Abstract
Resistance against infection by the fungus Aspergillus flavus Link in commercial maize (Zea mays L.) is the topic of many studies, but few studies have investigated the effects of A. flavus infection on gene expression levels in ear kernels. A crucial component of gene expression profiling by RT-qPCR is having a reliable set of reference genes that show relatively constant expression across the treatments and phenotypes under study. Currently, however, there is no published information on reference genes suitable for measuring changes in kernel gene expression levels after infection with A. flavus. Thus, in this study, six candidate reference genes (ACT1, β-Tub2, eIF4A2, TATA, EFIα, and GAPDH) were evaluated and ranked according to their expression stability. The genes were amplified from first-strand cDNA samples synthesized from kernels of two susceptible and two resistant maize lines that were either inoculated with A. flavus or water or not inoculated. Three software packages were used to calculate and rank the stability of expression for these genesgeNorm, NormFinder, and BestKeeper. The analysis revealed that the most stable genes to normalize expression levels from maize kernels responding to A. flavus inoculation and wounding were ACT1, EFIα, and eIF4A2.
Highlights
Many commercial maize (Zea mays L.) varieties are highly susceptible to fungal pathogens, including Aspergillus flavus Link
The analysis revealed that the most stable genes to normalize expression levels from maize kernels responding to A. flavus inoculation and wounding were ACT1, EFIα, and eIF4A2
Over 200 genes have been proposed in the current literature as candidates that may help the maize plant suppress the effects of A. flavus [2] or the production of aflatoxin
Summary
Many commercial maize (Zea mays L.) varieties are highly susceptible to fungal pathogens, including Aspergillus flavus Link This fungus produces aflatoxin, the accumulation of which causes critical health and economic problems [1]. One of the preferred methods to quantify gene expression patterns via RT-qPCR is the 2−∆∆Cq method proposed by Livak and Schmittgen [4] This method measures the relative change in target transcripts between a treated and an untreated control sample, and it relies heavily on the normalization of the acquired Cq (quantitation cycle) values of all samples. Without the adjustment of the variation in the reverse-transcription yields and efficiency of amplification of mRNA, the comparison across different samples is meaningless [5] This normalization is usually done using a baseline reference gene, which is stably expressed across all samples and treatments evaluated in the study. The correct selection and use of stably expressed reference genes are part of the MIQE (minimum information for publication of qPCR experiments) guidelines of expression studies [7], a surprising number of published gene expression data using RT-qPCR still rely on the use of a single unvalidated reference gene [8]
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