Dataset Information

The LUFS domain, its transcriptional regulator proteins, and drug resistance in the fungal pathogen Candida auris.

ABSTRACT: The LUFS domain (LUG/LUH, Flo8, single-strand DNA-binding protein [SSBP]) is a well-conserved and apparently ancient region found in diverse proteins and taxa. This domain, which has as its most obvious structural feature a series of three helices, has been identified in transcriptional regulator proteins of animals, plants, and fungi. Recently, in these pages (Wang et al., Protein Sci., 2019, 28:788-793), the first crystal structure of a LUFS domain was reported, for the human SSBP2, a transcriptional repressor. We briefly address how the new insights into LUFS structures might contribute to a better understanding of an important transcriptional activator of yeasts that contains the LUFS domain, Flo8, and consider how a focus on the LUFS domain and its variation could help us to understand etiologies of drug resistance in a recently emerged pathogenic fungus, Candida auris.

SUBMITTER: Misas E

PROVIDER: S-EPMC6798126 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

The LUFS domain, its transcriptional regulator proteins, and drug resistance in the fungal pathogen Candida auris.

Misas Elizabeth E Escandón Patricia P McEwen Juan G JG Clay Oliver K OK

Protein science : a publication of the Protein Society 20191101 11

The LUFS domain (LUG/LUH, Flo8, single-strand DNA-binding protein [SSBP]) is a well-conserved and apparently ancient region found in diverse proteins and taxa. This domain, which has as its most obvious structural feature a series of three helices, has been identified in transcriptional regulator proteins of animals, plants, and fungi. Recently, in these pages (Wang et al., Protein Sci., 2019, 28:788-793), the first crystal structure of a LUFS domain was reported, for the human SSBP2, a transcri ...[more]

PMID: 31503375

Similar Datasets

Project description:Candida auris is an emerging fungal pathogen capable of causing invasive infections in humans. Since its first appearance around 1996, it has been isolated in countries spanning five continents. C. auris is a yeast that has the potential to cause outbreaks in hospitals, can survive in adverse conditions, including dry surfaces and high temperatures, and has been frequently misidentified by traditional methods. Furthermore, strains have been identified that are resistant to two and even all three of the main classes of antifungals currently in use. Several nuclear genome assemblies of C. auris have been published representing different clades and continents, yet until recently, the mitochondrial genomes (mtDNA chromosomes) of this species and the closely related species of C. haemulonii, C. duobushaemulonii, and C. pseudohaemulonii had not been analyzed in depth. We used reads from PacBio and Illumina sequencing to obtain a de novo reference assembly of the mitochondrial genome of the C. auris clade I isolate B8441 from Pakistan. This assembly has a total size of 28.2 kb and contains 13 core protein-coding genes, 25 tRNAs and the 12S and 16S ribosomal subunits. We then performed a comparative analysis by aligning Illumina reads of 129 other isolates from South Asia, Japan, South Africa, and South America with the B8441 reference. The clades of the phylogenetic tree we obtained from the aligned mtDNA sequences were consistent with those derived from the nuclear genome. The mitochondrial genome revealed a generally low genetic variation within clades, although the South Asian clade displayed two sub-branches including strains from both Pakistan and India. In particular, the 86 isolates from Colombia and Venezuela had mtDNA sequences that were all identical at the base level, i.e., a single conserved haplotype or mitochondrial background that exhibited characteristic differences from the Pakistan reference isolate B8441, such as a unique 25-nt insert that may affect function.

Project description: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.

Dataset Information

The LUFS domain, its transcriptional regulator proteins, and drug resistance in the fungal pathogen Candida auris.

Publications

The LUFS domain, its transcriptional regulator proteins, and drug resistance in the fungal pathogen Candida auris.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets