Host mechanisms involved in cattle Escherichia coli O157 shedding: a fundamental understanding for reducing foodborne pathogen in food animal production
Bibliographic record
Abstract
The host mechanisms involved in Escherichia coli O157 super-shedding in cattle is largely unknown. In this study, the comparison of transcriptomes of intestinal tissues between super-shedders (SS) and cattle negative for E. coli O157 (NS) was performed, aiming to identify genes that are potentially associated with super-shedding. In total, 16,846 ± 639 (cecum) to 18,137 ± 696 (distal jejunum) were expressed throughout the intestine, with the expressed genes associated with immune functions more pronounced in the small intestine. In total, 351 differentially expressed (DE) genes were identified throughout the intestine between SS and NS, with 101 being up-regulated and 250 down-regulated in SS. Functional analysis revealed DE genes were involved in increased T-cell responses and cholesterol absorption in the distal jejunum and descending colon, and decreased B-cell maturation in the distal jejunum of SS. RNA-Seq based SNP discovery revealed that the mutations in seven DE genes involved in leukocyte activation and cholesterol transportation were associated with E. coli O157 shedding. Our findings suggest that T-cell responses and cholesterol metabolism in the intestinal tract may be associated with super-shedding phenomenon, and the SNPs in the DE genes are possibly associated with the observed gene expression difference between SS and NS.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".