First Canadian report of transmission of fluconazole-resistant Candida parapsilosis within two hospital networks confirmed by genomic analysis

Lisa R McTaggart; AliReza Eshaghi; Susy Hota; Susan M Poutanen; Jennie Johnstone; Domenica G De Luca; Amrita Bharat; Samir N Patel; Julianne V Kus

doi:10.1128/jcm.01161-23

. 2023 Dec 19;62(1):e01161-23. doi: 10.1128/jcm.01161-23

First Canadian report of transmission of fluconazole-resistant Candida parapsilosis within two hospital networks confirmed by genomic analysis

Lisa R McTaggart ¹, AliReza Eshaghi ¹, Susy Hota ^2,³, Susan M Poutanen ^3,^4,⁵, Jennie Johnstone ^5,⁶, Domenica G De Luca ^7,⁸, Amrita Bharat ^7,⁸, Samir N Patel ^1,⁵, Julianne V Kus ^1,^5,^✉

Editor: Kimberly E Hanson⁹

¹Public Health Ontario, Toronto, Ontario, Canada

²Infection Prevention and Control Department, University Health Network, Toronto, Ontario, Canada

³Department of Medicine, University of Toronto, Toronto, Ontario, Canada

⁴Department of Microbiology, University Health Network/Sinai Health, Toronto, Ontario, Canada

⁵Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Ontario, Canada

⁶Infection Prevention and Control Department, Sinai Health, Toronto, Ontario, Canada

⁷National Microbiology Laboratory, Public Health Agency of Canada, Winnipeg, Manitoba, Canada

⁸Department of Medical Microbiology and Infectious Diseases, University of Manitoba, Winnipeg, Manitoba, Canada

⁹University of Utah, Salt Lake City, Utah, USA

^✉

Address correspondence to Julianne V. Kus, julianne.kus@oahpp.ca

The authors declare no conflict of interest.

Roles

Lisa R McTaggart: Data curation, Formal analysis, Investigation, Methodology, Visualization, Writing – original draft, Writing – review and editing

AliReza Eshaghi: Formal analysis, Investigation, Methodology, Writing – review and editing

Susy Hota: Conceptualization, Investigation, Methodology, Resources, Writing – review and editing

Susan M Poutanen: Conceptualization, Investigation, Methodology, Resources, Writing – review and editing

Jennie Johnstone: Conceptualization, Investigation, Methodology, Writing – review and editing

Domenica G De Luca: Data curation, Formal analysis, Investigation, Methodology, Writing – review and editing

Amrita Bharat: Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Supervision, Writing – review and editing

Samir N Patel: Funding acquisition, Investigation, Methodology, Resources, Writing – review and editing

Julianne V Kus: Conceptualization, Formal analysis, Investigation, Methodology, Project administration, Resources, Supervision, Writing – original draft, Writing – review and editing

Kimberly E Hanson: Editor

PMCID: PMC10793253 PMID: 38112529

ABSTRACT

Candida parapsilosis is a common cause of non-albicans candidemia. It can be transmitted in healthcare settings resulting in serious healthcare-associated infections and can develop drug resistance to commonly used antifungal agents. Following a significant increase in the percentage of fluconazole (FLU)-nonsusceptible isolates from sterile site specimens of patients in two Ontario acute care hospital networks, we used whole genome sequence (WGS) analysis to retrospectively investigate the genetic relatedness of isolates and to assess potential in-hospital spread. Phylogenomic analysis was conducted on all 19 FLU-resistant and seven susceptible-dose dependent (SDD) isolates from the two hospital networks, as well as 13 FLU susceptible C. parapsilosis isolates from the same facilities and 20 isolates from patients not related to the investigation. Twenty-five of 26 FLU-nonsusceptible isolates (resistant or SDD) and two susceptible isolates from the two hospital networks formed a phylogenomic cluster that was highly similar genetically and distinct from other isolates. The results suggest the presence of a persistent strain of FLU-nonsusceptible C. parapsilosis causing infections over a 5.5-year period. Results from WGS were largely comparable to microsatellite typing. Twenty-seven of 28 cluster isolates had a K143R substitution in lanosterol 14-α-demethylase (ERG11) associated with azole resistance. As the first report of a healthcare-associated outbreak of FLU-nonsusceptible C. parapsilosis in Canada, this study underscores the importance of monitoring local antimicrobial resistance trends and demonstrates the value of WGS analysis to detect and characterize clusters and outbreaks. Timely access to genomic epidemiological information can inform targeted infection control measures.

KEYWORDS: Candida parapsilosis, outbreak, MycoSNP, GATK, antifungal susceptibility testing, fluconazole resistance, ERG11, phylogenomic analysis, microsatellite typing, whole genome sequencing

INTRODUCTION

Candida parapsilosis is a common cause of life-threatening invasive candidemia and an emerging public health concern. Since the early 2000s, the clinical prominence and incidence of C. parapsilosis and other non-albicans Candida species (spp.) have increased significantly. C. parapsilosis currently ranks as the second to fourth most prevalent cause of invasive Candida spp. infections, ranging from 5% to 30% depending on the country (1). In particular, patients admitted to intensive care units, neonates, transplant recipients, and patients hospitalized with COVID-19 or cancer are most at risk. The overall mortality rate for C. parapsilosis candidemia is 25%, although it may be higher in certain patient populations (2).

