Positive epistasis between co-infecting plasmids promotes plasmid survival in bacterial populations
Bibliographic record
Abstract
Plasmids have a key role in the horizontal transfer of genes among bacteria. Although plasmids are catalysts for bacterial evolution, it is challenging to understand how they can persist in bacterial populations over the long term because of the burden they impose on their hosts (the 'plasmid paradox'). This paradox is especially perplexing in the case of 'small' plasmids, which are unable to self-transfer by conjugation. Here, for the first time, we investigate how interactions between co-infecting plasmids influence plasmid persistence. Using an experimental model system based on interactions between a diverse assemblage of 'large' plasmids and a single small plasmid, pNI105, in the pathogenic bacterium Pseudomonas aeruginosa, we demonstrate that positive epistasis minimizes the cost associated with carrying multiple plasmids over the short term and increases the stability of the small plasmid over a longer time scale. In support of these experimental data, bioinformatic analysis showed that associations between small and large plasmids are more common than would be expected owing to chance alone across a range of families of bacteria; more generally, we find that co-infection with multiple plasmids is more common than would be expected owing to chance across a wide range of bacterial phyla. Collectively, these results suggest that positive epistasis promotes plasmid stability in bacterial populations. These findings pave the way for future mechanistic studies aimed at elucidating the molecular mechanisms of plasmid-plasmid interaction, and evolutionary studies aimed at understanding how the coevolution of plasmids drives the spread of plasmid-encoded traits.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".