Genomic reconstruction of an azole-resistant <i>Candida parapsilosis</i> outbreak and the creation of a multilocus sequence typing scheme: a retrospective observational and genomic epidemiology study
Bibliographic record
Abstract
Summary Background Fluconazole-resistant Candida parapsilosis has emerged as a significant healthcare-associated pathogen with a propensity to spread patient-to-patient and cause nosocomial outbreaks, similar to Candida auris . This study investigates a prolonged outbreak of fluconazole-resistant C. parapsilosis across multiple years and healthcare centers in Berlin, Germany. Methods In this retrospective observational study, we used whole-genome sequencing of isolates from the outbreak in Berlin and other regions within Germany and compared them with isolates from a global distribution to understand the molecular epidemiology of this outbreak. Additionally, we used the genomic dataset of global samples to identify loci with high discriminatory power to establish a multi-locus sequence typing (MLST) strategy for C. parapsilosis . Findings A clonal, azole-resistant strain of C. parapsilosis was observed causing 33 cases of invasive infection from 2018-2022 in multiple hospitals within the outbreak city. Whole genome sequencing revealed that outbreak strains were separated by an average of 36 single nucleotide variants, while outbreak strains differed from outgroup samples from Berlin and other regions of Germany by an average of 2,112 variants. Temporal and genomic reconstruction of the outbreak cases indicated that transfer of patients between healthcare facilities was likely responsible for the persistent reimportation of the drug-resistant clone and subsequent person-to-person transmission. German outbreak strains were closely related to strains responsible for an outbreak in Canada and to others isolated in the Middle East and East Asia. Including the outbreak clone, we identified three distinct ERG11 Y132F azole-resistant lineages in Germany, marking the first description of this azole-resistance in the country and its endemic status. Using the novel MLST strategy, a global collection of 386 isolates was categorized into 62 sequence types, with the outbreak strains all belonging to the same sequence type. Interpretation This study underscores the emergence of drug resistant fungal pathogens that can spread patient-to-patient within a healthcare system, but also around the globe. This highlights the importance of monitoring C. parapsilosis epidemiology globally and of continuous surveillance and rigorous infection control measures at the local scale. Through large-scale genomic epidemiology, our study offers a high-resolution view of how a drug-resistant clone behaved in a local healthcare system and how this clone fits into the global epidemiology of this pathogen. We also demonstrate the utility of the novel typing scheme for genetic epidemiology and outbreak investigations as a faster and less expensive alternative to whole genome sequencing. Funding German Federal Ministry for Education and Research, German Research Foundation, German Ministry of Health Research in context Evidence before this study We searched PubMed and Google Scholar from database inception to Apr 25, 2024, using the search terms “Candida parapsilosis”, “outbreak”, “azole resistance”, and/or “fluconazole” in PubMed and Google Scholar. We applied no language or study type restrictions. The epidemiology of candidemia has undergone dramatic changes in recent years. New pathogenic species, such as Candida auris , have emerged, and existing species like Candida parapsilosis have increased in prominence. There has also been a worrying increase in drug resistance among Candida species. Moreover, numerous drug-resistant outbreaks of C. parapsilosis have been reported worldwide and are challenging to control due to their prolonged and intermittent nature. The overwhelming majority of previous work has used microsatellite markers to infer genetic relationships among outbreaks strains, obscuring whether they are really clonal in nature, our understanding of the temporal and transmission dynamics of these outbreaks, and the genetic relationship between outbreak clones. Added value of this study This study adds to the existing evidence by utilizing whole genome sequencing in conjunction with hospital records to analyze a prolonged outbreak of clonal, azole-resistant C. parapsilosis that occurred across multiple years and medical centers. This study demonstrates that patient transfers can result in the reimportation of outbreak clones, posting a significant challenge for infection control. We also reveal that the outbreak clone is closely related to drug-resistant isolates from other continents, highlighting the global spread of drug-resistant C. parapsilosis . Furthermore, the study addresses the need for rapid strain differentiation in outbreak settings by establishing and validating a set of four loci for Sanger sequence-based typing, which provide a highly discriminatory tool for epidemiologic investigations. Implications of all the available evidence This study underscores the global challenge of azole-resistant C. parapsilosis and its importance as the causative agent of nosocomial outbreaks. Clinicians should be aware of the evolving epidemiology of C. parapsilosis and the prevalence of drug-resistant strains, emphasizing the importance of appropriate antifungal stewardship and infection control measures. The study emphasizes the challenges caused by inter-hospital transmission and their role in persistent outbreaks, highlighting the need for robust surveillance and coordination among healthcare facilities. While whole genome sequencing (WGS) is becoming more widely available, it is still not available in many settings due to cost, limitations in bioinformatic expertise, and the absence of standardized methodology and data interpretation. The establishment of a sequence-based typing scheme is a valuable tool for rapid assessment of samples, which can aid in outbreak tracking and containment efforts, and provide results more rapidly even in settings where WGS is available.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".