Fluconazole (FLU) is often used prophylactically in vulnerable patients at risk of invasive candidiasis and to treat many Candida spp. infections including those caused by C. parapsilosis. Before 2010, FLU-resistant C. parapsilosis infections were rarely detected. From 2010 to 2019, several studies recorded a growing prevalence (>10%) of FLU resistance among C. parapsilosis clinical isolates (2). Since 2019, the number of reports of FLU-resistant C. parapsilosis has been increasing (2), which is concerning given that infections with FLU-resistant strains are associated with higher mortality rates than with FLU-susceptible strains (3, 4).

Several recent studies have described outbreaks of hospital-associated FLU-resistant C. parapsilosis infections as a result of clonal dissemination, often to azole-naïve patients (3 –12). Transmission is attributed to the ability of C. parapsilosis to colonize and persist in healthcare environments including healthcare worker hands (2, 3, 10, 13). Many of the outbreaks spanned multiple years, suggesting the persistence of strains in undescribed environmental niches in hospital settings (3, 5, 9 –12). The role of biofilms in persistence in these environments is an area of study (2, 3, 7). Genetic analysis frequently attributes FLU nonsusceptibility to nonsynonymous mutation(s) in the ERG11 gene, which alters the sterol biosynthesis pathway (3 –6, 9, 11, 14, 15). Collectively, these studies indicate that FLU-resistant C. parapsilosis has emerged as a worldwide healthcare challenge, having evolved to outcompete FLU susceptible strains seemingly through a combination of FLU resistance and persistence in healthcare settings, which is intransigent to routine infection control strategies (2). Furthermore, recent reports of infections caused by echinocandin-resistant C. parapsilosis have intensified the concern over the potential for outbreaks caused by multidrug-resistant (MDR) strains (16 –19).

In this study, we describe a whole genome sequencing (WGS) investigation that was initiated following concerns raised by the infectious disease consultation service and antimicrobial stewardship team about an increasing number of FLU-nonsusceptible C. parapsilosis clinical isolates identified within two acute care hospital networks in Ontario that are in close geographic proximity. While most of the studies to date that investigate the genetic relatedness of C. parapsilosis isolates for the purpose of identifying outbreaks have utilized microsatellite typing (3 –7, 9 –11, 13, 15, 20 –23), here, we used the more discriminatory method of WGS single nucleotide variant (SNV) analysis. We show the utility of this analysis in identifying the cause of the increased number of FLU-nonsusceptible C. parapsilosis clinical isolates, distinguishing between alternate hypotheses of multiple independent evolutions of FLU resistance as a result of antimicrobial selection pressure or an outbreak due to horizontal transmission of FLU-nonsusceptible C. parapsilosis. Furthermore, we show that WGS SNV analysis has the added benefit of enabling interrogation of genomes for potential genetic causes of antifungal resistance.

MATERIALS AND METHODS

Setting

The study involved four acute care hospitals spanning two academic teaching hospital corporations (networks) in Toronto, Ontario. The hospitals provide a spectrum of tertiary care services including obstetrics, surgery, critical care, cancer care and transplant (multi-organ and bone marrow), and range in size with the largest having approximately 460 inpatient beds. The two hospital networks are in close geographic proximity to each other, have frequent patient transfers between sites, and share many clinical services, including medical consultation services.

Isolate identification and susceptibility testing

FLU susceptibility trends were evaluated from a data set of 874 clinical isolates of C. parapsilosis from sterile body sites submitted to Public Health Ontario Laboratory (PHOL) for antifungal susceptibility testing (AFST) from January 2016 to July 2021. This study start date was chosen based on a retrospective look-back of FLU-nonsusceptible C. parapsilosis isolates from the submitting hospitals as well as the availability of the identified isolates for WGS investigation and to represent circumstances prior to infection prevention and control intervention to address the outbreak. The end date was the last month prior to the implementation of infection prevention and control measures.

PHOL serves as the reference microbiology laboratory for the province of Ontario, and it performs the majority of AFST for hospital and community laboratories; yeast isolates from blood and sterile sites are routinely sent for susceptibility testing. The data set represented the first isolate from each patient submitted to PHO for AFST within a 6-month period, defined as an infection episode in this study. Additional isolates received from the same patient within 6 months were excluded from the data set. Identification of C. parapsilosis isolates was performed by Bruker matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-ToF MS) using a MALDI Biotyper Reference Library v5-v9 (Bruker Daltonics, Billerica, MA, USA) (24). FLU susceptibility was determined as a component of routine in vitro antifungal susceptibility testing using the Sensititre YeastOne Y09 panels (Thermo Fisher Scientific, Waltham, MA, USA) with minimum inhibitory concentration (MIC) results read at 24 h as previously described (25, 26). Results were interpreted using breakpoints from the Clinical Laboratory Standards Institute (CLSI) (27). Cochran-Armitage test of trend and chi-square tests for yearly comparisons were conducted in R v4.2.1 (28) with packages DescTools (29) and Rcompanion (30), respectively.

