Biologia plantarum 64: 838-844, 2020 | DOI: 10.32615/bp.2020.153

Reference gene selections for real time quantitative PCR analysis of gene expression in different oat tissues and under salt stress

Z.L. DUAN1, W.H. HAN2, L. YAN2, B. WU2,*
1 Yan'an Institute of Agricultural Sciences, Yan'an 716000, P.R. China
2 Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100081, P.R. China

Appropriate choice of reference genes for data normalization is of critical importance for accurate real time reverse transcription quantitative PCR (RT-qPCR) analysis of gene expression. Oat is an agriculturally important crop cultivated widely around the world for grain or forage, and appropriate reference genes for reliable gene expression analysis remain to be identified. In this study, we selected nine candidate reference genes based on available oat RNA-seq data. We then conducted a systematic evaluation of the relative expression stability of these genes in different tissues and under salt stress using statistical algorithms, geNorm, NormFinder, and BestKeeper. Our findings reveal that the highest-ranked reference genes for accurate data normalization should be selected according to specific sample subsets. For different tissues, the combination of two reference genes [elongation factor 1-alpha (EF1α) and serine/threonine protein phosphatase 2] was sufficient for accurate normalization. For salt stress treatment, the combination of two reference genes (EF1α and TATA-binding protein) was sufficient for accurate normalization. Moreover, the commonly used reference genes, actin and glyceraldehyde 3-phosphate dehydrogenase, were least suitable for data normalization of oat samples. Expression of a salt stress-inducible transcription factor Avena sativa WRKY2 was investigated to validate the reference genes identified in this study. This is the first systematic study of reference gene selection in cultivated oat and provides guidelines to obtain more accurate RT-qPCR results in this species.

Keywords: Avena sativa, BestKeeper, geNorm, NormFinder, RT-qPCR.

Received: July 14, 2020; Revised: September 25, 2020; Accepted: October 15, 2020; Prepublished online: December 15, 2020; Published online: December 16, 2020Show citation

ACS AIP APA ASA Harvard Chicago IEEE ISO690 MLA NLM Turabian Vancouver
DUAN, Z.L., HAN, W.H., YAN, L., & WU, B. (2020). Reference gene selections for real time quantitative PCR analysis of gene expression in different oat tissues and under salt stress. Biologia plantarum, 64, 838-844. doi: 10.32615/bp.2020.153.
Download citation
  • BibTeX (.bib)
  • Bookends (.ris)
  • EasyBib (.ris)
  • EndNote (.enw)
  • EndNote 8 (.xml)
  • ISI WoS (.isi)
  • Medlars (.medlars)
  • Mendeley (.ris)
  • MODS (.xml)
  • Papers (.ris)
  • RefWorks (.txt)
  • RefManager (.ris)
  • RIS (.ris)
  • MS Word (.xml)
  • Zotero (.ris)
    • Open full article

