摘要
目的:分析猫人参及其近缘种的核酮糖-1,5-二磷酸酯羧化酶基因(ribulose-1,5-bisphosphate carboxylase,rbcL)序列,探讨rbcL序列在猫人参基源鉴定中的应用。方法:采用试剂盒提取所有样品的脱氧核糖核酸,基于rbcL序列并采用DNAman、Editseq、MEGA-X等软件进行序列比对与分析,构建ML系统聚类树。结果:猫人参rbcL序列长度为507 bp,其中鸟嘧啶+胞嘧啶含量为43.59%和43.39%,猫人参及其近缘种共产生变异位点10个,聚类分析表明猫人参基源种对萼猕猴桃与大籽猕猴桃聚为一类,此二者又与葛枣猕猴桃聚为一类,其余近缘种聚为一类。结论:根据rbcL序列变异位点和系统聚类树可区分猫人参及其近缘种,rbcL序列可用于猫人参及其近缘种的分子鉴定。
Objective:To analyze the ribulose-1,5-bisphosphate carboxylase(rbcL)sequence of Maorenshen(Actinidia Valvata Dunn.Radix)and its related species,and to explore the application of rbcL sequences in the origin identification of Maorenshen.Methods:The Deoxyribo Nucleic Acid of all samples was extracted by reagent test kit.Based on the rbcL sequence,DNAman,Editseq,MEGA-X and other software were used for sequence alignment and analysis.Constructing ML system clustering tree.Results:The length of rbcL sequence of Maorenshen was 507 bp,in which the content of guanosine+cytosine were 43.59%and 43.39%.Morenshen and its related species produced 10 variation sites.Cluster analysis showed that Actinidia valvata Dunn.and Actinidia macrosperma C.F.Liang.which was the basic species of Morenshen clustered together,then clustered with Actinidia(Sieb.et Zucc.)Maxim.,and the other related species clustered together.Conclusion:According to the rbcL sequence variation site and clustering tree,the Maorenshen and its related species can be distinguished,and the rbcL sequence can be used for the molecular identification of Maorenshen and its related species.
作者
张晓芹
毛佳乐
王慧玉
雷后兴
蓝艳
ZHANG Xiaoqin;MAO Jiale;WANG Huiyu;LEI Houxing;LAN Yan(Lishui Hospital of Traditional Chinese Medicine Affiliated to Zhejiang Chinese Medicine University,Lishui 323000,China)
出处
《山东中医药大学学报》
2022年第6期758-765,共8页
Journal of Shandong University of Traditional Chinese Medicine
基金
浙江省基础公益项目(编号:LGF20H280005)
丽水市重点研发项目(编号:2020ZDYF15)
丽水市科技计划项目(编号:2021SJZC039)。
关键词
猫人参
核酮糖-1
5-二磷酸酯羧化酶基因序列
分子鉴定
近缘种
序列分析
Maorenshen(Actinidia Valvata Dunn.Radix)
ribulose-1,5-bisphosphate carboxylase sequence
molecular identification
related species
sequential analysis