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Planta

, Volume 232, Issue 1, pp 145–153 | Cite as

Evaluation of candidate reference genes for expression studies in Pisum sativum under different experimental conditions

  • José V. Die
  • Belén Román
  • Salvador Nadal
  • Clara I. González-Verdejo
Original Article

Abstract

Reverse transcription quantitative real-time polymerase chain reaction is the most accurate measure of gene expression in biological systems. The data are analyzed through a process called normalization. Internal standards are essential for determining the relative gene expression in different samples. For this purpose, reference genes are selected based on their constitutive expression across samples. At present, there has not yet been any reference gene identified in any organism that is universally optimal across different tissue types or disease situations. Our goal was to test the regulation of 11 potential references for pea. These included eight commonly used and three new candidates. Twenty-six samples, including different tissues, treatments and genotypes, were addressed in this analysis. For reliable data normalization, the most suitable combination of reference genes in each experimental set was constructed with at least two out the five more stably expressed references in the whole experimental series (i.e. protein phosphatase 2A, β-tubulin, GH720838, actin and GH720808). To validate the determined measure of gene-stability, the gene-specific variation was calculated using different normalization factors. The most non-specific variation was removed when the most stable genes were used, highlighting the importance of the adequate choice of internal controls in gene expression experiments. The set of reference genes presented here will provide useful guidelines as starting point for reference gene selection in pea studies under conditions other than those tested here.

Keywords

Normalization Pea Quantitative PCR Reference genes Stable expression 

Abbreviations

Cq

Quantification cycle

CV

Coefficient of variation

E

PCR amplification efficiency

EF-

Elongation factor 1α

GAPDH

Glyceraldehyde-3-phosphate dehydrogenase

PLC

Phospholipase

PP2A

Protein phosphatase 2A

qPCR

Quantitative PCR

NF

Normalization factor

RQ

Relative quantity

RT

Reverse transcription

SD

Standard deviation

SE

Standard error

Notes

Acknowledgments

Financial support was provided by the Spanish project RTA2007-00009. J.V. D and C.I. G-V are researchers funded by the ‘Juan de la Cierva’ programme of the Spanish MICINN. The authors acknowledge the USDA-ARS (Washington State University) for the supply of Ps624 seeds.

Supplementary material

425_2010_1158_MOESM1_ESM.doc (906 kb)
Supplementary material 1 (DOC 906 kb)

