Genomic studies of Candida for improved outbreak detection and diagnostics

Funding period: 2023-2025
Lead: Amrita Bharat
Total GRDI funding: $738,200

Research need

Candida is a common fungal pathogen. Infections can be mild or result in life threatening sepsis. Antifungal resistance is an under-recognized type of antimicrobial resistance. As the use of antifungal drugs increase, more Candida infections are resistant to treatment with antifungal drugs. Three species cause >80% of Candida bloodstream infections: Candida albicans, Candida glabrata and Candida parapsilosis. Recently, multidrug resistant Candida auris has rapidly emerged and has caused large hospital outbreaks around the globe. All four species are on the WHO list of priority fungal pathogens. About one third of C. auris isolates are multidrug resistant (resistance to two classes). Resistance is also increasingly observed in C. glabrata and C. parapsilosis.

Project summary

In 2017, Dr. Bharat established a Mycology Unit at NML, which provides genomics-based laboratory support for outbreak investigations and detection of resistance in Candida spp. Dr. Bharat received a pilot grant in GRDI-7, to sequence a collection of the top three species of Candida (C. albicans, C. parapsilosis, and C. glabrata) and all known Canadian isolates of C. auris. We published several manuscripts and abstracts on the two haploid species (one copy of the chromosome).10–13 The objectives of this proposal are to develop bioinformatic methods for analyzing diploid Candida species (two copies of the chromosome), find the genetic basis of amphotericin B resistance, carry out a follow up genomic study of C. auris, and conduct genomic analyses of C. albicans, and C. parapsilosis in Canada. These analyses will focus on improved outbreak detection, genomic strain typing and molecular detection of antifungal resistance from genome sequences.

Outputs and outcomes

Foundational knowledge gained from this project will help NML's fledgling Mycology Unit provide reliable support for infection prevention and control of infections due to C. auris and other Candida species. Another output is the identification of a set of resistance determinants to guide prediction of antifungal resistance from genome sequences or development ofa diagnostic PCR for resistance to guide treatment decisions for improved patient care. Molecular detection of antifungal resistance is frequently requested by end users. New technologies such as pipelines for AMR prediction and phylogenetic analysis of fungal genomes will be transferred to provincial public health laboratories.