Skip to main content
Log in

Comprehensive selection of reference genes for quantitative gene expression analysis during seed development in Brassica napus

  • Original Paper
  • Published:
Plant Cell Reports Aims and scope Submit manuscript

Abstract

Key message

MicroRNAs have higher expression stability than protein-coding genes in B. napus seeds and are therefore good reference genes for miRNA and mRNA RT-qPCR analysis.

Abstract

Reverse transcription quantitative real-time polymerase chain reaction (RT-qPCR) has become the “gold standard” to gain insight into function of genes. However, the accuracy of the technique depends on appropriate reference genes for quantification analysis in different experimental conditions. Accumulation of microRNAs (miRNAs) has also been studied by RT-qPCR, but there are no reference genes currently validated for normalization of Brassica napus miRNA expression data. In this study, we selected 43 B. napus miRNAs and 18 previously validated mRNA reference genes. The expression stability of the candidate reference genes was evaluated in different tissue samples (stages of seed development, flowers, and leaves) using geNorm, NormFinder, and RefFinder analysis. The best-ranked reference genes for expression studies during seed development (miR167-1_2, miR11-1, miR159-1 and miR168-1) were used to asses the expression of miR03-1. Since candidate miRNAs showed higher expression stability than protein-coding genes in most of the tested conditions, the expression profile of DGAT1 gene was compared when normalized by the four most stable miRNAs reference genes and by the four most stable mRNA reference genes. The expected expression pattern of DGAT1 during seed development was achieved with the use of miRNA as reference genes. In conclusion, the most stable miRNA reference genes can be employed in the normalization of RT-qPCR quantification of miRNAs and protein-coding genes. This work is the first to perform a comprehensive survey of the stability of miRNA reference genes in B. napus and provides guidelines to obtain more accurate RT-qPCR results in B. napus seeds studies.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

  • Andersen CL, Jensen JL, Ørntoft 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  CAS  PubMed  Google Scholar 

  • Baud S, Lepiniec L (2010) Physiological and developmental regulation of seed oil production. Prog Lipid Res 49:235–249

    Article  CAS  PubMed  Google Scholar 

  • Borowski JM, Galli V, da Silva Messias R et al (2014) Selection of candidate reference genes for real-time PCR studies in lettuce under abiotic stresses. Planta 239:1187–1200

    CAS  PubMed  Google Scholar 

  • Bustin SA, Benes V, Garson JA et al (2009) The MIQE guidelines: minimum information for publication of quantitative real-time PCR experiments. Clin Chem 55:611–622

    Article  CAS  PubMed  Google Scholar 

  • Chen C, Ridzon DA, Broomer AJ et al (2005) Real-time quantification of microRNAs by stem-loop RT-PCR. Nucleic Acids Res 33:e179

    Article  PubMed Central  PubMed  Google Scholar 

  • Chen X, Truksa M, Shah S, Weselake RJ (2010) A survey of quantitative real-time polymerase chain reaction internal reference genes for expression studies in Brassica napus. Anal Biochem 405:138–140

    Article  CAS  PubMed  Google Scholar 

  • Chi X, Hu R, Yang Q et al (2012) Validation of reference genes for gene expression studies in peanut by quantitative real-time RT-PCR. Mol Gen Genom 287:167–176

    Article  CAS  Google Scholar 

  • Czechowski T, Stitt M, Altmann T (2005) Genome-wide identification and testing of superior reference genes for transcript normalization in Arabidopsis. Plant Physiol 139:5–17

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  • Dekkers BJ, Willems L, Bassel GW et al (2012) Identification of reference genes for RT-qPCR expression analysis in Arabidopsis and tomato seeds. Plant Cell Physiol 53:28–37

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  • Feng H, Huang X, Zhang Q et al (2012) Selection of suitable inner reference genes for relative quantification expression of microRNA in wheat. Plant Physiol Biochem 51:116–122

    Article  CAS  PubMed  Google Scholar 

  • Guénin S, Mauriat M, Pelloux J et al (2009) Normalization of qRT-PCR data: the necessity of adopting a systematic, experimental conditions-specific, validation of references. J Exp Bot 60:487–493

    Article  PubMed  Google Scholar 

  • Gutierrez L, Mauriat M, Guénin S et al (2008a) 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

    Article  CAS  PubMed  Google Scholar 

  • Gutierrez L, Mauriat M, Pelloux J et al (2008b) Towards a systematic validation of references in real-time RT-PCR. Plant cell 20:1734–1735

