DOI QR코드

DOI QR Code

Enrichment and verification of differentially expressed miRNAs in bursa of Fabricius in two breeds of duck

  • Luo, Jun ;
  • Liu, Junying ;
  • Liu, Hehe ;
  • Zhang, Tao ;
  • Wang, Jiwen ;
  • He, Hua ;
  • Han, Chunchun
  • Received : 2016.04.25
  • Accepted : 2016.09.15
  • Published : 2017.07.01

Abstract

Objective: The bursa of Fabricius (BF) is a central humoral immune organ belonging specifically to avians. Recent studies had suggested that miRNAs were active regulators involved in the immune processes. This study was to investigate the possible differences of the BF at miRNA level between two genetically disparate duck breeds. Methods: Using Illumina next-generation sequencing, the miRNAs libraries of ducks were established. Results: The results showed that there were 66 differentially expressed miRNAs and 28 novel miRNAs in bursa. A set of abundant miRNAs (i.e., let-7, miR-146a-5p, miR-21-5p, miR-17~92) which are involved in immunity and disease were detected and the predicted target genes of the novel miRNAs were associated with duck high anti-adversity ability. By gene ontology analysis and enriching KEGG pathway, the targets of differential expressed miRNAs were mainly involved in immunity and disease, supporting that there were differences in the BF immune functions between the two duck breeds. In addition, the metabolic pathway had the maximum enriched target genes and some enriched pathways that were related to cell cycle, protein synthesis, cell proliferation and apoptosis. It indicted that the difference of metabolism may be one of the reasons leading the immune difference between the BF of two duck breeds. Conclusion: This data lists the main differences in the BF at miRNAs level between two genetically disparate duck breeds and lays a foundation to carry out molecular assisted breeding of poultry in the future.

