Abstract
Candida auris (C. auris) is an emerging fungus associated with high morbidity. It has a unique transmission ability and is often resistant to multiple drugs. In this study, we evaluated the ability of different machine learning models to classify the drug resistance and predicted and ranked the drug resistance mutations of C. auris. Two C. auris strains were obtained. Combined with other 356 strains collected from the European Bioinformatics Institute (EBI) databases, the whole genome sequencing (WGS) data were analyzed by bioinformatics. Machine learning classifiers were used to build drug resistance models, which were evaluated and compared by various evaluation methods based on AUC value. Briefly, two strains were assigned to Clade III in the phylogenetic tree, which was consistent with previous studies; nevertheless, the phylogenetic tree was not completely consistent with the conclusion of clustering according to the geographical location discovered earlier. The clustering results of C. auris were related to its drug resistance. The resistance genes of C. auris were not under additional strong selection pressure, and the performance of different models varied greatly for different drugs. For drugs such as azoles and echinocandins, the models performed relatively well. In addition, two machine learning algorithms, based on the balanced test and imbalanced test, were designed and evaluated; for most drugs, the evaluation results on the balanced test set were better than on the imbalanced test set. The mutations strongly be associated with drug resistance of C. auris were predicted and ranked by Recursive Feature Elimination with Cross-Validation (RFECV) combined with a machine learning classifier. In addition to known drug resistance mutations, some new resistance mutations were predicted, such as Y501H and I466M mutation in the ERG11 gene and R278H mutation in the ERG10 gene, which may be associated with fluconazole (FCZ), micafungin (MCF), and amphotericin B (AmB) resistance, respectively; these mutations were in the “hot spot” regions of the ergosterol pathway. To sum up, this study suggested that machine learning classifiers are a useful and cost-effective method to identify fungal drug resistance-related mutations, which is of great significance for the research on the resistance mechanism of C. auris.
Highlights
Candida auris (C. auris) is an emerging fungal pathogen first isolated from the external ear canal of a 70-year-old female inpatient in Tokyo hospital (Satoh et al, 2009)
The phylogenetic NJ tree was divided into four clades starting from the root (Figure 1): clades: South Asia (Clade I), Clade II, Clade III, and Clade IV, which was consistent with the conclusions reported in previous literatures (Lockhart et al, 2017)
In the NJ tree, C1921 and C1922 from our laboratory were in Clade III, which was consistent with the phylogenetic tree constructed using Internal Transcribed Spacer (ITS) and D1/D2 Large Ribosomal Subunit Region previously (Chen et al, 2018)
Summary
Candida auris (C. auris) is an emerging fungal pathogen first isolated from the external ear canal of a 70-year-old female inpatient in Tokyo hospital (Satoh et al, 2009). C. auris outbreak has been reported in more than 30 countries worldwide (Rhodes et al, 2018; Tian et al, 2018; Escandon et al, 2019; Rhodes and Fisher, 2019). C. auris, known as “super fungus”, is a multidrug-resistant species associated with high mortality (Wang et al, 2018). All clades are characterized by distinct single nucleotide polymorphisms (SNPs), highlighting this pathogen’s independent and worldwide emergence (Lockhart et al, 2017). Except for Clade II, the other three clusters have been associated with an outbreak of invasive infection and multiple resistance. Clade II is predominantly an ear canal infection, and presents either single fluconazole resistance or susceptible (Kwon et al, 2019; Welsh et al, 2019)
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