Identification and validation of the reference genes in the echiuran worm Urechis unicinctus based on transcriptome...

Real-time quantitative PCR (RT-qPCR) is a crucial and widely used method for gene expression analysis. Selecting suitable reference genes is extremely important for the accuracy of RT-qPCR results. Commonly used reference genes are not always stable in various organisms or under different environmental conditions. With the increasing application of high-throughput sequencing, transcriptome analysis has become an effective method for identifying novel stable reference genes.

In this study, we identified candidate reference genes based on transcriptome data covering embryos and larvae of early development, normal adult tissues, and the hindgut under sulfide stress using the coefficient of variation (CV) method in the echiuran Urechis unicinctus, resulting in 6834 (15.82%), 7110 (16.85%) and 13880 (35.87%) candidate reference genes, respectively. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses revealed that the candidate reference genes were significantly enriched in cellular metabolic process, protein metabolic process and ribosome in early development and normal adult tissues as well as in cellular localization and endocytosis in the hindgut under sulfide stress. Subsequently, ten genes including five new candidate reference genes and five commonly used reference genes, were validated by RT-qPCR. The expression stability of the ten genes was analyzed using four methods (geNorm, NormFinder, BestKeeper, and ∆Ct). The comprehensive results indicated that the new candidate reference genes were more stable than most commonly used reference genes. The commonly used ACTB was the most unstable gene. The candidate reference genes STX12, EHMT1, and LYAG were the most stable genes in early development, normal adult tissues, and hindgut under sulfide stress, respectively. The log₂(TPM) of the transcriptome data was significantly negatively correlated with the Ct values of RT-qPCR (Ct =  - 0.5405 log₂(TPM) + 34.51), which made it possible to estimate the Ct value before RT-qPCR using transcriptome data.

Our study is the first to select reference genes for RT-qPCR from transcriptome data in Echiura and provides important information for future gene expression studies in U. unicinctus.