Analysis of qPCR reference gene stability determination methods and a practical approach for efficiency calculation on a turbot (Scophthalmus maximus) gonad dataset

BMC Genomics

Ana Viñas²  Paulino Martínez¹  Laura Sánchez¹  Belén G Pardo¹  Rosa M Cal³  Jorge Hernández-Urcera³  Diego Robledo² 

[1] Departamento de Genética, Facultad de Veterinaria, Universidad de Santiago de Compostela, 27002 Lugo, Spain;Departamento de Genética, Facultad de Biología (CIBUS), Universidad de Santiago de Compostela, 15782 Santiago de Compostela, Spain;Instituto Español de Oceanografía, Centro Oceanográfico de Vigo, 36390 Vigo, Spain

关键词: Gonad;    Scophthalmus maximus;    Turbot;    Amplification efficiency;    Reference genes;    qPCR;   

Others  :  1216338
DOI  :  10.1186/1471-2164-15-648

 received in 2013-03-19, accepted in 2014-07-25,  发布年份 2014

【 摘 要 】

Background

Gene expression analysis by reverse transcription quantitative PCR (qPCR) is the most widely used method for analyzing the expression of a moderate number of genes and also for the validation of microarray results. Several issues are crucial for a successful qPCR study, particularly the selection of internal reference genes for normalization and efficiency determination. There is no agreement on which method is the best to detect the most stable genes neither on how to perform efficiency determination. In this study we offer a comprehensive evaluation of the characteristics of reference gene selection methods and how to decide which one is more reliable when they show discordant outcomes. Also, we analyze the current efficiency calculation controversy. Our dataset is composed by gonad samples of turbot at different development times reared at different temperatures. Turbot (Scophthalmus maximus) is a relevant marine aquaculture European species with increasing production in the incoming years. Since females largely outgrow males, identification of genes related to sex determination, gonad development and reproductive behavior, and analysis of their expression profiles are of primary importance for turbot industry.

Results

We analyzed gene stability of six reference genes: RPS4, RPL17, GAPDH, ACTB, UBQ and B2M using the comparative delta-CT method, Bestkeeper, NormFinder and GeNorm approaches in gonad samples of turbot. Supported by descriptive statistics, we found NormFinder to be the best method, while on the other side, GeNorm results proved to be unreliable. According to our analysis, UBQ and RPS4 were the most stable genes, while B2M was the least stable gene. We also analyzed the efficiency calculation softwares LinRegPCR, LREanalyzer, DART and PCR-Miner and we recommend LinRegPCR for research purposes since it does not systematically overestimate efficiency.

Conclusion

Our results indicate that NormFinder and LinRegPCR are the best approaches for reference gene selection and efficiency determination, respectively. We also recommend the use of UBQ and RPS4 for normalization of gonad development samples in turbot.

【 授权许可】

   
2014 Robledo et al.; licensee BioMed Central Ltd.

【 图 表 】

Figure 1.

Figure 2.

