Abundance and Expression of Shiga Toxin Genes in Escherichia coli at the Recto-Anal Junction Relates to Host Immune Genes
Bibliographic record
Abstract
Shiga toxin (Stx) is the main virulence factor of Shiga toxin-producing Escherichia coli (STEC), and ruminants are the main reservoir of STEC. This study assessed the abundance and expression of Stx genes and the expression of host immune genes, aiming to determine factors affecting these measures and potential gene markers to differentiate Stx gene expression in the recto-anal junction of feedlot beef cattle. Rectal tissue and content samples were collected from 143 feedlot steers of three breeds (Angus, Charolais, and Kinsella Composite) over 2 consecutive years 2014 (n=71) and 2015 (n=72). The abundance and expression of stx1 and stx2 were quantified using qPCR and reverse-transcription-qPCR (RT-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 RT-qPCR. The stx1 gene abundance was only detected in tissue samples collected in year 2 and did not differ among breeds. The stx2 gene was detected in STEC from all samples collected in both years and did not vary among breeds. The abundance of stx1 and stx2 differed (P < 0.001) in content samples collected across breeds (stx1:AN>CH>KC, stx2: AN=CH>KC) in year 1, but not in year 2. Expression of stx2 was detected in 13 RAJ tissue samples (2014: n=6, 2015: n=7), while expression of stx1 was not detected. Correlation analysis showed that the expression of stx2 was negatively correlated with the expression of MS4A1 (R=-0.56, P=0.05) and positively correlated with the expression of LTB (R=0.60, P=0.05). The random forest model and Boruta method revealed that expression of selected immune genes could be predictive indicators of stx2 expression with prediction accuracy of MS4A1 >LTB >CCL21 >CD19. Our results indicate that the abundance of Stx could be affected by cattle breed and sampling year, suggesting that host genetics and environment may influence STEC colonization of the recto-anal junction of feedlot cattle. Additionally, the identified relationship between expressions of host immune genes and stx2 suggests that the host animal may regulate stx2 expression in colonizing STEC through immune functions.
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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".