Plant Molecular Biology

, Volume 63, Issue 5, pp 679–688 | Cite as

A combined strategy of “in silico” transcriptome analysis and web search engine optimization allows an agile identification of reference genes suitable for normalization in gene expression studies

  • Primetta Faccioli
  • Gian Paolo Ciceri
  • Paolo Provero
  • Antonio Michele Stanca
  • Caterina Morcia
  • Valeria Terzi
Article

Abstract

Traditionally housekeeping genes have been employed as endogenous reference (internal control) genes for normalization in gene expression studies. Since the utilization of single housekeepers cannot assure an unbiased result, new normalization methods involving multiple housekeeping genes and normalizing using their mean expression have been recently proposed. Moreover, since a gold standard gene suitable for every experimental condition does not exist, it is also necessary to validate the expression stability of every putative control gene on the specific requirements of the planned experiment. As a consequence, finding a good set of reference genes is for sure a non-trivial problem requiring quite a lot of lab-based experimental testing. In this work we identified novel candidate barley reference genes suitable for normalization in gene expression studies. An advanced web search approach aimed to collect, from publicly available web resources, the most interesting information regarding the expression profiling of candidate housekeepers on a specific experimental basis has been set up and applied, as an example, on stress conditions. A complementary lab-based analysis has been carried out to verify the expression profile of the selected genes in different tissues and during heat shock response. This combined dry/wet approach can be applied to any species and physiological condition of interest and can be considered very helpful to identify putative reference genes to be shortlisted every time a new experimental design has to be set up.

Keywords

“In silico” analysis Literature search Reference genes RNA quantification 

Notes

Acknowledgements

This work was supported by “AGRONANOTECH” (MiPAAF) project and by “VIGNA” project.

