Evaluation of Angelica decursiva reference genes under various stimuli for RT-qPCR data normalization
Abstract Angelica decursiva is one of the lending traditional Chinese medicinal plants producing coumarins. Notably, several studies have focused on the biosynthesis and not the RT-qPCR (quantitative real-time reverse transcription polymerase chain reaction) study of coumarins. This RT-qPCR techniqu...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2021-09-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-021-98434-6 |
| _version_ | 1818683138778333184 |
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| author | Yuedong He Yuan Zhong Zhenzhen Bao Weiqi Wang Xiaoqing Xu Yanan Gai Jie Wu |
| author_facet | Yuedong He Yuan Zhong Zhenzhen Bao Weiqi Wang Xiaoqing Xu Yanan Gai Jie Wu |
| author_sort | Yuedong He |
| collection | DOAJ |
| description | Abstract Angelica decursiva is one of the lending traditional Chinese medicinal plants producing coumarins. Notably, several studies have focused on the biosynthesis and not the RT-qPCR (quantitative real-time reverse transcription polymerase chain reaction) study of coumarins. This RT-qPCR technique has been extensively used to investigate gene expression levels in plants and the selection of reference genes which plays a crucial role in standardizing the data form the RT-qPCR analysis. In our study, 11 candidate reference genes were selected from the existing transcriptome data of Angelica decursiva. Here, four different types of statistical algorithms (geNorm, NormFinder, BestKeeper, and Delta Ct) were used to calculate and evaluate the stability of gene expression under different external treatments. Subsequently, RefFinder analysis was used to determine the geometric average of each candidate gene ranking, and to perform comprehensive index ranking. The obtained results showed that among all the 11 candidate reference genes, SAND family protein (SAND), protein phosphatase 2A gene (PP2A), and polypyrimidine tract-binding protein (PTBP) were the most stable reference genes, where Nuclear cap binding protein 2 (NCBP2), TIP41-like protein (TIP41), and Beta-6-tubulin (TUBA) were the least stable genes. To the best of our knowledge, this work is the first to evaluate the stability of reference genes in the Angelica decursiva which has provided an important foundation on the use of RT-qPCR for an accurate and far-reaching gene expression analysis in this medicinal plant. |
| first_indexed | 2024-12-17T10:29:59Z |
| format | Article |
| id | doaj.art-3616474c6f3041d19f860ffc183b0353 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-12-17T10:29:59Z |
| publishDate | 2021-09-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-3616474c6f3041d19f860ffc183b03532022-12-21T21:52:33ZengNature PortfolioScientific Reports2045-23222021-09-0111111010.1038/s41598-021-98434-6Evaluation of Angelica decursiva reference genes under various stimuli for RT-qPCR data normalizationYuedong He0Yuan Zhong1Zhenzhen Bao2Weiqi Wang3Xiaoqing Xu4Yanan Gai5Jie Wu6School of Pharmacy, Jiangsu Health Vocational CollegeSchool of Pharmacy, Jiangsu Health Vocational CollegeSchool of Pharmacy, Jiangsu Health Vocational CollegeSchool of Pharmacy, Jiangsu Health Vocational CollegeSchool of Pharmacy, Jiangsu Health Vocational CollegeInstitute of Botany, Jiangsu Province and Chinese Academy of SciencesSchool of Pharmacy, Jiangsu Health Vocational CollegeAbstract Angelica decursiva is one of the lending traditional Chinese medicinal plants producing coumarins. Notably, several studies have focused on the biosynthesis and not the RT-qPCR (quantitative real-time reverse transcription polymerase chain reaction) study of coumarins. This RT-qPCR technique has been extensively used to investigate gene expression levels in plants and the selection of reference genes which plays a crucial role in standardizing the data form the RT-qPCR analysis. In our study, 11 candidate reference genes were selected from the existing transcriptome data of Angelica decursiva. Here, four different types of statistical algorithms (geNorm, NormFinder, BestKeeper, and Delta Ct) were used to calculate and evaluate the stability of gene expression under different external treatments. Subsequently, RefFinder analysis was used to determine the geometric average of each candidate gene ranking, and to perform comprehensive index ranking. The obtained results showed that among all the 11 candidate reference genes, SAND family protein (SAND), protein phosphatase 2A gene (PP2A), and polypyrimidine tract-binding protein (PTBP) were the most stable reference genes, where Nuclear cap binding protein 2 (NCBP2), TIP41-like protein (TIP41), and Beta-6-tubulin (TUBA) were the least stable genes. To the best of our knowledge, this work is the first to evaluate the stability of reference genes in the Angelica decursiva which has provided an important foundation on the use of RT-qPCR for an accurate and far-reaching gene expression analysis in this medicinal plant.https://doi.org/10.1038/s41598-021-98434-6 |
| spellingShingle | Yuedong He Yuan Zhong Zhenzhen Bao Weiqi Wang Xiaoqing Xu Yanan Gai Jie Wu Evaluation of Angelica decursiva reference genes under various stimuli for RT-qPCR data normalization Scientific Reports |
| title | Evaluation of Angelica decursiva reference genes under various stimuli for RT-qPCR data normalization |
| title_full | Evaluation of Angelica decursiva reference genes under various stimuli for RT-qPCR data normalization |
| title_fullStr | Evaluation of Angelica decursiva reference genes under various stimuli for RT-qPCR data normalization |
| title_full_unstemmed | Evaluation of Angelica decursiva reference genes under various stimuli for RT-qPCR data normalization |
| title_short | Evaluation of Angelica decursiva reference genes under various stimuli for RT-qPCR data normalization |
| title_sort | evaluation of angelica decursiva reference genes under various stimuli for rt qpcr data normalization |
| url | https://doi.org/10.1038/s41598-021-98434-6 |
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