Interleukin‐1 receptor activates Rho kinase to disrupt epithelial tight junctions during <i>Helicobacter pylori</i> infection
Bibliographic record
Abstract
The ability of H. pylori (Hp) to modulate tight junction (TJ) permeability may contribute to the development of gastric ulcers and adenocarcinomas. Rho kinase (ROCK) and interleukin (IL)‐1β are known to modulate tight junction (TJ) function. While Hp induces IL‐1β expression, the role of this proinflammatory cytokine and ROCK in Hp‐mediated TJ disruption remains elusive. AIM This study investigated the role of ROCK and IL‐1β in Hp‐mediated TJ disruption in human gastric epithelial (HGE‐20) cells. RESULTS HGE‐20 monolayers challenged with either Hp or IL‐1β showed an increase in ROCK activity 6 h post infection, which coincided with redistribution and loss of claudin‐4. Inhibition of ROCK prior to bacterial challenge prevented these effects. Hp infection also induced IL‐1 receptor (IL‐1R) phosphorylation. Nevertheless, no increase in IL‐1β could be detected in supernatants of infected monolayers and treatment with co‐culture conditioned media failed to induce IL‐1R phosphorylation. While an IL‐1β neutralizing antibody did not prevent Hp‐induced IL‐1R activation, the use of an IL‐1R blocking antibody prior to infection prevented Hp‐mediated ROCK activation and claudin‐4 loss. CONCLUSION These findings suggest that the mechanism by which Hp disrupts gastric epithelial barrier structure involves IL‐1R ‐dependent activation of ROCK, which mediates claudin‐4 disruption. This work was funded by CIHR.
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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".