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Selection of the Most Stable Endogenous Control Genes for Microrna Quantitation in Chicken Ovarian Follicles Cover

Selection of the Most Stable Endogenous Control Genes for Microrna Quantitation in Chicken Ovarian Follicles

By: Ewa Ocłoń and  Anna Hrabia  
Open Access
|Jan 2020

References

  1. Alberti C., Cochella L. (2017). A framework for understanding the roles of miRNAs in animal development. Development, 144: 2548–2559.
  2. Andersen C.L., Jensen J.L., Ørntoft T.F. (2004). Normalisation of real-time quantitative reverse transcription-PCR data: a model-based variance estimation approach to identify genes suited for normalisation, applied to bladder and colon cancer data sets. Cancer Res., 64: 5245–5250.
  3. Androvic P., Valihrach L., Elling J., Sjoback R., Kubista M. (2017). Two-tailed RT-qPCR: a novel method for highly accurate miRNA quantification. Nucleic Acids Res., 45:e144.
  4. Bannister S.C., Tizard M.L., Doran T.J., Sinclair A.H., Smith C.A. (2009). Sexually dimorphic microRNA expression during chicken embryonic gonadal development. Biol. Reprod., 81: 165–176.
  5. Bartel D.P. (2004). MicroRNAs: genomics, biogenesis, mechanism, and function. Cell, 116: 281–297.
  6. Burnside J., Ouyang M., Anderson A., Bernberg E., Lu C., Meyers B.C., Green P.J., Markis M., Isacs G., Huang E., Morgan R.W. (2008). Deep sequencing of chicken microRNAs. BMC Genomics, 9: 185.
  7. Bustin S.A., Benes V., Garson J.A., Hellemans J., Huggett J., Kubista M., Mueller R., Nolan T., Pfaffl M.W., Shipley G.L., Vandesompele J., Wittwer C.T. (2009). The MIQE guidelines: minimum information for publication of quantitative real-time PCR experiments. Clin. Chem., 55: 611–622.
  8. Darnell D.K., Kaur S., Stanislaw S., Konieczka J.H., Yatskievych T.A., Antin P.B. (2006). MicroRNA expression during chick embryo development. Dev. Dynam., 235: 3156–165.
  9. Fang G., Jia X., Li H., Tan S., Nie Q., Yu H., Yang Y. (2018). Characterization of microRNA and mRNA expression profiles in skin tissue between early-feathering and late-feathering chickens. BMC Genomics, 19: 399.
  10. Git A., Dvinge H., Salmon-Divon M., Osborne M., Kutter C., Hadfield J., Bertone P., Caldas C. (2010). Systematic comparison of microarray profiling, real-time PCR, and next-generation sequencing technologies for measuring differential microRNA expression. RNA, 16: 991–1006.
  11. Glazov E.A., Cottee P.A., Barris W.C., Moore R.J., Dalrymple B.P., Tizard M.L. (2008). A microRNA catalog of the developing chicken embryo identified by a deep sequencing approach. Genome Res., 18: 957–964.
  12. He L., Hannon G.J. (2004). MicroRNAs: small RNAs with a big role in gene regulation. Nat. Rev. Genet., 5: 522–531.
  13. Hicks J.A., Tembhurne P.A., Liu H.C. (2009). Identification of microRNA in the developing chick immune organs. Immunogenetics, 61: 231–240.
  14. Kang L., Cui X., Zhang Y., Yang C., Jiang Y. (2013). Identification of miRNAs associated with sexual maturity in chicken ovary by Illumina small RNA deep sequencing. BMC Genomics, 14:e352.
  15. Li H., Ma Z., Jia L., Li Y., Xu C., Wang T., Han R., Jiang R., Li Z., Sun G., Kang X., Liu X. (2016). Systematic analysis of the regulatory functions of microRNAs in chicken hepatic lipid metabolism. Sci. Rep., 6: 31766.
  16. Li T., Wang S., Wu R., Zhou X., Zhu D., Zhang Y. (2012). Identification of long non-protein coding RNAs in chicken skeletal muscle using next generation sequencing. Genomics, 99: 292–298.
  17. Lim W., Song G. (2014). Identification of novel regulatory genes in development of the avian reproductive tracts. PLosOne, 9(4):e96175.
