Metabolic adaptations of <i>Pseudomonas aeruginosa</i> during cystic fibrosis chronic lung infections
Bibliographic record
Abstract
Pseudomonas aeruginosa forms chronic infections in the lungs of cystic fibrosis (CF) patients, and is the leading cause of morbidity and mortality in patients with CF. Understanding how this opportunistic pathogen adapts to the CF lung during chronic infections is important to increase the efficacy of treatment and is likely to increase insight into other long-term infections. Previous studies of P. aeruginosa adaptation and divergence in CF infections have focused on the genetic level, both identifying characteristic mutations and patterns of gene expression. However, these approaches are not sufficient to fully understand the metabolic changes that occur during long-term infection, as metabolic regulation is complex and takes place on different biological levels. We used untargeted metabolic profiling (metabolomics) of cell supernatants (exometabolome analysis, or metabolic footprinting) to compare 179 strains, collected over time periods ranging from 4 to 24 years for the individual patients, representing a series of mostly clonal lineages from 18 individual patients. There was clear evidence of metabolic adaptation to the CF lung environment: acetate production was highly significantly negatively associated with length of infection. For amino acids, which are available to the bacterium in the lung environment, the tendency of isolates to evolve more efficient uptake was related to the biosynthetic cost of producing each metabolite; conversely, for the non-mammalian metabolite trehalose, isolates had significantly reduced tendency to utilize this compound with length of infection. However, as well as adaptation across patients, there was also a striking degree of metabolic variation between the different clonal lineages: in fact, the patient the strains were isolated from was a greater source of variance than length of infection for all metabolites observed. Our data highlight the potential for metabolomic investigation of complex phenotypic adaptations during infection.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".