Tsr Chemoreceptor Interacts With IL-8 Provoking E. coli Transmigration Across Human Lung Epithelial Cells
Bibliographic record
Abstract
Bacterial colonization of epithelial surfaces and subsequent transmigration across the mucosal barrier are essential for the development of infection. We hypothesized that the methyl-accepting proteins (MCPs), known as chemoreceptors expressed on Escherichia coli (E. coli) bacterial surface, play an important role in mediating bacterial transmigration. We demonstrated a direct interaction between human interleukin-8 (IL-8) and Tsr receptor, a major MCP chemoreceptor. Stimulation of human lung epithelial cell monolayer with IL-8 resulted in increased E. coli adhesion and transmigration of the native strain (RP437) and a strain expressing only Tsr (UU2373), as compared to a strain (UU2599) with Tsr truncation. The augmented E. coli adhesion and migration was associated with a higher expression of carcinoembryonic antigen-related cell adhesion molecule 6 and production of inflammatory cytokines/chemokines, and a lower expression of the tight junction protein claudin-1 and the plasma membrane protein caveolin-1 in lung epithelial cells. An increased E. coli colonization and pulmonary cytokine production induced by the RP437 and UU2373 strains was attenuated in mice challenged with the UU2599 strain. Our results suggest a critical role of the E. coli Tsr chemoreceptor in mediating bacterial colonization and transmigration across human lung epithelium during development of pulmonary infections.
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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 | 0.001 |
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".