摘要
旨在建立稳定、可靠的宽叶缬草ISSR反应体系。以10个水平单因素试验为基础,应用L16(45)正交设计对反应体系中4个主要因子在4个水平上进行组合优化,并采用直观法、极差法和方差法对试验结果进行分析;利用优化的反应体系,筛选引物以及最佳退火温度;同时,以9份不同产地宽叶缬草样品对反应体系进行稳定性检测。结果显示,宽叶缬草25μL ISSR最佳反应体系为:2.5μL 10×Taq Buffer,2.0 mmol/L Mg2+,0.28 mmol/L d NTPs,0.3μmol/L引物,1.75 U Taq DNA聚合酶,60 ng DNA模板,dd H2O补齐;影响大小依次是:Mg2+>引物>d NTPs>Taq DNA聚合酶;确定了各引物的最佳退火温度以及筛选出扩增条带清晰稳定的17条ISSR引物;稳定性实验表明,该体系稳定可靠。优化出宽叶缬草ISSR最佳反应体系并筛选出理想的引物。
This study is to establish a stable and reliable ISSR system for Valeriana officinalis L.var. latifolia Miq. Based on the single factor experiment in 10 levels, L16 （ 45 ） orthogonal design was applied to conduct combinatorial optimization in 4 levels of 4 major factors, and the results were analyzed by visual method, range analysis and variance method. The primer and the optimal annealing temperature were screened by optimized reaction system. The stability and reliability of this system were tested by 9 samples from different origins. The optimal ISSR conditions in the experiments were as following： in 25μL reaction system containing 2.5 ~tL 10 x Taq Buffer, 2.0 mmol/L Mg^2＋, 0.28 mmol/ L dNTPs, 0.3μmol/L primer, 1.75 U Taq DNA polymerase, 60 ng DNA template, and ddH20 completed. The order of the effect by the factors was in Mg2＋〉 primer 〉 dNTP s 〉 Taq DNA polymerase. The optimal annealing temperature for each primer was determined, and 17 primers with distinct and stable amplified bands were screened. Stability test showed that the optimized system was stable and reliable. By this study we may reach the conclusion that the optimal ISSR reaction system for V. officinalis var. latifolia was generated, and the desired primers also were screened, which may provide the technical foundation for the application of ISSR molecular marker technology in the study of genetic diversity of V. officinalis var. latifolia.
出处
《生物技术通报》
CAS
CSCD
北大核心
2015年第7期69-75,共7页
Biotechnology Bulletin
基金
贵州省中药攻关项目(黔科合ZY[2011]3008号)
贵州省高层次人才特助经费(TZJF-2010-053号)
关键词
宽叶缬草
ISSR
单因素试验
正交设计
体系优化
Valeriana officinalis L. var. latifolia Miq
ISSR
single factor experiment
orthogonal design
optimization
