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Evaluation of candidate reference genes for normalization of quantitative RT-PCR in soybean tissues under various
Dung Tien Le1, Donavan L Aldrich, Babu Valliyodan
1Signaling Pathway Research Unit, RIKEN Plant Science Center, Yokohama, Kanagawa, Japan.
Abstract:
Quantitative RT-PCR can be a very sensitive and powerful technique for measuring differential gene expression. Changes in gene expression induced by abiotic stresses are complex and multifaceted, which make determining stably expressed genes for data normalization difficult. To identify the most suitable reference genes for abiotic stress studies in soybean, 13 candidate genes collected from literature were evaluated for stability of expression under dehydration, high salinity, cold and ABA (abscisic acid) treatments using delta CT and geNorm approaches. Validation of reference genes indicated that the best reference genes are tissue- and stress-dependent. With respect to dehydration treatment, the Fbox/ABC, Fbox/60s gene pairs were found to have the highest expression stability in the root and shoot tissues of soybean seedlings, respectively. Fbox and 60s genes are the most suitable reference genes across dehydrated root and shoot tissues. Under salt stress the ELF1b/IDE and Fbox/ELF1b are the most stably expressed gene pairs in roots and shoots, respectively, while 60s/Fbox is the best gene pair in both tissues. For studying cold stress in roots or shoots, IDE/60s and Fbox/Act27 are good reference gene pairs, respectively. With regard to gene expression analysis under ABA treatment in either roots, shoots or across these tissues, 60s/ELF1b, ELF1b/Fbox and 60s/ELF1b are the most suitable reference genes, respectively. The expression of ELF1b/60s, 60s/Fbox and 60s/Fbox genes was most stable in roots, shoots and both tissues, respectively, under various stresses studied. Among the genes tested, 60s was found to be the best reference gene in different tissues and under various stress conditions. The highly ranked reference genes identified from this study were proved to be capable of detecting subtle differences in expression rates that otherwise would be missed if a less stable reference gene was used.
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