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Identification of lncRNAs Differentially Expressed during Natural and Induced Estrus in Sheep

DOI: 10.18805/IJAR.B-1347    | Article Id: B-1347 | Page : 1421-1429
Citation :- Identification of lncRNAs Differentially Expressed during Natural and Induced Estrus in Sheep.Indian Journal of Animal Research.2021.(55):1421-1429
Bujun Mei, Rong Liu meibujun@163.com
Address : Department of Agriculture, Hetao College, Bayannur, 015000, People’s Republic of China.
Submitted Date : 12-01-2021
Accepted Date : 20-07-2021

Abstract

Background: The manipulation of the estrous cycle or induction of estrus is a commonly used technique in sheep industry. The goal of this study was to identify and characterize differences of non-coding RNAs (lincRNAs) expression between induced estrus and natural estrus using the BGISEQ-500 plat form in 7 Mongolian sheep, which will provide insights into the regulation mechanisms of lncRNAs in different reproduction mode of sheep.
Methods: During the late spring, ovarian, pituitary, hypothalamic, pineal and uterine tissue samples were collected from four artificially induced estrus and three naturally estrus Mongolian sheep. Total RNA was extracted from the five tissues using TRIzol reagent (Invitrogen) and treated with DNase I following the manufacturer’s instructions. A total of 35 sheep samples were sequenced using the BGISEQ-500 plat form. Bioinformatics methods were used to analysis expression difference analysis between groups, SNP and InDel, alternative splicing, lncRNA’s miRNA precursor prediction, lncRNA target gene and family prediction.
Result: 211 novel lncRNAs were systematically identified using RNA-Seq technology. Meanwhile, we found that there are diversifications of lncRNAs in induced estrus vs. nature estrus of ewes. Therefore, we predict that, under the action of exogenous hormones, many physiological processes of ewes may be affected to varying degrees through the change of LncRNA to a variety of pathways.

Keywords

Ewe Induced estrus lncRNAs Natural estrus Reproductive traits Sheep

References

  1. Bu, D., Yu, K., Sun, S., Xie, C., Skogerbo, G., Miao, R., Xiao, H., Liao, Q., Luo, H., Zhao, G., Zhao, H., Liu, Z., Liu, C., Chen, R. and Zhao, Y. (2012). NONCODE v3.0: Integrative annotation of long noncoding RNAs. Nucleic Acids Res. 40(Database issue): D210-215.
  2. Burge, S.W., Daub, J., Eberhardt, R., Tate, J., Barquist, L., Nawrocki, E.P., Eddy, S.R., Gardner, P.P. and Bateman, A. (2013). Rfam 11.0: 10 years of RNA families. Nucleic Acids Res. 41(Database issue): D226-232.
  3. Chu, Q., Zhou, B., Xu, F., Chen, R., Shen, C., Liang, T., Li, Y. and Schinckel, A.P. (2017). Genome-wide differential mRNA expression profiles in follicles of two breeds and at two stages of estrus cycle of gilts. Sci. Rep. 7(1): 5052.
  4. Conesa, A., Gotz, S., Garcia-Gomez, J. M., Terol, J., Talon, M. and Robles, M. (2005). Blast2GO: A universal tool for annotation, visualization and analysis in functional genomics research. Bioinformatics. 21(18): 3674-3676.
  5. Finn, R.D., Coggill, P., Eberhardt, R.Y., Eddy, S.R., Mistry, J., Mitchell, A.L., Potter, S.C., Punta, M., Qureshi, M., Sangrador- Vegas, A., Salazar, G. A., Tate, J. and Bateman, A. (2016). The Pfam protein families database: towards a more sustainable future. Nucleic Acids Res. 44(D1): D279-285.
  6. Griffiths-Jones, S., Grocock, R.J., van Dongen, S., Bateman, A. and Enright, A.J. (2006). miRBase: microRNA sequences, targets and gene nomenclature. Nucleic Acids Res. 34(Database issue): D140-144.
  7. Kim, D., Langmead, B. and Salzberg, S.L. (2015). HISAT: A fast spliced aligner with low memory requirements. Nat Methods. 12(4): 357-360.
  8. Kong, L., Zhang, Y., Ye, Z. Q., Liu, X. Q., Zhao, S. Q., Wei, L. and Gao, G. (2007). CPC: Aassess the protein-coding potential of transcripts using sequence features and support vector machine. Nucleic Acids Res. 35 (Web Server issue): W345-349.
  9. Langmead, B. and Salzberg, S.L. (2012). Fast gapped-read alignment with Bowtie 2. Nat. Methods. 9(4): 357-359.
  10. Li, B. and Dewey, C.N. (2011). RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome. BMC Bioinformatics, 12: 323.
  11. Malik, Z.S., Dalal, D.S., Patil, C.S. and Dahiya, S.P. (2017). Genetic studies on growth, reproduction and wool production traits in Harnali sheep. Indian Journal of Animal Research. 51(5): 813-816.
  12. Nawrocki, E.P., Kolbe, D.L. and Eddy, S.R. (2009). Infernal 1.0: inference of RNA alignments. Bioinformatics. 25(10): 1335-1337.
  13. Pertea, M., Pertea, G.M., Antonescu, C.M., Chang, T.C., Mendell, J.T. and Salzberg, S.L. (2015). StringTie enables improved reconstruction of a transcriptome from RNA-seq reads. Nat. Biotechnol. 33(3): 290-295.
  14. Wang, L., Feng, Z., Wang, X., Wang, X. and Zhang, X. (2010). DEGseq: An R package for identifying differentially expressed genes from RNA-seq data. Bioinformatics. 26(1): 136-138.
  15. Wei, Y., Lang, J., Zhang, Q., Yang, C. R., Zhao, Z. A., Zhang, Y., Du, Y. and Sun, Y. (2019). DNA methylation analysis and editing in single mammalian oocytes. Proceedings of the National Academy of Sciences of the United States of America. 116(20): 9883-9892.

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