【 参考文献 】

• [1]Spiess AN, Feig C, Ritz C: Highly accurate sigmoidal fitting of real-time PCR data by introducing a parameter for asymmetry. BMC Bioinformatics 2008, 9:221.
• [2]Bustin SA, Benes V, Garson JA, Hellemans J, Huggett J, Kubista M, Mueller R, Nolan T, Pfaffl MW, Shipley GL, Vandesompele J, Wittwer CT: The MIQE guidelines: minimum information for publication of quantitative real-time PCR experiments. Clin Chem 2009, 55:4.
• [3]Vandesompele J, De Preter K, Pattyn F, Poppe B, Van Roy N, De Paepe A, Speleman F: Accurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple internal control genes. Genome Biol 2002, 3:RESESRCH0034.
• [4]Glare EM, Divjak M, Bailey MJ, Walters EH: Beta-Actin and GAPDH housekeeping gene expression in asthmatic airways is variable and not suitable for normalizing mRNA levels. Thorax 2002, 57:765-770.
• [5]Dang W, Sun L: Determination of internal controls for quantitative real time RT-PCR analysis of the effect of Edwardsiella tarda infection on gene expression in turbot (Scophthalmus maximus). Fish Shellfish Inmunol 2011, 30:720-728.
• [6]Silver N, Best S, Jiang J, Thein SL: Selection of housekeeping genes for gene expression studies in human reticulocytes using real-time PCR. BMC Mol Biol 2006, 7:33.
• [7]Pfaffl MW, Tichopad A, Prgomet C, Neuvians TP: Determination of stable housekeeping genes, differentially regulated target genes and sample integrity: BestKeeper – Excel-based tool using pair-wise correlations. Biotechnol Lett 2004, 26:509-515.
• [8]Andersen CL, Jensen JL, Orntoft TF: 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 2004, 64:5245-5250.
• [9]Caradec J, Sirab N, Keumeugni C, Moutereau S, Chimingqi M, Matar C, Revaud D, Bah M, Manivet P, Conti M, Loric S: Desperate house genes: the dramatic example of hypoxia. Br J Cancer 2010, 102:1037-1043.
• [10]Peirson SN, Butler JN, Foster RG: Experimental validation of novel and conventional approaches to quantitative real-time PCR data analysis. Nucleic Acids Res 2003, 31:e73.
• [11]Rutledge RG, Côte C: Mathematics of quantitative kinetic PCR and the application of standard curves. Nucleic Acids Res 2003, 31:e93.
• [12]Ramakers C, Ruijter JM, Deprez RHL, Moorman AFM: Assumption-free analysis of quantitative real-time polymerase chain reaction (PCR) data. Neurosci Lett 2003, 339:62-66.
• [13]Booth CS, Pienaar E, Termaat JR, Whitney SE, Louw TM, Viljoen HJ: Efficiency of the polymerase chain reaction. Chem Eng Sci 2010, 65:4996-5006.
• [14]Ruijter JM, Ramakers C, Hoogaars WMH, Karlen Y, Bakker O, van den Hoff MJB, Moorman AFM: Amplification efficiency: linking baseline and bias in the analysis of quantitative PCR data. Nucleic Acids Res 2009, 37:e45.
• [15]Rutledge RG: A java program for LRE-based real-time qPCR that enables large-scale absolute quantification. PLoS One 2011, 6:e17636.
• [16]Zhao S, Fernald RD: Comprehensive Algorithm for quantitative Real-time polymesare chain reaction. J Comput Biol 2005, 12:1047-1064.
• [17]Cerdà J, Manchado M: Advances in genomics for flatfish aquaculture. Genes Nutr 2013, 8:5-17.
• [18]FAO: The state of world fisheries and aquaculture 2010. 2009. http://www.fao.org/docrep/013/i1820e/i1820e00.htm webcite
• [19]Bouza C, Hermida M, Pardo BG, Vera M, Fernández C, de la Herrán R, Navajas-Pérez R, Álvarez-Dios J, Gómez-Tato A, Martínez P: An expressed sequence tag (EST)-enriched genetic map of turbot (Scophthalmus maximus): a useful framework for comparative genomics across model and farmed teleosts. BMC Genet 2012, 13:54.
• [20]Piferrer F, Felip A, Blazquez M: Control genético y fisiológico de las proporciones de sexos de los teleósteos y su aplicación en acuicultura. In Aulas del Mar, Acuicultura, Biología Marina. Edited by Zamora S, Agulleiro y García O. Murcia: University of Murcia; 1995:75-109.
