The Two-Component System CprRS Senses Cationic Peptides and Triggers Adaptive Resistance in Pseudomonas aeruginosa Independently of ParRS
Bibliographic record
Abstract
Cationic antimicrobial peptides pass across the outer membrane by interacting with negatively charged lipopolysaccharide (LPS), leading to outer membrane permeabilization in a process termed self-promoted uptake. Resistance can be mediated by the addition of positively charged arabinosamine through the action of the arnBCADTEF operon. We recently described a series of two-component regulators that lead to the activation of the arn operon after recognizing environmental signals, including low-Mg(2+) (PhoPQ, PmrAB) or cationic (ParRS) peptides. However, some peptides did not activate the arn operon through ParRS. Here, we report the identification of a new two-component system, CprRS, which, upon exposure to a wide range of antimicrobial peptides, triggered the expression of the LPS modification operon. Thus, mutations in the cprRS operon blocked the induction of the arn operon in response to several antimicrobial peptides independently of ParRS but did not affect the response to low Mg(2+). Distinct patterns of arn induction were identified. Thus, the responses to polymyxins were abrogated by either parR or cprR mutations, while responses to other peptides, including indolicidin, showed differential dependency on the CprRS and ParRS systems in a concentration-dependent manner. It was further demonstrated that, following exposure to inducing antimicrobial peptides, cprRS mutants did not become adaptively resistant to polymyxins as was observed for wild-type cells. Our microarray studies demonstrated that the CprRS system controlled a quite modest regulon, indicating that it was quite specific to adaptive peptide resistance. These findings provide greater insight into the complex regulation of LPS modification in Pseudomonas aeruginosa, which involves the participation of at least 4 two-component systems.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".