References

  1. Andrade MA, Bork P (2000) Automated extraction of information in molecular biology. FEBS Lett 476:12–17PubMedCrossRefGoogle Scholar
  2. Atienza SG, Faccioli P, Perrotta G, Dalfino G, Zschiesche W, Humbeck K, Stanca AM, Cattivelli L (2004) Large scale analysis of transcripts abundance in barley subjected to several single and combined abiotic stress conditions. Plant Sci 167:1359–1365CrossRefGoogle Scholar
  3. Brin S, Page L (1998) The anatomy of a large-scale hypertextual web search engine. Comput Netw 30:107–117Google Scholar
  4. Busch W, Wunderlich M, Schöffl F (2005) Identification of novel heat shock factor-dependent genes and biochemical pathways in Arabidopsis thaliana. Plant J 41:1–14PubMedCrossRefGoogle Scholar
  5. Bustin SA (2002) Quantification of mRNA using real-time reverse transcription PCR (RT-PCR): trends and problems. J Mol Endocrinol 29:23–39PubMedCrossRefGoogle Scholar
  6. Calishain T, Dornfest R (2004) Google Hacks, 2nd edn. O’Reilly, SebastopolGoogle Scholar
  7. Close TJ, Wanamaker SI, Caldo RA, Turner SM, Ashlock DA, Dickerson JA, Wing RA, Muehlbauer GJ, Kleinhofs A, Wise RP (2004) A new resource for cereal genomics: 22K barley GeneChip comes of age. Plant Physiol 134:960–968PubMedCrossRefGoogle Scholar
  8. Coker JS, Davis E (2003) Selection of candidate housekeeping controls in tomato plants using EST data. BioTechniques 35(4):740–748PubMedGoogle Scholar
  9. Czechowski T, Stitt M, Altmann T, Udvardi MK, Scheible W (2005) Genome-wide identification and testing of superior reference genes for transcript normalization in Arabidopsis. Plant Physiol 139:5–17PubMedCrossRefGoogle Scholar
  10. 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
  11. Druka A, Muehlbauer G, Druka I, Caldo R, Baumann U, Rostoks N, Schreiber A, Wise R, Close T, Kleinhofs A, Graner A, Schulman A, Langridge P, Sato K, Hayes P, McNicol J, Marshall D, Waugh R (2006) An atlas of gene expression from seed to seed through barley development. Funct Integr Genomics 6:202–211PubMedCrossRefGoogle Scholar
  12. Faccioli P, Pecchioni N, Cattivelli L, Stanca AM, Terzi V (2001) Expressed sequence tags from cold-acclimatized barley identify novel plant genes. Plant Breed 120:497–502CrossRefGoogle Scholar
  13. Faccioli P, Lagonigro MS, De Cecco L, Stanca AM, Alberici R, Terzi V (2002) Analysis of differential expression of barley ESTs during cold acclimatization using microarray technology. Plant Biol 4:630–639CrossRefGoogle Scholar
  14. Faccioli P, Provero P, Herrmann C, Stanca AM, Morcia C, Terzi V (2005) From single genes to co-expression networks: extracting knowledge from barley functional genomics. Plant Mol Biol 58(5):739–750PubMedCrossRefGoogle Scholar
  15. Iskandar HM, Simpson RS, Casu RE, Bonnett GD, MacLean DJ, Manners JM (2004) Comparison of reference genes for quantitative real-time polymerase chain reaction analysis of gene expression in sugarcane. Plant Mol Biol Rep 22:325–337Google Scholar
  16. Jones LJ, Yue ST, Cheung C, Singer VL (1998) RNA quantitation by fluorescence-based solution assay: RiboGreen reagent characterization. Anal Biochem 265:368–374PubMedCrossRefGoogle Scholar
  17. Kim S, Kim T (2003) Selection of optimal internal controls for gene expression profiling of liver disease. BioTechniques 35:456–460PubMedGoogle Scholar
  18. 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
  19. Livak KL, Schmittgen TD (2001) Analysis of relative gene expression data using real-time quantitative PCR and the 2–ΔΔCt method. Methods 25:402–408PubMedCrossRefGoogle Scholar
  20. Lohmann C, Eggers-Schumacher G, Wunderlich M, Schöffl F (2004) Two different heat shock transcription factors regulate immediate early expression of stress genes in Arabidopsis. Mol Genet Genomics 271:11–21PubMedCrossRefGoogle Scholar
  21. Marmiroli N, Lorenzoni C, Stanca AM, Terzi V (1989) Preliminary study of the inheritance of temperature stress proteins in barley (Hordeum vulgare L.). Plant Sci 62:147–156CrossRefGoogle Scholar
  22. Nicot N, Hausman J, 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(421):2907–2914PubMedCrossRefGoogle Scholar
  23. Ohl F, Jung M, Radonić A, Sachs M, Loening SA, Jung K (2006) Identification and validation of suitable endogenous reference genes for gene expression studies of human bladder cancer. J Urol 175:1915–1920PubMedCrossRefGoogle Scholar
