Selection and Validation of Reference Genes for Quantitative Real-time PCR in Gentiana macrophylla

Abstract

Real time quantitative PCR (RT-qPCR or qPCR) has been extensively applied for analyzing gene expression because of its accuracy, sensitivity, and high throughput. However, the unsuitable choice of reference gene(s) can lead to a misinterpretation of results. We evaluated the stability of 10 candidates - five traditional housekeeping genes (UBC21, GAPC2, EF-1α4, UBQ10, and UBC10) and five novel genes (SAND1, FBOX, PTB1, ARP, and Expressed1) - using the transcriptome data of Gentiana macrophylla. Common statistical algorithms ΔC t, GeNorm, NormFinder, and BestKeeper were run with samples collected from plants under various experimental conditions. For normalizing expression levels from tissues at different developmental stages, GAPC2 and UBC21 had the highest rankings. Both SAND1 and GAPC2 proved to be the optimal reference genes for roots from plants exposed to abiotic stresses while EF-1α4 and SAND1 were optimal when examining expression data from the leaves of stressed plants. Based on a comprehensive ranking of stability under different experimental conditions, we recommend that SAND1 and EF-1α4 are the most suitable overall. In this study, to find a suitable reference gene and its real-time PCR assay for G. macrophylla DNA content quantification, we evaluated three target genes including WRKY30, G10H, and SLS, through qualitative and absolute quantitative PCR with leaves under elicitors stressed experimental conditions. Arbitrary use of reference genes without previous evaluation can lead to a misinterpretation of the data. Our results will benefit future research on the expression of genes related to secoiridoid biosynthesis in this species under different experimental conditions.

DOI: 10.3389/fpls.2016.00945

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@inproceedings{He2016SelectionAV, title={Selection and Validation of Reference Genes for Quantitative Real-time PCR in Gentiana macrophylla}, author={Yihan He and Hailing Yan and Wenping Hua and Yaya Huang and Zhezhi Wang}, booktitle={Front. Plant Sci.}, year={2016} }