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Selecting reference genes in RT-qPCR based on equivalence tests: a network based approach.

ABSTRACT: Because quantitative reverse transcription PCR (RT-qPCR) gene expression data are compositional, amounts of quantified RNAs must be normalized using reference genes. However, the two most used methods to select reference genes (NormFinder and geNorm) ignore the compositional nature of RT-qPCR data, and often lead to different results making reliable reference genes selection difficult. We propose a method, based on all pairwise equivalence tests on ratio of gene expressions, to select genes that are stable enough to be used as reference genes among a set a candidate genes. This statistical procedure controls the error of selecting an inappropriate gene. Application to 30 candidate reference genes commonly used in human studies, assessed by RT-qPCR in RNA samples from lymphoblastoid cell lines of 14 control subjects and 26 patients with bipolar disorder, allowed to select 7 reference genes. This selection was consistent with geNorm's ranking, less with NormFinder's ranking. Our results provide an important fundamental basis for reference genes identification using sound statistics taking into account the compositional nature of RT-qPCR data. The method, implemented in the SARP.compo package for R (available on the CRAN), can be used more generally to prove that a set of genes shares a common expression pattern.

SUBMITTER: Curis E

PROVIDER: S-EPMC6838083 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature

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Selecting reference genes in RT-qPCR based on equivalence tests: a network based approach.

Curis Emmanuel E Nepost Calypso C Grillault Laroche Diane D Courtin Cindie C Laplanche Jean-Louis JL Etain Bruno B Marie-Claire Cynthia C

Scientific reports 20191107 1

Because quantitative reverse transcription PCR (RT-qPCR) gene expression data are compositional, amounts of quantified RNAs must be normalized using reference genes. However, the two most used methods to select reference genes (NormFinder and geNorm) ignore the compositional nature of RT-qPCR data, and often lead to different results making reliable reference genes selection difficult. We propose a method, based on all pairwise equivalence tests on ratio of gene expressions, to select genes that ...[more]

PMID: 31700128

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Project description:BackgroundThe key to understanding changes in gene expression levels using reverse transcription real-time quantitative polymerase chain reaction (RT-qPCR) relies on the ability to rationalize the technique using internal control genes (ICGs). However, the use of ICGs has become increasingly problematic given that any genes, including housekeeping genes, thought to be stable across different tissue types, ages and treatment protocols, can be regulated at transcriptomic level. Our interest in prenatal glucocorticoid (GC) effects on fetal growth has resulted in our investigation of suitable ICGs relevant in this model. The usefulness of RNA18S, ACTB, HPRT1, RPLP0, PPIA and TUBB as ICGs was analyzed according to effects of early dexamethasone (DEX) treatment, gender, and gestational age by two approaches: (1) the classical approach where raw (i.e., not normalized) RT-qPCR data of tested ICGs were statistically analyzed and the best ICG selected based on absence of any significant effect; (2) used of published algorithms. For the latter the geNorm Visual Basic application was mainly used, but data were also analyzed by Normfinder and Bestkeeper. In order to account for confounding effects on the geNorm analysis due to co-regulation among ICGs tested, network analysis was performed using Ingenuity Pathway Analysis software. The expression of RNA18S, the most abundant transcript, and correlation of ICGs with RNA18S, total RNA, and liver-specific genes were also performed to assess potential dilution effect of raw RT-qPCR data. The effect of the two approaches used to select the best ICG(s) was compared by normalization of NR3C1 (glucocorticoid receptor) mRNA expression, as an example for a target gene.ResultsRaw RT-qPCR data of all the tested ICGs was significantly reduced across gestation. TUBB was the only ICG that was affected by DEX treatment. Using approach (1) all tested ICGs would have been rejected because they would initially appear as not reliable for normalization. However, geNorm analysis (approach 2) of the ICGs indicated that the geometrical mean of PPIA, HPRT1, RNA18S and RPLPO can be considered a reliable approach for normalization of target genes in both control and DEX treated groups. Different subset of ICGs were tested for normalization of NR3C1 expression and, despite the overall pattern of the mean was not extremely different, the statistical analysis uncovered a significant influence of the use of different normalization approaches on the expression of the target gene. We observed a decrease of total RNA through gestation, a lower decrease in raw RT-qPCR data of the two rRNA measured compared to ICGs, and a positive correlation between raw RT-qPCR data of ICGs and total RNA. Based on the same amount of total RNA to performed RT-qPCR analysis, those data indicated that other mRNA might have had a large increase in expression and, as consequence, had artificially diluted the stably expressed genes, such as ICGs. This point was demonstrated by a significant negative correlation of raw RT-qPCR data between ICGs and liver-specific genes.ConclusionThe study confirmed the necessity of assessing multiple ICGs using algorithms in order to obtain a reliable normalization of RT-qPCR data. Our data indicated that the use of the geometrical mean of PPIA, HPRT1, RNA18S and RPLPO can provide a reliable normalization for the proposed study. Furthermore, the dilution effect observed support the unreliability of the classical approach to test ICGs. Finally, the observed change in the composition of RNA species through time reveals the limitation of the use of ICGs to normalize RT-qPCR data, especially if absolute quantification is required.

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Selecting reference genes in RT-qPCR based on equivalence tests: a network based approach.

Publications

Selecting reference genes in RT-qPCR based on equivalence tests: a network based approach.

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