Journal of Genetics

, Volume 97, Issue 5, pp 1433–1444 | Cite as

Selection and validation of reference genes for normalization of qRT-PCR gene expression in wheat (Triticum durum L.) under drought and salt stresses

  • Jamshidi Goharrizi KiarashEmail author
  • Henry Dayton Wilde
  • Farzane Amirmahani
  • Mohammad Mehdi Moemeni
  • Maryam Zaboli
  • Maryam Nazari
  • Sayyed Saeed Moosavi
  • Mina Jamalvandi
Research Article


Eight candidate housekeeping genes were examined as internal controls for normalizing expression analysis of durum wheat (Triticum durum L.) under drought and salinity stress conditions. Quantitative real-time PCR was used to analyse gene expression of multiple stress levels, plant ages (24 and 50 days old), and plant tissues (leaf and root). The algorithms BestKeeper, NormFinder, GeNorm, the delta Ct method and the RefFinder were applied to determine the stability of candidate genes. Under drought stress, the most stable reference genes were glyceraldehyde-3 phosphate, ubiquitin and \(\beta \)-tubulin2, whereas under salinity stress conditions, eukaryotic elongation factor 1-\(\alpha \), glyceraldehyde-3 phosphate and actin were identified as the most stable reference genes. Validation with stress-responsive genes NAC29 and NAC6 demonstrated that the expression level of target genes could be determined reliably with combinations of up to three of the reference genes. This is the first report on reference genes appropriate for quantification of target gene expression in T. durum under drought and salt stresses. Results of this investigation may be applicable to other Triticum species.


drought stress housekeeping genes quantitative real-time PCR salt stress transcription factors Triticum durum 



We are grateful to Dr Ali Hadipour, Dr Arman Salehi and Dr Negar Salehi for editing this paper and especially Mrs Sheryl Nikpoor and Navid Jamshidi for their valuable comments. Also, we are thankful to anonymous referees who helped us to improve our paper.

Supplementary material

12041_2018_1042_MOESM1_ESM.docx (2.3 mb)
Supplementary material 1 (docx 2327 KB)


