Cloning and evaluation of reference genes for quantitative real-time PCR analysis in .

Quantitative real-time reverse transcription PCR (RT-qPCR) has been widely used in the detection and quantification of gene expression levels because of its high accuracy, sensitivity, and reproducibility as well as its large dynamic range. However, the reliability and accuracy of RT-qPCR depends on accurate transcript normalization using stably expressed reference genes. is a perennial plant with a high content of konjac glucomannan (KGM) in its corm. This crop has been used as a food source and as a traditional medicine for thousands of years. Without adequate knowledge of gene expression profiles, there has been no report of validated reference genes in . In this study, nine genes that are usually used as reference genes in other crops were selected as candidate reference genes. These putative sequences of these genes were cloned by the use of degenerate primers. The expression stability of each gene was assessed in different tissues and under two abiotic stresses (heat and waterlogging) in and . Three distinct algorithms were used to evaluate the expression stability of the candidate reference genes. The results demonstrated that , , and were the best reference genes under heat stress in . Furthermore, , , , and were the best reference genes in waterlogged conditions. By comparing different tissues from all samples, we determined that , and were stable in these sets. In addition, the suitability of these reference genes was confirmed by validating the expression of a gene encoding the small heat shock protein , which is related to heat stress in . In sum, and were the two best reference genes for normalizing mRNA levels in different tissues and under various stress treatments, and we suggest using one of these genes in combination with 1 or 2 reference genes associated with different biological processes to normalize gene expression. Our results will provide researchers with appropriate reference genes for further gene expression quantification using RT-qPCR in .