CYP2C19型
熔化曲线分析
高分辨率熔体
基因型
生物
聚合酶链反应
检出限
桑格测序
DNA提取
计算生物学
色谱法
DNA
基因
遗传学
化学
DNA测序
作者
Jianguang Guo,Weixin You,Kangfeng Lin,Qinghan Li,Xiangju Guo,Shuai Wang,Ya Bian,Wenjing Ren,Shouxin Zhang,Yanping Wang,Boan Li
标识
DOI:10.1002/biot.202300207
摘要
Abstract Drug‐metabolizing enzymes play an important role in the metabolism of drugs in vivo. Their activity is an important factor affecting the rate of drug metabolism, which directly determines the intensity and persistence of drug action. Patients taking medication can be divided into different metabolic types through detection of CYP2C19 drug‐metabolizing enzyme gene polymorphisms, which can then be used for medication guidance for clopidogrel. Here, we describe a detection method based on real‐time polymerase chain reaction (PCR). This method uses multicolor melting curve analysis to accurately identify different mutation sites and genotypes of CYP2C19 * 2, CYP2C19 * 3, and CYP2C19 * 17. The detection limit of plasmid samples was 1 copies μL −1 ; that of genomic samples was 0.1 ng μL −1 . The system can detect nine types of CYP2C19 * 2/3/17 at three sites in one tube, quickly achieving detection within 1 h. Combined with the sample release agent, sample extraction was completed in 5 s, achieving rapid diagnosis without extraction for timely diagnosis and treatment. Furthermore, the system is not limited to blood samples and can also be applied to oropharyngeal and saliva samples, increasing sampling diversity and convenience. When using clinical blood samples (n = 93), the detection system we established was able to quickly and accurately identify different genotypes, and the accuracy and effectiveness of the detection were confirmed by Sanger sequencing. Due to its accuracy, rapidity, simple operation, and low cost, detection technology based on real‐time polymerase amplification combined with melting curve analysis is expected to become a powerful tool for detecting and guiding clopidogrel use in countries with limited resources.
科研通智能强力驱动
Strongly Powered by AbleSci AI