化学
分子反转探针
链霉亲和素
单核苷酸多态性
碱基对
DNA
寡核苷酸
分子生物学
计算生物学
遗传学
生物素
生物化学
基因型
基因
生物
作者
Yunshan Zhang,Shijie Xu,Ma Luo,Jian Chen,L. Wang,Fang Yang,Jing Ye,Jichong Liu,Bingxiao He,Lin Weng,Shuang Li,Diming Zhang
标识
DOI:10.1021/acs.analchem.4c01049
摘要
Single-nucleotide polymorphism (SNP) is widely used in the study of disease-related genes and in the genetic study of animal and plant strains. Therefore, SNP detection is crucial for biomedical diagnosis and treatment as well as for molecular design breeding of animals and plants. In this regard, this article describes a novel technique for detecting SNP using flap endonuclease 1 (FEN 1) as a specific recognition element and catalytic hairpin assembly (CHA) cascade reaction as a signal amplification strategy. The mutant target (MT) was hybridized with a biotin-modified upstream probe and hairpin-type downstream probe (DP) to form a specific three-base overlapping structure. Then, FEN 1 was employed for three-base overlapping structure-specific recognition, namely, the precise SNP site identification and the 5′ flap of DP dissociation. After dissociation, the hybridized probes were magnetically separated by a streptavidin–biotin complex. Especially, the ability to establish such a hairpin-type DP provided a powerful tool that could be used to hide the cut sequence (CS) and avoid false-positive signals. The cleaved CS initiated the CHA reaction and allowed superior fluorescence signal generation. Owing to the high specificity of FEN 1 for single base recognition, only the MT could be distinguished from the wild-type target and mismatched DNA. Owing to the dual signal amplification, as low as 0.36 fM MT and 1% mutation abundance from the mixtures could be detected, respectively. Furthermore, it could accurately identify SNPs from human cancer cells, as well as soybean leaf genome extracts. This strategy paves the way for the development of more precise and sensitive tools for diagnosing early onset diseases as well as molecular design breeding tools.
科研通智能强力驱动
Strongly Powered by AbleSci AI