Direct and indirect control of Rho-dependent transcription termination by the<i>Escherichia coli lysC</i>riboswitch
Bibliographic record
Abstract
Bacterial riboswitches are molecular structures that play a crucial role in controlling gene expression to maintain cellular balance. TheEscherichia coli lysCriboswitch has been previously shown to regulate gene expression through translation initiation and mRNA decay. Recent research suggests thatlysCgene expression is also influenced by Rho-dependent transcription termination. Through a series of in silico, in vitro, and in vivo experiments, we provide experimental evidence that thelysCriboswitch directly and indirectly modulates Rho transcription termination. Our study demonstrates that Rho-dependent transcription termination plays a significant role in the cotranscriptional regulation oflysCexpression. Together with previous studies, our work suggests thatlysCexpression is governed by a lysine-sensing riboswitch that regulates translation initiation, transcription termination, and mRNA degradation. Notably, both Rho and RNase E target the same region of the RNA molecule, implying that RNase E may degrade Rho-terminated transcripts, providing a means to selectively eliminate these incomplete messenger RNAs. Overall, this study sheds light on the complex regulatory mechanisms used by bacterial riboswitches, emphasizing the role of transcription termination in the control of gene expression and mRNA stability.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".