The fate of laterally transferred genes: Life in the fast lane to adaptation or death
Bibliographic record
Abstract
Large-scale genome arrangement plays an important role in bacterial genome evolution. A substantial number of genes can be inserted into, deleted from, or rearranged within genomes during evolution. Detecting or inferring gene insertions/deletions is of interest because such information provides insights into bacterial genome evolution and speciation. However, efficient inference of genome events is difficult because genome comparisons alone do not generally supply enough information to distinguish insertions, deletions, and other rearrangements. In this study, homologous genes from the complete genomes of 13 closely related bacteria were examined. The presence or absence of genes from each genome was cataloged, and a maximum likelihood method was used to infer insertion/deletion rates according to the phylogenetic history of the taxa. It was found that whole gene insertions/deletions in genomes occur at rates comparable to or greater than the rate of nucleotide substitution and that higher insertion/deletion rates are often inferred to be present at the tips of the phylogeny with lower rates on more ancient interior branches. Recently transferred genes are under faster and relaxed evolution compared with more ancient genes. Together, this implies that many of the lineage-specific insertions are lost quickly during evolution and that perhaps a few of the genes inserted by lateral transfer are niche specific.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".