Mobilizable genomic islands, different strategies for the dissemination of multidrug resistance and other adaptive traits
Bibliographic record
Abstract
Mobile genetic elements are near ubiquitous DNA segments that revealed a surprising variety of strategies for their propagation among prokaryotes and between eukaryotes. In bacteria, conjugative elements were shown to be key drivers of evolution and adaptation by efficiently disseminating genes involved in pathogenicity, symbiosis, metabolic pathways, and antibiotic resistance. Conjugative plasmids of the incompatibility groups A and C (A/C) are important vehicles for the dissemination of antibiotic resistance and the consequent global emergence and spread of multi-resistant pathogenic bacteria. Beyond their own mobility, A/C plasmids were also shown to drive the mobility of unrelated non-autonomous mobilizable genomic islands, which may also confer further advantageous traits. In this commentary, we summarize the current knowledge on different classes of A/C-dependent mobilizable genomic islands and we discuss other DNA hitchhikers and their implication in bacterial evolution. Furthermore, we glimpse at the complex genetic network linking autonomous and non-autonomous mobile genetic elements, and at the associated flow of genetic information between bacteria.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.012 | 0.010 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".