For WGS, 59 isolates were selected from the collection of C. parapsilosis isolates submitted to PHOL for AFST. Isolates were labeled with a letter designating the submitting hospital laboratory and a number designating sequential WGS study isolates received from unique patients from that submitter. All isolates were derived from sterile sites (57 blood isolates, 1 ascitic fluid, and 1 synovial fluid) and were from unique patients with the exception of a single pair of isolates collected 1 week apart from the same patient and included in the WGS analysis as a control. To examine the genetic relatedness of FLU-nonsusceptible clinical isolates from two Ontario acute care hospital networks (submitters A, B), we sequenced all (n = 19) (17 blood isolates, 1 ascitic fluid, 1 synovial fluid isolate) of the FLU-resistant isolates (MIC ≥8 µg/mL) and seven of eight susceptible-dose dependent (SDD) blood isolates (MIC = 4 µg/mL) received from the two hospital networks and fulfilling the above criteria. (A single stored, frozen SDD isolate was not viable upon retrieval and, therefore, unavailable for WGS.) We also sequenced 13 FLU susceptible (MIC ≤2 µg/mL) blood isolates from the two hospital networks randomly selected from among the C. parapsilosis isolates submitted to PHOL for AFST from January 1, 2016 to July 31, 2021. Additionally, we included WGS data for 7 FLU-resistant, 1 FLU SDD, and 12 FLU susceptible blood isolates submitted from other Ontario hospital laboratories from October 1, 2016 to March 31, 2019, which had been selected to represent a variety of antifungal susceptibility profiles and had been sequenced for another purpose.

Whole-genome sequencing and phylogenomic analysis

Total genomic DNA was extracted using the ZymoBIOMICS DNA Miniprep Kit (Zymo Research, Irvine, CA, USA) or the Epicentre Total DNA and RNA Extraction Kit (Illumina, San Diego, CA, USA). DNA was used as input for WGS library preparation using the Nextera XT DNA Library Preparation Kit (Illumina, San Diego, CA, USA) using the manufacturer’s instructions. Sequencing was performed on MiSeq and NextSeq 500/550 instruments using the MiSeq Reagent Kit v3 (600 cycles) and NextSeq 500/550 Mid Output Kit v2.5 (300 cycles), respectively (Illumina). Raw reads for WGS are available in the NCBI Sequence Read Archive (SRA) under Bioproject accession numbers PRJNA592373 and PRJNA1011319. The SRA accession numbers as well as quality control metrics recommended for the MycoSNP pipeline (see below) (31) for individual samples are listed in Table S1.

Since fungal WGS SNV phylogenomic analysis as a method to determine genetic relatedness is a new technique, we conducted the analysis using two approaches. For the first approach, whole genome single nucleotide variants (SNVs) were identified from raw FASTQ sequences using the MycoSNP pipeline, which is a published method with design parameters optimized for fungal WGS and demonstrated usefulness in determining genetic relatedness in another Candida spp., namely Candida auris (31). However, the pipeline excludes heterozygous SNVs as ambiguous, thus eliminating potentially useful data. The MycoSNP pipeline employs the Burrows-Wheeler Alignment (BWA) Tool to map FASTQ reads to a repeat-masked reference genome to a repeat-masked reference genome of C. parapsilosis CDC317 (32) from the Candida Genome Database (33) followed by variant calling with Genome Analysis Toolkit (GATK) HaplotypeCaller. Variants are filtered using a customized script and output as a multi-FASTA of concatenated SNVs. Phylogenomic analysis of the MycoSNP multi-FASTA SNV output file was conducted using the neighbor-joining (NJ) method in MEGA11 with 500 bootstrap replications (34). For the NJ analysis, all positions containing gaps and missing data were eliminated (complete deletion option). While a cutoff of <12 SNVs has previously been used to define a genetically related cluster representing recent transmission for C. auris (35), this cut off has not yet been defined for C. parapsilosis, so here, a cluster was arbitrarily defined as a group of isolates with <20 SNV differences.

For the second approach, reference-based genome mapping and SNV calling were performed according to the GATK Best Practices for germline short variant discovery using GATK v3.8. Although this approach is based on GATK Best Practices, the parameters have not been optimized for fungal WGS. However, it does retain heterozygous SNVs for inclusion in phylogenomic analysis and genomic interrogation for potential genetic mechanisms of antifungal resistance. Briefly, paired-end reads of each isolate were mapped against C. parapsilosis CDC317 using BWA version 0.7.17 (36) with the BWA-MEM algorithm and Picard tools v 2.9.0 (http://broadinstitute.github.io/picard). HaplotypeCaller was employed in Genomic Variant Call Format (GVCF) mode followed by joint genotyping of all isolates of each species. SNVs were hard filtered based on the following parameters: QD <2.0, FS >60.0, MQ <40.0, SOR >3.0, MQRankSum ≤12.5, ReadPosRankSum ≤8.0, and read depth (DP) <10. SNVs were converted into multi-FASTA format using vcf-to-tab of VCFtools (37) and a custom perl script (38). Following removal of constant sites, maximum-likelihood (ML) phylogenomic analysis of whole genome SNVs was conducted in IQ-TREE 2 with 1000 ultrafast bootstrap approximations using options for ModelFinder Plus (MFP) and correcting for ascertainment bias (ASC) (39 –41). Phylogenomic trees were visualized and annotated in R with ggtree (42). A cluster was defined as a clade with 100% bootstrap support and ML distance ≤0.0055 between isolates.

