Investigating the role of RNA structures in transcriptional pausing using in vitro assays and in silico analyses
Bibliographic record
Abstract
Transcriptional pausing occurs across the bacterial genome but the importance of this mechanism is still poorly understood. Only few pauses were observed during the previous decades, leaving an important gap in understanding transcription mechanisms. Using the well-known Escherichia coli hisL and trpL pause sites as models, we describe here the relation of pause sites with upstream RNA structures suspected to stabilize pausing. We find that the transcription factor NusA influences the pause half-life at leuL, pheL and thrL pause sites. Using a mutagenesis approach, we observe that transcriptional pausing is affected in all tested pause sites, suggesting that the upstream RNA sequence is important for transcriptional pausing. Compensatory mutations assessing the presence of RNA hairpins did not yield clear conclusions, indicating that complex RNA structures or transcriptional features may be playing a role in pausing. Moreover, using a bioinformatic approach, we explored the relation between a DNA consensus sequence important for pausing and putative hairpins among thousands of pause sites in E. coli. We identified 2125 sites presenting hairpin-dependent transcriptional pausing without consensus sequence, suggesting that this mechanism is widespread across E. coli. This study paves the way to understand the role of RNA structures in transcriptional pausing.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".