Validation of Novel Reference Genes for Reverse Transcription Quantitative Real-Time PCR in Drought-Stressed Sugarcane. (2nd June 2014)

• Record Type: Journal Article
• Authors: Silva, Roberta Lane de Oliveira; Silva, Manassés Daniel; Ferreira Neto, José Ribamar Costa; Nardi, Claudia Huerta de; Chabregas, Sabrina Moutinho; Burnquist, William Lee; Kahl, Günter; Benko-Iseppon, Ana Maria; Kido, Ederson Akio
• Other Names: Loor Juan J. Academic Editor.
• Is Part Of: TheScientificWorldjournal. Volume 2014(2014)
• Publication Date: 2014-06-02
• Subjects: Science -- Periodicals; Technology -- Periodicals; Medicine -- Periodicals; 505
• DOI: 10.1155/2014/357052
• Languages: English
• ISSNs: 2356-6140

Abstract

Abstract : One of the most challenging aspects of RT-qPCR data analysis is the identification of reliable reference genes. Ideally, they should be neither induced nor repressed under different experimental conditions. To date, few reference genes have been adequately studied for sugarcane ( Saccharum spp.) using statistical approaches. In this work, six candidate genes ( α TUB, GAPDH, H1, SAMDC, UBQ, and 25S rRNA) were tested for gene expression normalization of sugarcane root tissues from drought-tolerant and -sensitive accessions after continuous dehydration (24 h). By undergoing different approaches (GeNorm, NormFinder, and BestKeeper), it was shown that most of them could be used in combinations for normalization purposes, with the exception of SAMDC. Nevertheless three of them (H1, α TUB, and GAPDH) were considered the most reliable reference genes. Their suitability as reference genes validated the expression profiles of two targets (AS and PFP α 1), related to SuperSAGE unitags, in agreement with results revealed by previous in silico analysis. The other two sugarcane unitags (ACC oxidase and PIP1-1), after salt stress (100 mM NaCl), presented their expressions validated in the same way. In conclusion, these reference genes will be useful for dissecting gene expression in sugarcane roots under abiotic stress, especially in transcriptomic studies using SuperSAGE or RNAseq approaches.