Abstract

Cryptomeria fortunei has become one of the main timber afforestation species in subtropical high-altitude areas of China due to its fast growth, good material quality, and strong adaptability, showing broad application prospects. Quantitative real-time PCR (qRT-PCR) is the most accurate and widely used gene expression evaluation technique, and selecting appropriate reference genes (RGs) is essential for normalizing qRT-PCR results. However, suitable RGs for gene expression normalization in C. fortunei have not been reported. Here, we tested the expression stability for 12 RGs in C. fortunei under various experimental conditions (simulated abiotic stresses (cold, heat, drought, and salinity) and hormone treatments (methyl jasmonate, abscisic acid, salicylic acid, and gibberellin) and in different tissues (stems, tender needles, needles, cones, and seeds) using four algorithms (delta Ct, geNorm, NormFinder, and BestKeeper). Then, geometric mean rankings from these algorithms and the RefFinder program were used to comprehensively evaluate RG stability. The results indicated CYP, actin, UBC, and 18S as good choices for studying C. fortunei gene expression. qRT-PCR analysis of the expression patterns of three target genes (CAT and MAPK1/6) further verified that the selected RGs were suitable for gene expression normalization. This study provides an important basis for C. fortunei gene expression standardization and quantification.

Highlights

  • Quantitative real-time PCR has the characteristics of high sensitivity, high efficiency, and convenient operation and can be used to accurately analyze experimental results [1,2,3,4]

  • These results showed that all 12 pairs of primers met the requirements of Quantitative real-time PCR (qRT-PCR) and could be used in further analysis

  • CYP and ubiquitinconjugating enzyme (UBC) showed the highest stability under heat stress, under GA3 treatment, under MeJA/SA treatment, under hormone treatments, and under abiotic stress/in all samples

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Summary

Introduction

Quantitative real-time PCR (qRT-PCR) has the characteristics of high sensitivity, high efficiency, and convenient operation and can be used to accurately analyze experimental results [1,2,3,4]. It is one of the most commonly used methods and the most important method for investigating gene expression. QRT-PCR results are affected by many variable factors, such as RNA template, reverse transcription efficiency, primer specificity, protocol variability, and data normalization and analysis method. The main problems caused by inconsistent data normalization and analysis are widely ignored [3]. It is very important to compare the expression levels of all tested genes with the reference genes (RGs) to maximize the reproducibility of data analysis to obtain more accurate and reliable analysis data.

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