Involvement of Quorum Sensing and Heat-Stable Enterotoxin a in Cell Damage Caused by a Porcine Enterotoxigenic <i>Escherichia coli</i> Strain
Bibliographic record
Abstract
Enterotoxigenic Escherichia coli (ETEC) strains with K88 fimbriae are often associated with the outbreaks of diarrhea in newborn and weaned piglets worldwide. In the present study, we observed that 10⁸ CFU/ml of K88(+) ETEC strain JG280 caused more death of pig intestinal IPEC-J2 cells than did 10⁹ CFU/ml, suggesting that ETEC-induced cell death was cell density dependent and that quorum sensing (QS) may play a role in pathogenesis. Subsequent investigations demonstrated a positive correlation between autoinducer 2 (AI-2) activity of JG280 and death of IPEC-J2 cells during the infection for up to 3 h. However, there was a negative correlation between AI-2 activity and expression of the JG280 enterotoxin genes estA and estB when IPEC-J2 cells were exposed to the pathogen at 10⁸ CFU/ml. We therefore cloned the luxS gene (responsible for AI-2 production) from JG280 and overexpressed it in E. coli DH5α, because deletion of the luxS gene was retarded by the lack of suitable antibiotic selection markers and the resistance of this pathogen to a wide range of antibiotics. The addition of culture fluid from E. coli DH5α with the overexpressed luxS reduced cell death of IPEC-J2 cells by 10⁸ CFU/ml JG280. The addition also reduced the estA expression by JG280. Nonpathogenic K88(+) strain JFF4, which lacks the enterotoxin genes, caused no death of IPEC-J2 cells, although it produced AI-2 activity comparable to that produced by JG280. These results suggest the involvement of AI-2-mediated quorum sensing in K88(+) ETEC pathogenesis, possibly through a negative regulation of STa production.
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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".