A decade after the emergence of Candida auris: what do we know?

ElBaradei, Amira

doi:10.1007/s10096-020-03886-9

Cited by 18 publications

(20 citation statements)

References 95 publications

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“…Thanks to the study of the C. auris genome, orthologous of virulence factors involved in the formation of biofilms, antifungal resistance, and phenotypic change have been identified, which are already known in C. albicans [1,20,24,29]. The formation of biofilms is especially suggested to have an important role in antifungal resistance in C. auris, including resistance to AMB, due to the possibility of being housed in this treatment-resistant cells which reduce drugs bioavailability [1,29,36,43,44]. Muñoz et al, 2018 showed in their study that SIT1, PGA7, and RBT5 are positively regulated during the formation of biofilms in C. auris, thus suggesting that cell wall reorganization could be a response to antifungal treatment [39].…”

Section: Polyenesmentioning

confidence: 99%

“…Bhattacharya et al, 2020 propose replicative aging as another antifungal resistance mechanism in C. auris, as it origins stem cells and daughter cells that are phenotypically different, where old cells become more tolerant to AMB [34,44].…”

Section: Polyenesmentioning

confidence: 99%

“…It has been reported that fungal cells may develop resistance to 5-FC as a result of mutations that occur mainly in genes involved in the absorption and metabolism of 5-FC: FCY2, which encodes the purine-cytosine permease; FCY1, which encodes the cytosine deaminase, and FUR1 encoding the uracil phosphoribosyl transferase [44,65]. Resistance can be primary when related to a decrease in drug absorption by mutations in the gene FCY2, and secondary when there is a limitation in the conversion of 5FC into 5FU, or 5-fluorouridine monophosphate (5-FUMP) by alterations in FCY1 and FUR1 genes respectively [66].…”

Section: -Fluorocytosinementioning

confidence: 99%

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Antifungal Resistance in Candida auris: Molecular Determinants

et al. 2020

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Since Candida auris integrates strains resistant to multiple antifungals, research has been conducted focused on knowing which molecular mechanisms are involved. This review aims to summarize the results obtained in some of these studies. A search was carried out by consulting websites and online databases. The analysis indicates that most C. auris strains show higher resistance to fluconazole, followed by amphotericin B, and less resistance to 5-fluorocytosine and caspofungin. In C. auris, antifungal resistance to amphotericin B has been linked to an overexpression of several mutated ERG genes that lead to reduced ergosterol levels; fluconazole resistance is mostly explained by mutations identified in the ERG11 gene, as well as a higher number of copies of this gene and the overexpression of efflux pumps. For 5-fluorocytosine, it is hypothesized that the resistance is due to mutations in the FCY2, FCY1, and FUR1 genes. Resistance to caspofungin has been associated with a mutation in the FKS1 gene. Finally, resistance to each antifungal is closely related to the type of clade to which the strain belongs.

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Section: Polyenesmentioning

confidence: 99%

Section: Polyenesmentioning

confidence: 99%

Section: -Fluorocytosinementioning

confidence: 99%

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Antifungal Resistance in Candida auris: Molecular Determinants

et al. 2020

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“…Clinically, invasive fungal infections are usually treated with three classes of antifungal agents: echinocandins, azoles, and polyenes (ElBaradei, 2020). Fluconazole (FCZ) resistance is the most common.…”

Section: Introductionmentioning

confidence: 99%

Application of Machine Learning Classifier to Candida auris Drug Resistance Analysis

Wang

et al. 2021

Front. Cell. Infect. Microbiol.

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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.

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“…Misidentification of C. auris , which unavoidably induces inappropriate and delayed treatment, is another important reason for these outbreaks. Unlike other yeasts, C. auris has often been confused with other pathogens such as C. parapsilosis , C. haemulonii , and C. sake by commercial systems ( Lee et al, 2011 ; Kathuria et al, 2015 ; Mizusawa et al, 2017 ; Adams et al, 2018 ; Snayd et al, 2018 ; ElBaradei, 2020 ). Reliable identification methods involve systems with updated databases and molecular methods, which target the specific sequences of C. auris ( Kathuria et al, 2015 ; Kordalewska et al, 2017 ; Sexton et al, 2018 ; Forsberg et al, 2019 ; Lima et al, 2019 ; Lone and Ahmad, 2019 ).…”

Section: Introductionmentioning

confidence: 99%

A Comparative Transcriptome Between Anti-drug Sensitive and Resistant Candida auris in China

Zhou

Lin

et al. 2021

Front. Microbiol.

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Candida auris emerged as a pathogenic species of fungus that causes severe and invasive outbreaks worldwide. The fungus exhibits high intrinsic resistance rates to various first-line antifungals, and the underlying molecular mechanism responsible for its multidrug resistance is still unclear. In this study, a transcriptomic analysis was performed between two C. auris isolates that exhibited different anti-drug patterns by RNA-sequencing, namely, CX1 (anti-drug sensitive) and CX2 (resistant). Transcriptomic analysis results revealed 541 upregulated and 453 downregulated genes in the resistant C. auris strain compared with the susceptible strain. In addition, our findings highlight the presence of potential differentially expressed genes (DEGs), which may play a role in drug resistance, including genes involved in ergosterol and efflux pump biosynthesis such as SNQ2, CDR4, ARB1, MDR1, MRR1, and ERG genes. We also found that Hsp related genes were upregulated for expression in the anti-drug-resistant strain. Biofilm formation and growth conditions were also compared between the two isolates. Our study provides novel clues for future studies in terms of understanding multidrug resistance mechanisms of C. auris strains.

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A decade after the emergence of Candida auris: what do we know?

Cited by 18 publications

References 95 publications

Antifungal Resistance in Candida auris: Molecular Determinants

Antifungal Resistance in Candida auris: Molecular Determinants

Application of Machine Learning Classifier to Candida auris Drug Resistance Analysis

A Comparative Transcriptome Between Anti-drug Sensitive and Resistant Candida auris in China

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