    Supplementary files

    Download file6531_Duan_Suppl.pdf

    File size: 310.94 kB

    References

    1. Acevedo, R.M., Avico, E.H., Ruiz, O.A., Sansberro, P.A.: Assessment of reference genes for real-time quantitative PCR normalization in Ilex paraguariensis leaves during drought. - Biol. Plant. 62: 89-96, 2018. Go to original source...
    2. Andersen, C.L., Jensen, J.L., Ørntoft, T.F.: Normalization of real-time quantitative reverse transcription-PCR data: a model-based variance estimation approach to identify genes suited for normalization, applied to bladder and colon cancer data sets. - Cancer Res. 64: 5245-5250, 2004. Go to original source...
    3. Bustin, S.A.: Absolute quantification of mRNA using real-time reverse transcription polymerase chain reaction assays. - J. mol. Endocrinol. 25: 169-193, 2000. Go to original source...
    4. Guénin, S., Mauriat, M., Pelloux, J., Van Wuytswinkel, O., Bellini, C., Gutierrez, L.: Normalization of qRT-PCR data: the necessity of adopting a systematic, experimental conditions-specific, validation of references. - J. exp. Bot. 60: 487-493, 2009. Go to original source...
    5. Hruz, T., Wyss, M., Docquier, M., Pfaffl, M.W., Masanetz, S., Borghi, L., Verbrugghe, P., Kalaydjieva, L., Bleuler, S., Laule, O., Descombes, P., Gruissem, W., Zimmermann, P.: RefGenes: identification of reliable and condition specific reference genes for RT-qPCR data normalization. - BMC Genomics 12: 156, 2011. Go to original source...
    6. Jiang, J., Ma, S., Ye, N., Jiang, M., Cao, J., Zhang, J.: WRKY transcription factors in plant responses to stresses. - J. integr. Plant Biol. 59: 86-101, 2017. Go to original source...
    7. Kozera, B., Rapacz, M.: Reference genes in real-time PCR. - J. appl. Genet. 54: 391-406, 2013. Go to original source...
    8. Livak, K.J., Schmittgen, T.D.: Analysis of relative gene expression data using real-time quantitative PCR and the 2－ΔΔCT method. - Methods 25: 402-408, 2001. Go to original source...
    9. Loarce, Y., Navas, E., Paniagua, C., Fominaya, A., Manjón, J.L., Ferrer, E.: Identification of genes in a partially resistant genotype of Avena sativa expressed in response to Puccinia coronata infection. - Front. Plant Sci. 7: 731, 2016. Go to original source...
    10. Loskutov, I.G., Rines, H.W.: Avena. - In: Kole, C. (ed.): Wild Crop Relatives: Genomic and Breeding Resources. Pp.109-183. Springer, Berlin 2011. Go to original source...
    11. Manoli, A., Sturaro, A., Trevisan, S., Quaggiotti, S., Nonis, A.: Evaluation of candidate reference genes for qPCR in maize. - J. Plant Physiol. 169: 807-815, 2012. Go to original source...
    12. Moraes, G.P., Benitez, L.C., Do Amaral, M.N., Vighi, I.L., Auler, P.A., Da Maia, L.C., Bianchi, V.J., Braga, E.J.B.: Evaluation of reference genes for RT-qPCR studies in the leaves of rice seedlings under salt stress. - Genet. mol. Res. 14: 2384-2398, 2015. Go to original source...
    13. Niu, C.F., Wei, W., Zhou, Q.Y., Tian, A.G., Hao, Y.J., Zhang, W.K., Ma, B., Lin, Q., Zhang, Z.B., Zhang, J.S., Chen, S.Y.: Wheat WRKY genes TaWRKY2 and TaWRKY19 regulate abiotic stress tolerance in transgenic Arabidopsis plants. - Plant Cell Environ. 35: 1156-1170, 2012. Go to original source...
    14. Niu, X.P., Qi, J.M., Zhang, G.Y., Xu, J.T., Tao, A.F., Fang, P.P., Su, J.G.: Selection of reliable reference genes for quantitative real-time PCR gene expression analysis in jute (Corchorus capsularis) under stress treatments. - Front. Plant Sci. 6: 848, 2015. Go to original source...
    15. Nolan, T., Hands, R.E., Bustin, S.A.: Quantification of mRNA using real-time RT-PCR. - Nat. Protocols 1: 1559-1582, 2006. Go to original source...
    16. Paolacci, A.R., Tanzarella, O.A., Porceddu, E., Ciaffi, M.: Identification and validation of reference genes for quantitative RT-PCR normalization in wheat. - BMC mol. Biol. 10: 11, 2009. Go to original source...
    17. Pfaffl, M.W., Tichopad, A., Prgomet, C., Neuvians, T.P.: 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, 2004. Go to original source...
    18. Remans, T., Keunen, E., Bex, G.J., Smeets, K., Vangronsveld, J., Cuypers, A.: Reliable gene expression analysis by reverse transcription-quantitative PCR: reporting and minimizing the uncertainty in data accuracy. - Plant Cell 26: 3829-3837, 2014. Go to original source...
    19. Saha, P., Blumwald, E.: Assessing reference genes for accurate transcript normalization using quantitative real-time PCR in pearl millet [Pennisetum glaucum (L.) R. Br]. - PLoS ONE 9: e106308, 2014. Go to original source...
    20. Sgamma, T., Pape, J., Massiah, A., Jackson, S.: Selection of reference genes for diurnal and developmental time-course real-time PCR expression analyses in lettuce. - Plant Methods 12: 21, 2016. Go to original source...
    21. Smulders, M.J.M., Van de Wiel, C.C.M., Van den Broeck, H.C., Van der Meer, I.M., Israel-Hoevelaken, T.P.M., Timmer, R.D., Braun, S., Gilissen, L.J.W.J.: Oats in healthy gluten-free and regular diets: a perspective. - Food Res. Int. 110: e0196334, 2017.
    22. Stürzenbaum, S.R., Kille, P.: Control genes in quantitative molecular biological techniques: the variability of invariance. - Comp. Biochem. Physiol. B Biochem. mol. Biol. 130: 281-289, 2001. Go to original source...
    23. Suzuki, T., Higgins, P.J., Crawford, D.R.: Control selection for RNA quantitation. - Biotechniques 29: 332-337, 2000. Go to original source...
    24. Udvardi, M.K., Czechowski, T., Scheible, W.R.: Eleven golden rules of quantitative RT-PCR. - Plant Cell 20: 736-1737, 2008. Go to original source...
    25. Untergasser, A., Cutcutache, I., Koressaar, T., Ye, J., Faircloth, B.C., Remm, M., Rozen, S.G.: Primer3-new capabilities and interfaces. - Nucl. Acids Res. 40: e115, 2012. Go to original source...
    26. Vandesompele, J., De Preter, K., Pattyn, F., Poppe, B., Van Roy, N., De Paepe, A., Speleman, F.: Accurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple internal control genes. - Genome Biol. 3: research0034.1, 2002. Go to original source...
    27. Wu, B., Hu, Y., Huo, P., Zhang, Q., Chen, X., Zhang, Z.: Transcriptome analysis of hexaploid hulless oat in response to salinity stress. - PLoS ONE 12: e0171451, 2017. Go to original source...
    28. Xiao, Y., Rui, X., Xing, G., Wu, H., Li, W., Chen, X., Jiang, M., Dong, M.: Solid state fermentation with Cordyceps militaris SN-18 enhanced antioxidant capacity and DNA damage protective effect of oats (Avena sativa L.). - J. Funct. Foods 16: 58-73, 2015. Go to original source...

    Return to the content