Subtyping <i>Escherichia coli</i> Virulence Genes Isolated from Feces of Beef Cattle and Clinical Cases in Alberta
Bibliographic record
Abstract
Clinical outcomes of Shiga toxin (stx)–producing Escherichia coli infection are largely determined by virulence gene subtypes. This study used a polymerase chain reaction (PCR)–pyrosequencing assay to analyze single-nucleotide polymorphisms for subtyping three major virulence genes (stx1, stx2, eae) of pathogenic E. coli (O157, O26, O111, and O103) isolated from cattle over a 2-year interval (n = 465) and human clinical cases (n = 42) in western Canada. Most bovine isolates were PCR positive for at least one target virulence gene (367/465), whereas 100% of human isolates harbored eae in combination with at least one stx gene. Four Shiga toxin (1a, 2a, 2c, and 2e) and four eae (λ/γ1-eae, ɛ-eae, θ/γ2-eae, and β-eae) subtypes were identified in over 25 distinct virulence genotypes. Among cattle isolates, every serogroup, but O103, presented a dominant genotype (O157: stx1a+stx2a+λ/γ1-eae, O26: β-eae alone, and O111: stx1a+θ/γ2-eae). Similar patterns were found in human isolates, although it was not possible to establish a clear genotypic association between the two sources. Many O157 and non-O157 cattle isolates lacked stx genes; the absence was greater in non-O157 (75/258) and O157:non-H7 (19/40) than in O157:H7 strains (1/164). In addition, there was a greater diversity of virulence genotypes of E. coli isolated from cattle than those of human diseases, which could be due to sample characteristics (e.g., source and clinical condition). However, the majority of cattle strains had virulence profiles identical to those of clinical cases. Consequently, determining the presence of certain stx (stx1a and stx2a) and eae (λ/γ1-eae) subtypes known to cause human disease would be a valuable tool for risk assessment and prediction of disease outcome along the farm-to-fork continuum.
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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 | 0.001 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".