Identification of Molecular and Genetic Resistance Mechanisms in a Candida auris Isolate in a Tertiary Care Center in Türkiye

BACKGROUND

Candida auris is a multidrug-resistant pathogen that causes nosocomial outbreaks and high mortality. We conducted this study to investigate the molecular mechanisms of antifungal resistance in our clinical isolate of C. auris with a high level of resistance to three main classes of antifungals.

MATERIAL AND METHODS

A clinical C. auris isolate was identified by MALDI-TOF MS and antifungal susceptibilities were determined by the Sensititre YeastOne YO10 panel. After sequencing the whole genome of the microorganism with Oxford Nanopore NGS Technologies, a phylogenetic tree was drawn as a cladogram to detect where the C. auris clade to this study's assembly belongs.

RESULTS

The C. auris isolate in this study (MaCa01) was determined to be a part of the clade I (South Asian). The resistance-related genes indicated that MaCa01 would most likely be highly resistant to fluconazole (CDR1, TAC1b, and ERG11), none or little resistant to amphotericin B (AmpB) and echinocandins, and sensitive to flucytosine. The mutations found in the above-mentioned genes in the Türkiye C. auris isolate reveals an antifungal resistance pattern. This molecular resistance pattern was found consistent with the interpretation of MIC values of the antifungals according to CDC tentative breakpoints.

CONCLUSION

We detected the well-known antifungal resistance mutations, responsible for azole resistance in C. auris. Despite no ERG2, ERG6, and FKS mutation identified, the isolate was found to be resistant to AmpB and caspofungin based on the CDC tentative breakpoints which could be related to unidentified mutations.

De novo Nanopore read quality improvement using deep learning

Background

Long read sequencing technologies such as Oxford Nanopore can greatly decrease the complexity of de novo genome assembly and large structural variation identification. Currently Nanopore reads have high error rates, and the errors often cluster into low-quality segments within the reads. The limited sensitivity of existing read-based error correction methods can cause large-scale mis-assemblies in the assembled genomes, motivating further innovation in this area.

Results

Here we developed a Convolutional Neural Network (CNN) based method, called MiniScrub, for identification and subsequent “scrubbing” (removal) of low-quality Nanopore read segments to minimize their interference in downstream assembly process. MiniScrub first generates read-to-read overlaps via MiniMap2, then encodes the overlaps into images, and finally builds CNN models to predict low-quality segments. Applying MiniScrub to real world control datasets under several different parameters, we show that it robustly improves read quality, and improves read error correction in the metagenome setting. Compared to raw reads, de novo genome assembly with scrubbed reads produces many fewer mis-assemblies and large indel errors.

Conclusions

MiniScrub is able to robustly improve read quality of Oxford Nanopore reads, especially in the metagenome setting, making it useful for downstream applications such as de novo assembly. We propose MiniScrub as a tool for preprocessing Nanopore reads for downstream analyses. MiniScrub is open-source software and is available at https://bitbucket.org/berkeleylab/jgi-miniscrub.