Stability evaluation of reference genes for gene expression analysis by RT-qPCR in soybean under different conditions

文献类型： 外文期刊

第一作者： Wan, Qiao

作者： Wan, Qiao;Chen, Shuilian;Shan, Zhihui;Yang, Zhonglu;Chen, Limiao;Zhang, Chanjuan;Yuan, Songli;Hao, Qinnan;Zhang, Xiaojuan;Qiu, Dezhen;Chen, Haifeng;Zhou, Xinan

作者机构：

期刊名称：PLOS ONE （ 影响因子：3.24； 五年影响因子：3.788 ）

ISSN： 1932-6203

年卷期： 2017 年 12 卷 12 期

页码：

收录情况： SCI

摘要： Real-time quantitative reverse transcription PCR is a sensitive and widely used technique to quantify gene expression. To achieve a reliable result, appropriate reference genes are highly required for normalization of transcripts in different samples. In this study, 9 previously published reference genes (60S, Fbox, ELF1A, ELF1B, ACT11, TUA5, UBC4, G6PD, CYP2) of soybean [Glycine max (L.) Merr.] were selected. The expression stability of the 9 genes was evaluated under conditions of biotic stress caused by infection with soybean mosaic virus, nitrogen stress, across different cultivars and developmental stages. Delta Ct and geNorm algorithms were used to evaluate and rank the expression stability of the 9 reference genes. Results obtained from two algorithms showed high consistency. Moreover, results of pairwise variation showed that two reference genes were sufficient to normalize the expression levels of target genes under each experimental setting. For virus infection, ELF1A and ELF1B were the most stable reference genes for accurate normalization. For different developmental stages, Fbox and G6PD had the highest expression stability between two soybean cultivars (Tanlong No. 1 and Tanlong No. 2). ELF1B and ACT11 were identified as the most stably expressed reference genes both under nitrogen stress and among different cultivars. The results showed that none of the candidate reference genes were uniformly expressed at different conditions, and selecting appropriate reference genes was pivotal for gene expression studies with particular condition and tissue. The most stable combination of genes identified in this study will help to achieve more accurate and reliable results in a wide variety of samples in soybean.

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