Application of whole genome sequencing to query a potential outbreak of Elizabethkingia anophelis in Ontario, Canada
Bibliographic record
Abstract
Bioinformatic analysis of whole genome sequence (WGS) data is emerging as a tool to provide powerful insights for clinical microbiology. We used WGS data to investigate the genetic diversity of clinical isolates of the bacterial pathogen Elizabethkingia anophelis to query the existence of a single-strain outbreak in Ontario, Canada. The Public Health Ontario Laboratory (PHOL) provides reference identification of clinical isolates of bacteria for Ontario and prior to 2016 had not identified E. anophelis . In the wake of the Wisconsin outbreak of 2015–2016 for which a source was never elucidated, the identification of E. anophelis from clinical specimens from five Ontario patients gave reason to question the presence of an outbreak. Genomic comparisons based on core genome multi-locus sequence typing conclusively refuted the existence of an outbreak, since the 5 Ontario isolates were genetically dissimilar, representing at least 3 distinct sub-lineages scattered among a set of 39 previously characterized isolates. Further interrogation of the genomic data revealed multiple antimicrobial resistance genes. Retrospective reidentification via rpoB sequence analysis of 22 clinical isolates of Elizabethkingia spp. collected by PHOL from 2010 to 2018 demonstrated that E. anophelis was isolated from clinical specimens as early as 2010. The uptick in E. anophelis in Ontario was not due to an outbreak or increased incidence of the pathogen, but rather enhanced laboratory identification techniques and improved sequence databases. This study demonstrates the usefulness of WGS analysis as a public health tool to quickly rule out the existence of clonally related case clusters of bacterial pathogens indicative of single-strain outbreaks.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 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".