Evaluation of Autof MS 1000 and Vitek MS MALDI-TOF MS System in Identification of Closely-Related Yeasts Causing Invasive Fungal Diseases.
Evaluation of Autof MS 1000 and Vitek MS MALDI-TOF MS System in Identification of Closely-Related Yeasts Causing Invasive Fungal Diseases.
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Autof MS 1000 和 Vitek MS MALDI-TOF MS 系统在鉴定引起侵袭性真菌病的密切相关酵母中的评估
DOI：
10.3389/fcimb.2021.628828
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发表时间：
2021
影响因子：
5.7
通讯作者：
Xu YC
中科院分区：
文献类型：
--
作者：
Yi Q;Xiao M;Fan X;Zhang G;Yang Y;Zhang JJ;Duan SM;Cheng JW;Li Y;Zhou ML;Yu SY;Huang JJ;Chen XF;Hou X;Kong F;Kudinha T;Xu YC
基质辅助激光解吸电离飞行时间质谱（MALDI-TOF MS）是一种快速、准确、劳动强度小的微生物鉴定方法。然而，有有限的数据系统评价其有效性的鉴定遗传学密切相关的酵母菌种。在这项研究中，我们评估了两个市售的MALDI-TOF系统，Autof MS 1000和Vitek MS，用于鉴定密切相关的物种复合物中的酵母。共分离酵母菌1,228株，代表14种不同的5种复合菌，其中近平滑念珠菌复合菌479株，白色念珠菌复合菌323株，光滑念珠菌复合菌95株，海穆隆念珠菌复合菌16株（包括耳念珠菌2株），新生隐球菌复合菌315株，中国医院侵袭性真菌监测网（CHIF-NET）收集的样本进行研究。Autof MS 1000和Vitek MS系统正确鉴定了99.2%和89.2%的分离株，主要错误率分别为0.4%和1.6%，次要错误率分别为0.1%和3.5%。每种酵母复合物分别用Autof MS 1000和Vitek MS准确鉴定的分离物比例如下：白色念珠菌复合群99.4%对96.3%;近平滑复合体99.0%对79.1%，光滑复合体98.9%对94.7%，C. haemulonii复合体100%对93.8%;新生儿分别为99.4%和95.2%。总体而言，Autof MS 1000在酵母菌鉴定方面表现出良好的能力，而Vitek MS的鉴定准确性较低，特别是在鉴定遗传学上密切相关的物种复合体中的不太常见的物种方面。
Matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS) has been accepted as a rapid, accurate, and less labor-intensive method in the identification of microorganisms in clinical laboratories. However, there is limited data on systematic evaluation of its effectiveness in the identification of phylogenetically closely-related yeast species. In this study, we evaluated two commercially available MALDI-TOF systems, Autof MS 1000 and Vitek MS, for the identification of yeasts within closely-related species complexes. A total of 1,228 yeast isolates, representing 14 different species of five species complexes, including 479 of Candida parapsilosis complex, 323 of Candida albicans complex, 95 of Candida glabrata complex, 16 of Candida haemulonii complex (including two Candida auris), and 315 of Cryptococcus neoformans complex, collected under the National China Hospital Invasive Fungal Surveillance Net (CHIF-NET) program, were studied. Autof MS 1000 and Vitek MS systems correctly identified 99.2% and 89.2% of the isolates, with major error rate of 0.4% versus 1.6%, and minor error rate of 0.1% versus 3.5%, respectively. The proportion of isolates accurately identified by Autof MS 1000 and Vitek MS per each yeast complex, respectively, was as follows; C. albicans complex, 99.4% vs 96.3%; C. parapsilosis complex, 99.0% vs 79.1%; C glabrata complex, 98.9% vs 94.7%; C. haemulonii complex, 100% vs 93.8%; and C. neoformans, 99.4% vs 95.2%. Overall, Autof MS 1000 exhibited good capacity in yeast identification while Vitek MS had lower identification accuracy, especially in the identification of less common species within phylogenetically closely-related species complexes.