Extracellular DNA controls expression of <i>Pseudomonas aeruginosa</i> genes involved in nutrient utilization, metal homeostasis, acid pH tolerance, and virulence
Bibliographic record
Abstract
ABSTRACT Pseudomonas aeruginosa grows in extracellular DNA-enriched biofilms and infection sites. Extracellular DNA (eDNA) is generally considered a structural biofilm polymer required for aggregation and biofilm maturation. In addition, eDNA can sequester divalent metal cations, acidify the growth media, and serve as a nutrient source. Here we determine the transcriptome of planktonic P. aeruginosa grown in the presence of eDNA using RNA-seq. Transcriptome analysis identified 89 induced genes and 76 repressed genes in the presence of eDNA (FDR<0.05), and transcriptional lux fusions were used to confirm eDNA regulation. A large number of eDNA-induced genes appear to be involved in utilizing DNA as a nutrient. Several eDNA-induced genes are also induced by acidic pH 5.5, and growth in the presence of eDNA or acidic pH promoted an acid tolerance response in P. aeruginosa . The cyoABCDE terminal oxidase is induced at pH 5.5 and contributed to the acid tolerance phenotype. Quantitative metal analysis confirmed that DNA binds to diverse metals, which helps explain why many genes involved in a general uptake of metals were controlled by eDNA. Growth in the presence of eDNA also promoted intracellular bacterial survival and influenced virulence and during the acute infection model of fruit flies. The diverse functions of the eDNA-regulated genes underscore the important role of this extracellular polymer in promoting antibiotic resistance, virulence, acid tolerance, and nutrient utilization; phenotypes that 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".