References

  1. Brunner AM, Yakovlev IA, Strauss SH (2004) Validating internal controls for quantitative plant gene expression studies. BMC Plant Biol 4:14PubMedCrossRefGoogle Scholar
  2. Bustin SA (2000) Absolute quantification of mRNA using real-time reverse transcription polymerase chain reaction assays. J Mol Endocrinol 25:169–193PubMedCrossRefGoogle Scholar
  3. Bustin SA (2002) Quantification of mRNA using real-time reverse transcription PCR (RT-PCR): trends and problems. J Mol Endocrinol 29:23–39PubMedCrossRefGoogle Scholar
  4. Bustin SA, Benes V, Garson JA, Hellemans J, Huguett J, Kubista M, Mueller R et al (2009) The MIQE Guidelines: minimum information for publication of quantitative real-time PCR experiments. Clin Chem 55:611–622PubMedCrossRefGoogle Scholar
  5. Choi HK, Mun JH, Kim DJ, Uhm T, Zhu H, Baek JM, Mudge J et al (2004) Estimating genome conservation between crop and model legume species. Proc Natl Acad Sci 101:15289–15294PubMedCrossRefGoogle Scholar
  6. Cruz F, Kalaoun S, Nobile P, Colombo C, Almeida J, Barros L, Romano E et al (2009) Evaluation of coffee reference genes for relative expression studies by quantitative real-time RT-PCR. Mol Breeding 23:607–616CrossRefGoogle Scholar
  7. Czechowski T, Stitt M, Altman 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
  8. 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 37:112–119PubMedGoogle Scholar
  9. Dheda K, Huggett JF, Chang JS, Kim LU, Bustin SA, Johnson MA, Rook GAW, Zumla A (2005) The implications of using an inappropriate reference gene for real-time reverse transcription PCR data normalization. Anal Biochem 344:141–143PubMedCrossRefGoogle Scholar
  10. Die JV, Román B, Nadal S, Dita MA, González-Verdejo CI (2009) Expression analysis of Pisum sativum putative defence genes during Orobanche crenata infection. Crop Pasture Sci 60:490–498CrossRefGoogle Scholar
  11. Dombrowski J, Martin R (2009) Evaluation of reference genes for quantitative RT-PCR in Lolium temulentum under abiotic stress. Plant Sci 176:390–396CrossRefGoogle Scholar
  12. Expósito-Rodríguez M, Borges AA, Borges-Pérez A, Pérez 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. FAOSTAT data (2008) http://faostat.fao.org/. Accessed 9 March 2010
  14. Guénin 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. Gutierrez L, Mauriat M, Guénin S, Pelloux J, Lefebvre JF, Louvet R, Rusterucci C et al (2008a) The lack of a systematic validation of reference genes: a serious pitfall undervalued in reverse transcription-polymerase chain reaction (RT-PCR) analysis in plants. Plant Biotechnol J 6:609–618PubMedCrossRefGoogle Scholar
  16. Gutierrez L, Mauriat M, Pelloux J, Bellini C, Van Wuytswinkel O (2008b) Towards a systematic validation of references in real-time RT-PCR. Plant Cell 20:1734–1735PubMedCrossRefGoogle Scholar
  17. 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
  18. Hong SY, Seo PJ, Yang MS, Xiang F, Park CM (2008) Exploring valid reference genes for gene expression studies in Brachypodium distacyon by real-time PCR. BMC Plant Biol 8:112PubMedCrossRefGoogle Scholar
  19. Hu R, Fan C, Li H, Zhang Q, Fu Y-F (2009) Evaluation of putative reference genes for gene expression normalization in soybean by quantitative real-time RT-PCR. BMC Mol Biol 10:93PubMedCrossRefGoogle Scholar
  20. Hugget J, Dheda K, Bustin S, Zumla A (2005) Real-time RT-PCR normalisation; strategies and considerations. Genes Immun 6:279–284CrossRefGoogle Scholar
  21. Iskandar HM, Simpson RS, Casu RE, Bonnet GD, Maclean DJ, Manners JM (2004) Comparison of reference genes for quantitative real-time polymerase chain reaction analysis of gene in sugarcane. Plant Mol Biol Rep 22:325–337CrossRefGoogle Scholar
  22. Jain M (2009) Genome-wide identification of novel internal control genes for normalization of gene expression during various stages of development in rice. Plant Sci 176:702–706CrossRefGoogle Scholar
  23. Kakar K, Wandrey M, Czechowski T, Gaertner T, Scheible W-R, Stitt M, Torres-Jerez I et al (2008) A community resource for high-throughput quantitative RT-PCR analysis of transcription factor gene expression in Medicago truncatula. Plant Methods 4:18PubMedCrossRefGoogle Scholar
  24. Kim B, Nam H, Kim S, Chang YJ (2003) Normalization of reverse transcription quantitative-PCR with housekeeping genes in rice. Biotechnol Lett 25:1869–1872PubMedCrossRefGoogle Scholar
  25. Libault M, Thibivilliers S, Bilgin DD, Radwan O, Benitez M, Clough SJ, Stacey G (2008) Identification of four soybean reference genes for gene expression normalization. Plant Genome 1:44–54CrossRefGoogle Scholar
  26. Martin RC, Hollenbeck VG, Dombrowski JE (2008) Evaluation of reference genes for quantitative RT-PCR in Lolium perenne. Crop Sci 48:1881–1887CrossRefGoogle Scholar
  27. 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
  28. Nolan T, Hands RE, Bustin SA (2006) Quantification of mRNA using real-time RT-PCR. Nat Protoc 1:1559–1582PubMedCrossRefGoogle Scholar
  29. Paolacci AR, Tanzarella OA, Porceddu EP, Ciaffi M (2009) Identification and validation of reference genes for quantitative RT-PCR normalization in wheat. BMC Mol Biol 10:11PubMedCrossRefGoogle Scholar
  30. Ramakers C, Ruijter JM, Deprez RH, Moorman AF (2003) Assumption-free analysis of quantitative real-time polymerase chain reaction (PCR) data. Neurosci Lett 13:62–66CrossRefGoogle Scholar
  31. 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
  32. Remans T, Smeets K, Opdenakker K, Mathijsen D, Vangronsveld J, Cuypers A (2008) Normalisation of real-time RT-PCR gene expression measurements in Arabidopsis thaliana exposed to increased metal concentrations. Planta 227:1343–1349PubMedCrossRefGoogle Scholar
  33. Rubiales D, Moreno MT, Sillero JC (2005) Search for resistance to crenate broomrape (Orobanche crenata) in pea germplasm. Gen Res Crop Evol 52:853–861CrossRefGoogle Scholar
  34. Sambrook J, Fritsch EF, Maniatis T (1989) Molecular cloning: a laboratory manual. Cold Spring Harbor Laboratory Press, Cold Spring HarborGoogle Scholar
  35. Samuel Roberts Noble Foundation (2006) Medicago truncatula Handbook. In: Mathesius U, Journet EP, Sumner LW (eds) http://www.noble.org/MedicagoHandbook/. Accessed 9 March 2010
  36. Silveira ED, Alves-Ferreira M, Guimarães LA, Rodrigues da Silva F, Carneiro V (2009) Selection of reference genes for quantitative real-time PCR expression studies in the apomictic and sexual grass Brachiaria brizantha. BMC Plant Biol 9:84PubMedCrossRefGoogle Scholar
  37. Tong Z, Gao Z, 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
  38. Tu L, Zhang X, Liu D, Jin S, Cao J, Zhu L, Deng F, Tan J, Zhang C (2007) Suitable internal control genes for qRT-PCR normalization in cotton fiber development and somatic embryogenesis. Chin Sci Bull 52:3110–3117CrossRefGoogle Scholar
  39. Udvardi MK, Czechowski T, Scheible W-R (2008) Eleven golden rules of quantitative RT-PCR. Plant Cell 20:1736–1737PubMedCrossRefGoogle Scholar
  40. 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:RESEARCH0034PubMedCrossRefGoogle Scholar
  41. Wong ML, Medrano JF (2005) Real-time PCR for mRNA quantitation. BioTechniques 39:75–85PubMedCrossRefGoogle Scholar

Copyright information

© Springer-Verlag 2010

Authors and Affiliations

  • José V. Die
    • 1
    • 3
  • Belén Román
    • 1
  • Salvador Nadal
    • 2
  • Clara I. González-Verdejo
    • 1
  1. 1.Mejora y BiotecnologíaIFAPA “Alameda del Obispo”CórdobaSpain
  2. 2.Producción AgrariaIFAPA “Alameda del Obispo”CórdobaSpain
  3. 3.Mejora Genética VegetalInstituto de Agricultura Sostenible, Consejo Superior de Investigaciones CientíficasCórdobaSpain

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