Stedtfeld Robert D, Baushke Samuel W, Tourlousse Dieter M, Miller Sarah M, Stedtfeld Tiffany M, Gulari Erdogan, Tiedje James M, Hashsham Syed A
Department of Civil and Environmental Engineering, Michigan State University, East Lansing, MI 48824, USA.
Appl Environ Microbiol. 2008 Jun;74(12):3831-8. doi: 10.1128/AEM.02743-07. Epub 2008 Apr 18.
Development of quantitative PCR (QPCR) assays typically requires extensive screening within and across a given species to ensure specific detection and lucid identification among various pathogenic and nonpathogenic strains and to generate standard curves. To minimize screening requirements, multiple virulence and marker genes (VMGs) were targeted simultaneously to enhance reliability, and a predictive threshold cycle (C(T)) equation was developed to calculate the number of starting copies based on an experimental C(T). The empirical equation was developed with Sybr green detection in nanoliter-volume QPCR chambers (OpenArray) and tested with 220 previously unvalidated primer pairs targeting 200 VMGs from 30 pathogens. A high correlation (R(2) = 0.816) was observed between the predicted and experimental C(T)s based on the organism's genome size, guanine and cytosine (GC) content, amplicon length, and stability of the primer's 3' end. The performance of the predictive C(T) equation was tested using 36 validation samples consisting of pathogenic organisms spiked into genomic DNA extracted from three environmental waters. In addition, the primer success rate was dependent on the GC content of the target organisms and primer sequences. Targeting multiple assays per organism and using the predictive C(T) equation are expected to reduce the extent of the validation necessary when developing QPCR arrays for a large number of pathogens or other targets.
定量聚合酶链反应(QPCR)检测方法的开发通常需要在给定物种内部和不同物种之间进行广泛筛选,以确保在各种致病和非致病菌株之间实现特异性检测和清晰鉴定,并生成标准曲线。为了尽量减少筛选需求,同时针对多个毒力和标记基因(VMG)以提高可靠性,并开发了一个预测阈值循环(C(T))方程,用于根据实验C(T)计算起始拷贝数。该经验方程是在纳升体积的QPCR芯片(OpenArray)中使用Sybr green检测法开发的,并用针对来自30种病原体的200个VMG的220对先前未经验证的引物进行了测试。基于生物体的基因组大小、鸟嘌呤和胞嘧啶(GC)含量、扩增子长度以及引物3'端的稳定性,预测的和实验的C(T)之间观察到高度相关性(R(2) = 0.816)。使用由掺入从三种环境水样中提取的基因组DNA中的致病生物体组成的36个验证样本测试了预测C(T)方程的性能。此外,引物成功率取决于目标生物体的GC含量和引物序列。针对每个生物体进行多种检测并使用预测C(T)方程,有望减少为大量病原体或其他目标开发QPCR阵列时所需的验证范围。