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Selection of reference genes for use in quantitative reverse transcription PCR assays when using interferons in U87MG

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Abstract

Relative gene quantification by quantitative reverse transcription PCR (qRT-PCR) is an accurate technique only when a correct normalization strategy is carried out. Some of the most commonly genes used as reference have demonstrated variation after interferon (IFN) treatments. In this work we evaluated the suitability of seven reference genes (RGs) [glyceraldehyde-3-phosphate dehydrogenase (GAPDH), hydroxymethylbilane synthase (HMBS), β-2Microglobulin (B2M), ribosomal RNA subunits 18S and 28S, tyrosine 3-monooxygenase/tryptophan 5-monooxygenase activation protein, zeta polypeptide (YWHAZ) and the RNA helicase (DDX5)] for use in qRT-PCR assays in the glioblastoma-derived cell line U87MG treated with IFNα, IFNγ or a co-formulated combination of both IFNs (HeberPAG); untreated cell lines were included as control. Data was analyzed using geNorm and NormFinder softwares. The expression stability of the seven RGs decreased in order of DDX5/GAPDH/HMBS, 18S rRNA, YWHAZ, 28S rRNA and B2M. qRT-PCR analyses demonstrated that DDX5, GAPDH and HMBS were among the best stably expressed markers under all conditions. Both, geNorm and NormFinder, analyses proposed same RGs as the least variables. Evaluation of the expression levels of two target genes utilizing different endogenous controls, using REST-MCS software, revealed that the normalization method applied might introduce errors in the estimation of relative quantities. We concluded that when qRT-PCR is designed for studies of gene expression in U87MG cell lines treated with IFNs type I and II or their combinations, the use of all three GAPDH, HMBS and DDX5 (or their combinations in pairs) as RGs for data normalizations is recommended.

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References

  1. Pfaffl MW (2004) Quantification strategies in real-time PCR. In: Bustin SA (ed) A–Z of quantitative PCR, chap 3. International University Line (IUL), La Jolla

  2. Vandesompele J, Kubista M, Pfaffl MW (2009) Reference gene validation software for improved normalization. In: Edwards K, Saunders N, Logan J (eds) Real-time PCR: Current Technology and Applications. Caister Academic Press, Norfolk, pp 47–64

    Google Scholar 

  3. Suzuki T, Higgins PJ, Crawford DR (2000) Control selection for RNA quantitation. Biotechniques 29:332–337

    PubMed  CAS  Google Scholar 

  4. Spanakis E (1993) Problems related to the interpretation of autoradiographic data on gene expression using common constitutive transcripts as controls. Nucl Acids Res 21:3809–3819

    Article  PubMed  CAS  Google Scholar 

  5. Thellin O, Zorzi W, Lakaye B, De Borman B, Coumans B, Hennen G, Grisar T, Igout A, Heinen E (1999) Housekeeping genes as internal standards: use and limits. J Biotechnol 75:291–295

    Article  PubMed  CAS  Google Scholar 

  6. 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: research0034.1–0034.11

    Google Scholar 

  7. 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 64:5245–5250

    Article  PubMed  CAS  Google Scholar 

  8. de Jonge HJ, Fehrmann RS, de Bont ES, Hofstra RM, Gerbens F, Kamps WA, de Vries EG, van der Zee AG, te Meerman GJ, ter Elst A (2007) Evidence based selection of reference genes. PLoS One 2:e898

    Article  PubMed  Google Scholar 

  9. Su LJ, Chang CW, Wu YC, Chen KC, Lin CJ, Liang SC, Lin CH, Whang-Peng J, Hsu SL, Chen CH, Huang CYF (2007) Selection of DDX5 as a novel internal control for Q-RT-PCR from microarray data using a block bootstrap re-sampling scheme. BMC Genomics 8:140

    Article  PubMed  CAS  Google Scholar 

  10. de Veer MJ, Holko M, Frevel M, Walker E, Der S, Paranjape JM, Silverman RH, Williams BRG (2001) Functional classification of interferon-stimulated genes identified using microarrays. J Leukoc Biol 69:912–920

