Genomic Analysis of Shiga Toxin-Producing E. coli O157 Cattle and Clinical Isolates from Alberta, Canada
Bibliographic record
Abstract
Shiga toxin (stx) is the principal virulence factor of the foodborne pathogen, Shiga toxin-producing Escherichia coli (STEC) O157:H7 and is associated with various lambdoid bacterio (phages). A comparative genomic analysis was performed on STEC O157 isolates from cattle (n = 125) and clinical (n = 127) samples to characterize virulence genes, stx-phage insertion sites and antimicrobial resistance genes that may segregate strains circulating in the same geographic region. In silico analyses revealed that O157 isolates harboured the toxin subtypes stx1a and stx2a. Most cattle (76.0%) and clinical (76.4%) isolates carried the virulence gene combination of stx1, stx2, eae and hlyA. Characterization of stx1 and stx2-carrying phages in assembled contigs revealed that they were associated with mlrA and wrbA insertion sites, respectively. In cattle isolates, mlrA and wrbA insertion sites were occupied more often (77% and 79% isolates respectively) than in clinical isolates (38% and 1.6% isolates, respectively). Profiling of antimicrobial resistance genes (ARGs) in the assembled contigs revealed that 8.8% of cattle (11/125) and 8.7% of clinical (11/127) isolates harboured ARGs. Eight antimicrobial resistance genes cassettes (ARCs) were identified in 14 isolates (cattle, n = 8 and clinical, n = 6) with streptomycin (aadA1, aadA2, ant(3’’)-Ia and aph(3’’)-Ib) being the most prevalent gene in ARCs. The profound disparity between the cattle and clinical strains in occupancy of the wrbA locus suggests that this trait may serve to differentiate cattle from human clinical STEC O157:H7. These findings are important for stx screening and stx-phage insertion site genotyping as well as monitoring ARGs in isolates from cattle and clinical samples.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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 teacher head, 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".