Screening internal controls for expression analyses involving numerous treatments by combining statistical methods with reference gene selection tools
Real-time PCR is always the method of choice for expression analyses involving comparison of a large number of treatments. It is also the favored method for final confirmation of transcript levels followed by high throughput methods such as RNA sequencing and microarray. Our analysis comprised 16 different permutation and combinations of treatments involving four different Agrobacterium strains and three time intervals in the model plant Arabidopsis thaliana. The routinely used reference genes for biotic stress analyses in plants showed variations in expression across some of our treatments. In this report, we describe how we narrowed down to the best reference gene out of 17 candidate genes. Though we initiated our reference gene selection process using common tools such as geNorm, Normfinder and BestKeeper, we faced situations where these software-selected candidate genes did not completely satisfy all the criteria of a stable reference gene. With our novel approach of combining simple statistical methods such as t test, ANOVA and post hoc analyses, along with the routine software-based analyses, we could perform precise evaluation and we identified two genes, UBQ10 and PPR as the best reference genes for normalizing mRNA levels in the context of 16 different conditions of Agrobacterium infection. Our study emphasizes the usefulness of applying statistical analyses along with the reference gene selection software for reference gene identification in experiments involving the comparison of a large number of treatments.
KeywordsReference genes Arabidopsis Agrobacterium Stable expression Normalization Real-time PCR
We thank DST (Department of Science and Technology)-INSPIRE (Fellowship No.IF140978 and Grant No. IFA11-LSPA-04), India, for the doctoral fellowship of Joseph JT and project funding of Shah JM, respectively. We gratefully acknowledge K. Veluthambi (Madurai Kamaraj University, India) and Paul J. Hooykaas (Leiden University, the Netherlands) for providing Agrobacterium strains. We thank Maya N for preliminary support.
- 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. https://doi.org/10.1158/0008-5472.CAN-04-0496 CrossRefPubMedGoogle Scholar
- Guénin S, Mauriat M, Pelloux J, Van Wuytswinkel O, Bellini C, Gutierrez L (2009) Normalization of qRT-PCR data: the necessity of adopting a systematic, experimental conditions specific, validation of references. J Exp Bot 60:487–493. https://doi.org/10.1186/1471-2229-8-131 CrossRefPubMedGoogle Scholar
- Gutierrez L, Mauriat M, Guénin S, Pelloux J, Lefebvre J, Louvet R, Rusterucci C et al (2008) 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. https://doi.org/10.1111/j.1467-7652.2008.00346.x CrossRefPubMedGoogle Scholar
- Park SY, Vaghchhipawala Z, Vasudevan B, Lee LY, Shen Y, Singer K, Waterworth WM et al (2015) Agrobacterium T-DNA integration into the plant genome can occur without the activity of key non-homologous end-joining proteins. Plant J 81:934–946. https://doi.org/10.1111/tpj.12779 CrossRefPubMedPubMedCentralGoogle 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. https://doi.org/10.1023/B:BILE.0000019559.84305.47 CrossRefPubMedGoogle Scholar
- Remans T, Smeets K, Opdenakker K, Mathijsen D, Vangronsveld J, Cuypers A (2008) Normalisation of real-time RT-PCR gene expression measurements in Arabidopsis thaliana exposed to increased metal concentrations. Planta 227:1343–1349. https://doi.org/10.1007/s00425-008-0706-4 CrossRefPubMedPubMedCentralGoogle Scholar
- Shah JM, Ramakrishnan AM, Singh AK, Ramachandran S, Unniyampurath U, Jayshankar A et al (2015) Suppression of different classes of somatic mutations in Arabidopsis by vir gene-expressing Agrobacterium strains. BMC Plant Biol 15:210–223. https://doi.org/10.1186/s12870-015-0595-1 CrossRefPubMedPubMedCentralGoogle Scholar
- Veena Jiang H, Doerge RW, Gelvin SB (2003) Transfer of T-DNA and Vir proteins to plant cells by Agrobacterium tumefaciens induces expression of host genes involved in mediating transformation and suppresses host defense gene expression. Plant J 35:219–236. https://doi.org/10.1046/j.1365-313X.2003.01796.x CrossRefPubMedGoogle Scholar