Investigating the Role of Genetic Background and Niche of Origin in the Adaptation of Pseudomonas aeruginosa
Bibliographic record
Abstract
Prolonged respiratory infection by the opportunistic pathogen, Pseudomonas aeruginosa, is the major contributor to declining lung function and early mortality in individuals with cystic fibrosis (CF).Patients are believed to acquire their infections from the environment, upon which a single clone will often undergo long-term colonization in response to selection within the lung.It is unknown however, whether all genotypes are capable of causing infection, and whether all environmental strains are initially maladapted to life in the lung.To investigate these unknowns, we experimentally evolved 18 different environmental and CF-clinical genotypes within synthetic CF lung sputum (SCFM), to identify any genotypic and/or prior niche constraints on trait evolution and pathoadaptation.We found that genotype significantly constrained evolution within SCFM, which was evidenced through phylogenetic signal in the change of both traits and fitness.The traits showing the highest phylogenetic signal in this experiment included H2O2 resistance, biofilm formation and twitching.We also found that environmental strains evolve differently in SCFM compared to their CF-clinical counterparts.Environmental strains significantly decreased in H2O2 resistance, pigment production, and swimming whereas clinical strains slightly increased or remained unchanged.Furthermore, while the environmental strains often underwent greater fitness leaps than the clinical strains, this was not true for all strains.We hypothesize then that particular niches may better prime strains for causing infection over others.Altogether our results suggest there is widespread variation in the adaptation of P. aeruginosa, which is in part constrained by both genetic background, and niche of origin.
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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 | 0.000 |
| 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".