Adaptive Resistance to the “Last Hope” Antibiotics Polymyxin B and Colistin in <i>Pseudomonas aeruginosa</i> Is Mediated by the Novel Two-Component Regulatory System ParR-ParS
Bibliographic record
Abstract
As multidrug resistance increases alarmingly, polymyxin B and colistin are increasingly being used in the clinic to treat serious Pseudomonas aeruginosa infections. In this opportunistic pathogen, subinhibitory levels of polymyxins and certain antimicrobial peptides induce resistance toward higher, otherwise lethal, levels of these antimicrobial agents. It is known that the modification of lipid A of lipopolysaccharide (LPS) is a key component of this adaptive peptide resistance, but to date, the regulatory mechanism underlying peptide regulation in P. aeruginosa has remained elusive. The PhoP-PhoQ and PmrA-PmrB two-component systems, which control this modification under low-Mg2+ conditions, do not appear to play a major role in peptide-mediated adaptive resistance, unlike in Salmonella where PhoQ is a peptide sensor. Here we describe the identification and characterization of a novel P. aeruginosa two-component regulator affecting polymyxin-adaptive resistance, ParR-ParS (PA1799-PA1798). This system was required for activation of the arnBCADTEF LPS modification operon in the presence of subinhibitory concentrations of polymyxin, colistin, or the bovine peptide indolicidin, leading to increased resistance to various polycationic antibiotics, including aminoglycosides. This study highlights the complexity of the regulatory network controlling resistance to cationic antibiotics and host peptides in P. aeruginosa, which has major relevance in the development and deployment of cationic antimicrobials.
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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) | 0.000 | 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 | 0.000 |
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".