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Molecular Biology Reports

, Volume 46, Issue 6, pp 6003–6011 | Cite as

Selection of reliable reference genes for gene expression analysis in seeds at different developmental stages and across various tissues in Paeonia ostii

  • Chaoqiong LiEmail author
  • Lizong Hu
  • Xueqin Wang
  • Hongzhan Liu
  • Huihui Tian
  • Junsheng Wang
Original Article
  • 114 Downloads

Abstract

Paeonia ostii seeds have recently been identified as a new source of α-linolenic acid in China. Studying the gene expression patterns of unsaturated fatty acid-related genes would be helpful for understanding the mechanism of α-linolenic acid accumulation. Quantitative real-time polymerase chain reaction (qRT-PCR) is a useful method for reliably evaluating gene expression, and it is necessary to select reliable reference genes for data normalization in qRT-PCR analysis. In this study, we evaluated the expression stability of 12 candidate reference genes using four mathematical algorithms (∆Ct, BestKeeper, NormFinder, and geNorm). The web-based tool RefFinder was used to integrate the results and to provide a comprehensive ranking order. The expression stability ranking orders of reference genes were different caculated by these four algorithms, and the ranking order analyzed by the RefFinder was UBQ > Tip41 > UCE > EF-1α > α-TUB > PP2A > ACT > GAPDH > SAM > CYP > β-TUB > 18S at the different seed development stages, and UBQ > Tip41 > EF-1α > α-TUB > PP2A > UCE > GAPDH > SAM > ACT > CYP > 18S > β-TUB in P. ostii tissues. UBQ and Tip41 are the two most stable whereas 18S and β-TUB are the two least stable reference genes for gene expression in various tissues and seeds at different developmental stages in P. ostii.

Keywords

Paeonia ostii Reference gene Normalization qRT-PCR Seed development 

Notes

Acknowledgements

This work was supported by the National Natural Science Foundation of China (31500534), the Startup Fund for Advanced Talents of Zhoukou Normal University (ZKNU2014108), the School-Based Program of Zhoukou Normal University (ZKUNB115203), the Natural Science Foundation of Henan province (162300410346), and the Key Scientific Research Project in Colleges and Universities of Henan Province (15A180024).

Author contributions

CL and LH designed research; CL, LH, XW, HL, HT and JW performed experiments. CL and LH wrote the paper.

Compliance with ethical standards

Conflict of interest

All authors declare that they have no conflicts of interest.

Supplementary material

11033_2019_5036_MOESM1_ESM.tif (1.7 mb)
Electronic supplementary material 1 (TIFF 1728 kb)Fig. S1 Pairwise variation analysis by geNorm to determine the optimal number of reference genes needed for accurate normalization in seeds at different developmental stages and across various tissues in Paeonia ostii. Cutoff value = 0.15
11033_2019_5036_MOESM2_ESM.docx (17 kb)
Electronic supplementary material 2 (DOCX 17 kb)
11033_2019_5036_MOESM3_ESM.docx (17 kb)
Electronic supplementary material 3 (DOCX 17 kb)
11033_2019_5036_MOESM4_ESM.docx (17 kb)
Electronic supplementary material 4 (DOCX 17 kb)

