4.7 Article

Evaluation of Autof MS 1000 and Vitek MS MALDI-TOF MS System in Identification of Closely-Related Yeasts Causing Invasive Fungal Diseases

Journal

Publisher

FRONTIERS MEDIA SA
DOI: 10.3389/fcimb.2021.628828

Keywords

MALDI-TOF MS; closely-related yeasts; invasive fungal diseases; Autof MS 1000; Vitek MS

Funding

  1. National Major Science and Technology Project [2018ZX10712001]
  2. Beijing Hospitals Authority Youth Programe [QML20190301]
  3. Natural Science Foundation of China [81802042, 81971979, 81802049]
  4. Beijing Nova Program [Z201100006820127]
  5. Outstanding Young Talents Cultivation Program in Dongcheng District
  6. Beijing Key Clinical Specialty for Laboratory Medicine -Excellent Project [ZK201000]

Ask authors/readers for more resources

Matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS) is a rapid, accurate, and less labor-intensive method for identifying microorganisms, but its effectiveness in closely related yeast species has been limited. This study evaluated the performance of two commercially available MALDI-TOF systems, Autof MS 1000 and Vitek MS, in identifying yeasts within closely related species complexes. Autof MS 1000 showed good capacity for yeast identification, while Vitek MS had lower accuracy, particularly in less common species within phylogenetically closely related species complexes.
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.

Authors

I am an author on this paper
Click your name to claim this paper and add it to your profile.

Reviews

Primary Rating

4.7
Not enough ratings

Secondary Ratings

Novelty
-
Significance
-
Scientific rigor
-
Rate this paper

Recommended

No Data Available
No Data Available