PSI-8 Effect of breed on the abundance and expression of Shiga toxin in Escherichia coli from the recto-anal junction of feedlot beef cattle
Bibliographic record
Abstract
Abstract Shiga toxin (Stx) is the main virulence factor of Shiga toxin-producing E. coli (STEC), and ruminants including cattle are the main reservoir of STEC. This study aimed to assess whether cattle breed affects the abundance and expression of Stx and to determine whether the expression of host immune genes can serve as markers of STEC colonization. In total, 143 rectal tissue and content samples were collected from feedlot beef steers in 2014 (n = 71) and 2015 (n = 72) composed of three breeds (Angus, Charolais and Kinsella Composite) with differing feed efficiency. The abundance and expression of Stx1 and Stx2 by STEC associated with rectal tissue was quantified by qPCR and reverse-transcription-qPCR, respectively. Four immune genes (MS4A1, CCL21, CD19, and LTB), previously reported to be down-regulated in super-shedder cattle (i.e., > 104 cfu g-1) were selected and their expression was evaluated using qPCR. The abundance of stx1 and stx2 differed (P < 0.001) among breeds in rectal content samples collected in 2014, while no such difference was detected for 2015 samples. Correlation analysis showed that the expression of stx2 was negatively correlated with MS4A1 (R = -0.56, P = 0.05) and positively associated with LTB (R = 0.60, P = 0.05). The random forest model revealed that the expression of selected immune genes could be used as indicators of Stx2 expression and potential STEC colonization with prediction accuracy of MS4A1 >LTB >CCL21 >CD19. Our results indicate that the abundance and expression of Stx could be affected by cattle breed and the year of sampling, suggesting that host genetics and environment influence STEC colonization. The relationship between the expression of host genes associated with immunity and Stx by STEC expression suggests a role of host in STEC colonization, but further validation is needed to confirm the predictiveness of identified markers.
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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 | 0.001 | 0.000 |
| 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".