Keywords

Bursa of Fabricius;MiRNAs;Genetically Disparate Breeds;Gene Ontology;KEGG;Duck

References

  1. Korte J, Frohlich T, Kohn M, et al. 2D DIGE analysis of the bursa of Fabricius reveals characteristic proteome profiles for different stages of chicken B-cell development. Proteomics 2013;13:119-33. https://doi.org/10.1002/pmic.201200177
  2. Glick B, Chang TS, Jaap RG. The bursa of fabricius and antibody production. Poult Sci 1956;35:224-5. https://doi.org/10.3382/ps.0350224
  3. Yin T-B, Liu X-Y. Poultry immunology. Beijing, China: China Agriculture Science and Technique Press; 1999.
  4. Mustonen L, Alinikula J, Lassila O, Nera KP. Bursa of Fabricius. Encyclopedia of Life Science. Hoboken, NJ: Wiley; 2010.
  5. Ratcliffe MJ. Antibodies, immunoglobulin genes and the bursa of Fabricius in chicken B cell development. Dev Com Immunol 2006;30:101-18. https://doi.org/10.1016/j.dci.2005.06.018
  6. Bartel DP, Chen C-Z. Micromanagers of gene expression: the potentially widespread influence of metazoan microRNAs. Nat Rev Genet 2004;5:396-400. https://doi.org/10.1038/nrg1328
  7. Schickel R, Boyerinas B, Park S, Peter M. MicroRNAs: key players in the immune system, differentiation, tumorigenesis and cell death. Oncogene 2008;27:5959-74. https://doi.org/10.1038/onc.2008.274
  8. Hicks JA, Tembhurne PA, Liu H-C. Identification of microRNA in the developing chick immune organs. Immunogenetics 2009;61:231-40. https://doi.org/10.1007/s00251-009-0355-1
  9. Trakooljul N, Hicks J, Liu HC. Identification of target genes and pathways associated with chicken microRNA miR-143. Anim Genet 2010; 41:357-64.
  10. Chen C-Z, Li L, Lodish HF, Bartel DP. MicroRNAs modulate hematopoietic lineage differentiation. Science 2004;303:83-6. https://doi.org/10.1126/science.1091903
  11. Li Z-J, Zhang Y-P, Li Y, et al. Distinct expression pattern of miRNAs in Marek's disease virus infected-chicken splenic tumors and nontumorous spleen tissues. Res Vet Sci 2014;97:156-61. https://doi.org/10.1016/j.rvsc.2014.04.003
  12. Wang Y-S, Ouyang W, Pan Q-X, et al. Overexpression of microRNA gga-miR-21 in chicken fibroblasts suppresses replication of infectious bursal disease virus through inhibiting VP1 translation. Antiviral Res 2013;100:196-201. https://doi.org/10.1016/j.antiviral.2013.08.001
  13. Rodriguez A, Vigorito E, Clare S, et al. Requirement of bic/microRNA-155 for normal immune function. Science 2007;316:608-11. https://doi.org/10.1126/science.1139253
  14. Dahlberg JE, Lund E. Micromanagement during the innate immune response. Sci Signal 2007;2007:pe25-pe.
  15. Dinh H, Hong YH, Lillehoj HS. Modulation of microRNAs in two genetically disparate chicken lines showing different necrotic enteritis disease susceptibility. Vet Immunol Immunopathol 2014;159:74-82. https://doi.org/10.1016/j.vetimm.2014.02.003
  16. Tian F, Luo J, Zhang H, Chang S, Song J. MiRNA expression signatures induced by Marek's disease virus infection in chickens. Genomics 2012;99:152-9. https://doi.org/10.1016/j.ygeno.2011.11.004
  17. Wen M, Shen Y, Shi S, Tang T. miREvo: an integrative microRNA evolutionary analysis platform for next-generation sequencing experiments. BMC Bioinformatics 2012;13:140. https://doi.org/10.1186/1471-2105-13-140
  18. Friedlander MR, Mackowiak SD, Li N, Chen W, Rajewsky N. miRDeep2 accurately identifies known and hundreds of novel microRNA genes in seven animal clades. Nucleic Acids Res 2012;40:37-52. https://doi.org/10.1093/nar/gkr688
  19. Zhou L, Chen J, Li Z, et al. Integrated profiling of microRNAs and mRNAs: microRNAs located on Xq27. 3 associate with clear cell renal cell carcinoma. PLoS ONE 2010;5:e15224. https://doi.org/10.1371/journal.pone.0015224
  20. Wang L, Feng Z, Wang X, Wang X, Zhang X. DEGseq: an R package for identifying differentially expressed genes from RNA-seq data. Bioinformatics 2010;26:136-8. https://doi.org/10.1093/bioinformatics/btp612
  21. Livak KJ, Schmittgen TD. Analysis of relative gene expression data using real-time quantitative PCR and the $2^{-{\Delta}{\Delta}CT}$ method. Methods 2001;25:402-8. https://doi.org/10.1006/meth.2001.1262
  22. Green SB, Salkind NJ. Using SPSS for Windows and Macintosh: Analyzing and understanding data. Upper Saddle River, NJ: Prentice Hall Press; 2010.
  23. Li Y, Wang X, Yu J, et al. MiR-122 targets the vanin 1 gene to regulate its expression in chickens. Poult Sci 2016;95:1145-50. https://doi.org/10.3382/ps/pew039
  24. Bartel DP. MicroRNAs: genomics, biogenesis, mechanism, and function. Cell 2004;116:281-97. https://doi.org/10.1016/S0092-8674(04)00045-5
  25. Yu D-B, Jiang B-C, Jing G, et al. Identification of novel and differentially expressed microRNAs in the ovaries of laying and non-laying ducks. J Integr Agric 2013;12:136-46. https://doi.org/10.1016/S2095-3119(13)60214-2
  26. Zhang L, Nie Q, Su Y, et al. MicroRNA profile analysis on duck feather follicle and skin with high-throughput sequencing technology. Gene 2013;519:77-81. https://doi.org/10.1016/j.gene.2013.01.043
  27. Gu L, Xu T, Huang W, et al. Identification and profiling of microRNAs in the embryonic breast muscle of pekin duck. PloS one 2014;9:e86150. https://doi.org/10.1371/journal.pone.0086150
