Comparative Study between Transcriptionally- and Translationally-Acting Adenine Riboswitches Reveals Key Differences in Riboswitch Regulatory Mechanisms
Bibliographic record
Abstract
Many bacterial mRNAs are regulated at the transcriptional or translational level by ligand-binding elements called riboswitches. Although they both bind adenine, the adenine riboswitches of Bacillus subtilis and Vibrio vulnificus differ by controlling transcription and translation, respectively. Here, we demonstrate that, beyond the obvious difference in transcriptional and translational modulation, both adenine riboswitches exhibit different ligand binding properties and appear to operate under different regulation regimes (kinetic versus thermodynamic). While the B. subtilis pbuE riboswitch fully depends on co-transcriptional binding of adenine to function, the V. vulnificus add riboswitch can bind to adenine after transcription is completed and still perform translation regulation. Further investigation demonstrates that the rate of transcription is critical for the B. subtilis pbuE riboswitch to perform efficiently, which is in agreement with a co-transcriptional regulation. Our results suggest that the nature of gene regulation control, that is transcription or translation, may have a high importance in riboswitch regulatory mechanisms.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".