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Updated: Aug 6, 2026

Whole Genome Sequencing of Candida glabrata for Detection of Markers of Antifungal Drug Resistance
Published on: December 28, 2017
[Rapid detection of fluconazole resistance in Candida tropicalis using MALDI-TOF mass spectrometry]
Jie Hou1,2, Weilin Chen2, Liang Peng2
1Department of Laboratory Medicine, West China Tianfu Hospital of Sichuan University, Chengdu 610213, China.
Objectives:
To evaluate the efficacy of matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS)-based antifungal susceptibility testing (MS-AFST) for rapid detection of fluconazole resistance in Candida tropicalis.
Methods:
C. tropicalis isolates from patients with bloodstream infections at West China Hospital of Sichuan University (2018-2023) were collected and identified by chromogenic culture and MALDI-TOF MS. Using Clinical and Laboratory Standards Institute broth microdilution (BMD) method as the reference standard, we compared the performance of the Sensititre YeastOne chromogenic antifungal susceptibility testing and optimized MS-AFST based on the minimum profile change concentration (MPCC).
Results:
All the 41 isolates were confirmed as C. tropicalis, which showed an azole resistance rate of 36.59% and a proportion of non-wild-type strain of 68.29% with high cross-resistance to azoles. The categorical agreement (CA) and essential agreement (EA) between Sensititre YeastOne and CLSI BMD were both 100%, and their minimum inhibitory concentrations were highly correlated. The MPCC-based MS-AFST enabled rapid detection of fluconazole-resistant phenotype of C. tropicalis within approximately 3 h, demonstrating a CA of 92.68% and an EA of 90.24% both in comparison with the CLSI BMD reference method and Sensititre YeastOne; very major error\discrepancy occurred in two strains, and minor error\discrepancy occurred in one strain.
Conclusions:
MPCC-based MS-AFST enables rapid, reliable detection of fluconazole resistance in C. tropicalis with good agreement with the reference methods. However, classification errors remain, which should be improved by further technical optimization of this method and exploration of the underlying molecular mechanisms.
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