Chromosomal capture of beneficial genes drives plasmids toward ecological redundancy
Bibliographic record
Abstract
Plasmids are a ubiquitous feature of bacterial genomes, but the forces driving genes and phenotypes to become associated with plasmids are poorly understood. To address this problem, we compared the fitness effects of chromosomal and plasmid genes in the plant symbiont Rhizobium leguminosarum. The relative abundance of beneficial genes on plasmids was very low compared to the chromosome across niches that reflect key steps in plant colonization. Two lines of evidence support the hypothesis that this pattern emerges because evolutionary processes drive beneficial genes to move from plasmids to the bacterial chromosome. First, weakly beneficial genes that increased fitness in a single niche were evenly distributed between plasmids and the chromosome, whereas the chromosome was enriched for strongly beneficial genes that increased fitness across multiple niches. Second, beneficial genes were more prevalent on recently acquired plasmids compared to ancient plasmids. Our findings support a model in which bacterial lineages initially acquire plasmids due to the beneficial genes that they carry, but the movement of beneficial genes to the chromosome gradually erodes the ecological value of plasmids. These findings reconcile existing models of plasmids and highlight the challenge of understanding how plasmids can persist over the long term.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".