Identification and characterization of OmpT‐like proteases in uropathogenic <i>Escherichia coli</i> clinical isolates
Bibliographic record
Abstract
Bacterial colonization of the urogenital tract is limited by innate defenses, including the production of antimicrobial peptides (AMPs). Uropathogenic Escherichia coli (UPEC) resist AMP-killing to cause a range of urinary tract infections (UTIs) including asymptomatic bacteriuria, cystitis, pyelonephritis, and sepsis. UPEC strains have high genomic diversity and encode numerous virulence factors that differentiate them from non-UTI-causing strains, including ompT. As OmpT homologs cleave and inactivate AMPs, we hypothesized that UPEC strains from patients with symptomatic UTIs have high OmpT protease activity. Therefore, we measured OmpT activity in 58 clinical E. coli isolates. While heterogeneous OmpT activities were observed, OmpT activity was significantly greater in UPEC strains isolated from patients with symptomatic infections. Unexpectedly, UPEC strains exhibiting the greatest protease activities harbored an additional ompT-like gene called arlC (ompTp). The presence of two OmpT-like proteases in some UPEC isolates led us to compare the substrate specificities of OmpT-like proteases found in E. coli. While all three cleaved AMPs, cleavage efficiency varied on the basis of AMP size and secondary structure. Our findings suggest the presence of ArlC and OmpT in the same UPEC isolate may confer a fitness advantage by expanding the range of target substrates.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".