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Evaluation of reference genes for accurate qRT-PCR normalization in bitter gourd across different tissues and stress conditions

Year: 2025
Type: article
Abstract: BACKGROUND: Real-time quantitative reverse transcription PCR (qRT-PCR) is a widely used technique for gene expression analysis, and its accuracy depends critically on stable reference genes for normalization. Bitter gourd (Momordica charantia L.), an annual cucurbit valued for both nutritional and medicinal properties, has recently attracted increasing attention in molecular research. However, systematic evaluation of suitable reference genes in this species remains lacking. RESULTS: In this study, eight candidate reference transcripts-McFBX, McARMT, McEXO70, McALDH12A1, McCPR5, McDRB2, McBLOC1S1, and McNPF4.6-were identified through integrated analysis of three public bitter gourd transcriptome datasets, based on high expression abundance and low variability. Their expression stability was evaluated across tissues, genotypes, and treatment conditions of bitter gourd using three commonly applied algorithms: geNorm, NormFinder, and BestKeeper, and RefFinder was subsequently used to perform a comprehensive stability ranking of these reference transcripts. For tissue-specific qRT-PCR, all eight genes showed acceptable stability, with McFBX being the most stable, followed by McNPF4.6. Under stress and hormone treatments, McARMT and McNPF4.6 were identified as the most appropriate controls, while McEXO70 was best suited for complex experimental designs involving multiple tissues, genotypes, and treatments. CONCLUSION: By broadening the evaluation framework, this study systematically validated the universality and reliability of reference genes under diverse scenarios. These findings provide a robust set of internal controls for accurate qRT-PCR analyses in bitter gourd and will improve the reproducibility of future molecular studies in this species.
Authors: Shihan Zeng, Tian Xie, Xiaoqi Shu, Zhuning Zhou, Daiyue Yang, Hongrui Ma, Xuzhen Li, Feifan Chen, Libo Tian, Sang Shang
Institution: Hainan University
Language: English
Open Access status: gold
Estimated APC paid: $3,090
OpenAlex ID: W4417304637
FWCI: 0.261
Cites: 32
Cited by: 1
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