Genome Context as a Predictive Tool for Identifying Regulatory Targets of the TetR Family Transcriptional Regulators
Bibliographic record
Abstract
TetR family transcriptional regulators (TFRs) are found in most bacteria and archea. Most of the family members that have been investigated to date are repressors of their target genes, and the majority of these, like the well-characterized protein TetR, regulate genes that encode transmembrane efflux pumps. In many cases repression by TFR proteins is reversed through the direct binding of a small-molecule ligand. The number of TFRs in the public database has grown rapidly as a result of genome sequencing and there are now thousands of family members; however virtually nothing is known about the biology and biochemistry they regulate. Generally applicable methods for predicting their regulatory targets would assist efforts to characterize the family. Here, we investigate chromosomal context of 372 TFRs from three Streptomyces species. We find that the majority (250 TFRs) are transcribed divergently from one neighboring gene, as is the case for TetR and its target tetA. We explore predicted target gene product identity and intergenic separation to see which either correlates with a direct regulatory relationship. While intergenic separation is a critical factor in regulatory prediction the identity of the putative target gene product is not. Our data suggest that those TFRs that are <200 bp from their divergently oriented neighbors are most likely to regulate them. These target genes include membrane proteins (26% of which 22% are probable membrane-associated pumps), enzymes (60%), other proteins such as transcriptional regulators (1%), and proteins having no predictive sequence motifs (13%). In addition to establishing a solid foundation for identifying targets for TFRs of unknown function, our analysis demonstrates a much greater diversity of TFR-regulated biochemical functions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.002 | 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".