High-throughput LacZ/CPRG screen identifies novel potential antibiotics targeting Gram-negative bacterial envelopes to combat resistance
Bibliographic record
Abstract
Abstract Background Bacterial resistance, exacerbated by multidrug-resistant Gram-negative (GN) pathogens, poses a public health threat due to their impermeable envelopes, which block many antibiotics. Objectives We aimed to develop a high-throughput screening (HTS) method to identify small molecules targeting GN bacterial envelopes and assess their antibacterial potential. Methods Envelope disruption in Escherichia coli and Pseudomonas aeruginosa was assessed using a β -galactosidase (LacZ)/CPRG reporter assay in LB at 37°C. The assay was validated through screening the LOPAC 1280 and KD2 4761 compound libraries. Concentration–response relationships, permeabilisation constants (K 50 ), co-permeabilisation assays, minimal inhibitory concentration (MIC) measurements, and bacterial microscopy post-MICs were performed. Results The assay demonstrated robust performance, evidenced by high Z’-factor and signal-to-noise (S/N ratios. Screening identified 57 active compounds (1.2% of the library), including β -lactams and three non-antibiotic molecules—suloctidil, isorotenone, and alexidine—that exhibited concentration-dependent antibacterial activity. Alexidine showed the most potent activity, with the lowest K 50 (2.7×10 −3 mM) and MICs of 0.004 mM for E. coli and 0.015 mM for P. aeruginosa . Suloctidil and isorotenone induced spherical cell morphology, while alexidine induced a filamentous phenotype, indicative of envelope disruption. The assay also identified antibiotics for monotherapy and combination therapy, with ampicillin, alexidine, and suloctidil enhancing chloramphenicol’s efficacy against E. coli MG1655. Conclusions The LacZ/CPRG reporter assay effectively identified compounds targeting bacterial envelopes, including novel molecules with antibacterial activity against GN pathogens, making it a promising tool for antibiotic discovery or combination therapy.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".