A Forward Chemical Screen Identifies Antibiotic Adjuvants in <i>Escherichia coli</i>
Bibliographic record
Abstract
Multi-drug-resistant infections caused by Gram-negative pathogens are rapidly increasing, highlighting the need for new chemotherapies. Unlike Gram-positive bacteria, where many different chemical classes of antibiotics show efficacy, Gram-negatives are intrinsically insensitive to many antimicrobials including the macrolides, rifamycins, and aminocoumarins, despite intracellular targets that are susceptible to these drugs. The basis for this insensitivity is the presence of the impermeant outer membrane of Gram-negative bacteria in addition to the expression of pumps and porins that reduce intracellular concentrations of many molecules. Compounds that sensitize Gram-negative cells to "Gram-positive antibiotics", antibiotic adjuvants, offer an orthogonal approach to addressing the crisis of multi-drug-resistant Gram-negative pathogens. We performed a forward chemical genetic screen of 30,000 small molecules designed to identify such antibiotic adjuvants of the aminocoumarin antibiotic novobiocin in Escherichia coli. Four compounds from this screen were shown to be synergistic with novobiocin including inhibitors of the bacterial cytoskeleton protein MreB, cell wall biosynthesis enzymes, and DNA synthesis. All of these molecules were associated with altered cell shape and small molecule permeability, suggesting a unifying mechanism for these antibiotic adjuvants. The potential exists to expand this approach as a means to develop novel combination therapies for the treatment of infections caused by 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 | 0.000 |
| Research integrity | 0.001 | 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".