Co-evolution with <i>Staphylococcus aureus</i> leads to lipopolysaccharide alterations in <i>Pseudomonas aeruginosa</i>
Bibliographic record
Abstract
Detrimental and beneficial interactions between co-colonizing bacteria may influence the course of infections. In cystic fibrosis (CF) airways, Staphylococcus aureus prevails in childhood, whereas Pseudomonas aeruginosa progressively predominates thereafter. While a range of interactions has been identified, it is unclear if these represent specific adaptations or correlated responses to other aspects of the environment. Here, we investigate how P. aeruginosa adapts to S. aureus by evolving P. aeruginosa in the presence and absence of S. aureus. P. aeruginosa populations that evolved for 150 generations were sequenced and compared to the ancestor strain. Mutations in the Wsp signaling system were identified in both treatments and likely occurred because of low oxygen availability. Despite showing increased killing activity, wsp mutants were less fit in the presence of S. aureus. In contrast, mutations in lipopolysaccharide (LPS) biosynthesis occurred exclusively in co-cultures with S. aureus and conferred a fitness gain in its presence. Moreover, they increased resistance towards beta-lactam antibiotics. Strikingly, both mutations in wsp and LPS genes are observed in clinical isolates from CF-patients. Our results suggest that P. aeruginosa LPS mutations are a direct consequence of S. aureus imposed selection in vitro.
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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".