Transcriptional modulation of bacterial gene expression by subinhibitory concentrations of antibiotics
Bibliographic record
Abstract
Antibiotics such as erythromycin and rifampicin, at low concentrations, alter global bacterial transcription patterns as measured by the stimulation or inhibition of a variety of promoter-lux reporter constructs in a Salmonella typhimurium library. Analysis of a 6,500-clone library indicated that as many as 5% of the promoters may be affected, comprising genes for a variety of functions, as well as a significant fraction of genes with no known function. Studies of a selection of the reporter clones showed that stimulation varied depending on the nature of the antibiotic, the promoter, and what culture medium was used; the response differed on solid as compared with liquid media. Transcription was markedly reduced in antibiotic-resistant hosts, but the presence of mutations deficient in stress responses such as SOS or universal stress did not prevent antibiotic-induced modulation. The results show that small molecules may have contrasting effects on bacteria depending on their concentration: either the modulation of bacterial metabolism by altering transcription patterns or the inhibition of growth by the inhibition of specific target functions. Both activities could play important roles in the regulation of microbial communities. These studies indicate that the detection of pharmaceutically useful natural product inhibitors could be effectively achieved by measuring activation of transcription at low concentrations in high-throughput assays using appropriate bacterial promoter-reporter constructs.
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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) | 0.001 | 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) | 0.001 | 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".