Genomic reconstruction of an azole-resistant Candida parapsilosis outbreak and the creation of a multi-locus sequence typing scheme: a retrospective observational and genomic epidemiology study
Bibliographic record
Abstract
BACKGROUND: Fluconazole-resistant Candida parapsilosis has emerged as a significant health-care-associated pathogen with a propensity to spread patient to patient and cause nosocomial outbreaks, similar to Candida auris. This study investigates a long-lasting outbreak of fluconazole-resistant C parapsilosis that was initially detected in December, 2018, and January, 2019, and officially declared in November, 2019; lasted multiple years; and involved several health-care centres in Berlin, Germany. METHODS: In this retrospective, observational, and genomic epidemiology study, we used whole-genome sequencing (WGS) of isolates sent by German health-care facilities and laboratories to the National Reference Center for Invasive Fungal Infections (Jena, Germany) for antifungal susceptibility testing between Jan 1, 2016, and Dec 31, 2022. We included all potential outbreak samples (ie, isolates originating from Berlin that were resistant to fluconazole and voriconazole but susceptible to posaconazole) and all non-outbreak isolates that originated from outside of Berlin and were resistant to at least one azole. We also included a number of non-outbreak isolates from outside Berlin that were susceptible or resistant to azoles so that the total study dataset included a matching amount of outbreak and non-outbreak samples from Germany. We used admission and discharge records for patients involved in the outbreak and constructed a network of patient transfers in time and space. We used WGS data for included samples, complemented with WGS data for global samples obtained from the National Center for Biotechnology Information Sequence Read Archive, to construct single-nucleotide variant (SNV)-based phylogeny and perform SNV distance-based analyses. Additionally, we used the whole genomic dataset to identify loci with high discriminatory power to establish a multi-locus sequence typing (MLST) strategy for C parapsilosis. FINDINGS: We identified 38 clonal, azole-resistant isolates of C parapsilosis causing 33 cases of invasive infection during a 2018-22 outbreak in multiple hospitals in Berlin. We also sequenced the genomes of 37 non-outbreak isolates. WGS revealed that outbreak strains were separated by a mean of 36 SNVs (SD 20), whereas outbreak strains differed from outgroup samples from Berlin and other regions of Germany by a mean of 2112 SNVs (828). Temporal and genomic reconstruction of the outbreak cases indicated that transfer of patients between health-care facilities was probably 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 isolates from Kuwait, Türkiye, and South Korea. Including the outbreak clone, we identified three distinct azole-resistant lineages carrying ERG11 Y132F in Germany. We identified four 750 bp loci in CPAR2_101400, CPAR2_101470, CPAR2_108720, and CPAR2_808110 for inclusion in our MLST strategy. Application of the MLST method to a global collection of 386 isolates identified 62 sequence types, with the outbreak strains all belonging to the same sequence type. INTERPRETATION: This study underscores the emergence of drug-resistant C parapsilosis that can spread patient to patient within a health-care system, but also, possibly, internationally. Our findings highlight the importance of monitoring C parapsilosis epidemiology globally and of continuous surveillance and rigorous infection control measures at the local scale. We also developed a novel MLST scheme for genetic epidemiology and outbreak investigations, which could represent a faster and less expensive alternative to WGS. FUNDING: German Federal Ministry for Education and Research, German Research Foundation, and German Ministry of Health.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".