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  • Han X, Lu M, Chen Y, Zhan Z, Cui Q, Wang Y (2012) Selection of reliable reference genes for gene expression studies using real-time PCR in Tung tree during seed development. PLoS One 8:e43084

    Article  Google Scholar 

  • Huang D, Koh C, Feurtado JA et al (2013) MicroRNAs and their putative targets in Brassica napus seed maturation. BMC Genom 14:140

    Article  CAS  Google Scholar 

  • Körbes AP, Machado RD, Guzman F et al (2012) Identifying conserved and novel microRNAs in developing seeds of Brassica napus using deep sequencing. PLoS One 7:e50663

    Article  PubMed Central  PubMed  Google Scholar 

  • Kou SJ, Wu XM, Liu Z, Liu YL, Xu Q, Guo WW (2012) Selection and validation of suitable reference genes for miRNA expression normalization by quantitative RT-PCR in citrus somatic embryogenic and adult tissues. Plant Cell Rep 31(12):2151–2163

    Article  CAS  PubMed  Google Scholar 

  • Kulcheski FR, Marcelino-Guimaraes FC, Nepomuceno AL et al (2010) The use of microRNAs as reference genes for quantitative polymerase chain reaction in soybean. Anal Biochem 406:185–192

    Article  CAS  PubMed  Google Scholar 

  • Lao K, Xu NL, Yeung V et al (2006) Multiplexing RT-PCR for the detection of multiple miRNA species in small samples. Biochem Biophys Res Commun 343:85–89

    Article  CAS  PubMed  Google Scholar 

  • Le BH, Cheng C, Bui AQ et al (2010) Global analysis of gene activity during Arabidopsis seed development and identification of seed-specific transcription factors. Proc Natl Acad Sci USA 107:8063–8070

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  • Li Q, Fan CM, Zhang XM, Fu YF (2012) Validation of reference genes for real-time quantitative PCR normalization in soybean developmental and germinating seeds. Plant Cell Rep 31(10):1789–1798

    Article  CAS  PubMed  Google Scholar 

  • Li R, Hatanaka T, Yu K, Wu Y, Fukushige H, Hildebrand D (2013) Soybean oil biosynthesis: role of diacylglycerol acyltransferases. Funct Integr Genomics 13(1):99–113

    Article  CAS  PubMed  Google Scholar 

  • Lin YL, Lai ZX (2013) Evaluation of suitable reference genes for normalization of microRNA expression by real-time reverse transcription PCR analysis during longan somatic embryogenesis. Plant Physiol Biochem 66:20–25

    Article  CAS  PubMed  Google Scholar 

  • Livak KJ, Schmittgen TD (2001) Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)). Methods 25:402–408

    Article  CAS  PubMed  Google Scholar 

  • Lu C, Napier JA, Clemente TE, Cahoon EB (2011) New frontiers in oilseed biotechnology: meeting the global demand for vegetable oils for food, feed, biofuel, and industrial applications. Curr Opin in Biotechnol 22:252–259

    Article  CAS  Google Scholar 

  • Nodine MD, Bartel DP (2010) MicroRNAs prevent precocious gene expression and enable pattern formation during plant embryogenesis. Genes Dev 24:2678–2692

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  • Paolacci AR, Tanzarella OA, Porceddu E, Ciaffi M (2009) Identification and validation of reference genes for quantitative RT-PCR normalization in wheat. BMC Mol Biol 10:11

    Article  PubMed Central  PubMed  Google Scholar 

  • Peltier HJ, Latham GJ (2008) Normalization of microRNA expression levels in quantitative RT-PCR assays: identification of suitable reference RNA targets in normal and cancerous human solid tissues. RNA 14:844–852

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  • 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 26:509–515

    Article  CAS  PubMed  Google Scholar 

  • Ramakers C, Ruijter JM, Deprez RHL, Moorman AF (2003) Assumption-free analysis of quantitative real-time polymerase chain reaction (PCR) data. Neurosci Lett 339:62–66

    Article  CAS  PubMed  Google Scholar 

  • Rieu I, Powers SJ (2009) Real-time quantitative RT-PCR: design, calculations, and statistics. Plant Cell 21:1031–1033

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  • Ruijter JM, Ramakers C, Hoogaars WMH et al (2009) Amplification efficiency: linking baseline and bias in the analysis of quantitative PCR data. Nucleic Acids Res 37:e45

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  • Tang F, Hajkova P, Barton SC et al (2006) MicroRNA expression profiling of single whole embryonic stem cells. Nucleic Acids Res 34:e9

