The mycobacterial antibiotic resistance determinant WhiB7 acts as a transcriptional activator by binding the primary sigma factor SigA (RpoV)
Bibliographic record
Abstract
Tuberculosis therapeutic options are limited by the high intrinsic antibiotic resistance of Mycobacterium tuberculosis. The putative transcriptional regulator WhiB7 is crucial for the activation of systems that provide resistance to diverse antibiotic classes. Here, we used in vitro run-off, two-hybrid assays, as well as mutagenic, complementation and protein pull-down experiments, to characterize WhiB7 as an auto-regulatory, redox-sensitive transcriptional activator in Mycobacterium smegmatis. We provide the first direct biochemical proof that a WhiB protein promotes transcription and also demonstrate that this activity is sensitive to oxidation (diamide). Its partner protein for transcriptional activation was identified as SigA, the primary sigma factor subunit of RNA polymerase. Residues required for the interaction mapped to region 4 of SigA (including R515H) or adjacent domains of WhiB7 (including E63D). WhiB7's ability to provide a specific spectrum of antibiotic-resistance was dependent on these residues as well as its C-terminal AT-hook module that binds to an AT-rich motif immediately upstream of the -35 hexamer recognized by SigA. These experimentally established constrains, combined with protein structure predictions, were used to generate a working model of the WhiB7-SigA-promoter complex. Inhibitors preventing WhiB7 interactions could allow the use of previously ineffective antibiotics for treatment of mycobacterial diseases.
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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) | 0.001 | 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".