Abstract
Quantitative reverse transcription polymerase chain reaction (RT-qPCR), a sensitive technique for gene expression analysis, depends on the stability of the reference genes used for data normalization under different experimental conditions. Bursaphelenchus mucronatus, a pine-parasitic nematode varying in virulence, is widely distributed in natural pine forests throughout the northern hemisphere, but has not been investigated with respect to the identification of reference genes suitable for the normalization of RT-qPCR data. In the present study, eight candidate reference genes were analyzed in B. mucronatus under different habitat conditions and at different developmental stages. The expression stability of these genes was assessed by geNorm, NormFinder, BestKeeper, delta Cq, and RefFinder algorithms. In general, our results identified encoding beta-tubulin as the most stable gene. Moreover, pairwise analysis showed that three reference genes were sufficient to normalize the gene expression data under each set of conditions, with genes encoding beta-tubulin, 18S ribosomal RNA and ubiquitin-conjugating enzyme being the most suitable reference genes for different habitat conditions, whereas genes encoding beta-tubulin, histone, and 18S ribosomal RNA exhibited the most stable expression at different developmental stages. Validation of the selected reference genes was performed by profiling the expression of the fatty acid- and retinol-binding protein gene in different habitats, and by profiling the expression of the arginine kinase gene at different developmental stages. This first systematic analysis for the selection of suitable reference genes for RT-qPCR in B. mucronatus will facilitate future functional analyses and deep mining of genetic resources in this nematode.
Highlights
Quantitative reverse transcription polymerase chain reaction (RT-qPCR) is one of the most effective technologies used for quantifying gene expression in terms of transcript abundance and is characterized by high sensitivity and specificity, high reproducibility, and high-throughput capacity for a limited number of target genes (Heid et al, 1996; Livak and Schmittgen, 2001).Reference Genes in B. mucronatusRT-qPCR is a valuable method to analyze transcript abundance levels in different organisms (Giulietti et al, 2001) at different developmental stages (Huang et al, 2014; Wang et al, 2015) and under different physiological conditions
Based on the transcriptome de novo assembly sequences of B. mucronatus (Supplementary Table S1), two to three primer pairs were designed for each candidate reference gene to amplify one single PCR product, and confirmed by the dissociation assay following RT-qPCR
Previous research had shown that no single reference gene could be effectively used in the quantification of gene expression levels in all species or under all experimental conditions, because its expression levels always vary considerably under different experimental conditions (Suzuki et al, 2000; Kozera and Rapacz, 2013)
Summary
Quantitative reverse transcription polymerase chain reaction (RT-qPCR) is one of the most effective technologies used for quantifying gene expression in terms of transcript abundance and is characterized by high sensitivity and specificity, high reproducibility, and high-throughput capacity for a limited number of target genes (Heid et al, 1996; Livak and Schmittgen, 2001).Reference Genes in B. mucronatusRT-qPCR is a valuable method to analyze transcript abundance levels in different organisms (Giulietti et al, 2001) at different developmental stages (Huang et al, 2014; Wang et al, 2015) and under different physiological conditions. The most widely used method for normalization is to include one or a series of reference genes, whose expression is presumed to be stable under different experimental conditions. To obtain the available reference genes for qPCR, several statistical algorithms have been established to identify reference genes with stable expression levels, such as the delta Cq method, BestKeeper, geNorm, NormFinder, and RefFinder. NormFinder ranks candidate reference genes by calculating their stability value (SV) among samples in the given groups, and selects genes with a lower SV, considered to exhibit higher expression stability (Andersen et al, 2004). The RefFinder software comprehensively ranks the candidate reference genes based on the geometric mean (GM) values of the results from the four different statistical algorithms described above (Xie et al, 2012). With the help of these five statistical algorithms, much research on the validation of reference genes under different experimental conditions has been reported (Liu et al, 2017; Sadangi et al, 2017; Sprang et al, 2017; Mughal et al, 2018)
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