Characterization of tetracycline modifying enzymes using a sensitive in vivo reporter system
Bibliographic record
Abstract
BACKGROUND: Increasing our understanding of antibiotic resistance mechanisms is critical. To enable progress in this area, methods to rapidly identify and characterize antibiotic resistance conferring enzymes are required. RESULTS: We have constructed a sensitive reporter system in Escherichia coli that can be used to detect and characterize the activity of enzymes that act upon the antibiotic, tetracycline and its derivatives. In this system, expression of the lux operon is regulated by the tetracycline repressor, TetR, which is expressed from the same plasmid under the control of an arabinose-inducible promoter. Addition of very low concentrations of tetracycline derivatives, well below growth inhibitory concentrations, resulted in luminescence production as a result of expression of the lux genes carried by the reporter plasmid. Introduction of another plasmid into this system expressing TetX, a tetracycline-inactivating enzyme, caused a marked loss in luminescence due to enzyme-mediated reduction in the intracellular Tc concentration. Data generated for the TetX enzyme using the reporter system could be effectively fit with the known Km and kcat values, demonstrating the usefulness of this system for quantitative analyses. CONCLUSION: Since members of the TetR family of repressors regulate enzymes and pumps acting upon almost every known antibiotic and a wide range of other small molecules, reporter systems with the same design as presented here, but employing heterologous TetR-related proteins, could be developed to measure enzymatic activities against a wide range of antibiotics and other compounds. Thus, the assay described here has far-reaching applicability and could be adapted for high-throughput applications.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".