Attaching and Effacing Bacterial Effector NleC Suppresses Epithelial Inflammatory Responses by Inhibiting NF-κB and p38 Mitogen-Activated Protein Kinase Activation
Bibliographic record
Abstract
Enteropathogenic Escherichia coli (EPEC) and enterohemorrhagic E. coli are noninvasive attaching and effacing (A/E) bacterial pathogens that cause intestinal inflammation and severe diarrheal disease. These pathogens utilize a type III secretion system to deliver effector proteins into host epithelial cells, modulating diverse cellular functions, including the release of the chemokine interleukin-8 (IL-8). While studies have implicated the effectors NleE (non-locus of enterocyte effacement [LEE]-encoded effector E) and NleH1 in suppressing IL-8 release, by preventing NF-κB nuclear translocation, the impact of these effectors only partially replicates the immunosuppressive actions of wild-type EPEC, suggesting another effector or effectors are involved. Testing an array of EPEC mutants, we identified the non-LEE-encoded effector C (NleC) as also suppressing IL-8 release. Infection by ΔnleC EPEC led to exaggerated IL-8 release from infected Caco-2 and HT-29 epithelial cells. NleC localized to EPEC-induced pedestals, with signaling studies revealing NleC inhibits both NF-κB and p38 mitogen-activated protein kinase (MAPK) activation. Using Citrobacter rodentium, a mouse-adapted A/E bacterium, we found that ΔnleC and wild-type C. rodentium-infected mice carried similar pathogen burdens, yet ΔnleC strain infection led to worsened colitis. Similarly, infection with ΔnleC C. rodentium in a cecal loop model induced significantly greater chemokine responses than infection with wild-type bacteria. These studies thus advance our understanding of how A/E pathogens subvert host inflammatory responses.
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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 | 0.001 |
| 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".