Plant Cell, Tissue and Organ Culture (PCTOC)

, Volume 115, Issue 1, pp 13–22 | Cite as

Reference genes for quantitative real-time PCR analysis in the model plant foxtail millet (Setaria italica L.) subjected to abiotic stress conditions

  • Karunesh Kumar
  • Mehanathan Muthamilarasan
  • Manoj PrasadEmail author
Original Paper


Reference genes are standards for quantifying gene expression through quantitative real-time PCR (qRT-PCR); however, the variation observed in their expression levels is the major hindrance towards realising their effective use. Hence, a systematic validation of reference genes is required to ensure proper normalization. However, no such study has been conducted in foxtail millet [Setaria italica (L.)], which has recently emerged as a model crop for genetic and genomic studies. In the present study, 8 commonly used reference genes were evaluated, including 18S ribosomal RNA, elongation factor-1α, Actin2, alpha tubulin, beta tubulin, translation factor, RNA polymerase II and adenine phosphoribosyl transferase. Expression stability of candidate internal control genes was investigated under salinity and dehydration treatments. The results obtained suggested a wide range of Ct values and variable expression of all reference genes. geNorm and NormFinder analysis had revealed that Act2 and RNA POL II are suitable reference genes for salinity stress-related studies and EF- and RNA POL II are appropriate internal controls for dehydration stress-related expression analyses. These qualified reference genes has also been validated for relative quantification of 14-3-3 expression analysis which demonstrated their applicability. Thus, this is the first report on selection and validation of superior reference genes for qRT-PCR in foxtail millet under different abiotic stress conditions.


Abiotic stress Foxtail millet Reference genes Quantitative real time PCR (qRT-PCR) GeNorm NormFinder Setaria italica 



Grateful thanks are due to the Director, National Institute of Plant Genome Research (NIPGR), New Delhi, India for providing facilities. The authors work in this area was supported by the core Grant of NIPGR. Mr. Karunesh Kumar and Mr. Mehanathan Muthamilarasan acknowledge the award of Senior Research Fellowship and Junior Research Fellowship from Council of Scientific and Industrial Research and University Grants Commission, New Delhi, India, respectively.