Microsatellite typing

Multilocus microsatellite typing (MLMT) of C. parapsilosis isolates was performed as previously described using markers CP1, CP4, CP6, and B5 (22). Denatured PCR products were run on an ABI PRISM 3700 Genetic Analyzer (Thermo Fisher Scientific, Waltham, MA, USA) with the final size determined using the GeneMapper Software 6 (Thermo Fisher Scientific). Genetic relationships based on microsatellite size data were visualized using a minimum spanning tree based on categorical values constructed in BioNumerics v6.6 (Applied Maths, Sint-Martens-Latem, Belgium). A clonal complex was defined as a group of isolates with differences at ≤1 microsatellite locus (23).

Detection of amino acid substitutions in azole antifungal resistance targets

To identify potential genetic mechanism(s) of FLU resistance, SnpEff (43) was used to detect homozygous or heterozygous SNVs generated by the GATK custom pipeline that resulted in amino acid alterations in the ERG11 (CPAR2_303740), MDR1 (CPAR2_301760), MDR1B (CPAR2_603010), CDR1B (CPAR2_304370), MRR1 (CPAR2_807270), CDR1 (CPAR2_405290), TAC1 (CPAR2_303510), and UPC2 (CPAR2_207280).

RESULTS

Investigation of FLU susceptibility among C. parapsilosis isolates in Ontario revealed an increase in the proportion of nonsusceptible isolates from patients at two geographically close Ontario acute care hospital networks (submitters A, B) in 2020 and 2021. The percentage of FLU-nonsusceptible isolates from the two hospital networks increased significantly from 15% in 2016 to 47% in 2021 (Cochran-Armitage Z = 2.8943, P = 0.0038) (Fig. 1A). This is in contrast to the proportion of FLU-nonsusceptible isolates from other submitters across the province, which declined slightly from 27% in 2016 to 21% in 2021 (Cochran-Armitage Z = −2.0298, P = 0.0424) (Fig. 1B). Likewise, comparing 2020 to 2021, the percentage of FLU-nonsusceptible C. parapsilosis from the two hospital networks was 38% for 2020 and 47% for 2021 (January–July) compared to 15% and 21%, respectively, for all other Ontario submitters, which was significantly higher (2020 ꭓ² = 6.63, P = 0.0100; 2021 ꭓ² = 4.17, P = 0.0412) (Fig. 1).

Fig 1 — Number of FLU susceptible (MIC ≤2 µg/mL), susceptible-dose dependent (SDD) (MIC = 4 µg/mL), and FLU-resistant (MIC ≥8 µg/mL) *C. parapsilosis* isolates from two Ontario acute care hospital networks (A) and all other submitters (B) from 2016 to 2021 (January–July). The percentage of nonsusceptible isolates for each year is also displayed. *Statistically significant differences (P < 0.05) in yearly proportions of FLU-nonsusceptible isolates between the two Ontario acute care hospital networks and all other submitters. †Partial year, January–July.

Based on these findings, WGS SNV analysis using the MycoSNP pipeline was employed as part of the investigation to assess genetic similarity between isolates. All FLU-resistant (n = 19) and most (seven of eight) SDD isolates from the two hospital networks from January 2016 through July 2021 were included in the WGS analysis, together with other susceptible isolates from the same hospital facilities and FLU-nonsusceptible and susceptible isolates from across the province. Based on whole genome SNV phylogenomic analysis (Fig. 2A), the majority of FLU-nonsusceptible (25/26) isolates from the two hospital networks formed a genetically similar cluster, designated as Cluster I, together with two susceptible (MIC = 2 µg/mL) isolates from the same networks and a single FLU-resistant isolate (H01) from a different hospital (Fig. 2A). The isolates from Cluster I were collected over a period of 5.5 years from patients in a variety of wards across the two hospital networks (Fig. 3). Cluster I isolates were genetically similar to each other and distinct from other susceptible and nonsusceptible isolates in Ontario, including a single nonsusceptible isolate (B04) from hospital B (Fig. 2A). Based on the MycoSNP pipeline, the number of SNVs between outbreak isolates ranged from 0 to 19 with an average of seven SNV differences between isolates. There were 37–41 SNV differences between the isolates of Cluster I and isolate M01, and >157 SNV differences between the isolates of Cluster I and all other study isolates. As a control, two isolates (B12-1 and B12-2) included in Cluster I were from the same patient derived from specimens collected 1 week apart; the MycoSNP pipeline detected zero SNV differences between these isolates. WGS SNV analysis revealed a second cluster (Cluster II) of five genetically similar isolates from another hospital laboratory with zero or one SNV differences between isolates (Fig. 2A).

