Identification of reference genes for expression studies using quantitative RT-PCR in spathe tissue of Anthurium andraeanum (Hort.)

Reverse transcription quantitative real-time PCR (RT-qPCR) is a sensitive technique capable of accurately characterizing gene expression. However, the lack of suitable reference genes for sample normalization is an impediment to reliable expression studies. The aim of this study was to identify stable reference genes for RT-qPCR studies in Anthurium andraeanum (Hort.). Eight putative reference genes were partially cloned from anthurium using degenerate primers and RT-qPCR assays were developed. Total RNA was isolated from spathe tissues from different cultivars, color groups, developmental stages and harvesting times, and the reference genes were assayed using a RT-qPCR. Datasets were analyzed using ANOVA and reference genes were ranked based on stability using the coefficient of variation, geNorm and NormFinder approaches. CYP, UBQ5 and EF1α ranked amongst the most stable genes, except for studies involving development stages, where EF1α ranked poorly. Conversely, GAPDH displayed highly significant differences (P < 0.001) in expression across cultivars and color groups and ranked as the least stable. Gene expression levels varied with 18S > UBQ5 > (CYP, EF1α) > (ACT, IF4A) > (TUB, GAPDH). 18S expression levels were found to be 1000 fold greater than expression levels of UBQ5, the second highest expressed gene. geNorm pairwise variation analysis demonstrated that only two of the recommended reference genes were necessary for accurate normalization of RT-qPCR datasets.

► Eight putative reference genes were cloned from Anthurium andraeanum (Hort.). ► Expression stabilities of the genes were evaluated using RT-qPCR in different cultivars, color groups, development stages and harvesting times. ► CYP, UBQ5 and EF1α were the most stable, except for development stages, where EF1α ranked poorly. ► GAPDH was the least stable. ► Using two stable reference genes would facilitate accurate normalization of RT-qPCR datasets.