The specific transcriptomic response of porcine intestinal epithelial cells to <i>Escherichia coli</i> and <i>Salmonella enterica</i> Typhimurium infections is modulated by bovine colostrum fractions
Bibliographic record
Abstract
Abstract Enterotoxigenic Escherichiacoli (ETEC) and Salmonella enterica Typhimurium are both enteric pathogens, but the mechanisms by which they infect intestinal epithelium are different. Recently, we observed that bovine colostrum (BC) increases protection against enteric infections in weaned piglets. In this study, we described the transcriptomic response of porcine intestinal epithelial cells IPEC-J2 infected by each pathogens, and we measured the specific effect of defatted BC, as well as bovine serocolostrum and casein fractions (SC and CAS respectively) on IPEC-J2 transcriptomic response to ETEC and Salmonella by microarray analysis. We also measured the impact of BC, SC and CAS on pathogen-induced epithelial integrity loss using transepithelial electrical resistance (TEER), and on NF-kB transcriptional activity by luciferase assays. Results showed that transcriptomic profile of cells challenged with ETEC and Salmonella both induced genes involved in inflammatory response, while ETEC specifically induced the expression of genes involved in morphogenesis and response to lipids, and Salmonella specifically regulated genes involved in oxidative stress, migration and apoptotic process. The ETEC induction of inflammatory genes was decreased by BC and SC, while Salmonella induction of inflammatory genes was specifically reduced by BC and CAS fraction. BC prevented epithelial integrity disruption caused by both pathogens, while SC only prevented the one caused by Salmonella. Finally, BC and SC decreased NF-kB activity induced by each pathogen. Altogether, these results indicated that BC fractions modulate in a specific manner cellular mechanisms involved in intestinal epithelial response to these pathogens.
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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".