Reference gene selection for qRT-PCR assays inStellera chamaejasmesubjected to abiotic stresses and hormone treatments based on transcriptome datasets

Abstract

BackgroundStellera chamaejasme Linn, an important poisonous plant of the China grassland, is toxic to humans and livestock. The rapid expansion of S. chamaejasme has greatly damaged the grassland ecology and, consequently, seriously endangered the development of animal husbandry. To draft efficient prevention and control measures, it has become more urgent to carry out research on its adaptive and expansion mechanisms in different unfavorable habitats at the genetic level. Quantitative real-time polymerase chain reaction (qRT-PCR) is a widely used technique for studying gene expression at the transcript level; however, qRT-PCR requires reference genes (RGs) as endogenous controls for data normalization and only through appropriate RG selection and qRT-PCR can we guarantee the reliability and robustness of expression studies and RNA-seq data analysis. Unfortunately, little research on the selection of RGs for gene expression data normalization in S. chamaejasme has been reported.MethodIn this study, 10 candidate RGs namely, 18S, 60S, CYP, GAPCP1, GAPDH2, EF1B, MDH, SAND, TUA1, and TUA6, were singled out from the transcriptome database of S. chamaejasme, and their expression stability under three abiotic stresses (drought, cold, and salt) and three hormone treatments (abscisic acid, ABA; gibberellin, GA; ethephon, ETH) were estimated with the programs geNorm, NormFinder, and BestKeeper.ResultOur results showed that GAPCP1 and EF1B were the best combination for the three abiotic stresses, whereas TUA6 and SAND, TUA1 and CYP, GAPDH2 and 60S were the best choices for ABA, GA, and ETH treatment, respectively. Moreover, GAPCP1 and 60S were assessed to be the best combination for all samples, and 18S was the least stable RG for use as an internal control in all of the experimental subsets. The expression patterns of two target genes (P5CS2 and GI) further verified that the RGs that we selected were suitable for gene expression normalization.DiscussionThis work is the first attempt to comprehensively estimate the stability of RGs in S. chamaejasme. Our results provide suitable RGs for high-precision normalization in qRT-PCR analysis, thereby making it more convenient to analyze gene expression under these experimental conditions.

Identification of Suitable Reference Genes for RT-qPCR Assays in Liriodendron chinense (Hemsl.) Sarg

“…As a high throughput and accurate method of gene expression level analysis, RT-qPCR has been extensively applied in studies of functional genes across various samples and under different experimental conditions [2,3]. However, the accuracy of RT-qPCR analyses relies on the selection of suitable genes as internal control, because if an unstable gene is chosen as the internal control, the analysis yields unreliable results [31,32].…”

“…Reverse transcription quantitative polymerase chain reaction (RT-qPCR) has been extensively applied in gene expression analysis due to its high accuracy, high throughput, and high sensitivity [1][2][3]. To perform relative quantification via RT-qPCR, normalization is essential to eliminate experimental errors between samples [1], and the selection of appropriate internal genes has a significant influence on the relative quantification results [2]. If the expression pattern of the reference gene is unstable, then the accuracy of the assay will be reduced, and small changes in the expression of the target genes will be impossible to detect [1].…”

“…Reference genes usually have basic functions in cells or organisms and are normally expressed at relatively constant rates under different experimental conditions and across different organs [2,6]. Many genes, such as actin, beta-tubulin, elongation factor, polyubiquitin, eukaryotic translation initiation factor, glyceraldehyde-3-phosphate dehydrogenase, histone, ribosomal protein L, cyclophilin, and 18S RNA have been used as reference genes under different experimental conditions and in different species [2,[6][7][8][9][10][11][12][13][14][15] Different statistical algorithms have been developed to assess the expression stability of reference genes under certain experimental conditions for specific species; these algorithms include geNorm, BestKeeper, NormFinder, and RefFinder. These algorithms are widely employed to evaluate the expression stability of reference genes and identify reference genes that are consistent across various species such as Populus tomentosa Carr.…”

Reference gene selection in multiple Passiflora edulis tissues and under cold stress for RT- qPCR analysis

“…3, Fig. 5, Table 2) [8,23,51]. However, 18S could be used for data normalization in Bursaphelenchus mucronatus and C. sinensis under metal stress [45,49].…”

Selection and Validation of Reference Genes for the qRT-PCR Assays of Populus ussuriensis Gene Expression under Abiotic Stresses and Related ABA Treatment

“…Gene expression can be analyzed by several RNA quantification methods, including Northern blots, RNase protection assays, semi-quantitative reverse-transcription PCR, and quantitative real-time PCR (qRT-PCR), all of which rely on normalization procedures to quantitatively compare multiple samples [1]. qRT-PCR has become a common technique for gene expression analysis because of its rapidity, high sensitivity, and quantitative accuracy [2,3]. Moreover, qRT-PCR is regarded as the only effective way to measure the expression of genes with low mRNA copy number [4].…”