Whole Genome Sequencing demonstrates that Geographic Variation of Escherichia coli O157 Genotypes Dominates Host Association
Bibliographic record
Abstract
Genetic variation in an infectious disease pathogen can be driven by ecological niche dissimilarities arising from different host species and different geographical locations. Whole genome sequencing was used to compare E. coli O157 isolates from host reservoirs (cattle and sheep) from Scotland and to compare genetic variation of isolates (human, animal, environmental/food) obtained from Scotland, New Zealand, Netherlands, Canada and the USA. Nei's genetic distance calculated from core genome single nucleotide polymorphisms (SNPs) demonstrated that the animal isolates were from the same population. Investigation of the Shiga toxin bacteriophage and their insertion sites (SBI typing) revealed that cattle and sheep isolates had statistically indistinguishable rarefaction profiles, diversity and genotypes. In contrast, isolates from different countries exhibited significant differences in Nei's genetic distance and SBI typing. Hence, after successful international transmission, which has occurred on multiple occasions, local genetic variation occurs, resulting in a global patchwork of continental and trans-continental phylogeographic clades. These findings are important for three reasons: first, understanding transmission and evolution of infectious diseases associated with multiple host reservoirs and multi-geographic locations; second, highlighting the relevance of the sheep reservoir when considering farm based interventions; and third, improving our understanding of why human disease incidence varies across the world.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".