Antibiotics trigger initiation of SCCmec transfer by inducing SOS responses
Bibliographic record
Abstract
The rise of antimicrobial resistance limits therapeutic options for infections by methicillin-resistant staphylococci. The staphylococcal cassette chromosome mec (SCCmec) is a mobile genetic element as the only carrier of the methicillin-resistance determinants, the mecA or mecC gene. The use of antibiotics increases the spread of antibiotic resistance, but the mechanism by which antibiotics promote horizontal dissemination of SCCmec is largely unknown. In this study, we demonstrate that many antibiotics, including β-lactams, can induce the expression of ccrC1 and SCCmec excision from the bacterial chromosome. In particular, three widely used antibiotics targeting DNA replication and repair (sulfamethoxazole, ciprofloxacin and trimethoprim) induced higher levels of ccrC1 expression and higher rates of SCCmec excision even at low concentrations (1/8 × minimum inhibitory concentration). LexA was identified as a repressor of ccrC1 and ccrAB by binding to the promoter regions of ccrC1 and ccrAB. The activation of RecA after antibiotic induction alleviated the repression by LexA and increased the expression of ccrC1 or ccrAB, consequently increasing the excision frequency of the SCCmec for SCCmec transfer. These findings lead us to propose a mechanism by which antimicrobial agents can promote horizontal gene transfer of the mecA gene and facilitate the spread of methicillin resistance.
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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 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".