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首页> 外文期刊>Journal of Clinical Microbiology >Detection of Rare Drug Resistance Mutations by Digital PCR in a Human Influenza A Virus Model System and Clinical Samples
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Detection of Rare Drug Resistance Mutations by Digital PCR in a Human Influenza A Virus Model System and Clinical Samples

机译：通过数字PCR检测人类甲型流感病毒模型系统中的罕见耐药突变和临床样品

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Digital PCR (dPCR) is being increasingly used for the quantification of sequence variations, including single nucleotide polymorphisms (SNPs), due to its high accuracy and precision in comparison with techniques such as quantitative PCR (qPCR) and melt curve analysis. To develop and evaluate dPCR for SNP detection using DNA, RNA, and clinical samples, an influenza virus model of resistance to oseltamivir (Tamiflu) was used. First, this study was able to recognize and reduce off-target amplification in dPCR quantification, thereby enabling technical sensitivities down to 0.1% SNP abundance at a range of template concentrations, a 50-fold improvement on the qPCR assay used routinely in the clinic. Second, a method was developed for determining the false-positive rate (background) signal. Finally, comparison of dPCR with qPCR results on clinical samples demonstrated the potential impact dPCR could have on clinical research and patient management by earlier (trace) detection of rare drug-resistant sequence variants. Ultimately this could reduce the quantity of ineffective drugs taken and facilitate early switching to alternative medication when available. In the short term such methods could advance our understanding of microbial dynamics and therapeutic responses in a range of infectious diseases such as HIV, viral hepatitis, and tuberculosis. Furthermore, the findings presented here are directly relevant to other diagnostic areas, such as the detection of rare SNPs in malignancy, monitoring of graft rejection, and fetal screening.
机译：由于数字PCR（dPCR）与定量PCR（qPCR）和解链曲线分析等技术相比具有很高的准确性和精度，因此越来越多地用于定量分析包括单核苷酸多态性（SNP）在内的序列变异。为了开发和评估用于使用DNA，RNA和临床样品进行SNP检测的dPCR，使用了对奥司他韦（Tamiflu）耐药的流感病毒模型。首先，这项研究能够识别并减少dPCR定量中的脱靶扩增，从而使技术灵敏度在一定范围的模板浓度下降低至0.1％SNP丰度，是临床常规qPCR测定法的50倍。其次，开发了一种用于确定假阳性率（背景）信号的方法。最后，在临床样品上比较dPCR和qPCR结果表明，通过早期（痕迹）检测罕见的耐药序列变异，dPCR可能对临床研究和患者管理产生潜在影响。最终，这可以减少服用无效药物的数量，并有助于在可能的情况下及早转用替代药物。在短期内，此类方法可以增进我们对一系列感染性疾病（如HIV，病毒性肝炎和结核病）中微生物动力学和治疗反应的了解。此外，此处提出的发现与其他诊断领域直接相关，例如在恶性肿瘤中检测稀有SNP，监测移植物排斥和胎儿筛查。

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