Selection and validation of reference genes for measuring gene expression in Toona ciliata under different experimental conditions by quantitative real-time PCR analysis

Abstract

Background Before studying gene expression of different organisms, it is important to determine the best reference gene. At present, the most accurate method of detecting gene expression is quantitative real-time PCR (RT-qPCR). With this method, reference genes that are stable in different biological systems and under different conditions can be obtained. Toona ciliata Roem (T. ciliata). is a valuable and fast-growing timber specie. In this study, 20 reference genes were identified using RT-qPCR, as a primary prerequisite for future gene expression analysis. Four different methods, geNorm, NormFinder, BestKeeper, and RankAggreg were used to evaluate the expression stability of the 20 candidate reference genes in various tissues under different conditions. Results The experimental results showed that TUB-α was the most stably expressed reference gene across all samples and UBC17 was the most stable in leaves and young stems under Hypsipyla robusta (H. robusta) and methyl jasmonate (MeJA) treatments. In addition, PP2C59 and UBC5B were the best-performing genes in leaves under H. robusta treatment, while HIS1 and ACT7 were the best reference genes in young stems. The two best reference genes were 60S-18 and TUB-α after treatment at 4 °C. The expression of HIS6 and MUB1 was the most stable under PEG6000 treatment. The accuracy of the selected reference genes was verified using the transcription factor MYB3 (TcMYB3) gene. Conclusions This is the first report to verify the best reference genes for normalizing gene expression in T. ciliata under different conditions, which will facilitate future elucidation of gene regulations in this species.

Selection and validation of reference genes for quantitative real-time PCR of Quercus mongolica Fisch. ex Ledeb under abiotic stresses

“…GeNorm and NormFinder calculate the gene expression stability and then sort the candiate reference genes by SV, whereas BestKeeper calculates the results based on the SD and CV of the Ct value [ 24 – 26 ]. Because of the various algorithms employed in the three software packages, they have distinct rankings for identical experimental datasets [ 12 ], despite the fact that the analysis outcomes of NormFinder and GeNorm in this study hardly changed. For instance, the analysis of BestKeeper and GeNorm suggested that PP2A and UBC5 were the two optimal reference genes in drought-treated leaves of Q .…”

“…RefFinder was applied to integrate and create comprehensive candidate reference gene rankings in accordance with the geometric average of each gene weight counted using each program [ 35 ]. Many researchers also employ RankAggreg to count the ultimate ranking [ 8 , 11 , 12 ]. RankAggreg applies a genetic algorithm or cross-entropy Monte Carlo algorithm to calculate the ranking of reference genes [ 36 ].…”

“…The gene expression levels were normalized with the two most stable, as well as the two least stable, candidate reference genes identified from this study. Finally, we used the 2 -△△CT method to calculate the relative expression levels of the verified genes [ 12 ]. Statistical analyses were performed using one-way ANOVA with SPSS 21.0 software.…”

Selection of Suitable Reference Genes for Gene Expression Normalization Studies in Dendrobium huoshanense

“…They are usually used to calibrate qPCR results, so a stable reference gene is needed for qPCR. Nevertheless, several studies indicated that the expression levels of these traditional housekeeping genes varied widely in various experimental conditions among many species [ 32 , 33 , 34 , 35 , 36 , 37 , 38 ]. Consequently, selecting a stable and suitable reference gene has become increasingly crucial.…”

Reference gene screening for analyzing gene expression in the heart, liver, spleen, lung and kidney of forest musk deer

“…The expression of an “ideal” reference gene will not vary with treatment or physiological state in the tissue studied. However, several studies have shown that no perfect reference gene exists because the reference gene can be influenced by species, different tissues, developmental stages and diseases, etc [ 2 , 17 , 29 ]. Consequently, it is becoming an essential component of qPCR to systematically evaluate common and novel reference genes derived from genomewide analyses so as to improve the reliability of research results.…”