  24. Ozturk ZN, Talamé V, Deyholos M, Michalowski CB, Galbraith DW, Gozukirmizi N, Tuberosa R, Bohnert H (2002) Monitoring large-scale changes in transcript abundance in drought and salt-stressed barley. Plant Mol Biol 48:551–573CrossRefGoogle Scholar
  25. 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
  26. Quackenbush J (2002) Microarray data normalization and transformation. Nat Genet 32(4s):496–501PubMedCrossRefGoogle Scholar
  27. Quackenbush J, Cho J, Lee D, Liang F, Holt I, Karamycheva S, Parvizi B, Pertea G, Sultana R, White J (2001) The TIGR Gene Indices: analysis of gene transcript sequences in highly sampled eukaryotic species. Nucleic Acids Res 29(1):159–164PubMedCrossRefGoogle Scholar
  28. Rhoads RP, McManaman C, Ingvartsen KL, Boisclair YR (2003) The housekeeping genes GAPDH and cyclophilin are regulated by metabolic state in the liver of dairy cows. J Dairy Sci 86:3423–3429PubMedCrossRefGoogle Scholar
  29. Sætre R, Tveit A, Steigedal TS, Lægreid A (2005a) Semantic annotation of biomedical literature using Google. In: Gervasi O et al (eds) Computational science and its applications—ICCSA 2005, LNCS 3482. Springer, Berlin Heidelberg New York, pp 327–337Google Scholar
  30. Sætre R, Tveit A, Ranang MT, Steigedal TS, Thommensen L, Stunes K, Lægreid A (2005b) gProt: annotating protein interactions using Google and gene ontology. 9th International conference on knowledge-based & intelligent information & engineering systems (Lecture notes in artificial intelligence). Springer, Berlin Heidelberg New YorkGoogle Scholar
  31. Schmittgen TD, Zakrajsek BA (2000) Effect of experimental treatment on housekeeping gene expression: validation by real-time, quantitative RT-PCR. J Biochem Biophys Methods 46:69–81PubMedCrossRefGoogle Scholar
  32. Shen L, Gong J, Caldo RA, Nettleton D, Cook D, Wise RP, Dickerson JA (2005) BarleyBase—an expression profiling database for plant genomics. Nucleic Acid Res 33:D614–D618PubMedCrossRefGoogle Scholar
  33. Sirover MA (1999) New insights into an old protein: the functional diversity of mammalian glyceraldehyde-3-phosphate dehydrogenase. Biochim Biophys Acta 1432:159–184PubMedGoogle Scholar
  34. Stekel DJ, Git Y, Falciani F (2000) The comparison of gene expression from multiple cDNA libraries. Genome Res 10:2055–2061PubMedCrossRefGoogle Scholar
  35. Svensson JT, Crosatti C, Campoli C, Bassi R, Stanca AM, Close TJ, Cattivelli L (2006) Transcriptome analysis of cold acclimation in barley Albina and Xantha mutants. Plant Physiol 141:257–270PubMedCrossRefGoogle Scholar
  36. Szabo A, Perou CM, Karaca M, Perreard L, Quackenbush JF, Bernard PS (2004) Statistical modeling for selecting housekeeper genes. Genome Biol 5:R59PubMedCrossRefGoogle Scholar
  37. Ueda A, Kathiresan A, Bennett J, Takabe T (2006) Comparative transcriptome analysis of barley and rice under salt stress. Theor Appl Genet 112:1286–1294PubMedCrossRefGoogle Scholar
  38. 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):research0034.1–0034.11Google Scholar
  39. Volkov RA, Panchuk II, Schőffl 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 54:2343–2349PubMedCrossRefGoogle Scholar
  40. Walia H, Wilson C, Wahid A, Condamine P, Cui X, Close TJ (2006) Expression analysis of barley (Hordeum vulgare L.) during salinity stress. Funct Integr Genomics 6:143–156PubMedCrossRefGoogle Scholar
  41. Warrington J, Nair A, Mahadevappa M, Tsyganskaya M (2000) Comparison of human adult and fetal expression and identification of 535 housekeeping/maintenance genes. Physiol Genomics 2:143–147PubMedGoogle Scholar
  42. Wu X, Bayle JH, Olson D, Levine AJ (1993) The p53-mdm-2 autoregulatory feedback loop. Genes Dev 7:1126–1132PubMedGoogle Scholar
  43. Yan HZ, Liou RF (2006) Selection of internal control genes for real-time quantitative RT-PCR assays in the oomycete plant pathogen Phytophthora parasitica. Fungal Genet Biol 43:430–438PubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2006

Authors and Affiliations

  • Primetta Faccioli
    • 1
  • Gian Paolo Ciceri
    • 1
  • Paolo Provero
    • 2
  • Antonio Michele Stanca
    • 1
  • Caterina Morcia
    • 1
  • Valeria Terzi
    • 1
  1. 1.CRA, Experimental Institute for Cereal ResearchFiorenzuola d’ArdaItaly
  2. 2.Department of Genetics, Biology and Biochemistry, Molecular Biotechnology CenterUniversity of TurinTorinoItaly

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