  28. Cong L-X, Su J-Z, Xing S-Y, Zhao Z-H, Zhang J-Y. Detection of the expression and comparative study of miRNA-17a-* in H5N1 infected and uninfected SPF ducks. Heilongjiang Anim Sci Vet Med 2012;21:005.
  29. Li Z, Zhang J, Su J, et al. MicroRNAs in the immune organs of chickens and ducks indicate divergence of immunity against H5N1 avian influenza. FEBS Lett 2015;589:419-25. https://doi.org/10.1016/j.febslet.2014.12.019
  30. Smith KM, Guerau-de-Arellano M, Costinean S, et al. miR-29ab1 deficiency identifies a negative feedback loop controlling Th1 bias that is dysregulated in multiple sclerosis. J Immunol 2012;189:1567-76. https://doi.org/10.4049/jimmunol.1103171
  31. Baltimore D, Boldin MP, O'Connell RM, Rao DS, Taganov KD. MicroRNAs: new regulators of immune cell development and function. Nat Immunol 2008;9:839-45. https://doi.org/10.1038/ni.f.209
  32. Taganov KD, Boldin MP, Chang K-J, Baltimore D. NF-${\kappa}B$-dependent induction of microRNA miR-146, an inhibitor targeted to signaling proteins of innate immune responses. Proc Nat Acad Sci 2006;103: 12481-6. https://doi.org/10.1073/pnas.0605298103
  33. Lynn DJ, Winsor GL, Chan C, et al. InnateDB: facilitating systemslevel analyses of the mammalian innate immune response. Mol Syst Biol 2008;4.
  34. Yoneyama M, Kikuchi M, Natsukawa T, et al. The RNA helicase RIG-I has an essential function in double-stranded RNA-induced innate antiviral responses. Nat Immunol 2004;5:730-7. https://doi.org/10.1038/ni1087
  35. Baeuerle PA, Baichwal VR. NF-kB as a frequent target for immunosuppressive and anti-inflammatory molecules. Adv Immunol 1997;65:111-38.
  36. Honda K, Yanai H, Negishi H, et al. IRF-7 is the master regulator of type-I interferon-dependent immune responses. Nature 2005;434:772-7. https://doi.org/10.1038/nature03464
  37. Huang Y, Li Y, Burt DW, et al. The duck genome and transcriptome provide insight into an avian influenza virus reservoir species. Nat Genet 2013;45:776-83. https://doi.org/10.1038/ng.2657
  38. Georges SA, Biery MC, Kim S-Y, et al. Coordinated regulation of cell cycle transcripts by p53-Inducible microRNAs, miR-192 and miR-215. Cancer Res 2008;68:10105-12. https://doi.org/10.1158/0008-5472.CAN-08-1846
  39. Liu G, Friggeri A, Yang Y, et al. miR-147, a microRNA that is induced upon Toll-like receptor stimulation, regulates murine macrophage inflammatory responses. Proc Nat Acad Sci 2009;106:15819-24. https://doi.org/10.1073/pnas.0901216106
  40. Dong P, Kaneuchi M, Watari H, et al. MicroRNA-194 inhibits epithelial to mesenchymal transition of endometrial cancer cells by targeting oncogene BMI-1. Mol Cancer 2011;10:1. https://doi.org/10.1186/1476-4598-10-1
  41. Kumar R, Singh GK, Chauhan RS. Development of bursa of fabricius in relations to humoral immunity in chicken embryo. Indian J Anim Sci 2004;74:838-40.
  42. Singh S, Singh I, Singh G, Gangwar C, Kumar P. Postnatal development of bursa of Fabricius in relation to humoral Immunity in Keets. J Immunol Immunopathol 2011;13:42-5.
  43. Bangham CR. HTLV-1 infections. J Clin Pathol 2000;53:581-6. https://doi.org/10.1136/jcp.53.8.581
  44. Vojtek AB, Der CJ. Increasing complexity of the Ras signaling pathway. J Biol Chem 1998;273:19925-8. https://doi.org/10.1074/jbc.273.32.19925
  45. Zhang W, Liu HT. MAPK signal pathways in the regulation of cell proliferation in mammalian cells. Cell Res 2002;12:9-18. https://doi.org/10.1038/sj.cr.7290105
  46. Zhao J-H, Reiske H, Guan J-L. Regulation of the cell cycle by focal adhesion kinase. J Cell Biol 1998;143:1997-2008. https://doi.org/10.1083/jcb.143.7.1997
  47. Lukashev ME, Werb Z. ECM signalling: orchestrating cell behaviour and misbehaviour. Trends Cell Biol 1998;8:437-41. https://doi.org/10.1016/S0962-8924(98)01362-2
  48. Lau KS, Partridge EA, Grigorian A, et al. Complex N-glycan number and degree of branching cooperate to regulate cell proliferation and differentiation. Cell 2007;129:123-34. https://doi.org/10.1016/j.cell.2007.01.049
  49. Luna-Acosta JL, Alba-Betancourt C, Martinez-Moreno CG, et al. Direct antiapoptotic effects of growth hormone are mediated by PI3K/Akt pathway in the chicken bursa of Fabricius. Gen Comp Endocrinol 2015;224:148-59. https://doi.org/10.1016/j.ygcen.2015.07.010
  50. Lawson WE, Crossno PF, Polosukhin VV, et al. Endoplasmic reticulum stress in alveolar epithelial cells is prominent in IPF: association with altered surfactant protein processing and herpesvirus infection. Am J Physiol Lung Cell Mol Physiol 2008;294:L1119-L26. https://doi.org/10.1152/ajplung.00382.2007
  51. Ciechanover A, Orian A, Schwartz AL. Ubiquitin-mediated proteolysis: biological regulation via destruction. Bioessays 2000;22:442-51. https://doi.org/10.1002/(SICI)1521-1878(200005)22:5<442::AID-BIES6>3.0.CO;2-Q
  52. Dienz O, Eaton SM, Bond JP, et al. The induction of antibody production by IL-6 is indirectly mediated by IL-21 produced by CD4+ T cells. J Exp Med 2009;206:69-78. https://doi.org/10.1084/jem.20081571
  53. Ip YK, Chew SF. Ammonia production, excretion, toxicity, and defense in fish: a review. Front Physiol 2010;1:134.

Acknowledgement

Supported by : National Natural Science Foundation of China