Abstract
Reverse transcription coupled with real-time quantitative PCR (RT-qPCR) is a frequently used method for gene expression profiling. Reference genes (RGs) are commonly employed to normalize gene expression data. A limited information exist on the gene expression and profiling in developing barley caryopsis. Expression stability was assessed by measuring the cycle threshold (Ct) range and applying both the GeNorm (pair-wise comparison of geometric means) and Normfinder (model-based approach) principles for the calculation. Here, we have identified a set of four RGs suitable for studying gene expression in the developing barley caryopsis. These encode the proteins GAPDH, HSP90, HSP70 and ubiquitin. We found a correlation between the frequency of occurrence of a transcript in silico and its suitability as an RG. This set of RGs was tested by comparing the normalized level of β-amylase (β-amy1) transcript with directly measured quantities of the BMY1 gene product in the developing barley caryopsis. This panel of genes could be used for other gene expression studies, as well as to optimize β-amy1 analysis for study of the impact of β-amy1 expression upon barley end-use quality.
Highlights
Gene expression analysis is a major focus of current biological research and large data sets continue to be generated from the application of various analytical platforms [1,2]
In Silico Analysis Candidate Reference genes (RGs) selected according to their performance previously reported by Facciolli et al [25] and two RGs commonly used in different plant species were firstly analyzed in comparison to UniGene databases build #56 (Apr-2010)
All sequences corresponding to each of the candidate RGs were represented among the 23,542 entries present in UniGene database
Summary
Gene expression analysis is a major focus of current biological research and large data sets continue to be generated from the application of various analytical platforms [1,2]. Bustin et al [4] have emphasized that some of the apparent differences that emerge from many transcriptomic analyses are artefactual, due to uncontrolled variation in, among other things, sample preparation, nucleic acid isolation, cDNA synthesis and PCR amplification These factors contribute to a variable extent from poor reproducibility to inaccurate data [5,6,7]. Normalization is typically based on either the expression of a constitutively expressed gene or total RNA content The limitations of the latter are understood and its precision is highly dependent on the accurate quantification of the RNA content of the sample [8,9]. The former strategy can be extended to two or more RGs and various methods have been established to use RGs expression levels to correct raw expression data [10]
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