Application of a MALDI-TOF analysis platform (ClinProTools) for rapid and preliminary report of MRSA sequence types in Taiwan
Application of a MALDI-TOF analysis platform (ClinProTools) for rapid and preliminary report of MRSA sequence types in Taiwan
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DOI：
10.7717/peerj.5784
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发表时间：
2018-11-07
期刊：
影响因子：
2.7
通讯作者：
Lu, Jang-Jih
中科院分区：
文献类型：
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作者：
Wang, Hsin-Yao;Lien, Frank;Lu, Jang-Jih
背景资料：准确、快速地初步鉴定耐甲氧西林金黄色葡萄球菌（MRSA）的型别是控制感染的关键。然而，目前，昂贵，耗时，劳动密集型的方法用于MRSA分型。相比之下，基质辅助激光解吸电离飞行时间质谱（MALDI-TOF MS）是一个潜在的工具，初步谱系分型。该方法尚未标准化，其性能尚未在某些具有地理障碍的地区进行分析（例如，方法：从台湾多家参考医院获得306株MRSA分离株的质谱。测定了分离株的多位点序列类型（MIST）。使用ClinProTools软件分析光谱，以选择特征峰。此外，使用各种机器学习（ML）算法生成二进制和多类模型，用于对MRSA的主要MLST类型（ST 5，ST 59和ST 239）进行分类。结果：共识别并评价了10个具有最高区分能力的峰（m/z范围：2，082 - 6，594）。所有的单峰在MIST分型中显示出显著的区分能力。此外，二进制和多类ML模型取得了足够的准确性（82.80-94.40%的二进制模型和>81.00%的多类models.Conclusions）在分类主要MLST类型：MALDI-TOF MS分析和ML模型的组合是一个潜在的准确，客观，有效的工具，感染控制和爆发调查。
Background: The accurate and rapid preliminarily identification of the types of methicillin-resistant Staphylococcus aureus (MRSA) is crucial for infection control. Currently, however, expensive, time-consuming, and labor-intensive methods are used for MRSA typing. By contrast, matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF MS) is a potential tool for preliminary lineage typing. The approach has not been standardized, and its performance has not been analyzed in some regions with geographic barriers (e.g., Taiwan Island).Methods: The mass spectra of 306 MRSA isolates were obtained from multiple reference hospitals in Taiwan. The multilocus sequence types (MIST) of the isolates were determined. The spectra were analyzed for the selection of characteristic peaks by using the ClinProTools software. Furthermore, various machine learning (ML) algorithms were used to generate binary and multiclass models for classifying the major MLST types (ST5, ST59, and ST239) of MRSA.Results: A total of 10 peaks with the highest discriminatory power (m/z range: 2,082-6,594) were identified and evaluated. All the single peaks revealed significant discriminatory power during MIST typing. Moreover, the binary and multiclass ML models achieved sufficient accuracy (82.80-94.40% for binary models and >81.00% for multiclass models) in classifying the major MLST types.Conclusions: A combination of MALDI-TOF MS analysis and ML models is a potentially accurate, objective, and efficient tool for infection control and outbreak investigation.