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Molecular Genetics and Genomics

, Volume 283, Issue 3, pp 233–241 | Cite as

Stable internal reference genes for normalization of real-time RT-PCR in tobacco (Nicotiana tabacum) during development and abiotic stress

  • Gregor W. Schmidt
  • Sven K. Delaney
Original Paper

Abstract

Real-time RT-PCR is a powerful technique for the measurement of gene expression, but its accuracy depends on the stability of the internal reference gene(s) used for data normalization. Tobacco (Nicotiana tabacum) is an important model in studies of plant gene expression, but stable reference genes have not been well-studied in the tobacco system. We address this problem by analysing the expression stability of eight potential tobacco reference genes. Primers targeting each gene (18S rRNA, EF-1α, Ntubc2, α- and β-tubulin, PP2A, L25 and actin) were developed and optimized. The expression of each gene was then measured by real-time PCR in a diverse set of 22 tobacco cDNA samples derived from developmentally distinct tissues and from plants exposed to several abiotic stresses. L25 and EF-1α demonstrated the highest expression stability, followed by Ntubc2. Measurement of L25 and EF-1α was sufficient for accurate normalization in either the developmental or stress-treated samples, but Ntubc2 was also required when considering the entire sample set. Analysis of a tobacco circadian gene (NTCP-23) verified these reference genes in an additional context, and all techniques were optimized to enable a high-throughput approach. These results provide a foundation for the more accurate and widespread use of real-time RT-PCR in tobacco.

Keywords

Tobacco Real-time RT-PCR Normalization Internal reference gene Nicotiana 

Notes

Acknowledgments

The authors are indebted to Prof. Jeremy Timmis (University of Adelaide) for providing support for this project and critical reading of the manuscript. The authors also thank Prof. Dr. Habil. Wolfgang M. Schmidt (Hochschule für Bankwirtschaft, Frankfurt) for advice on mathematical and statistical analysis. This research was supported by the Australian Research Council’s Discovery Projects funding scheme (Project numbers DP0557496 and DP0667006).

