Dataset Information

Expressed repetitive elements are broadly applicable reference targets for normalization of reverse transcription-qPCR data in mice.

ABSTRACT: Reverse transcription quantitative PCR (RT-qPCR) is the gold standard method for gene expression analysis on mRNA level. To remove experimental variation, expression levels of the gene of interest are typically normalized to the expression level of stably expressed endogenous reference genes. Identifying suitable reference genes and determining the optimal number of reference genes should precede each quantification study. Popular reference genes are not necessarily stably expressed in the examined conditions, possibly leading to inaccurate results. Stably and universally expressed repetitive elements (ERE) have previously been shown to be an excellent alternative for normalization using classic reference genes in human and zebrafish samples. Here, we confirm that in mouse tissues, EREs are broadly applicable reference targets for RT-qPCR normalization, provided that the RNA samples undergo a thorough DNase treatment. We identified Orr1a0, Rltr2aiap, and Rltr13a3 as the most stably expressed mouse EREs across six different experimental conditions. Therefore, we propose this set of ERE reference targets as good candidates for normalization of RT-qPCR data in a plethora of conditions. The identification of widely applicable stable mouse RT-qPCR reference targets for normalization has great potential to facilitate future murine gene expression studies and improve the validity of RT-qPCR data.

SUBMITTER: Renard M

PROVIDER: S-EPMC5955877 | biostudies-literature | 2018 May

REPOSITORIES: biostudies-literature

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Expressed repetitive elements are broadly applicable reference targets for normalization of reverse transcription-qPCR data in mice.

Renard Marjolijn M Vanhauwaert Suzanne S Vanhomwegen Marine M Rihani Ali A Vandamme Niels N Goossens Steven S Berx Geert G Van Vlierberghe Pieter P Haigh Jody J JJ Decaesteker Bieke B Van Laere Jolien J Lambertz Irina I Speleman Frank F Vandesompele Jo J Willaert Andy A

Scientific reports 20180516 1

Reverse transcription quantitative PCR (RT-qPCR) is the gold standard method for gene expression analysis on mRNA level. To remove experimental variation, expression levels of the gene of interest are typically normalized to the expression level of stably expressed endogenous reference genes. Identifying suitable reference genes and determining the optimal number of reference genes should precede each quantification study. Popular reference genes are not necessarily stably expressed in the exami ...[more]

PMID: 29769563

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Project description:Quantitative PCR (qPCR) remains the most widely used technique for gene expression evaluation. Obtaining reliable data using this method requires reference genes (RGs) with stable mRNA level under experimental conditions. This issue is especially crucial in cancer studies because each tumor has a unique molecular portrait. The Cancer Genome Atlas (TCGA) project provides RNA-Seq data for thousands of samples corresponding to dozens of cancers and presents the basis for assessment of the suitability of genes as reference ones for qPCR data normalization. Using TCGA RNA-Seq data and previously developed CrossHub tool, we evaluated mRNA level of 32 traditionally used RGs in 12 cancer types, including those of lung, breast, prostate, kidney, and colon. We developed an 11-component scoring system for the assessment of gene expression stability. Among the 32 genes, PUM1 was one of the most stably expressed in the majority of examined cancers, whereas GAPDH, which is widely used as a RG, showed significant mRNA level alterations in more than a half of cases. For each of 12 cancer types, we suggested a pair of genes that are the most suitable for use as reference ones. These genes are characterized by high expression stability and absence of correlation between their mRNA levels. Next, the scoring system was expanded with several features of a gene: mutation rate, number of transcript isoforms and pseudogenes, participation in cancer-related processes on the basis of Gene Ontology, and mentions in PubMed-indexed articles. All the genes covered by RNA-Seq data in TCGA were analyzed using the expanded scoring system that allowed us to reveal novel promising RGs for each examined cancer type and identify several "universal" pan-cancer RG candidates, including SF3A1, CIAO1, and SFRS4. The choice of RGs is the basis for precise gene expression evaluation by qPCR. Here, we suggested optimal pairs of traditionally used RGs for 12 cancer types and identified novel promising RGs that demonstrate high expression stability and other features of reliable and convenient RGs (high expression level, low mutation rate, non-involvement in cancer-related processes, single transcript isoform, and absence of pseudogenes).

Project description:BackgroundA critical step in the RT-qPCR workflow for studying gene expression is data normalization, one of the strategies being the use of reference genes. This study aimed to identify and validate a selection of reference genes for relative quantification in Talaromyces versatilis, a relevant industrial filamentous fungus. Beyond T. versatilis, this study also aimed to propose reference genes that are applicable more widely for RT-qPCR data normalization in filamentous fungi.ResultsA selection of stable, potential reference genes was carried out in silico from RNA-seq based transcriptomic data obtained from T. versatilis. A dozen functionally unrelated candidate genes were analysed by RT-qPCR assays over more than 30 relevant culture conditions. By using geNorm, we showed that most of these candidate genes had stable transcript levels in most of the conditions, from growth environments to conidial germination. The overall robustness of these genes was explored further by showing that any combination of 3 of them led to minimal normalization bias. To extend the relevance of the study beyond T. versatilis, we challenged their stability together with sixteen other classically used genes such as β-tubulin or actin, in a representative sample of about 100 RNA-seq datasets. These datasets were obtained from 18 phylogenetically distant filamentous fungi exposed to prevalent experimental conditions. Although this wide analysis demonstrated that each of the chosen genes exhibited sporadic up- or down-regulation, their hierarchical clustering allowed the identification of a promising group of 6 genes, which presented weak expression changes and no tendency to up- or down-regulation over the whole set of conditions. This group included ubcB, sac7, fis1 and sarA genes, as well as TFC1 and UBC6 that were previously validated for their use in S. cerevisiae.ConclusionsWe propose a set of 6 genes that can be used as reference genes in RT-qPCR data normalization in any field of fungal biology. However, we recommend that the uniform transcription of these genes is tested by systematic experimental validation and to use the geometric averaging of at least 3 of the best ones. This will minimize the bias in normalization and will support trustworthy biological conclusions.

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Expressed repetitive elements are broadly applicable reference targets for normalization of reverse transcription-qPCR data in mice.

Publications

Expressed repetitive elements are broadly applicable reference targets for normalization of reverse transcription-qPCR data in mice.

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