Selection of reference genes for quantitative real-time PCR analysis of photosynthesis-related genes expression in Lilium regale

  • Wenkai Du
  • Fengrong Hu
  • Suxia YuanEmail author
  • Chun LiuEmail author
Research Article


Photosynthesis is closely related to the growth of plants. A stable reference gene is fundamental for studies of the molecular mechanism of photosynthesis in Lilium regale. Therefore, it is very important to select a suitable reference gene for qRT-PCR analysis on genes of photosynthetic system, chlorophyll biosynthetic pathway and chloroplast development in Lilium regale. Three kinds of tissues, leaves and bulbs (abnormal leaves) of tissue culture plantlets and cotyledons of seedlings of the wild-type and mutant Lilium regale were selected as materials for qRT-PCR. Six housekeeping genes were selected as candidate genes from transcriptome sequencing data of the wild-type and yellow seedling lethal mutant of Lilium regale. Finally, the expression stability of six candidate reference genes was analyzed using geNorm, NormFinder, and BestKeeper software, the comparative ∆Ct method, and the RefFinder program. The results showed that LrActin2 was the best reference gene for qRT-PCR analysis of photosynthesis-related genes expression in leaves of tissue culture plantlets and seedlings of Lilium regale. This study provided useful data for further research on molecular mechanism of photosynthesis in the Lilium.


Lilium regale Reference gene Quantitative real-time PCR Photosynthesis 



This work was supported by the Science and Technology Innovation Program of the Chinese Academy of Agricultural Sciences (CAAS-ASTIP-2018-IVFCAAS), the National Center for Flower Improvement, and the Key Laboratory of Biology and Genetic Improvement of Horticultural Crops, Ministry of Agriculture, P. R. China.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.


