HelR is a helicase-like protein that protects RNA polymerase from rifamycin antibiotics
Bibliographic record
Abstract
Summary Rifamycin antibiotics such as rifampin are widely used for the management of tuberculosis and other bacterial infections. These drugs inhibit prokaryotic RNA polymerase (RNAP) by preventing elongation of mRNA resulting in cell death. Rifamycin resistance in the clinic is manifested primarily through amino acid substitutions in RNAP that decrease target affinity for the antibiotics. In contrast, environmental bacteria possess a wide variety of highly specific rifamycin enzyme-mediated resistance mechanisms that modify and inactivate the antibiotics by glycosylation, phosphorylation, ADP-ribosylation, or hydroxylation. Expression of rifamycin resistance is controlled by a common 19bp cis -acting rifamycin associated element (RAE) upstream of inactivating genes. Guided by the presence of RAE sequences, we identify an unprecedented ATP-dependent mechanism of rifamycin resistance that acts not by antibiotic inactivation but by protecting the RNAP target. We show that Streptomyces venezuelae encodes a helicase-like protein, HelR, which confers broad spectrum rifamycin resistance. Furthermore, HelR is essential for promoting rifamycin tolerance at inhibitory concentrations, enabling bacterial evasion of the toxic properties of these antibiotics. HelR forms a complex with RNAP in vivo and rescues transcription inhibition by rifampin in vitro. We synthesized a rifamycin photoprobe and demonstrated that HelR directly displaces rifamycins from RNAP. HelR-encoding genes associated with RAEs are broadly distributed in actinobacteria, including many opportunistic Mycobacterial pathogens, which cannot currently be treated with rifamycins. This first report of an RNAP protection protein conferring antibiotic resistance and offers guidance for developing new rifamycin antibiotics that can avoid this mechanism.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".