    PubMed  Google Scholar 

  11. Samarajiwa SA, Forster S, Auchettl K, Hertzog PJ (2009) INTERFEROME: the database of interferon regulated genes. Nucl Acids Res 37:D852–D857

    Article  PubMed  CAS  Google Scholar 

  12. Rozen S, Skaletsky HJ (2000) Primer3 on the WWW for general users and for biologist programmers. In: Krawetz S, Misener S (eds) Bioinformatics Methods and protocols: methods in molecular biology. Humana Press, Totowa, pp 365–386

    Google Scholar 

  13. Nolan T, Hands RE, Bustin SA (2006) Quantification of mRNA using real-time RT-PCR. Nat Protoc 1:1559–1582

    Article  PubMed  CAS  Google Scholar 

  14. Ruijter JM, Ramakers C, Hoogaars WMH, Karlen Y, Bakker O, van den Hoff MJB, Moorman AFM (2009) Amplification efficiency: linking baseline and bias in the analysis of quantitative PCR data. Nucl Acids Res 37:e45

    Article  PubMed  CAS  Google Scholar 

  15. Pfaffl M (2001) A new mathematical model for relative quantification in real-time RT-PCR. Nucl Acids Res 29:e45

    Article  PubMed  CAS  Google Scholar 

  16. Pfaffl M, Horgan GW, Dempfle L (2002) Relative expression software tool (REST©) for group-wise comparison and statistical analysis of relative expression results in real-time PCR. Nucl Acids Res 30:e36

    Article  PubMed  Google Scholar 

  17. Valente V, Teixeira SA, Neder L, Okamoto OK, Oba-Shinjo SM, Marie SKN, Scrideli CA, Paçó-Larson ML, Carlotti CG Jr (2009) Selection of suitable housekeeping genes for expression analysis in glioblastoma using quantitative RT-PCR. BMC Mol Biol 10:17

    Article  PubMed  Google Scholar 

  18. Kwon MJ, Oh E, Lee S, Roh MR, Kim SE, Lee Y, Choi Y-L, In Y-H, Park T, Koh SS, Shin YK (2009) Identification of Novel reference genes using multiplatform expression data and their validation for quantitative gene expression analysis. PLoS One 4:e6162

    Article  PubMed  Google Scholar 

  19. Derveaux S, Vandesompele J, Hellemans J (2010) How to do successful gene expression analysis using real-time PCR. Methods 50:227–230

    Article  PubMed  CAS  Google Scholar 

  20. Dezso Z, Nikolsky Y, Sviridov E, Shi W, Serebriyskaya T, Dosymbekov D, Bugrim A, Rakhmatulin E, Brennan RJ, Guryanov A, Li K, Blake J, Samaha RR, Nikolskaya T (2008) A comprehensive functional analysis of tissue specificity of human gene expression. BMC Biol 6:49. doi:10.1186/1741-7007-6-49

    Article  PubMed  Google Scholar 

  21. Clark MJ, Homer N, O’Connor BD, Chen Z, Eskin A, Lee H, Merriman B, Nelson SF (2010) U87MG decoded: the genomic sequence of a cytogenetically aberrant human cancer cell line. PLoS Genet 6:e1000832

    Article  PubMed  Google Scholar 

  22. Abulrob A, Giuseppin S, Andrade MF, McDermid A, Moreno M, Stanimirovic D (2004) Interactions of EGFR and caveolin-1 in human glioblastoma cells: evidence that tyrosine phosphorylation regulates EGFR association with caveolae. Oncogene 23:6967–6979

    Article  PubMed  CAS  Google Scholar 

  23. Dráberová E, Vinopal S, Morfini G, Liu PS, Sládková V, Sulimenko T, Burns MR, Solowska J, Kulandaivel K, de Chadarévian JP, Legido A, Mörk SJ, Janáček J, Baas PW, Dráber P, Katsetos CD (2011) Microtubule-severing ATPase spastin in glioblastoma: increased expression in human glioblastoma cell lines and inverse roles in cell motility and proliferation. J Neuropathol Exp Neurol 70:811–826

