Horizontal transfer of antibiotic resistance genes in clinical environments
Bibliographic record
Abstract
A global medical crisis is unfolding as antibiotics lose effectiveness against a growing number of bacterial pathogens. Horizontal gene transfer (HGT) contributes significantly to the rapid spread of resistance, yet the transmission dynamics of genes that confer antibiotic resistance are poorly understood. Multiple mechanisms of HGT liberate genes from normal vertical inheritance. Conjugation by plasmids, transduction by bacteriophages, and natural transformation by extracellular DNA each allow genetic material to jump between strains and species. Thus, HGT adds an important dimension to infectious disease whereby an antibiotic resistance gene (ARG) can be the agent of an outbreak by transferring resistance to multiple unrelated pathogens. Here, we review the small number of cases where HGT has been detected in clinical environments. We discuss differences and synergies between the spread of plasmid-borne and chromosomal ARGs, with a special consideration of the difficulties of detecting transduction and transformation by routine genetic diagnostics. We highlight how 11 of the top 12 priority antibiotic-resistant pathogens are known or predicted to be naturally transformable, raising the possibility that this mechanism of HGT makes significant contributions to the spread of ARGs. HGT drives the evolution of untreatable "superbugs" by concentrating ARGs together in the same cell, thus HGT must be included in strategies to prevent the emergence of resistant organisms in hospitals and other clinical settings.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".