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首页> 美国卫生研究院文献>Applied and Environmental Microbiology >Optimization of Analytical Parameters for Inferring Relationships among Escherichia coli Isolates from Repetitive-Element PCR by Maximizing Correspondence with Multilocus Sequence Typing Data
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Optimization of Analytical Parameters for Inferring Relationships among Escherichia coli Isolates from Repetitive-Element PCR by Maximizing Correspondence with Multilocus Sequence Typing Data

机译：通过与多基因座序列键入数据的对应关系最大化来推断重复元素PCR的大肠杆菌分离株之间关系的分析参数的优化

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Repetitive-element PCR (rep-PCR) is a method for genotyping bacteria based on the selective amplification of repetitive genetic elements dispersed throughout bacterial chromosomes. The method has great potential for large-scale epidemiological studies because of its speed and simplicity; however, objective guidelines for inferring relationships among bacterial isolates from rep-PCR data are lacking. We used multilocus sequence typing (MLST) as a “gold standard” to optimize the analytical parameters for inferring relationships among Escherichia coli isolates from rep-PCR data. We chose 12 isolates from a large database to represent a wide range of pairwise genetic distances, based on the initial evaluation of their rep-PCR fingerprints. We conducted MLST with these same isolates and systematically varied the analytical parameters to maximize the correspondence between the relationships inferred from rep-PCR and those inferred from MLST. Methods that compared the shapes of densitometric profiles (“curve-based” methods) yielded consistently higher correspondence values between data types than did methods that calculated indices of similarity based on shared and different bands (maximum correspondences of 84.5% and 80.3%, respectively). Curve-based methods were also markedly more robust in accommodating variations in user-specified analytical parameter values than were “band-sharing coefficient” methods, and they enhanced the reproducibility of rep-PCR. Phylogenetic analyses of rep-PCR data yielded trees with high topological correspondence to trees based on MLST and high statistical support for major clades. These results indicate that rep-PCR yields accurate information for inferring relationships among E. coli isolates and that accuracy can be enhanced with the use of analytical methods that consider the shapes of densitometric profiles.
机译：重复元素PCR（rep-PCR）是一种基于选择性扩增散布在整个细菌染色体上的重复遗传元件进行细菌分型的方法。该方法具有快速，简便的特点，在大规模流行病学研究中具有巨大的潜力。但是，缺乏从rep-PCR数据推断细菌分离株之间关系的客观指南。我们使用多基因座序列分型（MLST）作为“金标准”，以优化分析参数，以从rep-PCR数据推断大肠杆菌分离株之间的关系。我们基于其rep-PCR指纹图谱的初步评估，从一个大型数据库中选择了12个分离株来代表大范围的成对遗传距离。我们使用这些相同的分离物进行了MLST，并系统地改变了分析参数，以最大程度地提高rep-PCR推断的关系和MLST推断的关系之间的对应性。比较光密度分布图形状的方法（“基于曲线的方法”）在数据类型之间的一致性值始终比基于共享和不同波段计算相似性指标的方法一致的值更高（最大对应性分别为84.5％和80.3％） 。与“带共享系数”方法相比，基于曲线的方法在适应用户指定的分析参数值的变化方面也更加健壮，并且增强了rep-PCR的可重复性。对rep-PCR数据进行系统进化分析，得出树与基于MLST的树具有较高的拓扑对应性，并且对主要进化枝具有较高的统计支持。这些结果表明，rep-PCR可以得出准确的信息以推断大肠杆菌分离株之间的关系，并且可以通过使用考虑光密度分布图形状的分析方法来提高准确性。

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