    PubMed  Google Scholar 

  24. Nakai E, Park K, Yawata T, Chihara T, Kumazawa A, Nakabayashi H, Shimizu K (2009) Enhanced MDR1 expression and chemoresistance of cancer stem cells derived from glioblastoma. Cancer Invest 27:901–908

    Article  PubMed  CAS  Google Scholar 

  25. Moreno MJ, Ball M, Andrade MF, Mcdermid A, Stanimirovic DB (2006) Insulin-like growth factor binding protein-4 (IGFBP-4) is a novel anti-angiogenic and anti-tumorigenic mediator secreted by dibutyryl cyclic AMP (dB-cAMP)-differentiated glioblastoma cells. GLIA 53:845–857

    Article  PubMed  Google Scholar 

  26. Momeny M, Malehmir M, Zakidizaji M, Ghasemi R, Ghadimi H, Shokrgozar MA, Emami AH, Nafissi S, Ghavamzadeh A, Ghaffari SH (2010) Silibinin inhibits invasive properties of human glioblastoma U87MG cells through suppression of cathepsin B and nuclear factor kappa B-mediated induction of matrix metalloproteinase 9. Anticancer Drugs 21:252–260

    Article  PubMed  CAS  Google Scholar 

  27. Sharma V, Dixit D, Koul N, Mehta VS, Sen E (2011) Ras regulates interleukin-1β-induced HIF-1α transcriptional activity in glioblastoma. J Mol Med (Berl) 89:123–136

    Article  CAS  Google Scholar 

  28. Haseley A, Boone S, Wojton J, Yu L, Yoo JY, Yu J, Kurozumi K, Glorioso JC, Caligiuri MA, Kaur B (2012) Extracellular matrix protein CCN1 limits oncolytic efficacy in glioma. Cancer Res 72:1353–1362

    Article  PubMed  CAS  Google Scholar 

  29. Tricarico C, Pinzani P, Bianchi S, Paglierani M, Distante V, Pazzagli M, Bustin SA, Orlando C (2002) Quantitative real-time reverse transcription polymerase chain reaction: normalization to rRNA or single housekeeping genes is inappropriate for human tissue biopsies. Anal Biochem 309:293–300

    Article  PubMed  CAS  Google Scholar 

  30. Baltz KM, Krusch M, Bringmann A, Brossart P, Mayer F, Kloss M, Baessler T, Kumbier I, Peterfi A, Kupka S, Kroeber S, Menzel D, Radsak MP, Rammensee HG, Salih HR (2007) Cancer immunoediting by GITR (glucocorticoid-induced TNF-related protein) ligand in humans: NK cell/tumor cell interactions. FASEB J 21:2442–2454

    Article  PubMed  CAS  Google Scholar 

  31. Osborn BL, Olsen HS, Nardelli B, Murray JH, Zhou JXH, Garcia A, Moody G, Zaritskaya LS, Sung C (2002) Pharmacokinetic and pharmacodynamic studies of a human serum albumin-interferon-α fusion protein in cynomolgus monkeys. J Pharmacol Exp Ther 303:540–548

    Article  PubMed  CAS  Google Scholar 

  32. Oliveira JG, Prados RZ, Guedes ACM, Ferreira PCP, Kroon EG (1999) The housekeeping gene glyceraldehydes-3-phosphate dehydrogenase is inappropriate as internal control in comparative studies between skin tissue and cultured skin fibroblasts using Northern blot analysis. Arch Dermatol Res 291:659–661

    Article  PubMed  CAS  Google Scholar 

  33. Valenti MT, Bertoldo F, Carbonare LD, Azzarello G, Zenari S, Zanatta M, Balducci E, Vinante O, Cascio VL (2006) The effect of bisphosphonates on gene expression: GAPDH as a housekeeping or a new target gene? BMC Cancer 6:49