  1. Abdolshahi R., Safarian A., Nazari M., Pourseyedi S. and Mohamadi-Nejad G. 2013 Screening drought-tolerant genotypes in bread wheat (Triticum aestivum L.) using different multivariate methods. Arch. Agron. Soil Sci. 59, 685–704.CrossRefGoogle Scholar
  2. Araus J., Slafer G., Reynolds M. and Royo C. 2002 Plant breeding and drought in C3 cereals: what should we breed for? Ann. Bot. 89, 925–940.CrossRefGoogle Scholar
  3. Baloglu M. C., Oz M., Oktem H. A. and Yucel M. 2012 Expression analysis of TaNAC69-1 and TtNAMB-2, wheat NAC family transcription factor genes under abiotic stress conditions in durum wheat (Triticum turgidum). Plant Mol. Biol. Rep. 30, 1246–1252.CrossRefGoogle Scholar
  4. Brennan J. P., Aw-Hassan A., Quade K. J. and Nordblom T. L. 2002 Impact of ICARDA research on Australian agriculture. Econ. Res. Rep. 11, NSW Agriculture, Wagga Wagga.Google Scholar
  5. Bustin S. A., Benes V., Garson J. A., Hellemans J., Huggett J., Kubista M. et al. 2009 The MIQE guidelines: minimum information for publication of quantitative real-time PCR experiments. Clin. Chem. 55, 611–622.CrossRefGoogle Scholar
  6. Cattivelli L., Rizza F., Badeck F.-W., Mazzucotelli E., Mastrangelo A. M., Francia E. et al. 2008 Drought tolerance improvement in crop plants: an integrated view from breeding to genomics. Field Crops Res. 105, 1–14.CrossRefGoogle Scholar
  7. Chen J., Rider D. A. and Ruan R. 2006 Identification of valid housekeeping genes and antioxidant enzyme gene expression change in the aging rat liver. J. Gerontol. A Biol. Sci. Med. Sci. 61, 20–27.CrossRefGoogle Scholar
  8. Chen Y., Hu B., Tan Z., Liu J., Yang Z., Li Z. et al. 2015 Selection of reference genes for quantitative real-time PCR normalization in creeping bentgrass involved in four abiotic stresses. Plant Cell Rep. 34, 1825–1834.CrossRefGoogle Scholar
  9. De Spiegelaere W., Dern-Wieloch J., Weigel R., Schumacher V., Schorle H., Nettersheim D. et al. 2015 Reference gene validation for RT-qPCR, a note on different available software packages. PLoS One. 10, e0122515.CrossRefGoogle Scholar
  10. Dudley L. and Shani U. 2003 Modeling plant response to drought and salt stress. Vadose Zone J. 2, 751–758.CrossRefGoogle Scholar
  11. Fischer M., Skowron M. and Berthold F. 2005 Reliable transcript quantification by real-time reverse transcriptase-polymerase chain reaction in primary neuroblastoma using normalization to averaged expression levels of the control genes HPRT1 and SDHA. J. Mol. Diagn. 7, 89–96.CrossRefGoogle Scholar
  12. Gachon C., Mingam A. and Charrier B. 2004 Real-time PCR: what relevance to plant studies? J. Exp. Bot. 55, 1445–1454.CrossRefGoogle Scholar
  13. Galli V., Borowski J. M., Perin E. C., da Silva Messias R., Labonde J., dos Santos Pereira I. et al. 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
  14. Goossens K., Van Poucke M., Van Soom A., Vandesompele J., Van Zeveren A. and Peelman L. J. 2005 Selection of reference genes for quantitative real-time PCR in bovine preimplantation embryos. BMC Dev. Biol. 5, 27.CrossRefGoogle Scholar
  15. Guénin S., Mauriat M., Pelloux J., Van Wuytswinkel O., Bellini C. and Gutierrez L. 2009 Normalization of qRT-PCR data: the necessity of adopting a systematic, experimental conditions-specific, validation of references. J. Exp. Bot. 60, 487–493.CrossRefGoogle Scholar
  16. Guo J., Ling H., Wu Q., Xu L. and Que Y. 2014 The choice of reference genes for assessing gene expression in sugarcane under salinity and drought stresses. Sci. Rep. 4, 7042.CrossRefGoogle Scholar
  17. Gutierrez L., Mauriat M., Guénin S., Pelloux J., Lefebvre J. F., Louvet R. et al. 2008 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, 609–618.CrossRefGoogle Scholar
  18. Huggett J., Dheda K., Bustin S. and Zumla A. 2005 Real-time RT-PCR normalisation; strategies and considerations. Genes Immun. 6, 279–284.CrossRefGoogle Scholar
  19. Iskandar H. M., Simpson R. S., Casu R. E., Bonnett G. D., Maclean D. J. and Manners J. M. 2004 Comparison of reference genes for quantitative real-time polymerase chain reaction analysis of gene expression in sugarcane. Plant Mol. Biol. Rep. 22, 325–337.CrossRefGoogle Scholar
  20. Jain M., Nijhawan A., Tyagi A. K. and Khurana J. P. 2006 Validation of housekeeping genes as internal control for studying gene expression in rice by quantitative real-time PCR. Biochem. Biophys. Res. Commun. 345, 646–651.CrossRefGoogle Scholar
  21. Joshi S. and Nimbalkar J. 1983 Effect of salt stress on growth and yield in Cajanus cajan L. Plant Soil. 74, 291–294.CrossRefGoogle Scholar
  22. Kavousi H. R., Marashi H. and Bagheri A. 2009 Expression of phenylpropanoid pathway genes in chickpea defense against race 3 of Ascochyta rabiei. Plant Pathol. J. 8, 127–132.CrossRefGoogle Scholar
  23. Knight H. and Knight M. R. 2001 Abiotic stress signalling pathways: specificity and cross-talk. Trends Plant Sci. 6, 262–267.CrossRefGoogle Scholar
  24. Kumar K., Muthamilarasan M. and Prasad M. 2013 Reference genes for quantitative real-time PCR analysis in the model plant foxtail millet (Setariaitalica L.) subjected to abiotic stress conditions. Plant Cell Tissue Organ Cult. 115, 13–22.CrossRefGoogle Scholar
  25. Li Q. F., Sun S. S., Yuan D. Y., Yu H. X., Gu M. H. and Liu Q. Q. 2010 Validation of candidate reference genes for the accurate normalization of real-time quantitative RT-PCR data in rice during seed development. Plant Mol. Biol. Rep. 28, 49.CrossRefGoogle Scholar
