<i>Helicobacter pylori</i> Cytotoxin-Associated Gene A Activates the Signal Transducer and Activator of Transcription 3 Pathway <i>In vitro</i> and <i>In vivo</i>
Bibliographic record
Abstract
Persistent infection with Helicobacter pylori confers an increased risk for the development of gastric cancer. However, the exact mechanisms whereby this bacterium causes carcinogenesis have not been completely elucidated. Recent evidence indicates that aberrant activation of the signal transducers and activators of transcription 3 (STAT3) signaling pathway may play a role in gastric carcinogenesis. Therefore, we hypothesized that H. pylori infection modulates STAT3 signaling, favoring gastric cancer development. In epithelial cells infected with H. pylori, STAT3 was activated, as assessed by immunoblotting for phosphorylated STAT3, immunofluorescence of translocated STAT3, fluorescence recovery after photobleaching, and luciferase activation in transfected cells. Activation was dependent on translocation but not phosphorylation of cytotoxin-associated gene A (CagA) in host cells. Activation seemed to be receptor-mediated because preincubation of cells with the interleukin-6 (IL-6) receptor superantagonist sant7 or inhibition of gp130 by a monoclonal antibody prevented H. pylori-mediated STAT3 activation. However, activation was not related to autocrine activation by IL-6 or IL-11. CagA+ wild-type H. pylori, but not the noncarcinogenic cagA- mutant, activated STAT3 in gastric epithelial cells in vivo in the gerbil model of H. pylori-mediated gastric carcinogenesis. Collectively, these results indicate that H. pylori CagA activates the STAT3 signaling pathway in vitro and in vivo, providing a potential mechanism by which chronic H. pylori infection promotes the development of gastric cancer.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".