Extracellular DNA Acidifies Biofilms and Induces Aminoglycoside Resistance in Pseudomonas aeruginosa
Bibliographic record
Abstract
Biofilms consist of surface-adhered bacterial communities encased in an extracellular matrix composed of DNA, exopolysaccharides, and proteins. Extracellular DNA (eDNA) has a structural role in the formation of biofilms, can bind and shield biofilms from aminoglycosides, and induces antimicrobial peptide resistance mechanisms. Here, we provide evidence that eDNA is responsible for the acidification of Pseudomonas aeruginosa planktonic cultures and biofilms. Further, we show that acidic pH and acidification via eDNA constitute a signal that is perceived by P. aeruginosa to induce the expression of genes regulated by the PhoPQ and PmrAB two-component regulatory systems. Planktonic P. aeruginosa cultured in exogenous 0.2% DNA or under acidic conditions demonstrates a 2- to 8-fold increase in aminoglycoside resistance. This resistance phenotype requires the aminoarabinose modification of lipid A and the production of spermidine on the bacterial outer membrane, which likely reduce the entry of aminoglycosides. Interestingly, the additions of the basic amino acid L-arginine and sodium bicarbonate neutralize the pH and restore P. aeruginosa susceptibility to aminoglycosides, even in the presence of eDNA. These data illustrate that the accumulation of eDNA in biofilms and infection sites can acidify the local environment and that acidic pH promotes the P. aeruginosa antibiotic resistance phenotype.
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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".