Horizontal gene transfer, segregation loss and the speed of microbial adaptation
Bibliographic record
Abstract
Abstract Microbial adaptation is driven by the circulation of mobile genetic elements (MGEs) among bacteria. On the one hand, MGEs can be viewed as selfish genes that spread like infectious diseases in a host population. On the other hand, the horizontal transfer and the loss of these MGEs is often viewed as a form of sexual reproduction that reshuffles genetic diversity in a way that may sometimes be adaptive for bacteria cells. Here, we show how these two perspectives can be reconciled using a single unified framework capturing the dynamics of multiple, interacting MGEs. We apply this framework to study how interactions between MGEs affecting rates of horizontal gene transfer and segregation loss shape the short- and long-term evolutionary dynamics of MGEs and the bacteria population. We show these interactions produce non-random MGE associations that can speed up or slow down microbial adaptation depending on the evolutionary conflicts between MGEs as well as between MGEs and their bacterial hosts. Moreover, we show how these interactions affect the evolutionary potential of the bacteria population. We discuss the implications of these predictions for the community response to environmental stressors such as antibiotic treatment or vaccination campaigns as well as the evolution of accessory genomes.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".