Uropathogenic E.coli (UPEC) Infection Induces Proliferation through Enhancer of Zeste Homologue 2 (EZH2)
Bibliographic record
Abstract
UNLABELLED: Host-pathogen interactions can induce epigenetic changes in the host directly, as well as indirectly through secreted factors. Previously, uropathogenic Escherichia coli (UPEC) was shown to increase DNA methyltransferase activity and expression, which was associated with methylation-dependent alterations in the urothelial expression of CDKN2A. Here, we showed that paracrine factors from infected cells alter expression of another epigenetic writer, EZH2, coordinate with proliferation. Urothelial cells were inoculated with UPEC, UPEC derivatives, or vehicle (mock infection) at low moi, washed, then maintained in media with Gentamycin. Urothelial conditioned media (CM) and extracellular vesicles (EV) were isolated after the inoculations and used to treat naïve urothelial cells. EZH2 increased with UPEC infection, inoculation-induced CM, and inoculation-induced EV vs. parallel stimulation derived from mock-inoculated urothelial cells. We found that infection also increased proliferation at one day post-infection, which was blocked by the EZH2 inhibitor UNC1999. Inhibition of demethylation at H3K27me3 had the opposite effect and augmented proliferation. CONCLUSION: Uropathogen-induced paracrine factors act epigenetically by altering expression of EZH2, which plays a key role in early host cell proliferative responses to infection.
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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) | 0.002 | 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".