  26. Ma S., Niu H., Liu C., Zhang J., Hou C. and Wang D. 2013 Expression stabilities of candidate reference genes for RT-qPCR under different stress conditions in soybean. PLoS One. 8, e75271.CrossRefGoogle Scholar
  27. Mafra V., Kubo K. S., Alves-Ferreira M., Ribeiro-Alves M., Stuart R. M., Boava L. P. et al. 2012 Reference genes for accurate transcript normalization in citrus genotypes under different experimental conditions. PLoS One. 7, e31263.CrossRefGoogle Scholar
  28. Mallona I., Lischewski S., Weiss J., Hause B. and Egea-Cortines M. 2010 Validation of reference genes for quantitative real-time PCR during leaf and flower development in Petunia hybrida. BMC Plant Biol. 10, 4.CrossRefGoogle Scholar
  29. Manoli A., Sturaro A., Trevisan S., Quaggiotti S. and Nonis A. 2012 Evaluation of candidate reference genes for qPCR in maize. J. Plant Physiol. 169, 807–815.CrossRefGoogle Scholar
  30. Mir R. R., Zaman-Allah M., Sreenivasulu N., Trethowan R. and Varshney R. K. 2012 Integrated genomics, physiology and breeding approaches for improving drought tolerance in crops. Theor. Appl. Genet. 125, 625–645.CrossRefGoogle Scholar
  31. Mittler R. 2006 Abiotic stress, the field environment and stress combination. Trends Plant Sci. 11, 15–19.CrossRefGoogle Scholar
  32. Mohammadi R., Farshadfar E. and Amri A. 2015 Interpreting genotype \(\times \) environment interactions for grain yield of rainfed durum wheat in Iran. Crop J. 3, 526–535.CrossRefGoogle Scholar
  33. Paolacci A. R., Tanzarella O. A., Porceddu E. and Ciaffi M. 2009 Identification and validation of reference genes for quantitative RT-PCR normalization in wheat. BMC Mol. Biol. 10, 11.CrossRefGoogle Scholar
  34. Pfaffl M. W., Tichopad A., Prgomet C. and Neuvians T. P. 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
  35. Radonić A., Thulke S., Mackay I. M., Landt O., Siegert W. and Nitsche A. 2004 Guideline to reference gene selection for quantitative real-time PCR. Biochem. Biophys. Res. Commun. 313, 856–862.CrossRefGoogle Scholar
  36. Shivhare R. and 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
  37. Scholtz J. J. and Visser B. 2012 Reference gene selection for qPCR gene expression analysis of rust-infected wheat. Physiol. Mol. Plant Pathol. 81, 22–25.CrossRefGoogle Scholar
  38. Silver N., Best S., Jiang J. and Thein S. L. 2006 Selection of housekeeping genes for gene expression studies in human reticulocytes using real-time PCR. BMC Mol. Biol. 7, 33.CrossRefGoogle Scholar
  39. Sinha P., Singh V. K., Suryanarayana V., Krishnamurthy L., Saxena R. K. and Varshney R. K. 2015 Evaluation and validation of housekeeping genes as reference for gene expression studies in pigeonpea (Cajanus cajan) under drought stress conditions. PLoS One. 10, e0122847.CrossRefGoogle Scholar
  40. Sun H. P., Li F., Ruan Q.-m. and Zhong X.-h. 2016 Identification and validation of reference genes for quantitative real-time PCR studies in Hedera helix L. Plant Physiol. Biochem. 108, 286–294.CrossRefGoogle Scholar
  41. Tuberosa R. 2012 Phenotyping for drought tolerance of crops in the genomics era. Front. Physiol. 3, 347.CrossRefGoogle Scholar
  42. Vandesompele J., De Preter K., Pattyn F., Poppe B., Van Roy N., De Paepe A. et al. 2002 Accurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple internal control genes. Genome Biol. 3 (
  43. Volkov R. A., Panchuk I. I. and Schöffl F. 2003 Heat-stress-dependency and developmental modulation of gene expression: the potential of house-keeping genes as internal standards in mRNA expression profiling using real-time RT-PCR. J. Exp. Bot. 54, 2343–2349.CrossRefGoogle Scholar
  44. Wei L., Miao H., Zhao R., Han X., Zhang T. and Zhang H. 2013 Identification and testing of reference genes for Sesame gene expression analysis by quantitative real-time PCR. Planta 237, 873–889.CrossRefGoogle Scholar
  45. Xia N., Zhang G., Sun Y.-F., Zhu L., Xu L.-S., Chen X.-M. et al. 2010 TaNAC8, a novel NAC transcription factor gene in wheat, responds to stripe rust pathogen infection and abiotic stresses. Physiol. Mol. Plant Pathol. 74, 394–402.CrossRefGoogle Scholar
  46. Yang Z., Chen Y., Hu B., Tan Z. and Huang B. 2015 Identification and validation of reference genes for quantification of target gene expression with quantitative real-time PCR for tall fescue under four abiotic stresses. PLoS One. 10, e0119569.CrossRefGoogle Scholar
  47. Zhang S., Zeng Y., Yi X. and Zhang Y. 2016 Selection of suitable reference genes for quantitative RT-PCR normalization in the halophyte Halostachys caspica under salt and drought stress. Sci. Rep6, 30363.CrossRefGoogle Scholar

Copyright information

© Indian Academy of Sciences 2018

Authors and Affiliations

  • Jamshidi Goharrizi Kiarash
    • 1
    Email author
  • Henry Dayton Wilde
    • 2
  • Farzane Amirmahani
    • 3
  • Mohammad Mehdi Moemeni
    • 1
  • Maryam Zaboli
    • 4
  • Maryam Nazari
    • 5
  • Sayyed Saeed Moosavi
    • 5
  • Mina Jamalvandi
    • 6
  1. 1.Department of Plant Breeding, Yazd BranchIslamic Azad UniversityYazdIran
  2. 2.Institute of Plant Breeding, Genetics, and GenomicsUniversity of GeorgiaAthensUSA
  3. 3.Faculty of Sciences, Genetic Division, Department of BiologyUniversity of IsfahanIsfahanIran
  4. 4.Faculty of Science, Department of ChemistryUniversity of BirjandBirjandIran
  5. 5.Department of Agronomy and Plant Breeding, Faculty of AgricultureBu-Ali Sina UniversityHamedanIran
  6. 6.Department of Genetic, Science and Research BranchIslamic Azad UniversityTehranIran

Personalised recommendations