Lu Sha, Mirchevska Gordana, Phatak Sayali S, Li Dongmei, Luka Janos, Calderone Richard A, Fonzi William A
Dermatology Department, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China.
Institute for Microbiology and Parasitology, University Sts Cyril and Methodius, Skopje, Macedonia.
PLoS One. 2017 Mar 6;12(3):e0173320. doi: 10.1371/journal.pone.0173320. eCollection 2017.
Fungal infections are a global problem imposing considerable disease burden. One of the unmet needs in addressing these infections is rapid, sensitive diagnostics. A promising molecular diagnostic approach is high-resolution melt analysis (HRM). However, there has been little effort in leveraging HRM data for automated, objective identification of fungal species. The purpose of these studies was to assess the utility of distance methods developed for comparison of time series data to classify HRM curves as a means of fungal species identification. Dynamic time warping (DTW), first introduced in the context of speech recognition to identify temporal distortion of similar sounds, is an elastic distance measure that has been successfully applied to a wide range of time series data. Comparison of HRM curves of the rDNA internal transcribed spacer (ITS) region from 51 strains of 18 fungal species using DTW distances allowed accurate classification and clustering of all 51 strains. The utility of DTW distances for species identification was demonstrated by matching HRM curves from 243 previously identified clinical isolates against a database of curves from standard reference strains. The results revealed a number of prior misclassifications, discriminated species that are not resolved by routine phenotypic tests, and accurately identified all 243 test strains. In addition to DTW, several other distance functions, Edit Distance on Real sequence (EDR) and Shape-based Distance (SBD), showed promise. It is concluded that DTW-based distances provide a useful metric for the automated identification of fungi based on HRM curves of the ITS region and that this provides the foundation for a robust and automatable method applicable to the clinical setting.
真菌感染是一个全球性问题，带来了相当大的疾病负担。应对这些感染的未满足需求之一是快速、灵敏的诊断方法。一种有前景的分子诊断方法是高分辨率熔解分析（HRM）。然而，在利用HRM数据进行真菌物种的自动化、客观鉴定方面，所做的工作很少。这些研究的目的是评估为比较时间序列数据而开发的距离方法的效用，以将HRM曲线分类作为真菌物种鉴定的一种手段。动态时间规整（DTW）最初是在语音识别背景下引入的，用于识别相似声音的时间失真，它是一种弹性距离度量，已成功应用于广泛的时间序列数据。使用DTW距离比较18种真菌的51个菌株的rDNA内转录间隔区（ITS）区域的HRM曲线，能够对所有51个菌株进行准确分类和聚类。通过将243个先前鉴定的临床分离株的HRM曲线与标准参考菌株的曲线数据库进行匹配，证明了DTW距离在物种鉴定中的效用。结果揭示了一些先前的错误分类，区分了常规表型测试无法分辨的物种，并准确鉴定了所有243个测试菌株。除了DTW，其他几个距离函数，即实序列编辑距离（EDR）和基于形状的距离（SBD），也显示出了前景。得出的结论是，基于DTW的距离为基于ITS区域的HRM曲线自动鉴定真菌提供了一种有用的度量标准，这为适用于临床环境的强大且可自动化的方法奠定了基础。
