Antagonistic drug interactions protect commensal <i>Bacteroidaceae</i> from macrolides via an RND-type efflux pump
Bibliographic record
Abstract
Antibiotics are essential to modern medicine, but their broad-spectrum activity can unintentionally disrupt the gut microbiota. This collateral damage may be alleviated by antagonistic drug interactions, in which specific compounds used in combination therapies selectively protect beneficial gut microbes from antibiotic activity. Using efflux pump inhibitors, transcriptomic and proteomic analyses, and targeted gene deletions, we show that a variety of non-antibiotic pharmaceuticals—from diverse therapeutic classes and at sub-inhibitory concentrations—can protect multiple Bacteroidales species from macrolide antibiotics. In Bacteroidaceae, this protection is mediated by a resistance-nodulation-division (RND)–type efflux pump, which is induced by the non-antibiotic drug but not by macrolides alone. Notably, protection persists even after the non-antibiotic drug is removed, and prolonged exposure results in stable macrolide resistance that is dependent on the RND-type efflux pump. Our findings illustrate how non-antibiotic drugs can inadvertently activate otherwise silent detoxification systems in gut microbes, uncovering resistance mechanisms that arise without antibiotic selection or gene transfer. While this can be harnessed to protect the microbiome during antibiotic therapy, it also reveals hidden resistance phenotypes that may escape detection in standard antimicrobial resistance assays.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".