Molecular Biology Reports

, Volume 40, Issue 4, pp 3395–3407 | Cite as

Application of qRT-PCR and RNA-Seq analysis for the identification of housekeeping genes useful for normalization of gene expression values during Striga hermonthica development

  • M. Fernández-AparicioEmail author
  • K. Huang
  • E. K. Wafula
  • L. A. Honaas
  • N. J. Wickett
  • M. P. Timko
  • C. W. dePamphilis
  • J. I. Yoder
  • J H. Westwood


Striga is a root parasitic weed that attacks many of the staple crops in Africa, India and Southeast Asia, inflicting tremendous losses in yield and for which there are few effective control measures. Studies of parasitic plant virulence and host resistance will be greatly facilitated by the recent emergence of genomic resources that include extensive transcriptome sequence datasets spanning all life stages of S. hermonthica. Functional characterization of Striga genes will require detailed analyses of gene expression patterns. Quantitative real-time PCR is a powerful tool for quantifying gene expression, but correct normalization of expression levels requires identification of control genes that have stable expression across tissues and life stages. Since no S. hermonthica housekeeping genes have been established for this purpose, we evaluated the suitability of six candidate housekeeping genes across key life stages of S. hermonthica from seed conditioning to flower initiation using qRT-PCR and high-throughput cDNA sequencing. Based on gene expression analysis by qRT-PCR and RNA-Seq across heterogeneous Striga life stages, we determined that using the combination of three genes, UBQ1, PP2A and TUB1 provides the best normalization for gene expression throughout the parasitic life cycle. The housekeeping genes characterized here provide robust standards that will facilitate powerful descriptions of parasite gene expression patterns.


Reference genes Witchweed Quantitative RT-PCR normalization RNA-Seq Parasitic plants 



Coefficient of variation




Expressed sequence tags


Glyceraldehyde-3-phosphate dehydrogenase-2


Growth regulator 24: strigol analogue


Maximum fold change of expression


Phosphoprotein phosphatase 2A subunit A3


Parasitic plant genome project


Quantitative real-time PCR


RNase L inhibitor protein


High-throughput sequencing of RNA


Reads per kilobase per million mapped reads


Beta tubulin 1


Beta tubulin 5


Ubiquitin 1



This research was supported by an International Outgoing European Marie Curie postdoctoral fellowship (PIOF-GA-2009-252538) to M Fernández-Aparicio, a NSF Plant Genome award (DBI-0701748) to JH Westwood, CW dePamphilis, MP Timko and JI Yoder, and a grant from the U.S. Department of Agriculture (Hatch Project no. 135798) to JH Westwood.


