Extracellular DNA-induced antimicrobial peptide resistance mechanisms in Pseudomonas aeruginosa
Bibliographic record
Abstract
Extracellular DNA (eDNA) is in the environment, bodily fluids, in the matrix of biofilms, and accumulates at infection sites. eDNA can function as a nutrient source, a universal biofilm matrix component, and an innate immune effector in eDNA traps. In biofilms, eDNA is required for attachment, aggregation, and stabilization of microcolonies. We have recently shown that eDNA can sequester divalent metal cations, which has interesting implications on antibiotic resistance. eDNA binds metal cations and thus activates the Mg(2+)-responsive PhoPQ and PmrAB two-component systems. In Pseudomonas aeruginosa and many other Gram-negative bacteria, the PhoPQ/PmrAB systems control various genes required for virulence and resisting killing by antimicrobial peptides (APs), including the pmr genes (PA3552-PA3559) that are responsible for the addition of aminoarabinose to lipid A. The PA4773-PA4775 genes are a second DNA-induced cluster and are required for the production of spermidine on the outer surface, which protects the outer membrane from AP treatment. Both modifications mask the negative surface charges and limit membrane damage by APs. DNA-enriched biofilms or planktonic cultures have increased antibiotic resistance phenotypes to APs and aminoglycosides. These dual antibiotic resistance and immune evasion strategies may be expressed in DNA-rich environments and contribute to long-term survival.
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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 | 0.000 |
| 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".