Abstract

Reference genes are used for the correction of qRT-PCR data, and it is necessary to investigate the optimum reference gene under certain conditions. The expression levels of seven traditional reference genes ACT1, ACT2, GAPDH, 18S rRNA, UBQ, TUB and CYP were analyzed using qRT-PCR in different varieties, tissues, developmental stages and hormone (or pollen polysaccharide) treatments in kiwifruit. Gene expression stability was assessed with the help of three common software (geNorm, NormFinder, BestKeeper), and the minimum number of reference genes necessary for normalization was also determined. GAPDH, ACT1 and ACT2 were selected as reference genes for different genotypes of kiwifruit. GAPDH and UBQ were the best combinations of reference genes for root, stem, leaf, flower and fruit. GAPDH and ACT1 could be the preferred reference genes for normalization of qRT-PCR data during fruit development. The pairing of ACT1 and UBQ constituted the optimal combination of reference genes in kiwifruit treated with different hormones (or pollen polysaccharide). This study provides a new and reliable option for the use of reference genes in the analysis of gene expression patterns of interest in kiwifruit.

Highlights

  • Quantitative real-time PCR has become a mainstream method for gene expression analysis because of its high sensitivity, high specificity, good reproducibility, ease of operation and short time consumption [1,2,3]

  • ‘Jinshi 1’ potted live annual seedlings were treated with two hormones (melatonin (MT): M8600 and 14-hydroxybrassinosterol (HBR): B29511), and one pollen polysaccharide (PP), which are some of the substances that are beneficial in improving plant stress resistance, and the latter two substances are receiving a lot of attention

  • The amplification efficiency of the genes was calculated by performing Quantitative real-time PCR (qRT-PCR) using a 5-fold gradient dilution of an equal volume of mixed cDNA of all kiwifruit samples for testing as the template, followed by plotting the standard curve

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Summary

Introduction

Quantitative real-time PCR (qRT-PCR) has become a mainstream method for gene expression analysis because of its high sensitivity, high specificity, good reproducibility, ease of operation and short time consumption [1,2,3]. When performing gene expression analysis on multiple samples, it is often necessary to ensure that the samples have the same RNA quality, cDNA yield and gene amplification efficiency, but in practice, it is often difficult to meet these conditions [4,5]. To eliminate differences in RNA quality, cDNA yield and gene amplification efficiency between samples, it is often necessary to introduce reference genes to normalize the qRT-PCR data [6]. The use of inappropriate reference genes for normalization can lead to bias in the quantitative data [10]. In order to obtain more reliable results, one or more reference genes need to be selected for calibration in the experiment [11,12]

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