Fig 2 — The phrase "two hospital networks (A, B)" should be in plain text. This "(A, B)" does NOT refer to parts A and B of fig. 2 of whole genome SNVs of *C. parapsilosis* by MycoSNP and NJ method constructed in MEGA11. The percentage of trees of 500 bootstrap replications in which the associated taxa clustered together is shown next to the branches. The tree is drawn to scale, with branch lengths measured in the number of substitutions per site. All positions containing gaps and missing data were eliminated (complete deletion option). There were a total of 7,094 positions in the final data set. This analysis involved 60 nucleotide sequences from isolates, which are assigned a letter to designate the submitter and a number to denote sequential isolates from the same submitter. Branch tips are annotated to designate samples from the two hospital networks (A, B) FLU susceptibility [R = resistant (MIC ≥8 µg/mL), SDD = susceptible-dose dependent (MIC = 4 µg/mL), S = susceptible (MIC ≤2 µg/mL)] and a homozygous (K143R) or heterozygous (K143/K143R) ERG11 substitution at position 143. Two clusters (Cluster I, Cluster II) of FLU-nonsusceptible isolates are denoted. (B) Phylogenomic analysis of whole genome SNVs generated by a GATK custom pipeline and ML method constructed in IQ-TREE 2. The percentage of trees of 1,000 ultrafast bootstrap replications in which the associated taxa clustered together is shown next to the branches. The tree is drawn to scale with branch lengths measured by ML distance estimation. There were a total of 10,204 positions in the final data set, and the tree was constructed using the TVM + F + ASC + R2 best-fit model. Sequences from isolates and clusters are labeled as in (A).

Fig 3 — Characteristics of Cluster I isolates including specimen type, hospital network and ward, specimen collection date, FLU MIC, interpretation, and homozygous (K143R) or heterozygous (K143/K143R) ERG11 substitution correlated with ML phylogenomic analysis of whole genome SNVs generated by a GATK custom pipeline.

WGS data were also analyzed with a custom pipeline utilizing GATK Best Practices for germline short variant discovery and ML phylogenomic analysis. The advantage of this approach is that it retained heterozygous SNVs that were eliminated by the previous approach that used the MycoSNP pipeline. Since C. parapsilosis is diploid (32), heterozygous SNVs are expected. The phylogenomic trees generated by MycoSNP/NJ and GATK/ML were very similar (Fig. 2B). The isolates within each of the two clusters were identical with 100% bootstrap support for branches. Phylogenomic analysis by GATK/ML suggested that Cluster I isolates were organized into five subgroups (Fig. 2B). There was no correlation between phylogenomic subgroup and hospital ward; however, there was some chronological association with three isolates (B01, A02, B01) from early 2016 forming a distinct basal cluster with 100% bootstrap support (Fig. 3).

The results of the WGS analysis were validated using the more commonly used typing method for C. parapsilosis, MLMT, which utilizes a small number of genetic loci. Several studies have conducted MLMT of C. parapsilosis and identified outbreaks based on identical or similar microsatellite profiles (3 –6, 9, 20, 22, 23). The minimum spanning tree identified two clonal complexes (I and II) based on the definition that isolates are part of the same clonal complex if they differ at ≤1 microsatellite locus. While the five isolates in Cluster II by WGS analysis were similarly grouped into a clonal complex by MLMT, the isolates contained in Cluster I by WGS phylogenomic analysis differed slightly from those included in Clonal complex I by MLMT. Isolate B19 was included in Cluster I by WGS analysis (Fig. 2A and B) but was excluded from the Clonal complex I by MLMT because it had differences at two microsatellite loci compared to other isolates in the complex (Fig. 4). Similarly, isolate M01 was excluded from Cluster I by WGS phylogenomic analysis (Fig. 2A and B) but was included in Clonal complex I by MLMT because it differed from other isolates at only one locus.

Fig 4 — Minimum spanning tree constructed from microsatellite genotypes treated as categorical values. Isolates are color-coded based on FLU susceptibility [R = resistant (MIC ≥8 µg/mL), SDD = susceptible-dose dependent (MIC = 4 µg/mL), S = susceptible (MIC ≤2 µg/mL)]. Genetically identical isolates are merged together into a single circle. Solid, dashed, and dotted branches indicated differences at 1, 2, or 3 loci, respectively. Clonal complexes were defined as a group of isolates with microsatellite genotypes that differ at ≤1 microsatellite locus. Two clonal complexes (Clonal complex I, Clonal complex II) of FLU-nonsusceptible isolates are denoted.

