The impact of genetic background during laboratory evolution of <i>Pseudomonas aeruginosa</i> in a cystic fibrosis-like environment
Bibliographic record
Abstract
Genetic background has the potential to influence both the tempo and trajectory of adaptive change: Different genotypes of a given species may adopt varied solutions to the same environmental challenge, or they may approach the same solution at different rates. Laboratory selection has been widely used to experimentally examine the evolutionary consequences of variation in genetic background, although largely using genotypes differing by only a few mutations. Here, we leverage natural variation in the bacterium Pseudomonas aeruginosa to investigate whether different adaptive solutions are accessible from distant points of departure on an adaptive landscape. We evolved 17 diverse genotypes in a laboratory medium that partially mimics the lung sputum of cystic fibrosis patients, and we measured changes in 10 phenotypes as well as in fitness. Using phylogenetically informed analyses, we found that genetic background impacted the tempo, but not the trajectory, of phenotypic evolution: Different starting genotypes converged toward similar phenotypes, but at varying rates. Our findings add to a growing body of evidence supporting widespread diminishing return epistasis during adaptation. The importance of genetic background toward the trajectory of adaptation remains inconsistent across experimental systems and conditions.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".