• [21]Haffray P, Lebege E, Jeu S, Guennoc M, Guiguen Y, Baroiller JF, Fostier A: Genetic determination and temperature effects on turbot Scophthalmus maximus sex differentiation: an investigation using steroid sex-inverted males and females. Aquaculture 2009, 294:30-36.
• [22]Martinez P, Bouza C, Hermida M, Fernandez J, Toro MA, Vera M, Pardo B, Millan A, Fernandez C, Vilas R, Vinas A, Sanchez L, Felip A, Piferrer F, Ferreiro I, Cabaleiro S: Identification of the major sex-determining region of turbot (Scophthalmus maximus). Genetics 2009, 183:1443-1452.
• [23]Millán A, Gómez-Tato A, Pardo BG, Fernández C, Bouza C, Vera M, Álvarez-Dios JA, Cabaleiro S, Lamas J, Lemos ML, Martínez P: Gene expression profiles of spleen, liver and head kidney in turbot (Scophthalmus maximus) along the infection process with Aeromonas salmonicida using an immune-enriched oligo-microarray. Mar Biotechnol (NY) 2011, 13:1099-1114.
• [24]Pardo BG, Millán A, Gómez-Tato A, Fernández C, Bouza C, Alvarez-Dios JA, Cabaleiro S, Lamas J, Leiro JM, Martínez P: Gene expression profiles of spleen, liver and head kidney in turbot (Scophthalmus maximus) along the infection process with Philasterides dicentrarchi using an immune-enriched oligo-microarray. Mar Biotechnol (NY) 2012, 14:570-582.
• [25]Domínguez B, Pardo BG, Noia M, Millán A, Gómez-Tato A, Martínez P, Leiro J, Lamas J: Microarray analysis of the inflammatory and immune responses in head kidney turbot leucocytes treated with resveratrol. Int Immunopharmacol 2013, 15:588-596.
• [26]Øvergård A, Nerland AH, Patel S: Evaluation of potential reference genes for real time RT-PCR studies in Atlantic halibut (Hippoglossus hippoglossus); during development, in tissues of healthy and NNV-injected fish, and in anterior kidney leucocytes. BMC Mol Biol 2010, 11:36.
• [27]Infante C, Matsuoka MP, Asensio E, Cañavate JP, Reith M, Manchado M: Selection of housekeeping genes for gene expression studies in larvae from flatfish using real-time PCR. BMC Mol Biol 2008, 9:28.
• [28]Sorby LA, Andersen SN, Bukholm IR, Jacobsen MB: Evaluation of suitable reference genes for normalization of real-time reverse transcription PCR analysis in colon cancer. J Exp Clin Cancer Res 2010, 29:144.
• [29]Silberberg G, Baruch K, Navon R: Detection of stable reference genes for real-time PCR analysis in schizophrenia and bipolar disorder. Anal Biochem 2009, 391:91-97.
• [30]Chen D, Pan X, Xiao P, Farwell MA, Zhang B: Evaluation and identification of reliable reference genes for pharmacogenomics, toxicogenomics, and small RNA expression analysis. J Cell Physiol 2011, 226:2469-2477.
• [31]Zhang Y, Chen D, Smith MA, Zhang B, Pan X: Selection of reliable reference genes in Caenorhabditis elegans for analysis of nanotoxicity. PLoS One 2012, 7:e31849.
• [32]McCurley AT, Callard GV: Characterization of housekeeping genes in zebrafish: male-female differences and effects of tissue type, developmental stage and chemical treatment. BMC Mol Biol 2008, 9:102.
• [33]Lievens A, Van Aelst S, Van den Bulcke M, Goetghebeur E: Enhance analysis of real-time PCR data by using a variable efficiency model: FPK-PCR. Nucleic Acids Res 2011, 40:1-15.
• [34]Rutledge RG, Stewart D: A kinetic-based sigmoidal model for the polymerase chain reaction and its application to high-capacity absolute quantitative real-time PCR. BMC Biotechnol 2008, 8:47.
• [35]Louw TM, Booth CS, Pienaar E, TerMaat JR, Whitney SE, Viljoen HJ: Experimental validation of a fundamental model for PCR efficiency. Chem Eng Sci 2011, 66:1783-1789.
• [36]Ruijter JM, Pfaffl MW, Zhao S, Spiess AN, Boggy G, Blom J, Rutledge RG, Sisti D, Lievens A, De Preter K, Derveaux S, Hellemans J, Vandesompele J: Evaluation of qPCR curve analysis methods for reliable biomarker discovery: Bias, resolution, precision, and implications. Methods 2013, 59:32-46.
• [37]The R project homepage http://www.r-project.org webcite
• [38]Kubista M, Sindelka R, Tichopad A, Besgkvist A, Lindh D, Forootan A: The prime technique. real-time PCR data analysis. GIT Lab J 2007, 9–10:33-35.