  1. Andersen CL, Jencsen JL, Ørntoft 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. Can Res 64:5245. CrossRefGoogle Scholar
  2. Bustin SA (2000) Absolute quantification of mRNA using real-time reverse transcription polymerase chain reaction assays. J Mol Endocrinol 25(2):169–193. CrossRefGoogle Scholar
  3. Bustin SA (2002) Quantification of mRNA using real-time reverse transcription PCR (RT-PCR): trends and problems. J Mol Endocrinol 29(1):23–39. CrossRefGoogle Scholar
  4. Delporte M, Legrand G, Hibert JL, Gagneul D (2015) Selection and validation of reference genes for quantitative real-time PCR analysis of gene expression in Cichorium intybus. Front Plant Sci 6:651. CrossRefGoogle Scholar
  5. Deng LT, Wu YL, Li JC, OuYang KX, Ding MM, Zhang JJ, Li SQ, Lin MF, Chen HB, Hu XS, Chen XY (2016) Screening reliable reference genes for RT-qPCR analysis of gene expression in Moringa oleifera. PLoS ONE 11(8):e0159458. CrossRefGoogle Scholar
  6. Fajardo TVM, Vanni MF, Nickel O (2017) Absolute quantification of viruses by TaqMan real-time RT-PCR in grapevines. Ciência Rural 47:e20161063. Google Scholar
  7. Galli V, Borowski JM, Perin EC, Messias RS, Labonde J, Pereira IS, Silva SDA, 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 554:205–214. CrossRefGoogle Scholar
  8. Huggett J, Dheda K, Bustin S, Zumla A (2005) Real-time RT-PCR normalisation; strategies and considerations. Genes Immun 6:279–284. CrossRefGoogle Scholar
  9. Jiang TT, Gao YH, Tong ZK (2015) Selection of reference genes for quantitative real-time PCR in Lycoris. Acta Hortic Sin 42:1129–1138. Google Scholar
  10. Karuppaiya P, Yan XX, Liao W, Wu J, Chen F, Tang L (2017) Identification and validation of superior reference gene for gene expression normalization via RT-qPCR in staminate and pistillate flowers of Jatropha curcas-A biodiesel plant. PLoS ONE 12(2):e0172460. CrossRefGoogle Scholar
  11. Kudo T, Sasaki Y, Terashima S, Matsuda-Imai N, Takano T, Saito M, Kanno M, Ozaki S, Suwabe K, Suzuki G, Watanabe M, Matsuoka M, Takayama S, Yano K (2016) Identification of reference genes for quantitative expression analysis using large-scale RNA-seq data of Arabidopsis thaliana and model crop plants. Genes Genet Syst 91(2):111–125. CrossRefGoogle Scholar
  12. Lekshmy S, Jha SK (2017) Selection of reference genes suitable for qRT-PCR expression profiling of biotic stress, nutrient deficiency and plant hormone responsive genes in bread wheat. Indian J Plant Physiol 22:101–106. CrossRefGoogle Scholar
  13. Li XY, Cheng JY, Zhang J, 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(10):e0141323. CrossRefGoogle Scholar
  14. Liu Q, Wei C, Zhang MF, Jia GX (2016) Evaluation of putative reference genes for quantitative real-time PCR normalization in Lilium regale during development and under stress. PeerJ 4:e1837. CrossRefGoogle Scholar
  15. Livak KJ, Schmittgen TD (2001) Analysis of relative gene expression data using real-time quantitative PCR and the 2−ΔΔCT method. Methods 25:402–408. CrossRefGoogle Scholar
  16. Lu ZH, Xi MJ, Jiang YL, Yu RP, Zhou XH, Tian M, Yang X, Gui M (2017) The preliminary study of chlorine mutant in Dendranthema morifolium. Journay Southwest For Univ 37:60–68. Google Scholar
  17. Luo HL, Luo LP, Guan BC, Li EX, Xiong DJ, Sun BT, Peng K, Yang BY (2014) Evaluation of candidate reference genes for RT-qPCR in lily (Lilium brownii). J Pomol Hortic Sci 89:345–351. Google Scholar
  18. Ma JH, Sun Y, Wang YG, Duan YH (2017) Screening of reference genes for qRT-PCR analysis in Sorghum (Sorghum bicolor) under low nitrogen stress. J Agric Biotechnol 25:805–812. Google Scholar
  19. Mangeot-peter L, Legay S, Hausman JF, Esposito S, Gb Guerriero (2016) Identification of reference genes for qRT-PCR data normalization in Cannabis sativa stem tissues. Int J Mol Sci 17:1556. CrossRefGoogle Scholar
  20. Murashige T, Skoog F (1962) A revised medium for rapid growth and bio assays with tobacco tissue cultures. Physiol Plant 15(3):473–497CrossRefGoogle Scholar
  21. Nikalje GC, Srivastava AK, Sablok G, Pandey GK, Nikam TD, Suprasanna P (2017) Identification and validation of reference genes for quantitative real-time PCR under salt stress in a halophyte, Sesuvium portulacastrum. Plant Gene 13:18–24. CrossRefGoogle Scholar
  22. Pang QQ, Li ZL, Luo SB, Chen RY, Jin QM, Li ZX, Li DM, Sun BJ, Sun GW (2017) Selection and stability analysis of reference gene for qRT-PCR in eggplant under high temperature stress. Acta Hortic Sin 44:475–486. Google Scholar
  23. Pfaffl MW (2001) A new mathematical model for relative quantification in real-time RT-PCR. Nucleic Acids Res 29:e45. CrossRefGoogle Scholar
  24. 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 26:509–515. CrossRefGoogle Scholar
  25. Reddy PS, Reddy DS, Sivasakthi K, Bhatnagar-mathur P, Vadez V, Sharma KK (2016) Evaluation of sorghum [Sorghum bicolor (L.)] reference genes in various tissues and under abiotic stress conditions for quantitative real-time PCR data normalization. Front Plant Sci 7:529. Google Scholar
  26. Shivhare R, Lata C (2016) Selection of suitable reference genes for assessing gene expression in pearl millet under different abiotic stresses and their combinations. Sci Rep 6:23036. CrossRefGoogle Scholar
  27. 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 7:33. CrossRefGoogle Scholar
  28. Stanton KA, Edger PP, Puzey JR, Kinser T, Cheng P, Vernon DM, ForsthoefelNR Cooley AM (2017) A whole-transcriptome approach to evaluating reference genes for quantitative gene expression studies: a case study in mimulus. G3 Genes Genomes Genet 7:1085–1095. Google Scholar
  29. Suzuki T, Higgins PJ, Crawford DR (2000) Control selection for RNA quantitation. Biotechniques 29:332–337CrossRefGoogle Scholar
  30. Vandesompele J, De PK, Pattyn F, Poppe B, Roy NV, De PA, Speleman F (2002) Accurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple internal control genes. Genome Biol 3(7):research0034.1–research0034.11. CrossRefGoogle Scholar
  31. Wong ML, Medrano JF (2005) Real-time PCR for mRNA quantitation. Biotechniques 39:75–88. CrossRefGoogle Scholar
  32. Wu JY, He B, Du YJ, Li WC, Wei YZ (2017) Analysis method of systematically evaluating stability of reference genes using geNorm, NormFinder and BestKeeper. Mod Agric Sci Technol 5:278–281. Google Scholar
  33. Xu LF, Xu H, Cao YW, Yang PP, Feng YY, Tang YC, Yuan SX, Ming J (2017) Validation of reference genes for quantitative real-time PCR during bicolor tepal development in asiatic hybrid lilies (Lilium spp.). Front Plant Sci 8:669. CrossRefGoogle Scholar
  34. Yang D, Li Q, Wang GX, Ma QH, Zhu LQ (2017) Reference genes selection and system establishment for real-time qPCR analysis in Ping’ou Hybrid Hazelnut (C. heterophylla Fisch. × C. avellana L.). Sci Agric Sin 50:2399–2410. Google Scholar
  35. Zeng XY, Tang R, Guo HR, Ke SW, Teng B, Hung YH, Xu ZJ, Xie XM, Hsieh TF, Zhang XQ (2017) A naturally occurring conditional albino mutant in rice caused by defects in the plastid-localized OsABCI8 transporter. Plant Mol Biol 94:137–148. CrossRefGoogle Scholar
  36. Zhang J, Gai MZ, Xue BY, Jia NN, Wang CX, Wang JX, Sun HM (2017) The use of miRNAs as reference genes for miRNA expression normalization during Lilium somatic embryogenesis by real-time reverse transcription PCR analysis. Plant Cell Tissue Organ Cult 129:105–118. CrossRefGoogle Scholar

Copyright information

© Prof. H.S. Srivastava Foundation for Science and Society 2019

Authors and Affiliations

  1. 1.Institute of Vegetables and FlowersChinese Academy of Agricultural SciencesBeijingChina
  2. 2.College of Landscape ArchitectureNanjing Forestry UniversityNanjingChina

Personalised recommendations