Dual ligand/receptor interactions activate urothelial defenses against uropathogenic E. coli
Bibliographic record
Abstract
During urinary tract infection (UTI), the second most common bacterial infection, dynamic interactions take place between uropathogenic E. coli (UPEC) and host urothelial cells. While significant strides have been made in the identification of the virulence factors of UPEC, our understanding of how the urothelial cells mobilize innate defenses against the invading UPEC remains rudimentary. Here we show that mouse urothelium responds to the adhesion of type 1-fimbriated UPEC by rapidly activating the canonical NF-κB selectively in terminally differentiated, superficial (umbrella) cells. This activation depends on a dual ligand/receptor system, one between FimH adhesin and uroplakin Ia and another between lipopolysaccharide and Toll-like receptor 4. When activated, all the nuclei (up to 11) of a multinucleated umbrella cell are affected, leading to significant amplification of proinflammatory signals. Intermediate and basal cells of the urothelium undergo NF-κB activation only if the umbrella cells are detached or if the UPEC persistently express type 1-fimbriae. Inhibition of NF-κB prevents the urothelium from clearing the intracellular bacterial communities, leading to prolonged bladder colonization by UPEC. Based on these data, we propose a model of dual ligand/receptor system in innate urothelial defenses against UPEC.
Stored with the screening record, where it is evidence for the labels above.
How this classification was reachedexpand
The three-model screen
all 5,600 screened works →All three models called this out of scope.
Molecular immunology of urothelial defense against uropathogenic E. coli.
This is a biomedical study of urothelial defenses against infection.
Host–pathogen immunology study of urothelial defenses against UPEC.
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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".