Tobramycin adaptation alters the antibiotic susceptibility of <i>Pseudomonas aeruginosa</i> quorum sensing-null mutants
Bibliographic record
Abstract
The opportunistic bacterium Pseudomonas aeruginosa uses the LasR-I quorum sensing system to increase resistance to the aminioglycoside antibiotic tobramycin. Paradoxically, lasR-null mutants are commonly isolated from chronic human infections treated with tobramycin, suggesting there may be a mechanism allowing the lasR-null mutants to persist under tobramycin selection. We hypothesized that the effects of inactivating lasR on tobramycin resistance might be dependent on the presence or absence of other gene mutations in that strain, a phenomenon known as epistasis. To test this hypothesis, we inactivated lasR in several highly tobramycin-resistant isolates from long-term evolution experiments. We show that the effects of ΔlasR on tobramycin resistance are strain dependent. The effects can be attributed to a point mutation in the gene encoding the translation elongation factor fusA1 (G61A nucleotide substitution), which confers a strong selective advantage to lasR-null PA14 under tobramycin selection. This fusA1 G61A mutation results in increased activity of the MexXY efflux pump and expression of the mexXY regulator ArmZ. The fusA1 mutation can also modulate ΔlasR mutant resistance to two other antibiotics, ciprofloxacin and ceftazidime. Our results demonstrate the importance of epistatic gene interactions on antibiotic susceptibility of lasR-null mutants. These results support of the idea that gene interactions might play a significant role in the evolution of quorum sensing in P. aeruginosa.
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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".