The majority of isolates in Cluster I and Cluster II were FLU nonsusceptible. Therefore, we screened for amino acid substitutions in targets that are associated with decreased susceptibility to FLU, namely lanosterol 14-α-demethylase (ERG11), a key enzyme in the Candida spp. ergosterol biosynthesis pathway targeted by azoles (14), major facilitator superfamily transporters (MDR1, MDR1B), and ATP-binding cassette (ABC) transporters (CDR1, CDR1B) that can act as azole efflux pumps (44) and transcription factors (MRR1, TAC1, UPC2) thought to control the transcription of ERG11, CDR1, MDR1, CDR1B, and MDR1B genes (7, 14). The results are summarized in Table S2. Of note, the majority (27/28) of Cluster I isolates, including all (26/26) FLU-nonsusceptible isolates, had a homozygous or heterozygous K143R substitution in ERG11 (Fig. 3), which was not present in any susceptible or other nonsusceptible study isolates. The majority of FLU-nonsusceptible isolates with sufficient sequencing depth coverage of the CDR1 gene (29/30) had an N1132D substitution in CDR1 including Cluster I and Cluster II isolates and M01 (Table S2). This substitution was not observed in FLU-nonsusceptible isolate B04 or in any susceptible isolates. Isolate B04 displayed a heterozygous G490R substitution in TAC1 that was not observed in any other susceptible or nonsusceptible isolates.

DISCUSSION

In this study, we describe a prolonged outbreak of FLU-resistant C. parapsilosis in two closely linked acute care academic hospital networks in Ontario, Canada. Prompted by concerns from the infectious disease consultation service and antimicrobial stewardship team about a suspected increase in cases of FLU-nonsusceptible C. parapsilosis, a retrospective investigation confirmed a significant increase in FLU nonsusceptibility among C. parapsilosis isolates from the two hospital networks in 2020 and 2021 (January–July). Rates of C. parapsilosis FLU nonsusceptibility among clinical isolates range from 2% to 5% worldwide (1, 45), 4.9% Canada-wide (46), and were 17% in Ontario from 2014 to 2018 (25); whereas, the two hospital networks exhibited rates of 38% (2020) and 47% (January–July 2021). Subsequent WGS and phylogenomic analysis demonstrated that infections were caused by a group of genetically highly similar isolates comprising a single cluster (Cluster I) distinct from other susceptible and nonsusceptible C. parapsilosis clinical isolates in Ontario. Based on phylogenomic analysis, the isolates of Cluster I were recovered over a period of at least 5.5 years and included patients from various wards of the two hospital networks as well as at least one case from a different healthcare institution. The results suggest that the outbreak of C. parapsilosis infections was caused by clonal dissemination of a persistent strain of FLU-nonsusceptible C. parapsilosis pervasive in the healthcare environment in multiple hospitals in Ontario.

Several outbreaks of FLU-nonsusceptible C. parapsilosis infections in healthcare institutions have been previously described. The outbreaks often span multiple years and have been reported from various countries worldwide (3, 5 –12, 18, 20). To our knowledge, this is the first report of an outbreak due to clonal dissemination of FLU-nonsusceptible C. parapsilosis in Canada. Identification of genetically related clusters both in this study and in previous studies suggests that the outbreaks are often caused by clonal dissemination of a single or a few strains to multiple patients (3 –12, 20). Collectively, these studies suggest that the outbreaks caused by clonal dissemination of FLU-resistant C. parapsilosis are a global problem. Strains can persist in healthcare settings for long periods despite aggressive infection control measures and often demonstrate horizontal transmission with the potential to generate substantial numbers of infections with high mortality rates (2). Recent reports of echinocandin-resistant and multidrug-resistant C. parapsilosis due to FKS1 (β-1,3-D-glucan synthase) substitutions (16 –19) intensify the concern of outbreaks that are even more difficult to treat. FLU-resistant C. parapsilosis is not generally included in hospital infection prevention and control surveillance programs, and case clusters may be missed.

This outbreak occurred in 2020–2021 during the COVID-19 pandemic. An increased incidence of FLU-resistant C. parapsilosis candidemia in some healthcare institutions during the COVID-19 pandemic has been documented (6, 7, 20, 47). Significant strain on hospital resources during the COVID-19 pandemic may have contributed to the selection for and spread of FLU-resistant C. parapsilosis clones during this period (2). Moreover, some studies suggest that patients admitted to hospital due to COVID-19 are more susceptible to FLU-resistant C. parapsilosis fungemia (7, 47); thus, the nature of the patient population may have facilitated outbreaks during the COVID-19 pandemic years.

Most studies postulate that C. parapsilosis outbreaks are caused by dissemination of outbreak strains from colonized healthcare worker hands and/or the hospital environment (2, 3, 10, 13). Although some studies have demonstrated that approximately one-third of healthcare worker hands are colonized by C. parapsilosis (8, 13), most investigations that have genotyped clinical isolates, including this study, did not include isolates from healthcare workers or the hospital environment (no isolates were available for testing as part of this investigation). The two genetic fingerprinting studies that did include isolates from healthcare worker hands in their outbreak investigations noted that while the genotypes of most isolates from healthcare worker hands and patients were unrelated, there were a few cases in which they were identical (8, 13).

