Identification of candidate reference genes for quantitative RT-PCR in Miscanthus sinensis subjected to various abiotic stresses.

Clicks: 260
ID: 102066
2020
Quantitative real-time PCR (qRT-PCR) has been widely used for studying gene expression at the transcript level. Its accuracy usually relies on the reference genes that are utilized for data normalization. Miscanthus sinensis, a perennial C4 grass with high biomass and strong resistance to adversities, is often utilized as a high value energy crop. However, no reliable reference genes have been investigated for normalizing gene expression for this species. In this study, 12 candidate reference genes were selected to identify their stability under five different abiotic stress treatments (drought, salt, cadmium, chromium and arsenic) by using geNorm, NormFinder, BestKeeper and RefFinder softwares. The results showed that 18S rRNA and Unigene33312 were the best reference genes under drought treatments. Unigene33312 and Unigene33024 were found to be the most stably expressed genes under salt stress and Cd stress. Moreover, Unigene33024 and PP2A were the most suitable reference genes under Cr stress and Unigene33024 and Sb09g019750 were deemed more suitable reference genes under As stress. In total, considering all the samples, Unigene33024 and PP2A were the most stable genes while ACTIN and Unigene26576 were the least stable reference genes for internal control. The expression patterns of two target genes (Cu/Zn SOD and CAT) were used to further verify those selected reference genes under different conditions. The results showed that the most and the least stable reference genes had clearly different expression patterns. This work comprehensively estimated the stability of reference genes in M. sinensis which may give insight to the reference genes selection in other tissues as well as other related varieties. These suggested reference genes would assist in further putative gene expression validation in M. sinensis.
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zhong2020identificationmolecular Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Zhong, Minyi;Yang, Xinying;Hu, Yiyue;Huang, Linkai;Peng, Yan;Li, Zhou;Liu, Qiuxu;Wang, Xia;Zhang, Xinquan;Nie, Gang;
Journal molecular biology reports
Year 2020
DOI 10.1007/s11033-020-05392-9
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