The inhibition of COPII trafficking is important for intestinal epithelial tight junction disruption during enteropathogenic Escherichia coli and Citrobacter rodentium infection
Bibliographic record
Abstract
Enterohemorrhagic Escherichia coli (EHEC) and enteropathogenic E. coli (EPEC) are bacterial pathogens that cause severe illnesses in humans. Citrobacter rodentium is a related mouse pathogen that serves as a small animal model for EPEC and EHEC infections. EPEC, EHEC and C. rodentium translocate bacterial virulence proteins directly into host intestinal cells via a type III secretion system (T3SS). Non-LEE-encoded effector A (NleA) is a T3SS effector that is common to EPEC, EHEC and C. rodentium. NleA interacts with and inhibits the mammalian COPII complex, impairing cellular secretion; this interaction is required for bacterial virulence. Although diarrhea is a hallmark of EPEC, EHEC and C. rodentium infections, the underlying mechanisms are not well characterized. One of the essential functions of the intestine is to maintain a barrier between the lumen and submucosa. Tight junctions seal the space between adjacent epithelial cells creating this barrier. Consequently, it is thought that the disruption of intestinal epithelial tight junctions by EPEC, EHEC, and C. rodentium could result in a loss of barrier function. In this study, we demonstrate that NleA mediated COPII inhibition is required for EPEC- and C. rodentium-mediated disruption of tight junction proteins and increases in fecal water content.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".