Boyle Brian, Dallaire Nancy, MacKay John
Centre d'Etude de la Forêt, Institut de biologie intégrative et des systèmes, Pav, CE Marchand, Université Laval, Quebec City, QC G1V 0A6, Canada.
BMC Biotechnol. 2009 Aug 28;9:75. doi: 10.1186/1472-6750-9-75.
Robust designs of PCR-based molecular diagnostic assays rely on the discrimination potential of sequence variants affecting primer-to-template annealing. However, for accurate quantitative PCR (qPCR) assessment of gene expression in populations with gene polymorphisms, the effects of sequence variants within primer binding sites must be minimized. This dichotomy in PCR applications prompted us to design experiments to specifically address the quantitative nature of PCR amplifications with oligonucleotides containing mismatches.
We performed qPCR reactions with several primer-target combinations and calculated ratios of molecules obtained with mismatch oligonucleotides to the average obtained with perfect match primer pairs. Amplifications were performed with genomic DNA and complementary DNA samples from different genotypes to validate the findings obtained with plasmid DNA. Our results demonstrate that PCR amplifications are driven by probabilities of oligonucleotides annealing to target sequences. Empiric probabilities can be measured for any primer pair. Alternatively, for primers containing mismatches, probabilities can be measured for individual primers and calculated for primer pairs.
The ability to evaluate priming (and mispriming) rates and to predict their impacts provided a precise and quantitative description of assay performance. Priming probabilities were also found to be a good measure of analytical specificity.
基于聚合酶链式反应(PCR)的分子诊断检测的稳健设计依赖于影响引物与模板退火的序列变异的区分潜力。然而,对于基因多态性人群中基因表达的准确定量PCR(qPCR)评估,引物结合位点内序列变异的影响必须最小化。PCR应用中的这种二分法促使我们设计实验,以专门解决含错配寡核苷酸的PCR扩增的定量性质。
我们用几种引物-靶标组合进行了qPCR反应,并计算了用错配寡核苷酸获得的分子与用完美匹配引物对获得的分子的平均比值。用来自不同基因型的基因组DNA和互补DNA样本进行扩增,以验证用质粒DNA获得的结果。我们的结果表明,PCR扩增是由寡核苷酸与靶序列退火的概率驱动的。可以测量任何引物对的经验概率。或者,对于含错配的引物,可以测量单个引物的概率并计算引物对的概率。
评估引发(和错配引发)率并预测其影响的能力提供了对检测性能的精确和定量描述。引发概率也被发现是分析特异性的良好指标。