Lateral Transfer of Genes and Gene Fragments in Staphylococcus Extends beyond Mobile Elements
Bibliographic record
Abstract
The widespread presence of antibiotic resistance and virulence among Staphylococcus isolates has been attributed in part to lateral genetic transfer (LGT), but little is known about the broader extent of LGT within this genus. Here we report the first systematic study of the modularity of genetic transfer among 13 Staphylococcus genomes covering four distinct named species. Using a topology-based phylogenetic approach, we found, among 1,354 sets of homologous genes examined, strong evidence of LGT in 368 (27.1%) gene sets, and weaker evidence in another 259 (19.1%). Within-gene and whole-gene transfer contribute almost equally to the topological discordance of these gene sets against a reference phylogeny. Comparing genetic transfer in single-copy and in multicopy gene sets, we observed a higher frequency of LGT in the latter, and a substantial functional bias in cases of whole-gene transfer (little such bias was observed in cases of fragmentary genetic transfer). We found evidence that lateral transfer, particularly of entire genes, impacts not only functions related to antibiotic, drug, and heavy-metal resistance, as well as membrane transport, but also core informational and metabolic functions not associated with mobile elements. Although patterns of sequence similarity support the cohesion of recognized species, LGT within S. aureus appears frequently to disrupt clonal complexes. Our results demonstrate that LGT and gene duplication play important parts in functional innovation in staphylococcal 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 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".