Abstract
Quantitative analysis of gene expression is a fundamental experimental approach in many fields of plant biology, but it requires the use of internal controls representing constitutively expressed genes for reliable transcript quantification. In this study, we identified fifteen putative reference genes from an A. angustifolia transcriptome database. Variation in transcript levels was first evaluated in silico by comparing read counts and then by quantitative real-time PCR (qRT-PCR), resulting in the identification of six candidate genes. The consistency of transcript abundance was also calculated applying geNorm and NormFinder software packages followed by a validation approach using four target genes. The results presented here indicate that a diverse set of samples should ideally be used in order to identify constitutively expressed genes, and that the use of any two reference genes in combination, of the six tested genes, is sufficient for effective expression normalization. Finally, in agreement with the in silico prediction, a comprehensive analysis of the qRT-PCR data combined with validation analysis revealed that AaEIF4B-L and AaPP2A are the most suitable reference genes for comparative studies of A. angustifolia gene expression.