The Sensor Kinase CbrA Is a Global Regulator That Modulates Metabolism, Virulence, and Antibiotic Resistance in <i>Pseudomonas aeruginosa</i>
Bibliographic record
Abstract
Pseudomonas aeruginosa is an opportunistic pathogen that possesses a large arsenal of virulence factors enabling the pathogen to cause serious infections in immunocompromised patients, burn victims, and cystic fibrosis patients. CbrA is a sensor kinase that has previously been implied to play a role with its cognate response regulator CbrB in the metabolic regulation of carbon and nitrogen utilization in P. aeruginosa. Here it is demonstrated that CbrA and CbrB play an important role in various virulence and virulence-related processes of the bacteria, including swarming, biofilm formation, cytotoxicity, and antibiotic resistance. The cbrA deletion mutant was completely unable to swarm while exhibiting an increase in biofilm formation, supporting the inverse regulation of swarming and biofilm formation in P. aeruginosa. The cbrA mutant also exhibited increased cytotoxicity to human lung epithelial cells as early as 4 and 6 h postinfection. Furthermore, the cbrA mutant demonstrated increased resistance toward a variety of clinically important antibiotics, including polymyxin B, ciprofloxacin, and tobramycin. Microarray analysis revealed that under swarming conditions, CbrA regulated the expression of many genes, including phoPQ, pmrAB, arnBCADTEF, dnaK, and pvdQ, consistent with the antibiotic resistance and swarming impairment phenotypes of the cbrA mutant. Phenotypic and real-time quantitative PCR (RT-qPCR) analyses of a PA14 cbrB mutant suggested that CbrA may be modulating swarming, biofilm formation, and cytotoxicity via CbrB and that the CrcZ small RNA is likely downstream of this two-component regulator. However, as CbrB did not have a resistance phenotype, CbrA likely modulates antibiotic resistance in a manner independent of CbrB.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".