Single-Target Regulators Constitute the Minority Group of Transcription Factors in Escherichia coli K-12
Bibliographic record
Abstract
The identification of regulatory targets of all transcription factors (TFs) is critical for understanding the entire network of genome regulation. A total of approximately 300 TFs exist in the model prokaryote Escherichia coli K-12, but the identification of whole sets of their direct targets is impossible with use of in vivo approaches. For this end, the most direct and quick approach is to identify the TF-binding sites in vitro on the genome. We then developed and utilized the gSELEX screening system in vitro for identification of more than 150 E. coli TF-binding sites along the E. coli genome. Based on the number of predicted regulatory targets, we classified E. coli K-12 TFs into four groups, altogether forming a hierarchy ranging from a single-target TF (ST-TF) to local TFs, global TFs, and nucleoid-associated TFs controlling as many as 1,000 targets. Using the collection of purified TFs and a library of genome DNA segments from a single and the same E. coli K-12, we identified here a total of 11 novel ST-TFs, CsqR, CusR, HprR, NorR, PepA, PutA, QseA, RspR, UvrY, ZraR, and YqhC. The regulation of single-target promoters was analyzed in details for the hitherto uncharacterized QseA and RspR. In most cases, the ST-TF gene and its regulatory target genes are adjacently located on the E. coli K-12 genome, implying their simultaneous transfer in the course of genome evolution. The newly identified 11 ST-TFs and the total of 13 hitherto identified altogether constitute the minority group of TFs in E. coli K-12.
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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) | 0.001 | 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) | 0.001 | 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".