WhiB7, an Fe-S-dependent Transcription Factor That Activates Species-specific Repertoires of Drug Resistance Determinants in Actinobacteria
Bibliographic record
Abstract
WhiB-like (Wbl) proteins are well known for their diverse roles in actinobacterial morphogenesis, cell division, virulence, primary and secondary metabolism, and intrinsic antibiotic resistance. Gene disruption experiments showed that three different Actinobacteria (Mycobacterium smegmatis, Streptomyces lividans, and Rhodococcus jostii) each exhibited a different whiB7-dependent resistance profile. Heterologous expression of whiB7 genes showed these resistance profiles reflected the host's repertoire of endogenous whiB7-dependent genes. Transcriptional activation of two resistance genes in the whiB7 regulon, tap (a multidrug transporter) and erm(37) (a ribosomal methyltransferase), required interaction of WhiB7 with their promoters. Furthermore, heterologous expression of tap genes isolated from Mycobacterium species demonstrated that divergencies in drug specificity of homologous structural proteins contribute to the variation of WhiB7-dependent drug resistance. WhiB7 has a specific tryptophan/glycine-rich region and four conserved cysteine residues; it also has a peptide sequence (AT-hook) at its C terminus that binds AT-rich DNA sequence motifs upstream of the promoters it activates. Targeted mutagenesis showed that these motifs were required to provide antibiotic resistance in vivo. Anaerobically purified WhiB7 from S. lividans was dimeric and contained 2.1 ± 0.3 and 2.2 ± 0.3 mol of iron and sulfur, respectively, per protomer (consistent with the presence of a 2Fe-2S cluster). However, the properties of the dimer's absorption spectrum were most consistent with the presence of an oxygen-labile 4Fe-4S cluster, suggesting 50% occupancy. These data provide the first insights into WhiB7 iron-sulfur clusters as they exist in vivo, a major unresolved issue in studies of Wbl proteins.
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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".