Cationic antimicrobial peptides activate a two‐component regulatory system, PmrA‐PmrB, that regulates resistance to polymyxin B and cationic antimicrobial peptides in <i>Pseudomonas aeruginosa</i>
Bibliographic record
Abstract
The two-component regulatory system PhoP-PhoQ of Pseudomonas aeruginosa regulates resistance to cationic antimicrobial peptides, polymyxin B and aminoglycosides in response to low Mg2+ conditions. We have identified a second two-component regulatory system, PmrA-PmrB, that regulates resistance to polymyxin B and cationic antimicrobial peptides. This system responds to limiting Mg2+, and is affected by a phoQ, but not a phoP mutation. Inactivation of the pmrB sensor kinase and pmrA response regulator greatly decreased the expression of the operon encoding pmrA-pmrB while expression of the response regulator pmrA in trans resulted in increased activation suggesting that the pmrA-pmrB operon is autoregulated. Interposon mutants in pmrB, pmrA, or in an intergenic region upstream of pmrA-pmrB exhibited two to 16-fold increased susceptibility to polymyxin B and cationic antimicrobial peptides. The pmrA-pmrB operon was also found to be activated by a number of cationic peptides including polymyxins B and E, cattle indolicidin and synthetic variants as well as LL-37, a component of human innate immunity, whereas peptides with the lowest minimum inhibitory concentrations tended to be the weakest inducers. Additionally, we showed that the putative LPS modification operon, PA3552-PA3559, was also induced by cationic peptides, but its expression was only partially dependent on the PmrA-PmrB system. The discovery that the PmrA-PmrB two-component system regulates resistance to cationic peptides and that both it and the putative LPS modification system are induced by cationic antimicrobial peptides has major implications for the development of these antibiotics as a therapy for P. aeruginosa infections.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".