Abstract
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.
Highlights
Invasive fungal diseases (IFD) have become an emerging healthcare problem worldwide
A majority (96.3%, 311/323) of C. albicans complex isolates were correctly identified to the species level by Vitek MS
For C. parapsilosis complex, 79.1% (379/479) of the isolates were correctly identified to species level by Vitek MS, which was much less than that of Autof MS 1000 at 99.0% (474/ 479)
Summary
Invasive fungal diseases (IFD) have become an emerging healthcare problem worldwide. It is associated with high rates of morbidity and mortality in immunocompromised individuals and critically ill patients (Miceli et al, 2011; Lockhart et al, 2017). Some closely-related yeast species complexes are difficult to identify by conventional morphological or biochemical methods, and several of these cryptic species have distinct antifungal susceptibility profiles associated with specific clinical settings. These include Candida parapsilosis sensu stricto, Candida metapsilosis, Candida orthopsilosis, Lodderomyces elongisporus of the C. parapsilosis complex, Candida albicans and Candida dubliniensis of the C. albicans complex, Candida glabrata sensu stricto, Candida nivariensis, and Candida bracarensis of the C. glabrata complex, Cryptococcus neoformans, and Cryptococcus gattii of the C. neoformans complex, and Candida haemulonii, Candida duobushaemulonii, and Candida auris of the C. haemulonii complex ( refered as multidrug-resistant [MDR] complex) (Muñoz et al, 2018). The purpose of this study was to evaluate the accuracy of Autof MS 1000 and Vitek MS in the identification of yeasts causing IFDs, especially for pathogens within closely-related species complexes
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