References

  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. Bock R (2007) Plastid biotechnology: prospects for herbicide and insect resistance, metabolic engineering and molecular farming. Curr Opin Biotechnol 18:100–106PubMedCrossRefGoogle Scholar
  3. Brunner AM, Yakovlev IA, Strauss SH (2004) Validating internal controls for quantitative plant gene expression studies. BMC Plant Biol 4:14PubMedCrossRefGoogle Scholar
  4. Bustin SA (2002) Quantification of mRNA using real-time reverse transcription PCR (RT-PCR): trends and problems. J Mol Endocrinol 29:23–29PubMedCrossRefGoogle Scholar
  5. Bustin SA, Nolan T (2004) Pitfalls of quantitative real-time reverse-transcription polymerase chain reaction. J Biomol Tech 15:155–156PubMedGoogle Scholar
  6. Chuaqui RF et al (2002) Post-analysis follow-up and validation of microarray experiments. Nat Genet 32:509–514PubMedCrossRefGoogle Scholar
  7. Cortleven A, Remans T, Brenner WG, Valcke R (2009) Selection of plastid- and nuclear-encoded reference genes to study the effect of altered endogenous cytokinin content on photosynthesis genes in Nicotiana tabacum. Photosynth Res 102:21–29PubMedCrossRefGoogle 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. Dean JD, Goodwin PH, Hsiang T (2002) Comparison of relative RT-PCR and northern blot analyses to measure expression of beta-1, 3-glucanase in Nicotiana benthamiana infected with Colltotrichum destructivum. Plant Mol Biol Rep 20:347–356CrossRefGoogle Scholar
  10. Delaney SK, Orford SJ, Martin-Harris M, Timmis JN (2007) The fiber specificity of the cotton FSltp4 gene promoter is regulated by an AT-rich promoter region and the AT-hook transcription factor GhAT1. Plant Cell Physiol 48:1426–1437PubMedCrossRefGoogle Scholar
  11. Dheda K, Huggett JF, Bustin SA, Johnson MA, Rook G, Zumla A (2004) Validation of housekeeping genes for normalizing RNA expression in real-time PCR. Biotechniques 37:112–114, 116, 118–119Google Scholar
  12. Exposito-Rodriguez M, Borges AA, Borges-Perez A, Perez JA (2008) Selection of internal control genes for quantitative real-time RT-PCR studies during tomato development process. BMC Plant Biol 8:131PubMedCrossRefGoogle 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. Guenin S, Mauriat M, Pelloux J, Van Wuytswinkel 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
  15. 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
  16. Kanneganti V, Gupta AK (2008) Overexpression of OsiSAP8, a member of stress associated protein (SAP) gene family of rice confers tolerance to salt, drought and cold stress in transgenic tobacco and rice. Plant Mol Biol 66:445–462PubMedCrossRefGoogle Scholar
  17. Kasukabe N et al (2006) Expression and Ca2+ dependency of plasma membrane K+ channels of tobacco suspension cells adapted to salt stress. Plant Cell Physiol 47:1674–1677PubMedCrossRefGoogle Scholar
  18. Larionov A, Krause A, Miller W (2005) A standard curve based method for relative real time PCR data processing. BMC Bioinformatics 6:62–77Google Scholar
  19. Lee PD, Sladek R, Greenwood CMT, Hudson TJ (2002) Control genes and variability: absence of ubiquitous reference transcripts in diverse mammalian expression studies. Genome Res 12:292–297PubMedCrossRefGoogle Scholar
  20. Livak KJ, Schmittgen TD (2001) Analysis of relative gene expression data using real-time quantitative PCR and the 2(T)(−Delta Delta C) method. Methods 25:402–408PubMedCrossRefGoogle Scholar
  21. Lo E (2005) Gaussian error propagation applied to ecological data: post-ice-storm-downed woody biomass. Ecol Monogr 75:451–466CrossRefGoogle Scholar
  22. Lovdal T, Lillo C (2009) Reference gene selection for quantitative real-time PCR normalization in tomato subjected to nitrogen, cold, and light stress. Anal Biochem 387:238–242PubMedCrossRefGoogle Scholar
  23. Muller PY, Janovjak H, Miserez AR, Dobbie Z (2002) Processing of gene expression data generated by quantitative real-time RT-PCR. Biotechniques 32:1372–1379PubMedGoogle Scholar
  24. 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 56:2907–2914PubMedCrossRefGoogle Scholar
  25. Paolacci AR, Tanzarella OA, Porceddu E, Ciaffi M (2009) Identification and validation of reference genes for quantitative RT-PCR normalization in wheat. BMC Mol Biol 10:11PubMedCrossRefGoogle Scholar
  26. 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 26:509–515PubMedCrossRefGoogle Scholar
  27. 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 6:27–37Google Scholar
  28. Ribarits A, Abdullaev A, Tashpulatov A, Richter A, Heberle-Bors E, Touraev A (2007) Two tobacco proline dehydrogenases are differentially regulated and play a role in early plant development. Planta 225:1313–1324PubMedCrossRefGoogle Scholar
  29. Rutledge RG, Stewart D (2008) A kinetic-based sigmoidal model for the polymerase chain reaction and its application to high-capacity absolute quantitative real-time PCR. BMC Biotechnol 8:47PubMedCrossRefGoogle Scholar
  30. Sheppard AE et al (2008) Transfer of plastid DNA to the nucleus is elevated during male gametogenesis in tobacco. Plant Physiol 148:328–336PubMedCrossRefGoogle Scholar
  31. Suzuki T, Higgins PJ, Crawford DR (2000) Control selection for RNA quantitation. Biotechniques 29:332–337PubMedGoogle Scholar
  32. Tichopad A, Dilger M, Schwarz G, Pfaffl MW (2003) Standardized determination of real-time PCR efficiency from a single reaction set-up. Nucl Acids Res 31:e122Google Scholar
  33. Ueda T, Seo S, Ohashi Y, Hashimoto J (2000) Circadian and senescence-enhanced expression of a tobacco cysteine protease gene. Plant Mol Biol 44:649–657PubMedCrossRefGoogle Scholar
  34. Vandesompele J et al (2002) Accurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple internal control genes. Genome Biol 3:RESEARCH0034Google Scholar
  35. Volkov RA, Panchuk II, Schoffl F (2005) Small heat shock proteins are differentially regulated during pollen development and following heat stress in tobacco. Plant Mol Biol 57:487–502PubMedCrossRefGoogle Scholar

Copyright information

© Springer-Verlag 2010

Authors and Affiliations

  1. 1.Discipline of Genetics, School of Molecular and Biomedical ScienceUniversity of AdelaideAdelaideAustralia
  2. 2.Friedrich Schiller University of JenaJenaGermany
  3. 3.School of Biotechnology and Biomolecular SciencesUniversity of New South WalesSydneyAustralia

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