Identification and Validation of Reference Genes for RT-qPCR Analysis in Non-Heading Chinese Cabbage Flowers - Wang, Cheng; Cui, Hong-Mi; Huang, Tian-Hong; Liu, Tong-Kun; Hou, Xi-Lin; Li, Ying (Frontiers in Plant Science, 2016)
Quantification of mRNA transcript levels analysis is increasingly important in furthering our insight into complex metabolic pathways and signaling networks which underlie physiological and developmental processes. Quantitative reverse transcription-PCR (RT-qPCR) has been used as the main analysis technique to quantify mRNA transcript levels as well as validate high-throughput data due to its high sensitivity, accuracy and specificity in various fields of biological research (Bustin 2002; Artico et al. 2010). However, the accuracy of RT-qPCR is easily affected by various factors, such as the quality of mRNA samples, enzymatic efficiency in cDNA synthesis, the efficiency of amplification and the differences in transcriptional activity between the tissues or cells analyzed (Pfaffl 2001; Derveaux et al. 2010). To avoid bias in RT-qPCR analysis, reliable internal controls, termed reference genes that are steadily expressed in different experimental condition, are essential for normalization. Some of the commonly used reference genes in plants, such as beta-tubulin-4 (TUB4), polyubiquitin (UBQ), glyceraldehyde-3-phosphate dehydrogenase (GAPDH), 18S ribosomal RNA (18S rRNA), actin 7 (ACT7), DNAJ-like protein (DNAJ), and alpha tubulin-5 (TUA5), mostly involving in basic cellular processes, such as intermediary metabolism and protein translation and thus, have been widely adopted for gene expression analyses in different plant species (Die et al. 2010; Kozera and Rapacz 2013; Wei et al. 2013; Xiao et al. 2014; Delporte et al. 2015). In addition, some new reference genes including F-box protein (F-box), acyl carrier proteins (ACP), phosphoenolpyruvate carboxylase-related kinase 1 (PEPKR1), hypothetical proteins of unknown function (UNK1), SAND family protein (SAND), and TAP42-interacting protein of 41 kDa (TIP41), were identified and found to express stably (Libault et al. 2008; Chang et al. 2012; Nakashima et al. 2013). However, no single reference gene can always keep its stability under variable conditions (Kim et al. 2003; Argyropoulos et al. 2006; Fu et al. 2013; Niu et al. 2015). A systematic validation of the expression stability of candidate reference genes in each experimental system should be carried out before using these reference genes for normalization data in gene expression analysis. Meanwhile, several statistical algorithms, such as geNorm (Vandesompele et al. 2002), NormFinder (Andersen et al. 2004) and