Genetic and Chemical Screening Reveals Targets and Compounds to Potentiate Gram-Positive Antibiotics against Gram-Negative Bacteria
Bibliographic record
Abstract
Gram-negative bacteria are intrinsically resistant to a plethora of antibiotics that effectively inhibit the growth of Gram-positive bacteria. The intrinsic resistance of Gram-negative bacteria to classes of antibiotics, including rifamycins, aminocoumarins, macrolides, glycopeptides, and oxazolidinones, has largely been attributed to their lack of accumulation within cells due to poor permeability across the outer membrane, susceptibility to efflux pumps, or a combination of these factors. Due to the difficulty in discovering antibiotics that can bypass these barriers, finding targets and compounds that increase the activity of these ineffective antibiotics against Gram-negative bacteria has the potential to expand the antibiotic spectrum. In this study, we investigated the genetic determinants for resistance to rifampicin, novobiocin, erythromycin, vancomycin, and linezolid to determine potential targets of antibiotic-potentiating compounds. We subsequently performed a high-throughput screen of ∼50,000 diverse, synthetic compounds to uncover molecules that potentiate the activity of at least one of the five Gram-positive-targeting antibiotics. This led to the discovery of two membrane active compounds capable of potentiating linezolid and an inhibitor of lipid A biosynthesis capable of potentiating rifampicin and vancomycin. Furthermore, we characterized the ability of known inhibitors of lipid A biosynthesis to potentiate the activity of rifampicin against Gram-negative pathogens.
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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 | 0.001 |
| 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".