Antagonism between Front-Line Antibiotics Clarithromycin and Amikacin in the Treatment of Mycobacterium abscessus Infections Is Mediated by the <i>whiB7</i> Gene
Bibliographic record
Abstract
ABSTRACT Combinations of antibiotics, each individually effective against Mycobacterium abscessus , are routinely coadministered based on the concept that this minimizes the spread of antibiotic resistance. However, our in vitro data contradict this assumption and instead document antagonistic interactions between two antibiotics (clarithromycin and amikacin) used to treat M. abscessus infections. Clinically relevant concentrations of clarithromycin induced increased resistance to both amikacin and itself. The induction of resistance was dependent on whiB7 , a transcriptional activator of intrinsic antibiotic resistance that is induced by exposure to many different antibiotics. In M. abscessus , the deletion of whiB7 (MAB_3508c) resulted in increased sensitivity to a broad range of antibiotics. WhiB7 was required for transcriptional activation of genes that confer resistance to three commonly used anti- M. abscessus drugs: clarithromycin, amikacin, and tigecycline. The whiB7 -dependent gene that conferred macrolide resistance was identified as erm (41) (MAB_2297), which encodes a ribosomal methyltransferase. The whiB7 -dependent gene contributing to amikacin resistance was eis2 (MAB_4532c), which encodes a Gcn5-related N -acetyltransferase (GNAT). Transcription of whiB7 and the resistance genes in its regulon was inducible by subinhibitory concentrations of clarithromycin but not by amikacin. Thus, exposure to clarithromycin, or likely any whiB7 -inducing antibiotic, may antagonize the activities of amikacin and other drugs. This has important implications for the management of M. abscessus infections, both in cystic fibrosis (CF) and non-CF patients.
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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".