The utility of multiple molecular methods including whole genome sequencing as tools to differentiate Escherichia coli O157:H7 outbreaks
Bibliographic record
Abstract
A standardised method for determining Escherichia coli O157:H7 strain relatedness using whole genome sequencing or virulence gene profiling is not yet established. We sought to assess the capacity of either high-throughput polymerase chain reaction (PCR) of 49 virulence genes, core-genome single nt variants (SNVs) or k-mer clustering to discriminate between outbreak-associated and sporadic E. coli O157:H7 isolates. Three outbreaks and multiple sporadic isolates from the province of Alberta, Canada were included in the study. Two of the outbreaks occurred concurrently in 2014 and one occurred in 2012. Pulsed-field gel electrophoresis (PFGE) and multilocus variable-number tandem repeat analysis (MLVA) were employed as comparator typing methods. The virulence gene profiles of isolates from the 2012 and 2014 Alberta outbreak events and contemporary sporadic isolates were mostly identical; therefore the set of virulence genes chosen in this study were not discriminatory enough to distinguish between outbreak clusters. Concordant with PFGE and MLVA results, core genome SNV and k-mer phylogenies clustered isolates from the 2012 and 2014 outbreaks as distinct events. k-mer phylogenies demonstrated increased discriminatory power compared with core SNV phylogenies. Prior to the widespread implementation of whole genome sequencing for routine public health use, issues surrounding cost, technical expertise, software standardisation, and data sharing/comparisons must be addressed.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".