NDLI: Yeast Identification Algorithm Based on Use of the Vitek MS System Selectively Supplemented with Ribosomal DNA Sequencing: Proposal of a Reference Assay for Invasive Fungal Surveillance Programs in China

Content Provider	PubMed Central
Author	Zhang, Li Xiao, Meng Wang, He Gao, Ran Fan, Xin Brown, Mitchell Gray, Timothy J. Kong, Fanrong Xu, Ying-chun
Editor	Warnock, D. W.
Copyright Year	2014
Abstract	Sequence analysis of the internal transcribed spacer (ITS) region was employed as the gold standard method for yeast identification in the China Hospital Invasive Fungal Surveillance Net (CHIF-NET). It has subsequently been found that matrix-assisted laser desorption ionization–time of flight mass spectrometry (MALDI-TOF MS) is potentially a more practical approach for this purpose. In the present study, the performance of the Vitek MS v2.0 system for the identification of yeast isolates collected from patients with invasive fungal infections in the 2011 CHIF-NET was evaluated. A total of 1,243 isolates representing 31 yeast species were analyzed, and the identification results by the Vitek MS v2.0 system were compared to those obtained by ITS sequence analysis. By the Vitek MS v2.0 system, 96.7% (n = 1,202) of the isolates were correctly assigned to the species level and 0.2% (n = 2) of the isolates were identified to the genus level, while 2.4% (n = 30) and 0.7% (n = 9) of the isolates were unidentified and misidentified, respectively. After retesting of the unidentified and misidentified strains, 97.3% (n = 1,209) of the isolates were correctly identified to the species level. Based on these results, a testing algorithm that combines the use of the Vitek MS system with selected supplementary ribosomal DNA (rDNA) sequencing was developed and validated for yeast identification purposes. By employing this algorithm, 99.7% (1,240/1,243) of the study isolates were accurately identified with the exception of two isolates of Candida fermentati and one isolate of Cryptococcus gattii. In conclusion, the proposed identification algorithm could be practically implemented in strategic programs of fungal infection surveillance.
Related Links	http://dx.doi.org/10.1128/jcm.02543-13
Ending Page	577
Page Count	6
Starting Page	572
File Format	PDF
ISSN	00951137
e-ISSN	1098660X
Journal	Journal of Clinical Microbiology
Issue Number	2
Volume Number	52
Language	English
Publisher	American Society for Microbiology
Publisher Date	2014-02-01
Access Restriction	Open
Rights Holder	American Society for Microbiology
Subject Keyword	Microbiology (medical) Research in Higher Education
Content Type	Text
Resource Type	Article
Subject	Microbiology (medical)

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