Riboswitches: Ancient and Promising Genetic Regulators
Bibliographic record
Abstract
BAIT AND SWITCH: Metabolite-sensing riboswitches make use of RNA structural modulation to regulate gene expression, as illustrated in the scheme, in response to subtle changes in metabolite concentrations. This review describes the current knowledge about naturally occurring riboswitches and their growing potential as antibacterial cellular targets and as molecular biosensors. Newly discovered metabolite-sensing riboswitches have revealed that cellular processes extensively make use of RNA structural modulation to regulate gene expression in response to subtle changes in metabolite concentrations. Riboswitches are involved at various regulation levels of gene expression, such as transcription attenuation, translation initiation, mRNA splicing and mRNA processing. Riboswitches are found in the three kingdoms of life, and in various cases, are involved in the regulation of essential genes, which makes their regulation an essential part of cell survival. Because riboswitches operate without the assistance of accessory proteins, they are believed to be remnants of an ancient time, when gene regulation was strictly based on RNA, from which are left numerous "living molecular fossils", as exemplified by ribozymes, and more spectacularly, by the ribosome. Due to their nature, riboswitches hold high expectations for the manipulation of gene expression and the detection of small metabolites, and also offer an unprecedented potential for the discovery of novel classes of antimicrobial agents.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".