Requirement of the Pseudomonas aeruginosa CbrA Sensor Kinase for Full Virulence in a Murine Acute Lung Infection Model
Bibliographic record
Abstract
Pseudomonas aeruginosa is an opportunistic pathogen that is a major cause of respiratory tract and other nosocomial infections. The sensor kinase CbrA is a central regulator of carbon and nitrogen metabolism and in vitro also regulates virulence-related processes in P. aeruginosa. Here, we investigated the role of CbrA in two murine models of infection. In both peritoneal infections in leukopenic mice and lung infection models, the cbrA mutant was less virulent since substantially larger numbers of cbrA mutant bacteria were required to cause the same level of infection as wild-type or complemented bacteria. In contrast, in the chronic rat lung model the cbrA mutant grew and persisted as well as the wild type, indicating that the decrease of in vivo virulence of the cbrA mutant did not result from growth deficiencies on particular carbon substrates observed in vitro. In addition, a mutant in the cognate response regulator CbrB showed no defect in virulence in the peritoneal infection model, ruling out the involvement of certain alterations of virulence properties in the cbrA mutant including defective swarming motility, increased biofilm formation, and cytotoxicity, since these alterations are controlled through CbrB. Further investigations indicated that the mutant was more susceptible to uptake by phagocytes in vitro, resulting in greater overall bacterial killing. Consistent with the virulence defect, it took a smaller number of Dictyostelium discoideum amoebae to kill the cbrA mutant than to kill the wild type. Transcriptional analysis of the cbrA mutant during D. discoideum infection led to the conclusion that CbrA played an important role in the iron metabolism, protection of P. aeruginosa against oxidative stress, and the regulation of certain virulence factors.
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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 | 0.001 |
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".