Niche, not phylogeny, governs the response to oxygen availability among diverse Pseudomonas aeruginosa strains
Bibliographic record
Abstract
Pseudomonas aeruginosa, a ubiquitous opportunistic pathogen, is a leading cause of chronic infection of airways in cystic fibrosis (CF) patients. Chronic infections typically arise from colonization by environmental strains, followed by adaptation of P. aeruginosa to the conditions within the CF airway. It has been suggested that oxygen availability can be an important source of selection causing trait changes associated with the transition to chronic infection, but little data exist on the response of P. aeruginosa to varying levels of oxygen. Here, we use a diverse collection of P. aeruginosa strains recovered from both CF patients and environmental sources to evaluate the role of oxygen availability in driving adaptation to the CF lung while also accounting for phylogenetic relatedness. While we can detect a signal of phylogeny in trait responses to oxygen availability, niche of origin is a far stronger predictor. Specifically, strains isolated from the lungs of CF patients are more sensitive to external oxidative stress but more resistant to antibiotics under anoxic conditions. Additionally, many, though not all, patho-adaptive traits we assayed are insensitive to oxygen availability. Our results suggest that inferences about trait expression, especially those associated with the transition to chronic infection, depend on both the available oxygen and niche of origin of the strains being studied.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".