References

  1. 1.
    Chen FY, Li JJ (1998) Exportation of Chinese tree peonies (Mudan) and their development in other countries I: cultivated. J Northwest Norm Univ 34:109–116Google Scholar
  2. 2.
    Li SS, Wang LS, Shu QY, Wu J, Chen LG, Shao S, Yin DD (2015) Fatty acid composition of developing tree peony (Paeonia section Moutan DC.) seeds and transcriptome analysis during seed development. BMC Genomics 16:208CrossRefGoogle Scholar
  3. 3.
    Li XQ, Han JG, Liu Z, Liu QH, Hu YH (2014) Economic characteristics investigation and seed oil fatty acid composition analysis of Paeonia ostii plants in different areas. Cereals Oils 27:43–46Google Scholar
  4. 4.
    Qi JC, Zhou HM, Ma JQ, Li P (2005) Analysis of the chemical constituents in peony seed oil by GC-MS. J Cereals Oils 11:22–23Google Scholar
  5. 5.
    Bustin SA, Benes V, Nolan T, Pfaffl MW (2005) Quantitative real-time RT-PCR—a perspective. J Mol Endocrinol 34:597–601CrossRefGoogle Scholar
  6. 6.
    Gachon C, Mingam A, Charrier B (2004) Real-time PCR: what relevance to plant studies? J Expl Bot 55:1445–1454CrossRefGoogle Scholar
  7. 7.
    Kanakachari M, Solanke AU, Prabhakaran N, Ahmad I, Dhandapani G, Jayabalan N, Kumar PA (2016) Evaluation of suitable reference genes for normalization of qPCR gene expression studies in Brinjal (Solanum melongena L.) during fruit developmental stages. Appl Biochem Biotechnol 178:433–450CrossRefGoogle Scholar
  8. 8.
    Nicot N, Hausman JF, Hoffmann L, Evers D (2005) Housekeeping gene selection for real-time RT-PCR normalization in potato during biotic and abiotic stress. J Exp Bot 56:2907–2914CrossRefGoogle Scholar
  9. 9.
    Ute A, Kimd B (2008) Gene expression in Citrus sinensis (L.) Osbeck following infection with the bacterial pathogen Candidatus Liberibacter asiaticus causing Huanglongbing in Florida. Plant Sci 175:291–306CrossRefGoogle Scholar
  10. 10.
    Wang X, Liu A (2014) Expression of genes controlling unsaturated fatty acids biosynthesis and oil deposition in developing seeds of Sacha inchi (Plukenetia volubilis L.). Lipids 49:1019–1031CrossRefGoogle Scholar
  11. 11.
    Hu Y, Chen H, Luo C, Dong L, Zhang SW, He XH, Huang GX (2014) Selection of reference genes for real-time quantitative PCR studies of kumquat in various tissues and under abiotic stress. Sci Hortic 174:207–216CrossRefGoogle Scholar
  12. 12.
    Niu LJ, Tao YB, Chen MS, Fu QT, Li CQ, Dong YL, Wang XL, He HY, Xu ZF (2015) Selection of reliable reference genes for gene expression studies of a promising oilseed crop, Plukenetia volubilis, by real-time quantitative PCR. Int J Mol Sci 16:12513–12530CrossRefGoogle Scholar
  13. 13.
    Wang ML, Li QH, Xin HH, Chen X, Zhu XJ, Li XH (2017) Reliable reference genes for normalization of gene expression data in tea plants (Camellia sinensis) exposed to metal stresses. PLoS ONE 12:e0175863CrossRefGoogle Scholar
  14. 14.
    Zhu XY, Li XP, Chen WX, Chen JY, Lu WJ, Lei C, Fu DW (2012) Evaluation of new reference genes in papaya for accurate transcript normalization under different experimental conditions. PLoS ONE 7:e44405CrossRefGoogle Scholar
  15. 15.
    Silver N, Best S, Jiang J, Thein SL (2006) Selection of housekeeping genes for gene expression studies in human reticulocytes using real-time PCR. BMC Mol Biol 7:33CrossRefGoogle Scholar
  16. 16.
    Pfaffl MW, Tichopad A, Prgomet C, Neuvians TP (2004) Determination of stable housekeeping genes, differentially regulated target genes and sample integrity: BestKeeper—Excel-based tool using pair-wise correlations. Biotechnol Lett 26:509–515CrossRefGoogle Scholar
  17. 17.
    Vandesompele J, De Preter K, Pattyn F, Poppe B, Van Roy N, Paepe AD, Speleman F (2002) Accurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple internal control genes. Genome Biol.  https://doi.org/10.1186/gb-2002-3-7-research0034 CrossRefPubMedPubMedCentralGoogle Scholar
  18. 18.
    Andersen CL, Jensen JL, Orntoft TF (2004) 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 64:5245–5250CrossRefGoogle Scholar
  19. 19.
    Xie F, Xiao P, Chen D, Xu L, Zhang B (2012) miRDeepFinder: a miRNA analysis tool for deep sequencing of plant small RNAs. Plant Mol Biol 80:75–84CrossRefGoogle Scholar