    Article  PubMed Central  PubMed  Google Scholar 

  • Timoneda O, Balcells I, Córdoba S et al (2012) Determination of reference microRNAs for relative quantification in porcine tissues. PLoS One 7:e44413

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  • Troncoso-Ponce MA, Kilaru A, Cao X et al (2011) Comparative deep transcriptional profiling of four developing oilseeds. Plant J 68:1014–1027

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  • Udvardi MK, Czechowski T, Scheible WR (2008) Eleven golden rules of quantitative RT-PCR. Plant Cell 20(7):1736–1737

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  • Vandesompele J, De Preter K, Pattyn F et al (2002) Accurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple internal control genes. Genome Biol 3:1–11

    Article  Google Scholar 

  • Wang Z, Chen Y, Fang H, Shi H, Chen K, Zhang Z, Tan X (2014) Selection of reference genes for quantitative reverse transcription polymerase chain reaction normalization in Brassica napus under various stress conditions. Mol Genet Genomics 289(5):1023–1035

    Article  CAS  PubMed  Google Scholar 

  • Wei LQ, Xu WY, Deng ZY et al (2010) Genome-scale analysis and comparison of gene expression profiles in developing and germinated pollen in Oryza sativa. BMC Genom 11:338

    Article  Google Scholar 

  • Weselake RJ, Taylor DC, Rahman MH, Shah S, Laroche A, McVetty PB, Harwood JL (2009) Increasing the flow of carbon into seed oil. Biotech Adv 27:866–878

    Article  CAS  Google Scholar 

  • Willmann MR, Mehalick AJ, Packer RL, Jenik PD (2011) MicroRNAs regulate the timing of embryo maturation in Arabidopsis. Plant Physiol 155:1871–1884

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  • Yang H, Liu J, Huang S, Guo T, Deng L, Hua W (2014) Selection and evaluation of novel reference genes for quantitative reverse transcription PCR (qRT-PCR) based on genome and transcriptome data in Brassica napus L. Gene 538(1):113–122

    Article  CAS  PubMed  Google Scholar 

  • Zhao Y-T, Wang M, Fu S-X et al (2012) Small RNA profiling in two Brassica napus cultivars identifies microRNAs with oil production- and development-correlated expression and new small RNA classes. Plant Physiol 158:813–823

    Article  CAS  PubMed Central  PubMed  Google Scholar 

Download references

Acknowledgments

This work was financially supported by Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES-CNPq, Brazilian Ministry of Education), Conselho Nacional de Desenvolvimento Científico e Tecnológico (Genoprot-CNPq-MCT No. 559636/2009-1; CNPq-Universal No. 472575/2011-2), Fundação de Amparo à Pesquisa do Estado do Rio Grande do Sul (Agroestruturante-FAPERGS). APK, APC, and GLM were sponsored by research and Ph.D. grants from CAPES. RM and MM were sponsored by research grants from CNPq.

Conflict of interest

The authors declare that they have no conflict of interest.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Ana Paula Körbes.

Additional information

Communicated by M. Menossi.

Electronic supplementary material

Below is the link to the electronic supplementary material.

299_2015_1773_MOESM1_ESM.tif

Supplementary Fig. 1 Ranking of candidate reference genes based on NormFinder analysis for (a) stages of seed development, (b) flowers, (c) and leaf samples. Lower stability values indicate more stable gene expression (TIFF 672 kb)

299_2015_1773_MOESM2_ESM.pdf

Supplementary Table 1 Primer sequences and amplicon characteristics of 61 candidate reference genes tested for gene expression normalization (PDF 58 kb)

Supplementary Table 2 Accession numbers of B. napus protein-coding genes used in this study (PDF 135 kb)

299_2015_1773_MOESM4_ESM.xlsx

Supplementary Table 3 Ranking of candidate reference genes for all tissues sample set was based on gene expression stability calculated by NormFinder. Each tissue sample set was considered as a group for NormaFinder analysis (XLSX 16 kb)

Supplementary Table 4 BestKeeper descriptive statistics calculated by RefFinder (XLSX 23 kb)

299_2015_1773_MOESM6_ESM.docx

Supplementary Table 5 Gene expression stability of the best candidate reference genes for flower data set (top) and leaf data set (bottom) as assessed by RefFinder (DOCX 16 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Machado, R.D., Christoff, A.P., Loss-Morais, G. et al. Comprehensive selection of reference genes for quantitative gene expression analysis during seed development in Brassica napus . Plant Cell Rep 34, 1139–1149 (2015). https://doi.org/10.1007/s00299-015-1773-1

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00299-015-1773-1

Keywords

Navigation