Investigation of Transcription Repression and Small-Molecule Responsiveness by TetR-Like Transcription Factors Using a Heterologous<i>Escherichia coli</i>-Based Assay
Bibliographic record
Abstract
The SCO7222 protein and ActR are two of approximately 150 TetR-like transcription factors encoded in the Streptomyces coelicolor genome. Using bioluminescence as a readout, we have developed Escherichia coli-based biosensors that accurately report the regulatory activity of these proteins and used it to investigate their interactions with DNA and small-molecule ligands. We found that the SCO7222 protein and ActR repress the expression of their putative target genes, SCO7223 and actII-ORF2 (actA), respectively, by interacting with operator sequence in the promoters. The operators recognized by the two proteins are related such that O(7223) (an operator for SCO7223) could be bound by both the SCO7222 protein and ActR with similar affinities. In contrast, O(act) (an operator for actII-ORF2) was bound tightly by ActR and more weakly by the SCO7222 protein. We demonstrated ligand specificity of these proteins by showing that while TetR (but not ActR or the SCO7222 protein) interacts with tetracyclines, ActR (but not TetR or the SCO7222 protein) interacts with actinorhodin and related molecules. Through operator-targeted mutagenesis, we found that at least two nucleotide changes in O(7223) were required to disrupt its interaction with SCO7222 protein, while ActR was more sensitive to changes on O(act). Most importantly, we found that the interaction of each protein with wild-type and mutant operator sequences in vivo and in vitro correlated perfectly. Our data suggest that E. coli-based biosensors of this type should be broadly applicable to TetR-like transcription factors.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".