Bibliographic record
Abstract
Bacterial gene transfer agents (GTAs) are small virus-like particles that package DNA fragments and inject them into cells. They are encoded by gene clusters resembling defective prophages, with genes for capsid head and tail components. These gene clusters are usually assumed to be maintained by selection for the benefits of GTA-mediated recombination, but this has never been tested. We rigorously examined the potential benefits of GTA-mediated recombination, considering separately transmission of GTA-encoding genes and recombination of all chromosomal genes. In principle GTA genes could be directly maintained if GTA particles spread them to GTA– cells often enough to compensate for the loss of GTA-producing cells. However careful bookkeeping showed that losses inevitably exceed gains for two reasons. First, cells must lyse to release particles to the environment. Second, GTA genes are not preferentially replicated before DNA is packaged. A simulation model was then used to search for conditions where recombination of chromosomal genes makes GTA+ populations fitter than GTA– populations. Although the model showed that both synergistic epistasis and some modes of regulation could generate fitness benefits large enough to overcome the cost of lysis, these benefits neither allowed GTA+ cells to invade GTA– populations, nor allowed GTA+ populations to resist invasion by GTA– cells. Importantly, the benefits depended on highly improbable assumptions about the efficiencies of GTA production and recombination. Thus the selective benefits that maintain GTA gene clusters over many millions of years must arise from consequences other than transfer of GTA genes or recombination of chromosomal genes.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".