Selection and validation of suitable reference genes for qRT-PCR analysis in pear leaf tissues under distinct training systems

Zheng Liu, Kexin Cheng, Zhongqi Qin, Tao Wu, Xianming Li, Junfan Tu, Fuchen Yang, Hongyan Zhu and Li Yang

PLOS ONE, 2018, vol. 13, issue 8, 1-17

Abstract: Training systems generally alter tree architecture, which modulates light microclimate within the canopy, for the purpose of improving photosynthetic efficiency and fruit quality. Gene expression quantification is one of the most important methods for exploring the molecular mechanisms underlying the influence of training systems on pear photosynthesis, and suitable reference genes for gene expression normalization are a prerequisite for this method. In this study, the expression stability of nine common and four novel candidate genes were evaluated in 14 different pear leaf samples in two training systems, including those at four developmental stages (training_period) and from different parts of the trees (training_space), using two distinct algorithms, geNorm and NormFinder. Our results revealed that SKD1 (Suppressor of K+ Transport Growth Defect1)/ YLS8 (Yellow Leaf Specific 8) and ARM (Armadillo) were the most stable single reference genes for the ‘training_period’ and ‘training_space’ subsets, respectively, although these single genes were not as stable as the optimal pairs of reference genes, SKD1+YLS8 and ARM+YLS8, respectively. Furthermore, the expression levels of the PpsAPX (Ascorbate peroxidase) gene showed that the arbitrary use of reference genes without previous testing could lead to misinterpretation of data. This work constitutes the first systematic analysis regarding the selection of superior reference genes in training system studies, facilitating the elucidation of gene function in pear and providing valuable information for similar studies in other higher plants.

Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0202472

DOI: 10.1371/journal.pone.0202472

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