Characterization of a Rifampin-Inactivating Glycosyltransferase from a Screen of Environmental Actinomycetes
Bibliographic record
Abstract
Identifying and understanding the collection of all antibiotic resistance determinants presented in the global microbiota, the antibiotic resistome, provides insight into the evolution of antibiotic resistance and critical information for the development of future antimicrobials. The rifamycins are broad-spectrum antibiotics that target bacterial transcription by inhibition of RNA polymerase. Although mutational alteration of the drug target is the predominant mechanism of resistance to this family of antibiotics in the clinic, a number of diverse inactivation mechanisms have also been reported. In this report, we investigate a subset of environmental rifampin-resistant actinomycete isolates and identify a diverse collection of rifampin inactivation mechanisms. We describe a single isolate, WAC1438, capable of inactivating rifampin by glycosylation. A draft genome sequence of WAC1438 (most closely related to Streptomyces speibonae, according to a 16S rRNA gene comparison) was assembled, and the associated rifampin glycosyltransferase open reading frame, rgt1438, was identified. The role of rgt1438 in rifampin resistance was confirmed by its disruption in the bacterial chromosome, resulting in a loss of antibiotic inactivation and a 4-fold decrease in MIC. Interestingly, examination of the RNA polymerase β-subunit sequence of WAC1438 suggests that it harbors a resistant target and thus possesses dual mechanisms of rifamycin resistance. Using an in vitro assay with purified enzyme, Rgt1438 could inactivate a variety of rifamycin antibiotics with comparable steady-state kinetics constants. Our results identify rgt1438 as a rifampin resistance determinant from WAC1438 capable of inactivating an assortment of rifamycins, adding a new element to the rifampin resistome.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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 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".