Evaluation of whole-genome sequencing for outbreak detection of Verotoxigenic Escherichia coli O157:H7 from the Canadian perspective
Bibliographic record
Abstract
BACKGROUND: Rapid and accurate identification of Verotoxigenic Escherichia coli (VTEC) O157:H7 is dependent on well-established, standardized and highly discriminatory typing methods. Currently, conventional subtyping tests for foodborne bacterial pathogen surveillance are rapidly being replaced with whole-genome sequencing (WGS) in public health laboratories. The capacity of WGS to revolutionize global foodborne disease surveillance has positioned this tool to become the new gold standard; however, to ensure evidence standards for public health decision making can still be achieved, the performance of WGS must be thoroughly validated against current gold standard methods prior to implementation. Here we aim to verify the performance of WGS in comparison to pulsed-field gel electrophoresis (PFGE) and multiple-locus variable-number tandem repeat analysis (MLVA) for eight retrospective outbreaks of VTEC O157:H7 from the Canadian perspective. Since real-time implementation and routine use of WGS in public health laboratories is highly reliant on standardized data analysis tools, we also provide a comparative analysis of two popular methodologies for WGS analyses; an in-house developed single nucleotide variant phylogenomics (SNVPhyl) pipeline and the BioNumerics whole genome multilocus sequence typing (wgMLST) tool. To provide a useful and consistent starting point for examining laboratory-based surveillance data for VTEC O157:H7 in Canada, we also aim to describe the number of genetic differences observed among outbreak-associated isolates. RESULTS: WGS provided enhanced resolution over traditional subtyping methods, and accurately distinguished outbreak-related isolates from non-outbreak related isolates with high epidemiological concordance. WGS also illuminated potential linkages between sporadic cases of illness and contaminated food, and isolates spanning multiple years. The topologies generated by SNVPhyl and wgMLST were highly congruent with strong statistical support. Few genetic differences were observed among outbreak-related isolates (≤5 SNVs/ < 10 wgMLST alleles) unless the outbreak was suspected to be multi-strain. CONCLUSIONS: This study validates the superiority of WGS and indicates the BioNumerics wgMLST schema is suitable for surveillance and cluster detection of VTEC O157:H7. These findings will provide a useful and consistent starting point for examining WGS data for prospective laboratory-based surveillance of VTEC O157:H7, but however, the data will continue to be interpreted according to context and in combination with epidemiological and food safety evidence to inform public-health decision making in Canada.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".