  18. Liu L., Xiao Q., Gilbert E.R., Cui Z., Zhao X., Wang Y., Yin H., Li D., Zhang H., Zhu Q. (2018). Whole-transcriptome analysis of atrophic ovaries in broody chickens reveals regulatory pathways associated with proliferation and apoptosis. Sci. Rep., 8: 7231.
  19. Mansfield J.H., Harfe B.D., Nissen R., Obenauer J., Srineel J., Chaudhuri A., Farzan-Kashani R., Zuker M., Pasquinelli A.E., Ruvkun G., Sharp P.A., Tabin C.J., Mc Manus M.T. (2004). MicroRNA responsive ‘sensor’ transgenes uncover Hox- like and other developmentally regulated patterns of vertebrate microRNA expression. Nat. Genet., 36: 1079–1083.
  20. Meyer S.U., Pfaffl M.W., Ulbrich S.E. (2010). Normalisation strategies for microRNA profiling experiments: a ‘normal’ way to hidden layer of complexity? Biotechnol. Lett., 33: 1777–1788.
  21. Morozova O., Marra M.A. (2008). Applications of next-generation sequencing technologies in functional genomics. Genomics, 92: 255–264.
  22. Nothnick W.B. (2012). The role of micro-RNAs in the female reproductive tract. Reproduction, 143: 559–576.
  23. Pfaffl M.W. (2001). A new mathematical model for relative quantification in real-time RT-PCR. Nucleic Acids Res., 29:e45.
  24. Pfaffl M.W., Tichopad A., Prgomet C., Neuvians T.P. (2004). Determination of stable housekeeping genes, differentially regulated target genes and sample integrity: BestKeeper-Excel-based tool using pair-wise correlations. Biotechnol. Lett., 26: 509–515.
  25. Schmittgen T.D., Livak K.J. (2008). Analyzing real-time PCR data by the comparative C(T) method. Nat. Protoc., 3: 1101–1108.
  26. Sirotkin A.V., Kisová G., Brenaut P., Ovcharenko D., Grossmann R., Mlyncek M. (2014). Involvement of microRNA Mir15a in control of human ovarian granulosa cell proliferation, apoptosis, steroidogenesis, and response to FSH. MicroRNA, 3: 29–36.
  27. Tian F., Luo J., Zhang H., Chang S., Song J. (2012). MiRNA expression signatures induced by Marek’s disease virus infection in chickens. Genomics, 99: 152–159.
  28. Vandesompele J., De Preter K., Pattyn F., Poppe B., Van Roy N., De Paepe A., Speleman F. (2002). Accurate normalisation of real-time quantitative RT×PCR data by geometric averaging of multiple internal control genes. Genome Biol., 18:3, Research 0034.
  29. Wang Q., Gao Y., Ji X., Qi X., Qin L., Gao H., Wang Y., Wang X. (2013). Differential expression of microRNAs in avian leukosis virus subgroup J-induced tumors. Vet. Microbiol., 162: 232–238.
  30. Wang W., Wu K., Jia M., Sun S., Kang L., Zhang Q., Tang H. (2018). Dynamic changes in the global microRNAome and transcriptome identify key nodes associated with ovarian development in chickens. Front. Genet., 9: 491.
  31. Wu N., Gaur U., Zhu Q., Chen B., Xu Z., Zhao X., Yang M., Li D. (2017). Expressed microRNA associated with high rate of egg production in chicken ovarian follicles. Anim. Genet., 48: 2005–2016.
  32. Xu Q., Zhang Y., Chen Y., Tong Y.Y., Rong G.H., Huang Z.Y., Zhao R.X., Zhao W.M., Wu X.S., Chang G.B., Chen G.H. (2014). Identification and differential expression of microRNAs in ovaries of laying and broody geese (Anser cygnoides) by Solexa sequencing. PLosOne, 9(2):e87920.
DOI: https://doi.org/10.2478/aoas-2019-0070 | Journal eISSN: 2300-8733 | Journal ISSN: 1642-3402
Language: English
Page range: 109 - 123
Submitted on: Apr 24, 2019
Accepted on: Oct 1, 2019
Published on: Jan 28, 2020
Published by: National Research Institute of Animal Production
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year

© 2020 Ewa Ocłoń, Anna Hrabia, published by National Research Institute of Animal Production
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.