Reference Genes Screening and Gene Expression Patterns Analysis Involved in Gelsenicine Biosynthesis under Different Hormone Treatments in Gelsemium elegans .
Yao ZhangDetian MuLiya WangXujun WangIain W WilsonWenqiang ChenJinghan WangZhao-Ying LiuDeyou QiuQi TangPublished in: International journal of molecular sciences (2023)
Reverse transcription quantitative polymerase chain reaction (RT-qPCR) is an accurate method for quantifying gene expression levels. Choosing appropriate reference genes to normalize the data is essential for reducing errors. Gelsemium elegans is a highly poisonous but important medicinal plant used for analgesic and anti-swelling purposes. Gelsenicine is one of the vital active ingredients, and its biosynthesis pathway remains to be determined. In this study, G. elegans leaf tissue with and without the application of one of four hormones (SA, MeJA, ETH, and ABA) known to affect gelsenicine synthesis, was analyzed using ten candidate reference genes. The gene stability was evaluated using GeNorm, NormFinder, BestKeeper, ∆CT, and RefFinder. The results showed that the optimal stable reference genes varied among the different treatments and that at least two reference genes were required for accurate quantification. The expression patterns of 15 genes related to the gelsenicine upstream biosynthesis pathway was determined by RT-qPCR using the relevant reference genes identified. Three genes 8-HGO , LAMT , and STR , were found to have a strong correlation with the amount of gelsenicine measured in the different samples. This research is the first study to examine the reference genes of G. elegans under different hormone treatments and will be useful for future molecular analyses of this medically important plant species.
Keyphrases
- genome wide
- gene expression
- genome wide identification
- bioinformatics analysis
- dna methylation
- high resolution
- transcription factor
- machine learning
- poor prognosis
- magnetic resonance imaging
- patient safety
- copy number
- artificial intelligence
- spinal cord
- positron emission tomography
- electronic health record
- cell wall
- long non coding rna
- big data
- deep learning