Toll-Like Receptor 4 Contributes to Colitis Development but Not to Host Defense during <i>Citrobacter rodentium</i> Infection in Mice
Bibliographic record
Abstract
Enteropathogenic Escherichia coli (EPEC) and enterohemorrhagic E. coli (EHEC) are noninvasive bacterial pathogens that infect their hosts' intestinal epithelium, causing severe diarrheal disease. These infections also cause intestinal inflammation, although the mechanisms underlying the inflammatory response, as well as its potential role in host defense, are unclear. Since these bacteria are gram-negative, Toll-like receptor 4 (TLR4), the innate receptor for bacterial lipopolysaccharide may contribute to the host response; however, the role of TLR4 in the gastrointestinal tract is poorly understood, and its impact has yet to be tested against this family of enteric bacterial pathogens. Since EPEC and EHEC are human specific, we infected mice with Citrobacter rodentium, a mouse-adapted attaching and effacing (A/E) bacterium that infects colonic epithelial cells, causing colitis and epithelial hyperplasia, using a similar array of virulence proteins as EPEC and EHEC. We demonstrated that C. rodentium activates TLR4 and rapidly induced NF-kappaB nuclear translocation in host cells in a partially TLR4-dependent manner. Infection of TLR4-deficient mice revealed that TLR4-dependent responses mediate much of the inflammation and tissue pathology seen during infection, including the induction of the chemokines MIP-2 and MCP-1, as well as the recruitment of macrophages and neutrophils into the infected intestine. Surprisingly, spread of C. rodentium through the colon was delayed in TLR4-deficient mice, whereas the duration of the infection was unaffected, indicating that TLR4-mediated responses against this A/E pathogen are not host protective and are ultimately maladaptive to the host, contributing to both the morbidity and the pathology seen during infection.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".