  1. 1.
    Parker C (2009) Observations on the current status of Orobanche and Striga problems worldwide. Pest Manag Sci 65:453–459PubMedCrossRefGoogle Scholar
  2. 2.
    Ejeta G (2007) The Striga scourge in Africa: a growing problem. In: Ejeta G, Gressel J (eds) Integrating new technologies for Striga control: toward ending the witch-hunt. World Scientific Publishing Co., Hackensack, pp 3–16CrossRefGoogle Scholar
  3. 3.
    Westwood JH, Yoder JI, Timko MP, Depamphilis CW (2010) The evolution of parasitism in plants. Trends Plant Sci 15:227–235PubMedCrossRefGoogle Scholar
  4. 4.
    Press MC, Smith S, Stewart GR (1991) Carbon acquisition and assimilation in parasitic plants. Funct Ecol 5:278–283CrossRefGoogle Scholar
  5. 5.
    Brown R, Edwards M (1946) The germination of the seeds of Striga lutea. II. The effect of time of treatment and concentration of the host stimulant. Ann Bot 10:133–142Google Scholar
  6. 6.
    Vallance KB (1951) Studies on the germination of the seeds of Striga hermonthica. III. On the nature of pretreatment and after ripening. Ann Bot 15:109–128Google Scholar
  7. 7.
    Kust CA (1966) A germination inhibitor in Striga seeds. Weeds 14:327–329CrossRefGoogle Scholar
  8. 8.
    Babiker AGT, Cai T, Ejeta G, Butler LG, Woodson WR (1994) Enhancement of ethylene biosynthesis and germination with thidiazuron and some selected auxins in Striga asiatica seeds. Physiol Plant 91(529):536Google Scholar
  9. 9.
    Babiker AGT, Ejeta G, Butler LG, Woodson WR (1993) Ethylene biosynthesis and strigol-induced germination of Striga asiatica. Physiol Plant 88(359):365Google Scholar
  10. 10.
    Babiker AGT, Ma Y, Sugimoto Y, Inanaga S (2000) Conditioning period, CO2 and GR24 influence ethylene biosynthesis and germination of Striga hermonthica. Physiol Plant 109:75–80CrossRefGoogle Scholar
  11. 11.
    Poneleit LS, Dilley DR (1993) Carbon dioxide activation of 1-aminocyclopropane-1-carboxylate (ACC) oxidase in ethylene biosynthesis. Postharv Biol Technol 3:191–199CrossRefGoogle Scholar
  12. 12.
    Lynn DG, Chang M (1990) Phenolic signals in cohabitation: implications for plant development. Annu Rev Plant Physiol Plant Mol Biol 41:497–526CrossRefGoogle Scholar
  13. 13.
    Musselman LJ, Matteson PC, Fortune S (1983) Potential pollen vectors of Striga hermonthica (Scrophulariaceae) in West Africa. Ann Bot 51:859–862Google Scholar
  14. 14.
    Westwood JH, dePamphilis CW, Das M, Fernandez-Aparicio M, Honaas LA, Timko MP, Wickett NJ, Yoder JI (2012) The Parasitic Plant Genome Project: new tools for understanding the biology of Orobanche and Striga. Weed Sci 60:295–306CrossRefGoogle Scholar
  15. 15.
    Szövényi P, Rensing SA, Lang D, Wray GA, Shaw AJ (2011) Generation-biased gene expression in a bryophyte model system. Mol Biol Evol 28:803–812PubMedCrossRefGoogle Scholar
  16. 16.
    Srivastava A, Ohm RO, Oxiles L, Brooks F, Lawrence CB, Grigoriev IV, Cho Y (2012) A zinc-finger-family transcription factor, AbVf19, is required for the induction of a gene subset important for virulence in Alternaria brassicicola. Mol Plant Microbe Interact 25(4):443–452PubMedCrossRefGoogle Scholar
  17. 17.
    Schmidt A, Schmid MA, Grossniklaus U (2012) Analysis of plant germline development by high-throughput RNA profiling: technical advances and new insights. Plant J 70:18–29PubMedCrossRefGoogle Scholar
  18. 18.
    Johnson AW, Rosebery G, Parker C (1976) A novel approach to Striga and Orobanche control using synthetic germination stimulants. Weed Res 16:223–227CrossRefGoogle Scholar
  19. 19.
    Hewitt EJ (1966) Sand and water culture methods used in the studey of plant nutrition. Commonwealth Agricultural Bureaux, WallingfordGoogle Scholar
  20. 20.
    Gonzalez-Verdejo CI, Die JV, Nadal S, Jimenez-Marín A, Moreno MT, Roman B (2008) Selection of housekeeping genes for normalization by real-time RT-PCR: analysis of Or-MYB1 gene expression in Orobanche ramosa development. Anal Biochem 379:38–43CrossRefGoogle Scholar
  21. 21.
    Paolacci A, Tanzarella O, Porceddu E, Ciaffi M (2009) Identification and validation of reference genes for quantitative RT-PCR normalization in wheat. BMC Mol Biol 10:11PubMedCrossRefGoogle Scholar
  22. 22.
    Die JV, Roman B, Nadal S, Gonzalez-Verdejo C (2010) Evaluation of candidate reference genes for expression studies in Pisum sativum under different experimental conditions. Planta 232:145–153PubMedCrossRefGoogle Scholar
  23. 23.
    Giménez MJ, Pistón F, Atienza SG (2010) Identification of suitable reference genes for normalization of qPCR data in comparative transcriptomics analyses in the Triticeae. Planta 233:163–173PubMedCrossRefGoogle Scholar
