Architecture of Bacterial Promoters; The case of the Escherichia coli ogt Promoter
Bibliographic record
Abstract
All bacteria utilize RNA polymerase enzyme and transcription activator proteins to regulate gene expression in response to internal or external stress. Some bacterial promoters are regulated with only one transcription factor whilst two or more transcription activators regulate some other promoters. NarL is a transcription activator protein that activates the E. coli yeaR and ogt promoters in response to nitrate and nitrite induction in absence of oxygen. In the present study we have studied ogt1052 promoter, which is a derivative of ogt promoter containing only one NarL binding site very close to -35 element. Therefore, it is considered as class II activator dependent promoter just as yeaR promoter. A molecular structure of ogt1052 promoter was proposed which suggests that NarL binding site is located in opposite face of DNA that contains Alpha-CTD and sigma domain 4 of RNA polymerase enzyme required for promoter recognition. The aim of the present study was to study and test the suggested molecular model by creating point mutations at -35 element and deletion of one base pair in spacer region, to test whether sigma domain 4 is necessary to bind -35 hexamer in order to start transcription initiation, and to test whether NarL activates the promoter by interaction with Alpha-CTD in the opposite face of the DNA. Based on the result achieved, ogt1052 promoter is a class I promoter “dressed” like a class II promoter.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".