<b><i>Helicobacter pylori</i></b> Inhibits Dendritic Cell Maturation via Interleukin-10-Mediated Activation of the Signal Transducer and Activator of Transcription 3 Pathway
Bibliographic record
Abstract
Helicobacter pylori infects the human gastric mucosa causing a chronic infection that is the primary risk factor for gastric cancer development. Recent studies demonstrate that H. pylori promotes tolerogenic dendritic cell (DC) development indicating that this bacterium evades the host immune response. However, the signaling pathways involved in modulating DC activation during infection remain unclear. Here, we report that H. pylori infection activated the signal transducer and activator of transcription 3 (STAT3) pathway in murine bone marrow-derived DCs (BMDCs) and splenic DCs isolated ex vivo. Isogenic cagA-, cagE-, vacA- and urease-mutants exhibited levels of phosphoSTAT3 that were comparable to in the wild-type (WT) parent strain. H. pylori-infected BMDCs produced increased immunosuppressive IL-10, which activated STAT3 in an autocrine/paracrine fashion. Neutralization of IL-10 prevented H. pylori-mediated STAT3 activation in both BMDCs and splenic DCs. In addition, anti-IL-10 treatment of infected H. pylori-BMDCs was associated with increased CD86 and MHC II expression and enhanced proinflammatory IL-1β cytokine secretion. Finally, increased CD86 and MHC II expression was detected in H. pylori-infected STAT3 knockout DCs when compared to WT controls. Together, these results demonstrate that H. pylori infection induces IL-10 secretion in DCs, which activates STAT3, thereby modulating DC maturation and reducing IL-1β secretion. These findings identify a host molecular mechanism by which H. pylori can manipulate the innate immune response to potentially favor chronic infection and promote carcinogenesis.
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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) | 0.004 | 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".