The bacterial virulence factor CagA induces microbial dysbiosis that contributes to excessive epithelial cell proliferation in the Drosophila gut
Bibliographic record
Abstract
Gut microbiota facilitate many aspects of human health and development, but dysbiotic microbiota can promote hyperplasia and inflammation and contribute to human diseases such as cancer. Human patients infected with the gastric cancer-causing bacterium Helicobacter pylori have altered microbiota; however, whether dysbiosis contributes to disease in this case is unknown. Many H. pylori human disease phenotypes are associated with a potent virulence protein, CagA, which is translocated into host epithelial cells where it alters cell polarity and manipulates host-signaling pathways to promote disease. We hypothesized that CagA alone could contribute to H. pylori pathogenesis by inducing microbial dysbiosis that promotes disease. Here we use a transgenic Drosophila model of CagA expression to genetically disentangle the effects of the virulence protein CagA from that of H. pylori infection. We found that expression of CagA within Drosophila intestinal stem cells promotes excess cell proliferation and is sufficient to alter host microbiota. Rearing CagA transgenic flies germ-free revealed that the dysbiotic microbiota contributes to cell proliferation phenotypes and also elicits expression of innate immune components, Diptericin and Duox. Further investigations revealed interspecies interactions are required for this dysbiotic CagA-dependent microbiota to promote proliferation in CagA transgenic and healthy control Drosophila. Our model establishes that CagA can alter gut microbiota and exacerbate cell proliferation and immune phenotypes previously attributed to H. pylori infection. This work provides valuable new insights into the mechanisms by which interactions between a specific virulence factor and the resident microbiota can contribute to the development and progression of disease.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".