Candida glabrata is an increasingly encountered pathogen with importance as a species that exhibits resistance to the azoles as well as more recently, to the echinocandins1,2,3. Unlike the diploid C. albicans, the haploid genome of C. glabrata may allow it to acquire mutations and develop multi-drug resistance more easily. Co-resistance to both drug classes has also been reported4. Hence, early evaluation of antifungal susceptibility and detection of drug resistance in C. glabrata is crucial for correct, targeted therapy as well as in the context of antifungal stewardship to limit drivers of antimicrobial resistance1,5,6. Establishing an efficient workflow to rapidly detect the presence of confirmatory mutations linked to resistance biomarkers in resistant isolates will also help to improve prescribing decisions and clinical outcomes.
Antifungal susceptibility is usually assessed by measuring minimum inhibitory concentration (MIC) which is defined as the lowest drug concentration that results in a significant reduction in growth of a microorganism compared with that of a drug-free growth control. The Clinical and Laboratory Standards Institute (CLSI) and European Committee on Antimicrobial Susceptibility Testing (EUCAST) have standardized susceptibility testing methods in order to make MIC determination more accurate and consistent7,8. However, the utility of antifungal MIC remains limited especially for the echinocandins, in particular with regards to inter-laboratory comparisons where varying methodologies and conditions are used9. There is also uncertain correlation of MICs with response to echinocandin treatment and inability to distinguish WT (or susceptible) isolates from those harboring FKS mutations (echinocandin-resistant strains)10,11. Despite the availability of confirmatory single-gene PCRs and Sanger sequencing of antifungal resistance markers, realization of results is often delayed due to lack of simultaneous detection of multiple resistance markers5,12. Hence, concurrent detection of resistance-conferring mutations in different locations in the genome, enabled by whole genome sequencing-based analysis, offers significant advantages over current approaches.
Whole genome sequencing (WGS) has been successfully implemented to track disease transmission during outbreaks as well as an approach for genome-wide risk assessment and drug resistance testing in bacteria and viruses13. Recent advances in nucleic acid sequencing technology have made the whole genome sequencing (WGS) of pathogens in a clinically actionable turn-around-time both technically and economically feasible. DNA sequencing offers important advantages over other methods of pathogen identification and characterization employed in microbiology laboratories14,15,16. First, it provides a universal solution with high throughput, speed and quality. Sequencing can be applied to any of microorganisms and allows economies of scale at local or regional laboratories. Second, it produces data in a 'future-proof' format amenable to comparison at national and international levels. Finally, the potential utility of WGS in medicine has been augmented by the rapid growth of public data bases containing reference genomes, which can be linked to equivalent data bases that contain additional clinical and epidemiological metadata17,18.
Recent studies have demonstrated the utility of WGS for identification of antifungal resistance markers from clinical isolates of Candida spp.10,19,20. This is mostly due to the availability of high-throughput benchtop sequencers, established bioinformatics pipelines and decreasing cost of sequencing21,22. The advantage of fungal WGS over Sanger sequencing is that WGS allows sequencing of multiple genomes on a single run. In addition, WGS of Candida genomes can identify novel mutations in drug targets, track genetic evolution, and emergence of clinically relevant sequence-types20,22,23. Most importantly, in cases of intrinsic multidrug resistance, WGS can assist in early detection of resistance-conferring mutations prior to treatment selection22,24.
Here, we examined the feasibility of WGS-enabled screening for mutations associated with drug resistance to different classes of antifungal agents. We present a methodology for the implementation of WGS from end-user and diagnostic mycology laboratory perspectives. We included in this analysis three isolate pairs cultured from three separate clinical cases in which in vitro resistance to the echinocandins and 5-flucytosine developed over time following antifungal treatment.