online access to ontology and annotation data. Bioinformatics 25:288-289. https://doi.org/10.1093/bioinformatics/btn615
  6. Carthew, R. W. 2006. Gene regulation by microRNAs. Curr. Opin. Genet. Dev. 16:203-208. https://doi.org/10.1016/j.gde.2006.02.012
  7. Chen, X., Q. Li, J. Wang, X. Guo, X. Jiang, Z. Ren, C. Weng, G. Sun, X. Wang, Y. Liu, L. Ma, J. Y. Chen, J. Wang, K. Zen, J. Zhang and C. Y. Zhang. 2009. Identification and characterization of novel amphioxus microRNAs by Solexa sequencing. Genome Biol. 10:R78. https://doi.org/10.1186/gb-2009-10-7-r78
  8. Fabian, M. R., N. Sonenberg and W. Filipowicz. 2010. Regulation of mRNA translation and stability by microRNAs. Annu. Rev. Biochem. 79:351-379. https://doi.org/10.1146/annurev-biochem-060308-103103
  9. Ji, Z., G. Wang, Z. Xie, C. Zhang and J. Wang. 2012. Identification and characterization of microRNA in the dairy goat (Capra hircus) mammary gland by Solexa deep-sequencing technology. Mol. Biol. Rep. 39:9361-9371. https://doi.org/10.1007/s11033-012-1779-5
  10. Jiang, P., H. Wu, W. Wang, W. Ma, X. Sun and Z. Lu. 2007. MiPred: classification of real and pseudo microRNA precursors using random forest prediction model with combined features. Nucleic Acids Res. 35:W339-W344. https://doi.org/10.1093/nar/gkm368
  11. Kozomara, A. and S. Griffiths-Jones. 2011. miRBase: integrating microRNA annotation and deep-sequencing data. Nucleic Acids Res. 39:D152-D157. https://doi.org/10.1093/nar/gkq1027
  12. Lai, E. C. 2002. MicroRNAs are complementary to 3′ UTR sequence motifs that mediate negative post-transcriptional regulation. Nat. Genet. 30:363-364. https://doi.org/10.1038/ng865
  13. Lee, R. C., R. L. Feinbaum and V. Ambros. 1993. The C. elegans heterochronic gene lin-4 encodes small RNAs with antisense complementarity to lin-14. Cell 75:843-854. https://doi.org/10.1016/0092-8674(93)90529-Y
  14. Lewis, B. P., I. H. Shih, M. W. Jones-Rhoades, D. P. Bartel and C. B. Burge. 2003. Prediction of mammalian microRNA targets. Cell 115:787-798. https://doi.org/10.1016/S0092-8674(03)01018-3
  15. Li, G., Y. Li, X. Li, X. Ning, M. Li and G. Yang. 2011a. MicroRNA identity and abundance in developing swine adipose tissue as determined by Solexa sequencing. J. Cell Biochem. 112:1318-1328. https://doi.org/10.1002/jcb.23045
  16. Li, R., Y. Li, K. Kristiansen and J. Wang. 2008. SOAP: Short Oligonucleotide alignment program. Bioinformatics 24:713-714. https://doi.org/10.1093/bioinformatics/btn025
  17. Li, T., R. Wu, Y. Zhang and D. Zhu. 2011b. A systematic analysis of the skeletal muscle miRNA transcriptome of chicken varieties with divergent skeletal muscle growth identifies novel miRNAs and differentially expressed miRNAs. BMC Genomics 12:186-205. https://doi.org/10.1186/1471-2164-12-186
  18. Li, Y., Z. Zhang, F. Liu, W. Vongsangnak, Q. Jing and B. Shen. 2012. Performance comparison and evaluation of software tools for microRNA deep-sequencing data analysis. Nucleic Acids Res. 40:4298-4305. https://doi.org/10.1093/nar/gks043
  19. Millar, A. A. and P. M. Waterhouse. 2005. Plant and animal microRNAs: similarities and differences. Funct. Integr. Genomics 5:129-135. https://doi.org/10.1007/s10142-005-0145-2
  20. Kanehisa, M., S. Goto, Y. Sato, M. Furumichi and M. Tanabe. 2012. KEGG for integration and interpretation of large-scale molecular datasets. Nucleic Acids Res. 40:D109-D114. https://doi.org/10.1093/nar/gkr988
  21. Schwab, R., J. F. Palatnik, M. Riester, C. Schommer, M. Schmid and D Weigel. 2005. Specific effects of microRNAs on the plant transcriptome. Dev. Cell 8:517-527. https://doi.org/10.1016/j.devcel.2005.01.018
  22. Yousef, M., L. Showe, M. Showe. 2009. A study of microRNAs in silico and in vivo: bioinformatics approaches to microRNA discovery and target identification. FEBS J. 276:2150-2156. https://doi.org/10.1111/j.1742-4658.2009.06933.x
  23. Zhang, B., E. J. Stellwag, X. Pan. 2009. Large-scale genome analysis reveals unique features of microRNAs. Gene 443:100-109. https://doi.org/10.1016/j.gene.2009.04.027
  24. Zhang, B. H., X. P. Pan, S. B. Cox, G. P. Cobb and T. A. Anderson. 2006. Evidence that miRNAs are different from other RNAs. Cell Mol. Life Sci. 63:246-254. https://doi.org/10.1007/s00018-005-5467-7

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