Biosensor-guided detection of outer membrane-specific antimicrobial activity against <i>Pseudomonas aeruginosa</i> from fungal cultures and medicinal plant extracts
Bibliographic record
Abstract
ABSTRACT Pseudomonas aeruginosa responds to sub-lethal antimicrobial exposure by inducing the expression of lipopolysaccharide (LPS) surface modifications that mask antibiotic binding sites and contribute to repair and resistance of the outer membrane (OM). We exploit these membrane damage-responsive operons in a biosensor approach used to discover new antimicrobials that specifically target the OM. Chromosomal transcriptional luxCDABE reporters from the pmr (polymyxin resistance; aminoarabinose LPS modification) and speD2E2 (spermidine synthesis) operons are induced by validated outer membrane-acting agents including cationic antimicrobial peptides, cation chelators, ascorbic acid, detergents, and cell wall synthesis inhibitors cycloserine and bacitracin. To identify novel sources of OM-disrupting antimicrobials, we used these OM damage-responsive biosensors to screen a panel of fungal culture supernatants for novel antimicrobial and biosensor activity. Biosensor activity was used to determine the optimal time point of antimicrobial production from fungal supernatants and to guide the purification of active fractions after size-exclusion chromatography. Water and ethanol extracts of Chinese medicinal plants also proved to be a source of biosensor activity. The pathogen box is a 400-member drug library of potential antimicrobials, but none of these compounds induced our OM damage biosensors. This novel, sensitive, cell-based screening assay has potential for future discovery of lead compounds that specifically target the outer membrane, which is a significant barrier to antibiotic entry into Gram-negative bacteria. IMPORTANCE New approaches are needed to discover novel antimicrobials, particularly antibiotics that target the Gram-negative outer membrane. By exploiting bacterial sensing and responses to outer membrane (OM) damage, we used a biosensor approach consisting of polymyxin resistance gene transcriptional reporters to screen natural products and a small drug library for biosensor activity that indicates damage to the OM. The diverse antimicrobial compounds that cause induction of the polymyxin resistance genes, which correlates with outer membrane damage, suggest that these LPS and surface modifications also function in short-term repair to sublethal exposure and are required against broad membrane stress conditions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".