Identification of Suitable Reference Genes for Gene Expression Normalization in the Quantitative Real-Time PCR Analysis of Sweet Osmanthus (Osmanthus fragrans Lour.)

摘要

Quantitative real-time PCR (RT-qPCR), a sensitive technique for quantifying gene expression, depends on the stability of the reference gene(s) used for data normalization. Several studies examining the selection of reference genes have been performed in ornamental plants but none in sweet osmanthus (Osmanthus fragrans Lour.). Based on transcriptomic sequencing data from O. fragrans buds at four developmental stages, six reference genes (OfACT, OfEF1α, OfIDH, OfRAN1, OfTUB, and OfUBC2) with stable expression (0.5 to 2 fold change in expression levels between any two developmental stages), as well as the commonly used reference gene Of18S, were selected as candidates for gene expression normalization in the RT-qPCR analysis of O. fragrans. For the normalization of RT-qPCR with two dyes, SYBR Green and EvaGreen, the expressional stability of seven candidate reference genes in 43 O. fragrans samples was analyzed using geNorm, NormFinder and BestKeeper. For RT-qPCR using SYBR Green, OfRAN1 and OfUBC2 were the optimal reference genes for all samples and different cultivars, OfACT and OfEF1α were suitable for different floral developmental stages, and OfACT was the optimal reference gene for different temperature treatments. The geometric mean values of the optimal reference gene pairs for the normalization of RT-qPCR are recommended to be used for all samples, different cultivars and different floral developmental stages in O. fragrans. For RT-qPCR using EvaGreen, OfUBC2 was the optimal reference gene for all samples and different cultivars, and OfACT was the optimal reference gene for different floral developmental stages and different temperature treatments. As the worst reference gene, Of18S should not be used as a reference gene in O. fragrans in the future. Our results provide a reference gene application guideline for O. fragrans gene expression characterization using RT-qPCR.