The vast majority of C. parapsilosis genotyping studies employ microsatellite markers (3 –6, 9, 20, 22, 23). However, the utility of microsatellite genotyping has recently been questioned following a report of identical genotypes from unrelated patients from distant geographic locations (10, 23). Either some genotypes are widely disseminated or microsatellites have low discriminatory power (10, 23). Whole genome SNV analysis has the potential for much greater discriminatory power and easier standardization between laboratories with the added benefit of in silico detection of antifungal resistance mutations. Guinea et al. (10) demonstrated that WGS and phylogenomic analysis correlated well with the microsatellite dendrogram topology, although greater intra-cluster genomic variability was observed. In this study, we also document the overall similarity in tree topology by microsatellite typing and whole genome SNV analysis. The two discrepancies between the two methods involved the different definitions of cluster and clonal complex for each typing method, resulting in the inclusion or exclusion of closely related isolates in the cluster or clonal complex.

The MycoSNP pipeline and NJ phylogenomic analysis clearly delineated the majority of FLU-nonsusceptible isolates from the two acute care hospital networks from the remaining FLU-susceptible and -nonsusceptible isolates from these hospitals and other healthcare institutions in Ontario. Based on our findings, we tentatively set a cutoff of <20 SNVs between isolates of the same cluster, which is similar to the <12 SNVs that suggest horizontal transmission of C. auris (35). The analysis also detected a previously unsuspected clonal outbreak (Cluster II) at another healthcare institution and also clearly identified an isolate from another healthcare institution as part of Cluster I. These findings highlight an advantage of whole genome SNV analysis in identifying genetically highly similar isolates without prior assumptions about possible sample linkages.

One challenge for whole genome SNV phylogenomic analysis is the treatment of heterozygous SNVs encountered in diploid yeasts such as C. parapsilosis (32). Here, we analyzed the whole genome SNVs using (i) the MycoSNP pipeline, which is optimized for fungal WGS and NJ analysis that omits heterozygous SNVs as ambiguous, and (ii) a GATK pipeline with generic SNV filtering parameters described in the GATK Best Practices workflow followed by ML analysis, which retains heterozygous SNVs. Encouragingly, both methods yielded similar phylogenomic results. Similar results from different SNV detection pipelines were also observed for C. auris (48). However, the GATK pipeline has the added benefit of facilitating detection of heterozygous SNVs in antifungal resistance markers.

Isolates from clonal outbreaks of FLU-nonsusceptible C. parapsilosis candidemia frequently carry the Y132F mutation in ERG11, which alters sterol biosynthesis (3 –6, 9, 11, 14, 15). All FLU-susceptible and -nonsusceptible isolates in this study exhibited the wild-type F132 in ERG11. However, all FLU-nonsusceptible isolates in Cluster I had a homozygous or heterozygous K143R substitution in ERG11, which likely mediated resistance to FLU. The K143R substitution in ERG11 has been previously noted among FLU-nonsusceptible C. parapsilosis clinical isolates, and expression of ERG11 carrying the K143R substitution in Saccharomyces cerevisiae resulted in elevated azole MICs (5). There were seven FLU-nonsusceptible isolates (Cluster II, M01, B04) in this study that had a wild-type ERG11. Therefore, we screened additional targets for SNVs causing amino acid substitutions in azole efflux pumps (MDR1, MDR1B, CDR1, and CDR1B) and transcription factors (MRR1, UPC2, TAC1) postulated to control transcription of ERG11 or azole efflux pump genes (14, 15, 44, 49). Interestingly, all isolates in Cluster I, Cluster II, and M01 had the N1132D substitution in CDR1, previously described as being associated with FLU resistance (50). In addition, FLU-nonsusceptible isolate B04 had a unique G490/G490R substitution in TAC1 not found in other isolates. Recently, Daneshnia et al. (7) described the L518F TAC1 substitution associated with FLU resistance in C. parapsilosis. Further study is needed to confirm whether these substitutions in CDR1 and TAC1 conferred FLU nonsusceptibility in our study isolates.

Our study is subject to limitations. Minimal patient and epidemiological information were available to corroborate the clonal dissemination hypothesized here based on genetic similarity between isolates or to ascertain potential causes of horizontal transmission. Lack of isolates from either healthcare worker hands or hospital environmental sources precluded the opportunity to identify environmental niche(s) or mode(s) of transmission. The success of post-intervention efforts that ensued following knowledge of this outbreak was not included in this study. Sequencing efforts were intentionally skewed toward nonsusceptible isolates, so this analysis does not provide an accurate portrayal of the genetic diversity of all clinical isolates of C. parapsilosis in Ontario.

In conclusion, phylogenomic analysis of whole genome SNVs depicted a genetically highly similar cluster of primarily FLU-nonsusceptible clinical isolates of C. parapsilosis from two acute care hospital networks and one other healthcare institution, warranting additional WGS surveillance in Ontario. The analysis provided evidence that the increased number of cases due to FLU-nonsusceptible C. parapsilosis was due to clonal dissemination of a single strain to various patients rather than independent selection events for FLU nonsusceptibility during antimicrobial treatment. The results emphasize the enhanced benefit of coordinating local antimicrobial resistance surveillance with WGS analysis to detect and characterize outbreaks of resistant fungi, which can, in turn, guide targeted infection control activities. Together with other recent reports of hospital-associated FLU-nonsusceptible C. parapsilosis outbreaks, this study serves to heighten the awareness of this emerging issue and prompt increased monitoring and surveillance.