  20. 20.
    Hofmann J, Grundler FMW (2007) Identification of reference genes for qRT-PCR studies of gene expression in giant cells and syncytia induced in Arabidopsis thaliana by Meloidogyne incognita and Heterodera schachtii. Nematology 9:317–323CrossRefGoogle Scholar
  21. 21.
    Jain M, Nijhawan A, Tyagi AK, Khurana JP (2006) Validation of housekeeping genes as internal control for studying gene expression in rice by quantitative real-time PCR. Biochem Bioph Res Commun 345:646–651CrossRefGoogle Scholar
  22. 22.
    Pabuayon IM, Yamamoto N, Trinidad JL, Longkumer T, Raorane ML, Kohli A (2016) Reference genes for accurate gene expression analyses across different tissues, developmental stages and genotypes in rice for drought tolerance. Rice 9:32CrossRefGoogle Scholar
  23. 23.
    Li J, Han JG, Hu YH, Yang J (2016) Selection of reference genes for quantitative real-time PCR during flower development in tree peony (Paeonia suffruticosa Andr.). Front Plant Sci 7:516PubMedPubMedCentralGoogle Scholar
  24. 24.
    Okuley J, Lightner J, Feldmann K, Yadav N, Lark E, Browse J (1994) Arabidopsis FAD2 gene encodes the enzyme that is essential for polyunsaturated lipid synthesis. Plant Cell 6:147–158PubMedPubMedCentralGoogle Scholar
  25. 25.
    Livak KJ, Schmittgen TD (2001) Analysis of relative gene expression data using real-time quantitative PCR and the 2−ΔΔCT method. Methods 25:402–408CrossRefGoogle Scholar
  26. 26.
    Narancio R, John U, Mason J, Spangenberg G (2018) Selection of optimal reference genes for quantitative RT-PCR transcript abundance analysis in white clover (Trifolium repens L.). Funct Plant Biol 45:737–744CrossRefGoogle Scholar
  27. 27.
    Cao J, Wang L, Lan H (2016) Validation of reference genes for quantitative RT-PCR normalization in Suaeda aralocaspica, an annual halophyte with heteromorphism and C4 pathway without Kranz anatomy. PeerJ 4:e1697CrossRefGoogle Scholar
  28. 28.
    Chang E, Shi SQ, Liu JF, Cheng TL, Xue L, Yang XY, Yang WJ, Lan Q, Jiang ZP (2012) Selection of reference genes for quantitative gene expression studies in Platycladus orientalis (Cupressaceae) using real-time PCR. PLoS ONE 7:e33278CrossRefGoogle Scholar
  29. 29.
    Yi SJ, Qian YQ, Han L, Sun ZY, Fan CM, Liu JX, Ju GS (2012) Selection of reliable reference genes for gene expression studies in Rhododendron micranthum Turcz. Sci Hortic 138:128–133CrossRefGoogle Scholar
  30. 30.
    Li XY, Cheng JY, Zhang J, da Silva JAT, Wang CX, Sun HM (2015) Validation of reference genes for accurate normalization of gene expression in Lilium davidii var. unicolor for real time quantitative PCR. PLoS ONE 10:e0141323CrossRefGoogle Scholar
  31. 31.
    Chen X, Mao YJ, Huang SW, Ni J, Lu WL, Hou JY, Wang YT, Zhao WW, Li MH, Wang QJ (2017) Selection of suitable reference genes for quantitative real-time PCR in Sapium sebiferum. Front Plant Sci 8:637CrossRefGoogle Scholar
  32. 32.
    Zhuang H, Fu Y, He W, Wang L, Wei Y (2015) Selection of appropriate reference genes for quantitative real-time PCR in Oxytropis ochrocephala Bunge using transcriptome datasets under abiotic stress treatments. Front Plant Sci 6:475CrossRefGoogle Scholar
  33. 33.
    Long X, He B, Gao X, Qin Y, Yang J, Fang Y, Qi J, Tang C (2015) Validation of reference genes for quantitative real-time PCR during latex regeneration in rubber tree. Gene 563:190–195CrossRefGoogle Scholar
  34. 34.
    Galli V, Borowski JM, Perin EC, Messias RD, Labonde J, Pereira ID, Silva SD, Rombaldi CV (2015) Validation of reference genes for accurate normalization of gene expression for real time-quantitative PCR in strawberry fruits using different cultivars and osmotic stresses. Gene 554:205–214CrossRefGoogle Scholar

Copyright information

© Springer Nature B.V. 2019

Authors and Affiliations

  1. 1.College of Life Science and AgronomyZhoukou Normal UniversityZhoukouChina
  2. 2.Key Laboratory of Plant Genetics and Molecular BreedingZhoukou Normal UniversityZhoukouChina
  3. 3.College of Journalism and MediaZhoukou Normal UniversityZhoukouChina

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