Enterohaemorrhagic, but not enteropathogenic, Escherichia coli infection of epithelial cells disrupts signalling responses to tumour necrosis factor-alpha
Bibliographic record
Abstract
Enterohaemorrhagic Escherichia coli (EHEC), serotype O157 : H7 is a non-invasive, pathogenic bacterium that employs a type III secretion system (T3SS) to inject effector proteins into infected cells. In this study, we demonstrate that EHEC blocks tumour necrosis factor-alpha (TNFα)-induced NF-κB signalling in infected epithelial cells. HEK293T and INT407 epithelial cells were challenged with EHEC prior to stimulation with TNFα. Using complementary techniques, stimulation with TNFα caused activation of NF-κB, as determined by luciferase reporter assay (increase in gene expression), Western blotting (phosphorylation of IκBα), immunofluorescence (p65 nuclear translocation) and immunoassay (CXCL-8 secretion), and each was blocked by EHEC O157 : H7 infection. In contrast, subversion of host cell signalling was not observed following exposure to either enteropathogenic E. coli, strain E2348/69 (O127 : H6) or the laboratory E. coli strain HB101. Heat-killed EHEC had no effect on NF-κB activation by TNFα. Inhibition was mediated, at least in part, by Shiga toxins and by the O157 plasmid, but not by the T3SS or flagellin, as demonstrated by using isogenic mutant strains. These findings indicate the potential for developing novel therapeutic targets to interrupt the infectious process.
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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".