<i>Escherichia coli</i> O157:H7 Strain Origin, Lineage, and Shiga Toxin 2 Expression Affect Colonization of Cattle
Bibliographic record
Abstract
Enterohemorrhagic Escherichia coli O157:H7 has evolved into an important human pathogen with cattle as the main reservoir. The recent discovery of E. coli O157:H7-induced pathologies in challenged cattle has suggested that previously discounted bacterial virulence factors may contribute to the colonization of cattle. The objective of the present study was to examine the impact of lineage type, cytotoxin activity, and cytotoxin expression on the amount of E. coli O157:H7 colonization of cattle tissue and cells in vitro. Using selected bovine- and human-origin strains, we determined that lineage type predicted the amount of E. coli O157:H7 strain colonization: lineage I > intermediate lineages > lineage II. All E. coli O157:H7 strain colonization was dose dependent, with threshold colonization at 10(3) to 10(5) CFU and maximum colonization at 10(7) CFU. We also determined that an as-yet-unknown factor of strain origin was the most dominant predictor of the amount of strain colonization in vitro. The amount of E. coli O157:H7 colonization was also influenced by strain cytotoxin activity and the inclusion of cytotoxins from lineage I or intermediate lineage strains increased colonization of a lineage II strain. There was a higher level of expression of the Shiga toxin 1 gene (stx(1)) in human-origin strains than in bovine-origin strains. In addition, lineage I strains expressed higher levels of the Shiga toxin 2 gene (stx(2)). The present study supports a role for strain origin, lineage type, cytotoxin activity, and stx(2) expression in modulating the amount of E. coli O157:H7 colonization of cattle.
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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 | 0.000 |
| 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 | 0.000 |
| 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".