Evolutionary Genomics of Niche-Specific Adaptation to the Cystic Fibrosis Lung in <i>Pseudomonas aeruginosa</i>
Bibliographic record
Abstract
The comparative genomics of the transition of the opportunistic pathogen Pseudomonas aeruginosa from a free-living environmental strain to one that causes chronic infection in the airways of cystic fibrosis (CF) patients remain poorly studied. Chronic infections are thought to originate from colonization by a single strain sampled from a diverse, globally distributed population, followed by adaptive evolution to the novel, stressful conditions of the CF lung. However, we do not know whether certain clades are more likely to form chronic infections than others and we lack a comprehensive view of the suite of genes under positive selection in the CF lung. We analyzed whole-genome sequence data from 1,000 P. aeruginosa strains with diverse ecological provenances including the CF lung. CF isolates were distributed across the phylogeny, indicating little genetic predisposition for any one clade to cause chronic infection. Isolates from the CF niche experienced stronger positive selection on core genes than those derived from environmental or acute infection sources, consistent with recent adaptation to the lung environment. Genes with the greatest differential positive selection in the CF niche include those involved in core cellular processes such as metabolism, energy production, and stress response as well as those linked to patho-adaptive processes such as antibiotic resistance, cell wall and membrane modification, quorum sensing, biofilms, mucoidy, motility, and iron homeostasis. Many genes under CF-specific differential positive selection had regulatory functions, consistent with the idea that regulatory mutations play an important role in rapid adaptation to novel environments.
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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 | 0.001 | 0.001 |
| 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".