Abstract
Strawberry (Fragaria spp) is an emerging model for the development of basic genomics and recombinant DNA studies among rosaceous crops. Functional genomic and molecular studies involve relative quantification of gene expression under experimental conditions of interest. Accuracy and reliability are dependent upon the choice of an optimal reference control transcript. There is no information available on validated endogenous reference genes for use in studies testing strawberry-pathogen interactions. Thirteen potential pre-selected strawberry reference genes were tested against different tissues, strawberry cultivars, biotic stresses, ripening and senescent conditions, and SA/JA treatments. Evaluation of reference candidate’s suitability was analyzed by five different methodologies, and information was merged to identify best reference transcripts. A combination of all five methods was used for selective classification of reference genes. The resulting superior reference genes, FaRIB413, FaACTIN, FaEF1α and FaGAPDH2 are strongly recommended as control genes for relative quantification of gene expression in strawberry. This report constitutes the first systematic study to identify and validate optimal reference genes for accurate normalization of gene expression in strawberry plant defense response studies.
Highlights
Transcriptomic analyses are essential in understanding complex molecular processes occurring in plants
Candidate genes were selected for further analysis based on inhouse data and information obtained from a range of microarrays experiments ([33], Amil-Ruiz et al, unpublished)
This work has mainly been focused to the evaluation of a set of potential strawberry reference genes for plant-defense response studies
Summary
Transcriptomic analyses are essential in understanding complex molecular processes occurring in plants. The data found by global expression techniques need to be considered carefully, typically using relative quantification of gene expression by quantitative reverse transcription (RTqPCR) This method is used as a primary source of in-depth molecular expression information for a smaller set of gene candidates due to its wide range of quantification, reproducibility, and higher precision and accuracy [1], [2], [3]. This approach requires knowledge of stably expressed reference genes for data normalization of target genes under specific experimental conditions. Either only gross changes in gene expression level are declared statistically significant, or the pattern of gene expression is inaccurately characterized [4], [5]
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