Bacterial resistance evolution by recruitment of super‐integron gene cassettes
Bibliographic record
Abstract
The capture and spread of antibiotic resistance determinants by integrons underlies the rapid evolution of multiple antibiotic resistance among diverse Gram-negative clinical isolates. The association of multiple resistance integrons (MRIs) with mobile DNA elements facilitates their transit across phylogenetic boundaries and augments the potential impact of integrons on bacterial evolution. Recently, ancestral chromosomal versions, the super-integrons (SIs), were found to be genuine components of the genomes of diverse bacterial species. SIs possess evolutionary characteristics and stockpiles of adaptive functions, including cassettes related to antibiotic resistance determinants previously characterized in clinical isolates, which suggest that MRIs and their resistance genes were originally recruited from SIs and their pool of amassed genes. However, the recombination activity of integrons has never been demonstrated in a bacterium other than Escherichia coli. We introduced a naturally occurring MRI (TpR, SulR) on a conjugative plasmid into Vibrio cholerae, a species known to harbour a SI. We show that MRIs can randomly recruit genes directly from the cache of SI cassettes. By applying a selective constraint for the development of antibiotic resistance, we demonstrate bacterial resistance evolution through the recruitment a novel, but phenotypically silent, chloramphenicol acetyltransferase gene from the V. cholerae SI and its precise insertion into the MRI. The resulting resistance profile (CmR, TpR, SulR) could then be disseminated by conjugation to other clinically relevant pathogens at high frequency. These results demonstrate that otherwise phenotypically sensitive strains may still be a genetic source for the evolution of resistance to clinically relevant antibiotics through integron-mediated recombination events.
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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 | 0.000 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".