Selection and Validation of Appropriate Reference Genes for Quantitative RT-PCR Analysis in Rubia yunnanensis Diels Based on Transcriptome Data

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Abstract

Real-time quantitative polymerase chain reaction (RT-qPCR) has been widely applied in gene expression and transcription abundance analysis because of its high sensitivity, good repeatability, and strong specificity. Selection of relatively stable reference genes is a precondition in order to obtain the reliable analysis results. However, little is known about evaluation of a set of reference genes through scientific experiments in Rubia plants. Here, 15 candidate reference genes were selected from R. yunnanensis transcriptome database and analyzed under abiotic stresses, hormone treatments, and different tissues. Among these 15 candidate reference genes, heterogeneous nuclear ribonucleoprotein (hnRNP), TATA binding protein (TBP), ribosomal protein L5 (RPL5), malate dehydrogenase (MDH), and elongation factor 1-alpha (EF-1α) were indicated as the five most stable reference genes by four statistical programs (geNorm, NormFinder, BestKeeper, and RefFinder). Ultimately, the validity of reference genes was confirmed by normalizing the expression of o-succinylbenzoate-CoA ligase (OSBL) and isochorismate synthase (ICS) involved in the anthraquinone biosynthesis pathway in different tissues and hormone treatments. Meanwhile, four other putative genes involved in the anthraquinone biosynthesis pathway were also normalized with the selected reference genes, which showed similar expression levels with those given by transcriptome data. This work is the first research that aims at a systematic validation on the stability of reference genes selected from R. yunnanensis transcriptome data and will be conducive to analyze gene expression in R. yunnanensis or other Rubia species.

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Yi, S., Lin, Q., Zhang, X., Wang, J., Miao, Y., & Tan, N. (2020). Selection and Validation of Appropriate Reference Genes for Quantitative RT-PCR Analysis in Rubia yunnanensis Diels Based on Transcriptome Data. BioMed Research International, 2020. https://doi.org/10.1155/2020/5824841

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