  1. 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 64:5245–5250PubMedCrossRefGoogle Scholar
  2. Argyropoulos D, Psallida C, Spyropoulos CG (2006) Generic normalization method for real-time PCR. Application for the analysis of the mannanase gene expressed in germinating tomato seed. FEBS J 273:770–777PubMedCrossRefGoogle Scholar
  3. Artico S, Nardeli SM, Brilhante O, Grossi-de-Sa MF, Alves-Ferreira M (2010) Identification and evaluation of new reference genes in Gossypium hirsutumfor accurate normalization of real-time quantitative RT-PCR data. BMC Plant Biol 10:49PubMedCrossRefGoogle Scholar
  4. Bas A, Forsberg G, Hammarström S, Hammarström ML (2004) Utility of the housekeeping genes 18SrRNA, beta-actin and glyceraldehyde-3-phosphate-dehydrogenase for normalization in real-time quantitative reverse transcriptase-polymerase chain reaction analysis of gene expression in human T lymphocytes. Scand J Immunol 59:566–573PubMedCrossRefGoogle Scholar
  5. Bennetzen JL, Wang JSH, Percifield R, Hawkins J, Pontaroli AC, Estep M et al (2012) Reference genome sequence of the model plant Setaria. Nat Biotech 30:555–561Google Scholar
  6. Bustin SA (2002) Quantification of mRNA using real-time reverse transcription PCR (RT-PCR): trends and problems. J Mol Endocrinol 29:23–39PubMedCrossRefGoogle Scholar
  7. Condori J, Nopo-Olazabal C, Medrano G, Medina-Bolivar F (2011) Selection of reference genes for qPCR in hairy root cultures of peanut. BMC Res Notes 4:392PubMedCrossRefGoogle Scholar
  8. Czechowski T, Stitt M, Altmann T, Udvardi MK, Scheible WR (2005) Genome-wide identification and testing of superior reference genes for transcript normalization in Arabidopsis. Plant Physiol 139:5–17PubMedCrossRefGoogle Scholar
  9. Devos KM, Gale MD (2000) Genome relationships: the grass model in current research. Plant Cell 12:637–646Google Scholar
  10. Doust AN, Kellogg EA, Devos KM, Bennetzen JL (2009) Foxtail millet: a sequence-driven grass model system. Plant Physiol 149:137–141PubMedCrossRefGoogle Scholar
  11. Fan C, Ma J, Guo Q, Li X, Wang H, Lu M (2013) Selection of reference genes for quantitative real-time PCR in bamboo (Phyllostachys edulis). PLoS ONE 8:e56573PubMedCrossRefGoogle Scholar
  12. Fink L, Seeger W, Ermert L, Hänze J, Stahl U, Grimminger F, Kummer W, Bohle RM (1998) Real-time quantitative RT-PCR after laser-assisted cell picking. Nat Med 4:1329–1333PubMedCrossRefGoogle Scholar
  13. Gachon C, Mingam A, Charrier B (2004) Real-time PCR: what relevance to plant studies? J Exp Bot 55:1445–1454PubMedCrossRefGoogle Scholar
  14. Gilsbach R, Kouta M, Bönisch H, Brüss M (2006) Comparison of in vitro and in vivo reference genes for internal standardization of real-time PCR data. Biotechniques 40:173–177PubMedCrossRefGoogle Scholar
  15. Ginzinger DG (2002) Gene quantification using real-time quantitative PCR: an emerging technology hits the mainstream. Exp Hematol 30:503–512PubMedCrossRefGoogle Scholar
  16. González-Verdejo CI, Die JV, Nadal S, Jiménez-Marín A, Moreno MT, Román B (2008) Selection of housekeeping genes for normalization by real-time RT-PCR: analysis of Or-MYB1 gene expression in Orobanche ramose development. Anal Biochem 379:176–181PubMedCrossRefGoogle Scholar
  17. Guénin S, Mauriat M, Pelloux J, VanWuytswinkel O, Bellini C, Gutierrez L (2009) Normalization of qRT-PCR data: the necessity of adopting a systematic, experimental conditions-specific, validation of references. J Exp Bot 60:487–493PubMedCrossRefGoogle Scholar
  18. Gutierrez L, Mauriat M, Gue′nin S, Pelloux J, Lefebvre JF, Louvet R, Rusterucci C, Moritz T, Guerineau F, Bellini C, Van Wuytswinkel O (2008) The lack of a systemic validation of reference genes: serious pitfall undervalued in reverse transcription–polymerase chain reaction (RT–PCR) analysis in plants. Plant Biotechnol J 6:609–618PubMedCrossRefGoogle Scholar
  19. Heid CA, Stevens J, Livak KJ, Williams PM (1996) Real time quantitative PCR. Genome Res 6:986–994PubMedCrossRefGoogle Scholar
  20. Hellemans J, Mortier G, De Paepe A, Speleman F, Vandesompele J (2007) qBase relative quantification framework and software for management and automated analysis of real-time quantitative PCR data. Genome Biol 8:R19PubMedCrossRefGoogle Scholar
  21. Higuchi R, Fockler C, Dolinger G, Watson R (1993) Kinetic PCR analysis: real time monitoring of DNA amplification reaction. Biotechnology 11:1026–1030PubMedCrossRefGoogle Scholar
  22. Huggett J, Dheda K, Bustin S, Zumla A (2005) Real-time RT-PCR normalisation; strategies and considerations. Genes Immun 6:279–284PubMedCrossRefGoogle Scholar
  23. 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 Biophys Res Commun 345:646–651PubMedCrossRefGoogle Scholar
  24. Jayaraman A, Puranik S, Rai NK, Vidapu S, Sahu PP, Lata C, Prasad M (2008) cDNA-AFLP analysis reveals differential gene expression in response to salt stress in foxtail millet (Setaria italica L.). Mol Biotechnol 40:241–251PubMedCrossRefGoogle Scholar
  25. Jian B, Liu B, Bi Y, Hou W, Wu C, Han T (2008) Validation of internal control for gene expression study in soybean by quantitative real-time PCR. BMC Mol Biol 9:59PubMedCrossRefGoogle Scholar
