Role of Bacterial Surface Components in the Pathogenicity of Proteus mirabilis in a Murine Model of Catheter-Associated Urinary Tract Infection
Bibliographic record
Abstract
Proteus mirabilis (PM) is a Gram-negative, rod-shaped bacterium that causes catheter-associated urinary tract infections (CAUTIs). The specific roles of bacterial surface components (BSCs) in PM pathogenicity and CAUTIs remain unknown. To address this knowledge gap, we utilized relevant in vitro adhesion/invasion models and a well-established murine model of CAUTI to assess the ability of wildtype (WT) and seven mutant strains (MSs) of PM with deficiencies in various genes encoding BSCs to undergo the infectious process (including adhesion to catheters) in both model systems. Overall, MSs adhesion to catheters and the different cell types tested was significantly reduced compared to WT, while no invasion of cells was evident at 24 h. In vivo, WT showed a greater number of planktonic (urine) bacteria, bacteria adherent to catheters, and bacteria adherent to/invading bladder tissue when compared to the MSs. Bacterial counts in urine for PMI3191 and waaE mutants were lower than that for WT and other MSs. The complementation of mutated BSC genes resulting in the biggest defects restored the invasion phenotype both in vitro and in vivo. BSCs play a critical role at various steps in the pathogenicity of PM including adhesion to indwelling medical devices and adhesion/invasion of urinary tissue in vivo.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".