High-throughput genome sequencing of two Listeria monocytogenes clinical isolates during a large foodborne outbreak
Bibliographic record
Abstract
BACKGROUND: A large, multi-province outbreak of listeriosis associated with ready-to-eat meat products contaminated with Listeria monocytogenes serotype 1/2a occurred in Canada in 2008. Subtyping of outbreak-associated isolates using pulsed-field gel electrophoresis (PFGE) revealed two similar but distinct AscI PFGE patterns. High-throughput pyrosequencing of two L. monocytogenes isolates was used to rapidly provide the genome sequence of the primary outbreak strain and to investigate the extent of genetic diversity associated with a change of a single restriction enzyme fragment during PFGE. RESULTS: The chromosomes were collinear, but differences included 28 single nucleotide polymorphisms (SNPs) and three indels, including a 33 kbp prophage that accounted for the observed difference in AscI PFGE patterns. The distribution of these traits was assessed within further clinical, environmental and food isolates associated with the outbreak, and this comparison indicated that three distinct, but highly related strains may have been involved in this nationwide outbreak. Notably, these two isolates were found to harbor a 50 kbp putative mobile genomic island encoding translocation and efflux functions that has not been observed in other Listeria genomes. CONCLUSIONS: High-throughput genome sequencing provided a more detailed real-time assessment of genetic traits characteristic of the outbreak strains than could be achieved with routine subtyping methods. This study confirms that the latest generation of DNA sequencing technologies can be applied during high priority public health events, and laboratories need to prepare for this inevitability and assess how to properly analyze and interpret whole genome sequences in the context of molecular epidemiology.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".