Modulation of virulence gene expression by cell wall active antibiotics in Staphylococcus aureus
Bibliographic record
Abstract
OBJECTIVES: To investigate the effect of subinhibitory concentrations of cell wall active antibiotics on virulence gene expression and biofilm formation in Staphylococcus aureus Newman and in laboratory strains. METHODS: Promoter regions of spa, lukE and agr RNAIII were cloned upstream of a modified luxABCDE reporter. Using disc diffusion assays, the effects of antibiotics were observed on gene expression and quantitative real-time PCR was employed to confirm the results. Assays were performed to measure biofilm formation in wild-type S. aureus and respective spa-deficient and small colony variant mutants in the presence of subinhibitory concentrations of antibiotics. RESULTS: Expression of spa and lukE was stimulated by subinhibitory concentrations of penicillin and cefalotin, while agr RNAIII expression was not affected. Denser biofilms were formed by S. aureus Newman and its small colony variant in the presence of subinhibitory concentrations of cefalotin. CONCLUSIONS: Subinhibitory concentrations of certain antibiotics have been shown to stimulate virulence gene expression in S. aureus; this may alter the progression of infection and thus render antimicrobial therapy unreliable. The use of appropriate combinations of antibiotics might be an approach to avoiding this situation. Promoter-lux reporters are sensitive tools for studying the modulation of transcription by antibiotic inhibitors, and could be used to predict novel therapeutic combinations for the treatment of infection.
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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 | 0.000 |
| 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".