Abstract
Due to its sensitivity and specificity, real-time quantitative PCR (qRT-PCR) is a popular technique for investigating gene expression levels in plants. Based on the Minimum Information for Publication of Real-Time Quantitative PCR Experiments (MIQE) guidelines, it is necessary to select and validate putative appropriate reference genes for qRT-PCR normalization. In the current study, three algorithms, geNorm, NormFinder, and BestKeeper, were applied to assess the expression stability of 10 candidate reference genes across five different tissues and three different abiotic stresses in Isatis indigotica Fort. Additionally, the IiYUC6 gene associated with IAA biosynthesis was applied to validate the candidate reference genes. The analysis results of the geNorm, NormFinder, and BestKeeper algorithms indicated certain differences for the different sample sets and different experiment conditions. Considering all of the algorithms, PP2A-4 and TUB4 were recommended as the most stable reference genes for total and different tissue samples, respectively. Moreover, RPL15 and PP2A-4 were considered to be the most suitable reference genes for abiotic stress treatments. The obtained experimental results might contribute to improved accuracy and credibility for the expression levels of target genes by qRT-PCR normalization in I. indigotica.
Highlights
Gene expression analysis is an effective strategy that offers certain important information related to gene functions in the plant life cycle and the signal pathways that regulate effective component accumulations in response to environmental changes (Miao et al, 2015; Shu et al, 2015; Yue et al, 2015; Vojta et al, 2016)
A total of 10 putative reference genes (UBC19, UBC22, UBC29, ACT7, PP2A-4, eIF2, APT3, AP-2, RPL15, and TUB4) from the transcriptome sequencing data of I. indigotica were selected as candidates for quantitative real-time PCR (qRT-PCR) normalization
Downregulation of the transcription level of IiYUC6 was noted in stem, leaf and root when normalized with RPL15, and similar expression patterns were observed when normalized with APT3 and RPL15
Summary
Gene expression analysis is an effective strategy that offers certain important information related to gene functions in the plant life cycle and the signal pathways that regulate effective component accumulations in response to environmental changes (Miao et al, 2015; Shu et al, 2015; Yue et al, 2015; Vojta et al, 2016). Selected methods can be applied to detect gene expression levels, including microarray/gene chips, RNA sequencing, semi-quantitative PCR (semi-qPCR), Northern blot, RNase protection analysis (RPA), and quantitative real-time PCR (qRT-PCR) (VanGuilder et al, 2008). Among these methods, qRT-PCR is considered one of the most popular techniques for analyzing gene expression levels due to its high sensitivity, specificity, reproducibility, accuracy, and high efficiency (Hu et al, 2014). These guidelines are most commonly used to select and normalize the reference gene for qRT-PCR analysis (Chao et al, 2012)
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