Abstract
In the investigation of the expression levels of target genes, reverse transcription quantitative real-time polymerase chain reaction (RT-qPCR) is the most accurate and widely used method. However, a normalization step is a prerequisite to obtain accurate quantification results from RT-qPCR data. Therefore, many studies regarding the selection of reference genes have been carried out. Recently, these studies have involved large-scale gene analysis methods such as microarray and next generation sequencing. In our previous studies, we analyzed large amounts of transcriptome data from the cynomolgus monkey. Using a modification of this large-scale transcriptome sequencing dataset, we selected and compared 12 novel candidate reference genes (ARFGAP2, ARL1, BMI1, CASC3, DDX3X, MRFAP1, ORMDL1, RSL24D1, SAR1A, USP22, ZC3H11A, and ZRANB2) and 4 traditionally used reference genes (ACTB, GAPDH, RPS19, and YWHAZ) in 13 different whole-body tissues by the 3 well-known programs geNorm, NormFinder, and BestKeeper. Combined analysis by these 3 programs showed that ADP-ribosylation factor GTPase activating protein 2 (ARFGAP2), morf4 family associated protein 1 (MRFAP1), and ADP-ribosylation factor-like 1 (ARL1) are the most appropriate reference genes for accurate normalization. Interestingly, 4 traditionally used reference genes were the least stably expressed in this study. For this reason, selection of appropriate reference genes is vitally important, and large-scale analysis is a good method for finding new candidate reference genes. Our results could provide reliable reference gene lists for future studies on the expression of various target genes in the cynomolgus monkey.
Highlights
Reverse transcription quantitative real-time polymerase chain reaction (RT-qPCR) is a widely used experimental method for the measurement of mRNA expression levels, because this method has several advantages such as specificity, accuracy, and costeffectiveness
These results showed that the expression levels of traditionally used reference genes such as glyceraldehyde-3-phosphate dehydrogenase (GAPDH), ACTB, and beta-2-microglobulin (B2M) are unstable, and new reference genes identified from microarray and transcriptome sequencing analyses showed high stability in different experimental species and conditions
Normalization is essential in order to obtain accurate gene expression data from reverse transcription (RT)-qPCR experiments
Summary
Reverse transcription quantitative real-time polymerase chain reaction (RT-qPCR) is a widely used experimental method for the measurement of mRNA expression levels, because this method has several advantages such as specificity, accuracy, and costeffectiveness. Gene expression data obtained using RT-qPCR can be affected by a number of parameters such as differing sample amounts, RNA quality, purity, enzymatic efficiency in reverse transcription, and PCR efficiency [1,2]. Traditional reference genes such as glyceraldehyde-3-phosphate dehydrogenase (GAPDH) and b-actin (ACTB) are frequently used for normalization These genes have been shown to have variable expression levels across tissue types and experimental conditions [1,3]. Studies have focused on the selection of new reference genes in various species such as human, rhesus monkey, dog, rat, Escherichia coli, and buckwheat using microarray and transcriptome sequencing analyses [4–9] These results showed that the expression levels of traditionally used reference genes such as GAPDH, ACTB, and beta-2-microglobulin (B2M) are unstable, and new reference genes identified from microarray and transcriptome sequencing analyses showed high stability in different experimental species and conditions. The selection of new reference genes using microarray and transcriptome sequencing data could provide more reliable and appropriate reference genes for the normalization of target gene expression levels
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