  26. Kubista M, Andrade JM, Bengtsson M, Forootan A, Jonak J, Lind K, Sindelka R, Sjoback R, Sjogreen B, Strombom L (2006) The real-time polymerase chain reaction. Mol Aspects Med 27:95–125PubMedCrossRefGoogle Scholar
  27. Lata C, Sahu PP, Prasad M (2010) Comparative transcriptome analysis of differentially expressed genes in foxtail millet (Setaria italica L.) during dehydration stress. Biochem Biophys Res Commun 393:720–727PubMedCrossRefGoogle Scholar
  28. Lata C, Jha S, Dixit V, Sreenivasulu N, Prasad M (2011) Differential antioxidative responses to dehydration-induced oxidative stress in core set of foxtail millet cultivars [Setaria italica (L.)]. Protoplasma 248:817–828PubMedCrossRefGoogle Scholar
  29. Lata C, Gupta S, Prasad M (2012) Foxtail millet: a model crop for genetic and genomic studies in bioenergy grasses. Crit Rev Biotechnol. doi: 10.3109/07388551.2012.716809 PubMedGoogle Scholar
  30. Libus J, Štorchová H (2006) Quantification of cDNA generated by reverse transcription of total RNA provides a simple alternative tool for quantitative RT-PCR normalization. Biotechniques 41:156–164PubMedCrossRefGoogle Scholar
  31. Liu Z, Ge X-X, Wu X-M, Kou S-J, Chai L-J, Guo WW (2013) Selection and validation of suitable reference genes for mRNA qRT-PCR analysis using somatic embryogenic cultures, floral and vegetative tissues in citrus. Plant Cell Tiss Organ Cult. doi: 10.1007/s11240-013-0288-0 Google Scholar
  32. Nicot N, Hausman J-F, Hoffmann L, Evers D (2005) Housekeeping gene selection for real-time RT-PCR normalization in potato during biotic and abiotic stress. J Exp Bot 56:2907–2914PubMedCrossRefGoogle Scholar
  33. Nolan T, Hands RE, Bustin SA (2006) Quantification of mRNA using real-time RT-PCR. Nat Protocols 1:1559–1582CrossRefGoogle Scholar
  34. Puranik S, Jha S, Srivastava PS, Sreenivasulu N, Prasad M (2011) Comparative transcriptome analysis of contrasting foxtail millet cultivars in response to short-term salinity stress. J Plant Physiol 168:280–287PubMedCrossRefGoogle Scholar
  35. Reid KE, Olsson N, Schlosser J, Peng F, Lund ST (2006) An optimized grapevine RNA isolation procedure and statistical determination of reference genes for real-time RT-PCR during berry development. BMC Plant Biol 6:27PubMedCrossRefGoogle Scholar
  36. Schena M, Shalon D, Davis RW, Brown PO (1995) Quantitative monitoring of gene expression patterns with a complementary DNA microarray. Science 270:467–470PubMedCrossRefGoogle Scholar
  37. Schmidt GW, Delaney SK (2010) Stable internal reference genes for normalization of real-time RT-PCR in tobacco (Nicotiana tabacum) during development and abiotic stress. Mol Genet Genomics 283:233–241PubMedCrossRefGoogle Scholar
  38. Sellars MJ, Vuocolo T, Leeton LA, Coman GJ, Degnan BM, Preston NP (2007) Real-time RT-PCR quantification of Kuruma shrimp transcripts: a comparison of relative and absolute quantification procedures. J Biotechnol 129:391–399PubMedCrossRefGoogle Scholar
  39. Singh R, Green MR (1993) Sequence-specific binding of transfer RNA by glyceraldehyde-3-phosphate dehydrogenase. Science 259:365–368PubMedCrossRefGoogle Scholar
  40. Suzuki T, Higgins PJ, Crawford DR (2000) Control selection for RNA quantitation. Biotechniques 29:332–337PubMedGoogle Scholar
  41. Thellin O, Zorzi W, Lakaye B, De Borman B, Coumans B, Hennen G, Grisar T, Igout A, Heinen E (1999) Housekeeping genes as internal standards: use and limits. J Biotechnol 75:291–295PubMedCrossRefGoogle Scholar
  42. Tong ZG, Gao ZH, Wang F, Zhou J, Zhang Z (2009) Selection of reliable reference genes for gene expression studies in peach using real-time PCR. BMC Mol Biol 10:71PubMedCrossRefGoogle Scholar
  43. Van Guilder HD, Vrana KE, Freeman WM (2008) Twenty-five years of quantitative PCR for gene expression analysis. Biotechniques 44:619–626Google Scholar
  44. Vandesompele J, De Preter K, Pattyn F, Poppe B, Van Roy N, De Paepe A, Speleman F (2002) Accurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple internal control genes. Genome Biol 3:7CrossRefGoogle Scholar
  45. Veistinen E, Liippo J, Lassila O (2002) Quantification of human Aiolos splice variants by real-time PCR. J Immunol Methods 271:113–123PubMedCrossRefGoogle Scholar
  46. Xu Y, Zhu X, Gong Y, Xu L, Wang Y, Liu L (2012) Evaluation of reference genes for gene expression studies in radish (Raphanus sativus L.) using quantitative real-time PCR. Biochem Biophys Res Commun 424:398–403PubMedCrossRefGoogle Scholar
  47. Zhang G, Liu X, Quan Z, Cheng S, Xu X, Pan S et al (2012) Genome sequence of foxtail millet (Setaria italica) provides insights into grass evolution and biofuel potential. Nat Biotech 30:549–554Google Scholar
  48. Zhu J, Zhang L, Li W, Han S, Yang W, Qi L (2013) Reference gene selection for quantitative real-time PCR normalization in Caragana intermedia under different abiotic stress conditions. PLoS ONE 8:e53196PubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  • Karunesh Kumar
    • 1
  • Mehanathan Muthamilarasan
    • 1
  • Manoj Prasad
    • 1
    Email author
  1. 1.National Institute of Plant Genome Research (NIPGR)New DelhiIndia

Personalised recommendations