Growth phenotypes of <i>Pseudomonas aeruginosa lasR</i> mutants adapted to the airways of cystic fibrosis patients
Bibliographic record
Abstract
The opportunistic pathogen Pseudomonas aeruginosa undergoes genetic change during chronic airway infection of cystic fibrosis (CF) patients. One common change is a mutation inactivating lasR, which encodes a transcriptional regulator that responds to a homoserine lactone signal to activate expression of acute virulence factors. Colonies of lasR mutants visibly accumulated the iridescent intercellular signal 4-hydroxy-2-heptylquinoline. Using this colony phenotype, we identified P. aeruginosa lasR mutants that emerged in the airway of a CF patient early during chronic infection, and during growth in the laboratory on a rich medium. The lasR loss-of-function mutations in these strains conferred a growth advantage with particular carbon and nitrogen sources, including amino acids, in part due to increased expression of the catabolic pathway regulator CbrB. This growth phenotype could contribute to selection of lasR mutants both on rich medium and within the CF airway, supporting a key role for bacterial metabolic adaptation during chronic infection. Inactivation of lasR also resulted in increased beta-lactamase activity that increased tolerance to ceftazidime, a widely used beta-lactam antibiotic. Loss of LasR function may represent a marker of an early stage in chronic infection of the CF airway with clinical implications for antibiotic resistance and disease progression.
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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 | 0.001 |
| 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".