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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".