Cellular reporter screens for inhibitors of Burkholderia pseudomallei targets in Pseudomonas aeruginosa
Bibliographic record
Abstract
To facilitate the discovery of new therapeutics for Burkholderia pseudomallei infections, we have developed cellular reporter screens for inhibitors of B. pseudomallei targets in the surrogate host Pseudomonas aeruginosa. Pseudomonas aeruginosa strains carrying deletions of essential genes were engineered to be dependent on the isopropyl-beta-D-thiogalactopyranoside (IPTG)-regulated expression of their B. pseudomallei orthologues on a broad-host-range plasmid. Pseudomonas aeruginosa genes which are upregulated in response to depletion of each target gene product, were fused to the Photorhabdus luminescens luxCDABE operon via pGSV3-lux-Sp(R) to generate reporter strains with increased bioluminescence upon target inhibition. A total of 11 of 19 B. pseudomallei genes complemented deletions of their orthologues in P. aeruginosa. The dependence of growth on IPTG levels varied from complete dependence (ftsQ, gyrA, glmU, secA) to slower growth in the absence of IPTG (coaD, efp, mesJ), to apparently normal growth in the absence of IPTG (ligA, lpxA, folA, ipk). Reporter screening strains have been constructed for three gene targets (gyrA, glmU, secA), and one (gyrA) has been applied to 68,000 compounds resulting in a primary hit rate of 0.5% and a confirmed hit rate of 0.06%, including several fluoroquinolones. These results provide proof of principle for surrogate cellular reporter screens as a useful approach to identify inhibitors of essential gene products.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".