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优化的McRAPD在鉴定9种常见于患者样本中的酵母菌种中的性能：自动化潜力

Performance of optimized McRAPD in identification of 9 yeast species frequently isolated from patient samples: potential for automation.

作者信息

Trtkova Jitka, Pavlicek Petr, Ruskova Lenka, Hamal Petr, Koukalova Dagmar, Raclavsky Vladislav

机构信息

Department of Microbiology, Palacky University and University Hospital Olomouc, Czech Republic.

出版信息

BMC Microbiol. 2009 Nov 10;9:234. doi: 10.1186/1471-2180-9-234.

DOI:10.1186/1471-2180-9-234
PMID:19903328
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC2779194/
Abstract

BACKGROUND

Rapid, easy, economical and accurate species identification of yeasts isolated from clinical samples remains an important challenge for routine microbiological laboratories, because susceptibility to antifungal agents, probability to develop resistance and ability to cause disease vary in different species. To overcome the drawbacks of the currently available techniques we have recently proposed an innovative approach to yeast species identification based on RAPD genotyping and termed McRAPD (Melting curve of RAPD). Here we have evaluated its performance on a broader spectrum of clinically relevant yeast species and also examined the potential of automated and semi-automated interpretation of McRAPD data for yeast species identification.

RESULTS

A simple fully automated algorithm based on normalized melting data identified 80% of the isolates correctly. When this algorithm was supplemented by semi-automated matching of decisive peaks in first derivative plots, 87% of the isolates were identified correctly. However, a computer-aided visual matching of derivative plots showed the best performance with average 98.3% of the accurately identified isolates, almost matching the 99.4% performance of traditional RAPD fingerprinting.

CONCLUSION

Since McRAPD technique omits gel electrophoresis and can be performed in a rapid, economical and convenient way, we believe that it can find its place in routine identification of medically important yeasts in advanced diagnostic laboratories that are able to adopt this technique. It can also serve as a broad-range high-throughput technique for epidemiological surveillance.

摘要

背景

从临床样本中快速、简便、经济且准确地鉴定酵母菌的种类，对于常规微生物实验室而言仍是一项重大挑战，因为不同种类的酵母菌对抗真菌药物的敏感性、产生耐药性的可能性以及致病能力各不相同。为克服现有技术的缺点，我们最近提出了一种基于随机扩增多态性DNA（RAPD）基因分型的创新方法来鉴定酵母菌种类，并将其命名为McRAPD（RAPD熔解曲线）。在此，我们评估了该方法在更广泛的临床相关酵母菌种类中的性能，并研究了对McRAPD数据进行自动化和半自动化解读以鉴定酵母菌种类的潜力。

结果

一种基于标准化熔解数据的简单全自动算法能正确鉴定80%的分离株。当该算法辅以对一阶导数图中决定性峰的半自动匹配时，87%的分离株能被正确鉴定。然而，对导数图进行计算机辅助视觉匹配表现最佳，平均98.3%的分离株能被准确鉴定，几乎与传统RAPD指纹图谱99.4%的性能相当。

结论

由于McRAPD技术无需凝胶电泳，且能以快速、经济和便捷的方式进行，我们认为它能够在有能力采用该技术的先进诊断实验室的医学重要酵母菌常规鉴定中占有一席之地。它还可作为一种广泛的高通量技术用于流行病学监测。

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