  24. 24.
    Dekkers BJW, Willems L, Bassel GW, van Bolderen-Veldkamp RP, Ligterink W, Hilhorst HWM et al (2012) Identification of reference genes for RT-qPCR expression analysis in Arabidopsis and tomato seeds. Plant Cell Physiol 53:28–37PubMedCrossRefGoogle Scholar
  25. 25.
    Wilkening S, Bader A (2004) Quantitative real-time polymerase chain reaction: methodical analysis and mathematical model. J Biomol Tech 15:107–111PubMedGoogle Scholar
  26. 26.
    Lu Y, Xie L, Chen J (2012) A novel procedure for absolute real-time quantification of gene expression patterns. Plant Methods 8:9. doi: 10.1186/1746-4811-8-9 PubMedCrossRefGoogle Scholar
  27. 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 7:33PubMedCrossRefGoogle Scholar
  28. 28.
    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
  29. 29.
    de Jonge HJM, Fehrmann RSN, de Bont ESJM, Hofstra RMW, Gerbens F et al (2007) Evidence based selection of housekeeping genes. PLoSONE 2(9):e898. doi: 10.1371/journal.pone.0000898 Google Scholar
  30. 30.
    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(7):RESEARCH0034Google Scholar
  31. 31.
    Kozarewa I, Ning ZM, Quail MA, Sanders MJ, Berriman M, Turner DJ (2009) Amplification-free illumina sequencing-library preparation facilitates improved mapping and assembly of (G plus C)-biased genomes. Nat Methods 6(4):291–295PubMedCrossRefGoogle Scholar
  32. 32.
    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
  33. 33.
    Nagalakshmi U, Wang Z, Waern K, Shou C, Raha D, Gerstein M, Snyder M (2008) The transcriptional landscape of the yeast genome defined by RNA sequencing. Science 320:1344–1349PubMedCrossRefGoogle Scholar
  34. 34.
    Haberhausen G, Pinsl J, Kuhn CC, Markert-Hahn C (1998) Comparative study of different standardization concepts in quantitative competitive reverse transcription-PCR assays. J Clin Microbiol 36:628–633PubMedGoogle Scholar
  35. 35.
    Thellin O, Zorzi W, Lakaye B, De Borman B, Coumans B, Hennen G, Grisar T, Igout A, Heinen E (1999) Housekeeping genes internal standards: use and limits. J Biotechnol 75:291–295PubMedCrossRefGoogle Scholar
  36. 36.
    Fleige S, Walf V, Huch S, Prgomet C, Sehm J, Pfaffl MW (2006) Comparison of relative mRNA quantification models and the impact of RNA integrity in quantitative real-time RT-PCR. Biotechnol Lett 28:1601–1613PubMedCrossRefGoogle Scholar
  37. 37.
    Schroeder A, Mueller O, Stocker S, Salowsky R, Leiber M, Gassmann M, Lightfoot S, Menzel W, Granzow M, Ragg T (2006) The RIN: an RNA integrity number for assigning integrity values to RNA measurements. BMC Mol Biol 7:3PubMedCrossRefGoogle Scholar
  38. 38.
    Riedmaier I, Bergmaier M, Pfaffl MW (2010) Comparison of two available platforms for determination of RNA quality. Biotechnol Biotechnol Equip 24(4):2154–2159CrossRefGoogle Scholar
  39. 39.
    Vermeulen J, De Preter K, Lefever S, Nuytens J, Derveaux S, Hellemans J et al (2011) Measurable impact of RNA quality on gene expression results from quantitative PCR. Nucleic Acids Res 39(9):e63. doi: 10.1093/nar/gkr065 PubMedCrossRefGoogle Scholar
  40. 40.
    Wong ML, Medrano JF (2005) Real-time PCR for mRNA quantification. Biotechniques 39:75–85PubMedCrossRefGoogle Scholar
  41. 41.
    Obrero A, Die JV, Roman B, Gomez P, Nadal S, Gonzalez-Verdejo CI (2011) Selection of reference genes for gene expression studies in Zucchini (Cucurbita pepo) Using qPCR. J Agric Food Chem 59(10):5402–5411PubMedCrossRefGoogle Scholar
  42. 42.
    Hruz T, Wyss M, Docquier M, Pfaffl MW, Masanetz S, Borghi L et al (2011) RefGenes: identification of reliable and condition specific reference genes for RT-qPCR data normalization. BMC Genomics 12:156PubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2012

Authors and Affiliations

  • M. Fernández-Aparicio
    • 1
    • 2
    Email author
  • K. Huang
    • 3
  • E. K. Wafula
    • 4
  • L. A. Honaas
    • 4
  • N. J. Wickett
    • 4
    • 6
  • M. P. Timko
    • 3
  • C. W. dePamphilis
    • 4
  • J. I. Yoder
    • 5
  • J H. Westwood
    • 2
  1. 1.Department of Plant BreedingIAS-CSIC, Institute for Sustainable AgricultureCórdobaSpain
  2. 2.Department of Plant Pathology, Physiology and Weed ScienceVirginia TechBlacksburgUSA
  3. 3.Department of BiologyUniversity of VirginiaCharlottesvilleUSA
  4. 4.Department of Biology, Institute for Molecular Evolutionary GeneticsPenn State UniversityPAUSA
  5. 5.Department of Plant SciencesUniversity of California-DavisDavisUSA
  6. 6.Chicago Botanic GardenGlencoeUSA

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