ACKNOWLEDGMENTS

Thanks to Ceylon Simon for assistance with patient data. This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

Contributor Information

Julianne V. Kus, Email: julianne.kus@oahpp.ca.

Kimberly E. Hanson, University of Utah, Salt Lake City, Utah, USA

DATA AVAILABILITY

Raw reads for WGS are available in the NCBI Sequence Read Archive (SRA) under Bioproject accession numbers PRJNA592373 and PRJNA1011319. Individual SRA accession numbers are listed in Table S1.

ETHICS APPROVAL

This investigation was conducted as part of Public Health Ontario’s legislated mandate to provide scientific and technical advice as well as operational support in emergency or outbreak situations (Ontario Agency for Health Protection and Promotion Act, SO 2007, Chapter 10) (https://www.ontario.ca/laws/statute/07o10). As this work is considered public health practice and not research, research ethics approval was not required. Specimens and associated data were anonymized prior to use by a Public Health Ontario data custodian and, accordingly, individual consent was not required for the secondary use of non-identifiable specimens and associated information.

SUPPLEMENTAL MATERIAL

The following material is available online at https://doi.org/10.1128/jcm.01161-23.

Table S1. jcm.01161-23-s0001.docx.

Phred quality score, percent GC content, mean depth of coverage, and SRA accession numbers of study isolates.

jcm.01161-23-s0001.docx^{(15.3KB, docx)}

DOI: 10.1128/jcm.01161-23.SuF1

Table S2. jcm.01161-23-s0002.xlsx.

Homozygous or heterozygous amino acid substitutions in targets ERG11, MDR1, MDR1B, CDR1, CDR1B, MRR1, TAC1, and UPC2 in study isolates compared to reference strain C. parapsilosis CDC317 based on SNVs in genome alignments.

jcm.01161-23-s0002.xlsx^{(24KB, xlsx)}

DOI: 10.1128/jcm.01161-23.SuF2

ASM does not own the copyrights to Supplemental Material that may be linked to, or accessed through, an article. The authors have granted ASM a non-exclusive, world-wide license to publish the Supplemental Material files. Please contact the corresponding author directly for reuse.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Table S1. jcm.01161-23-s0001.docx.

Phred quality score, percent GC content, mean depth of coverage, and SRA accession numbers of study isolates.

jcm.01161-23-s0001.docx^{(15.3KB, docx)}

DOI: 10.1128/jcm.01161-23.SuF1

Table S2. jcm.01161-23-s0002.xlsx.

jcm.01161-23-s0002.xlsx^{(24KB, xlsx)}

DOI: 10.1128/jcm.01161-23.SuF2

Data Availability Statement

Raw reads for WGS are available in the NCBI Sequence Read Archive (SRA) under Bioproject accession numbers PRJNA592373 and PRJNA1011319. Individual SRA accession numbers are listed in Table S1.

[B1] 1. Tóth R, Nosek J, Mora-Montes HM, Gabaldon T, Bliss JM, Nosanchuk JD, Turner SA, Butler G, Vágvölgyi C, Gácser A. 2019. Candida parapsilosis: from genes to the bedside. Clin Microbiol Rev 32:e00111-18. doi: 10.1128/CMR.00111-18 [DOI] [PMC free article] [PubMed] [Google Scholar]

[B2] 2. Daneshnia F, de Almeida Júnior JN, Ilkit M, Lombardi L, Perry AM, Gao M, Nobile CJ, Egger M, Perlin DS, Zhai B, Hohl TM, Gabaldón T, Colombo AL, Hoenigl M, Arastehfar A. 2023. Worldwide emergence of fluconazole-resistant Candida parapsilosis: current framework and future research roadmap. Lancet Microbe 4:e470–e480. doi: 10.1016/S2666-5247(23)00067-8 [DOI] [PMC free article] [PubMed] [Google Scholar]

[B3] 3. Thomaz DY, de Almeida JN, Lima GME, Nunes M de O, Camargo CH, Grenfell R de C, Benard G, Del Negro GMB. 2018. An azole-resistant Candida parapsilosis outbreak: clonal persistence in the intensive care unit of a Brazilian teaching hospital. Front Microbiol 9:2997. doi: 10.3389/fmicb.2018.02997 [DOI] [PMC free article] [PubMed] [Google Scholar]

[B4] 4. Arastehfar A, Hilmioğlu-Polat S, Daneshnia F, Pan W, Hafez A, Fang W, Liao W, Şahbudak-Bal Z, Metin DY, Júnior J de A, Ilkit M, Perlin DS, Lass-Flörl C. 2021. Clonal candidemia outbreak by Candida parapsilosis carrying Y132F in Turkey: evolution of a persisting challenge. Front Cell Infect Microbiol 11:676177. doi: 10.3389/fcimb.2021.676177 [DOI] [PMC free article] [PubMed] [Google Scholar]

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PERMALINK

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