    Article  PubMed  Google Scholar 

  34. Indraccolo S, Pfeffer U, Minuzzo S, Esposito G, Roni V, Mandruzzato S, Ferrari N, Anfosso L, Dell’Eva R, Noonan DM, Bianchi-Chieco L, Albini A y, Amadori A (2007) Identificaction of genes selectively regulated by IFNs in endothelial cells. J Immunol 178:1122–1135

    PubMed  CAS  Google Scholar 

  35. Tan H, Derrick J, Hong J, Sanda C, Grosse WM, Edenberg HJ, Taylor M, Seiwert S, Blatt LM (2005) Global transcriptional profiling demonstrates the combination of type I and type II interferon enhances antiviral and immune responses at clinically relevant doses. J Interferon Cytokine Res 25:632–649

    Article  PubMed  CAS  Google Scholar 

  36. Albertsmeyer AC, Kakkassery V, Spurr-Michaud S, Beeks O, Gipson IK (2010) Effect of pro-inflammatory mediators on membrane-associated mucins expressed by human ocular surface epithelial cells. Exp Eye Res 90:444e451

    Article  Google Scholar 

  37. Katsoulidis E, Mavrommatis E, Woodard J, Shields MA, Sassano A, Carayol N, Sawicki KT, Munshi HG, Platanias LC (2010) Role of interferon (IFN)-inducible Schlafen-5 in regulation of anchorage-independent growth and invasion of malignant melanoma cells. J Biol Chem 285:40333–40341

    Article  PubMed  CAS  Google Scholar 

  38. Juengel E, Bhasin M, Libermann T, Barth S, Michaelis M, Cinatl J, Jones J, Hudak L, Jonas D, Blaheta RA (2011) Alterations of the gene expression profile in renal cell carcinoma after treatment with the histone deacetylase-inhibitor valproic acid and interferon-alpha. World J Urol 29:779–786

    Article  PubMed  CAS  Google Scholar 

  39. Mane VP, Heuer MA, Hillyer P, Navarro MB, Rabin RL (2008) Systematic method for determining an ideal housekeeping gene for real-time PCR analysis. J Biomol Tech 19:342–347

    PubMed  Google Scholar 

  40. Tan H, Derrick J, Hong J, Sanda C, Grosse WM, Edenberg HJ, Taylor M, Seiwert S, Blatt LM (2005) Global Transcriptional profiling demonstrates the combination of type I and type II interferon enhances antiviral and immune responses at clinically relevant doses. J Interf Cytokine Res 25:632–649

    Article  CAS  Google Scholar 

  41. Sanda C, Weitzel P, Tsukahara T, Schaley J, Edenberg HJ, Stephens MA, Mcclintick JN, Blatt LM, Li L, Brodsky L, Taylor MW (2006) Differential gene induction by type I and type II interferons and their combination. J Interf Cytokine Res 26:462–472

    Article  CAS  Google Scholar 

  42. Vestergaard AL, Knudsen UB, Rosbach H, Martensen P (2010) Transcriptional expression of type-I interferon response genes and stability of housekeeping genes in the human endometrium and endometriosis. Mol Hum Reprod 17:243–254

    Article  PubMed  Google Scholar 

  43. Wen PY, Kesari S (2008) Malignant gliomas in adults. N Engl J Med 359:492–507

    Article  PubMed  CAS  Google Scholar 

  44. Kreth S, Heyn J, Grau S, Kretzschmar HA, Egensperger R, Kreth FW (2010) Identification of valid endogenous control genes for determining gene expression in human glioma. Neuro-Oncology 12:570–579

    Article  PubMed  CAS  Google Scholar 

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Acknowledgments

We would like to thank Amanda B. Colarte, Clara Y. Taylor, Adelaida Villarreal and Tamara Díaz for their technical support.

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Correspondence to Dania Vázquez-Blomquist.

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Vázquez-Blomquist, D., Fernández, J.R., Miranda, J. et al. Selection of reference genes for use in quantitative reverse transcription PCR assays when using interferons in U87MG. Mol Biol Rep 39, 11167–11175 (2012). https://doi.org/10.1007/s11033-012-2026-9

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  • DOI: https://doi.org/10.1007/s11033-012-2026-9

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