Bioluminescent Pseudomonas aeruginosa and Escherichia coli for whole-cell screening of antibacterial and adjuvant compounds
Bibliographic record
Abstract
Continued efforts to discover new antibacterial molecules are critical to achieve a robust pre-clinical pipeline for new antibiotics. Screening of compound or natural product extract libraries remains a widespread approach and can benefit from the development of whole cell assays that are robust, simple and versatile, and allow for high throughput testing of antibacterial activity. In this study, we created and validated two bioluminescent reporter strains for high-throughput screening, one in Pseudomonas aeruginosa , and another in a hyperporinated and efflux-deficient Escherichia coli . We show that the bioluminescent strains have a large dynamic range that closely correlates with cell viability and is superior to conventional optical density (OD 600 ) measurements, can detect dose-dependent antibacterial activity and be used for different drug discovery applications. We evaluated the assays’ performance characteristics (signal to background ratio, signal window, Z’ robust) and demonstrated their potential utility for antibiotic drug discovery in two examples. The P. aeruginosa bioluminescent reporter was used in a pilot screen of 960 repurposed compound libraries to identify adjuvants that potentiate the fluoroquinolone antibiotic ofloxacin. The E. coli bioluminescent reporter was used to test the antibacterial activity of bioactive bacterial supernatants and assist with bioassay-guided fractionation of the crude extracts.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".