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Optimization of Random Amplified Polymorphic DNA-PCR for Genotyping Salmonella enterica subspecies enterica serovar Typhi Using a Mathematical Approach

机译:应用数学方法优化肠型沙门氏菌亚种肠型血清型伤寒基因的随机扩增多态性DNA-PCR的优化

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Aim: A mathematical approach was employed to optimize and observe the interactive effects of Random Amplified Polymorphic DNA-PCR (RAPD-PCR) master mix with a view to resolving its limited reproducibility for reliable diagnostic and biomarker discovery for Salmonella Typhi. Methodology: A gradient PCR for RAPD primer annealing temperature was performed, and a rotatable centred central composite design (RCCCD) using Design Expert? software was used to generate 82 experiments with six replicated centre points. Master mix components optimized include concentrations of DNA template, PCR buffer, MgCl2, RAPD primer, dNTPs and Taq DNA polymerase. Results: The result of this study showed that significant interactions that yielded higher numbers of amplified DNA bands existed between PCR buffer and MgCl2; dNTPs and MgCl2; and RAPD primer and MgCl2. Although not statistically significant, good interactive relationships that recorded higher numbers of bands were recorded between Taq with MgCl2, and between RAPD primers and dNTPs. Reproducible RAPD-PCR results were obtained following the optimization with a co-efficient of variation (CV) value of 2.19%. Conclusion: It was concluded that stringent and interactive master mix optimization is necessary if the simplicity and cost effectiveness of RAPD-PCR is to be utilized, and RSM offers a rapid and cost effective solution to this potentially tedious task.
机译:目的:采用一种数学方法来优化和观察随机扩增多态性DNA-PCR(RAPD-PCR)预混液的相互作用,以解决其对于伤寒沙门氏菌的可靠诊断和生物标志物发现的有限再现性。方法:进行了针对RAPD引物退火温度的梯度PCR,并使用Design Expert进行了可旋转的中心中心复合设计(RCCCD)。 软件用于生成具有六个重复中心点的82个实验。优化的预混料成分包括DNA模板,PCR缓冲液,MgCl 2 ,RAPD引物,dNTP和Taq DNA聚合酶的浓度。结果:本研究结果表明,PCR缓冲液与MgCl 2 之间存在显着的相互作用,从而产生了更多的DNA扩增条带。 dNTPs和MgCl 2 ; RAPD引物和MgCl 2 。尽管Taq与MgCl 2 之间以及RAPD引物与dNTPs之间记录了良好的相互作用关系,但记录的条带数量较多。优化后获得了可再现的RAPD-PCR结果,变异系数(CV)值为2.19%。结论:结论是,要利用RAPD-PCR的简单性和成本效益,必须进行严格且交互式的预混料优化,而RSM为这一潜在的繁琐任务提供了快速且经济高效的解决方案。

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