Selection and Validation of Reference Genes for Gene Expression Studies in Codonopsis pilosula Based on Transcriptome Sequence Data

Abstract

Relative gene expression analyses by RT-qPCR (reverse transcription-quantitative PCR) are highly dependent on the reference genes in normalizing the expression data of target genes. Therefore, inappropriate endogenous control genes will lead to inaccurate target gene expression profiles, and the selection and validation of suitable internal reference genes becomes essential. In this study, we retrieved the commonly used reference genes in transcriptome datasets of Codonopsis pilosula by RNA-Seq (unpublished data), and selected 15 candidate reference genes according to the coefficient of variation (CV) and fold change (FC) value of gene expression. The expression levels of candidate reference genes, which is at different growth stages, undergoing cold stress and drought stress, was determined by RT-qPCR. The expression stability of these genes was evaluated using software packages and algorithms including ΔCt, geNorm, NormFinder and Bestkeeper. Then appropriate reference genes were screened and validated by target gene-UDGPase (UDP glucose pyrophosphorylase). The optimal RGs combinations of C. pilosula, including PP2A59γ, CPY20-1, UBCE32, RPL5B and UBC18 for developmental stage, RPL5B, RPL13 and PP2A59γ for cold treatment, RPL13 and PP2A59γ for drought treatment, were found and proposed as reference genes for future work. This paper laid foundations for both the selection of reference genes and exploration in metabolic mechanism of C. pilosula.

Figure 1

RT-qPCR Ct values and interquartile ranges. (A) Ct values for each RG in all samples. A line across the box depicts the median. The box indicates the 25% and 75% percentiles. Whiskers represent the maximum and minimum values, circles represent outliers and asterisks indicate extremes. (B) interquartile ranges indicate variability of Ct values among 25% and 75%.

Figure 2

Average expression stability values (M) and ranking of the candidate RGs calculated using geNorm. A lower value of the average expression stability indicates more stable expression.

Figure 3

Pairwise varation to determine the optimal number of control genes for accurate normalization. The pairwise variation (V_n/V_{n + 1}) was analyzed between the NFs (NFn and NFn+1) using geNorm software, where n is the number of genes involved in the NF. Red figures indicate the optimal number of genes for normalization.

Figure 4

Relative quantity of UDGPase in different developmental stage with different RGs after normalization. Samples A, B, C were normalized with a single top-ranked RG (TR)- PP2A59γ. samples D, E, F were normalized with a combination of multiple top-ranked RGs (TRs)- PP2A59γ, CPY20-1, UBCE32, RPL5B and UBC18, while samples G, H, I were normalized with a single low-ranked RG (LR)- ACTβ.

Figure 5

Relative quantity of UDGPase in cold stress with different RGs after normalization. Samples A, B, C were normalized with a single top-ranked RG (TR)- RPL5B. samples D, E, F were normalized with a combination of multiple top-ranked RGs (TRs)- RPL5B, RPL13 and PP2A59γ, while samples G, H, I were normalized with a single low-ranked RG (LR)- ACTβ.

Figure 6

Relative quantity of UDGPase in drought stress with different RGs after normalization. Samples A, B, C were normalized with a single top-ranked RG (TR) - RPL13. samples D, E, F were normalized with a combination of multiple top-ranked RGs (TRs)- RPL13 and PP2A59γ, while samples G, H, I were normalized with a single low-ranked RG (LR)- ACTβ.