In Silico Analysis of Shiga Toxin-Producing Escherichia coli O157:H7 Strains from Presumptive Super- and Low-Shedder Cattle
Bibliographic record
Abstract
Cattle are the primary reservoir for STEC O157, with some shedding >104 CFU/g in feces, a phenomenon known as super-shedding (SS). The mechanism(s) responsible for SS are not understood but have been attributed to the environment, host, and pathogen. This study aimed to compare genetic characteristics of STEC O157 strains from cattle in the same commercial feedlot pens with SS or low-shedding (LS) status. Strains from SS (n = 35) and LS (n = 28) collected from 11 pens in three feedlots were analyzed for virulence genes, Shiga toxin-carrying bacteriophage insertion sites, and phylogenetic relationships. In silico analysis showed limited variation regarding virulence gene profiles. Stx-encoding prophage insertion sites mrlA and wrbA for stx1a and stx2a, respectively, were all occupied, but two isolates had fragments of the stx-carrying phage in mrlA and wrbA loci without stx1a and stx2a. All strains screened for lineage-specific polymorphism assay (LSPA-6) were 111111, lineage I. Of the isolates, 61 and 2 were clades 1 and 8, respectively. Phylogenetic analysis revealed that pens with more than one SS had multiple distantly related clusters of SS and LS isolates. Although virulence genes and lineage were largely similar within and across feedlots, multiple genetic origins of strains within a single feedlot pen illustrate challenges for on-farm control of STEC.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.000 | 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".