Unprecedented tunability of riboswitch structure and regulatory function by sub-millimolar variations in physiological Mg2+
Bibliographic record
Abstract
Riboswitches are cis-acting regulatory RNA biosensors that rival the efficiency of those found in proteins. At the heart of their regulatory function is the formation of a highly specific aptamer-ligand complex. Understanding how these RNAs recognize the ligand to regulate gene expression at physiological concentrations of Mg2+ ions and ligand is critical given their broad impact on bacterial gene expression and their potential as antibiotic targets. In this work, we used single-molecule FRET and biochemical techniques to demonstrate that Mg2+ ions act as fine-tuning elements of the amino acid-sensing lysC aptamer's ligand-free structure in the mesophile Bacillus subtilis. Mg2+ interactions with the aptamer produce encounter complexes with strikingly different sensitivities to the ligand in different, yet equally accessible, physiological ionic conditions. Our results demonstrate that the aptamer adapts its structure and folding landscape on a Mg2+-tunable scale to efficiently respond to changes in intracellular lysine of more than two orders of magnitude. The remarkable tunability of the lysC aptamer by sub-millimolar variations in the physiological concentration of Mg2+ ions suggests that some single-aptamer riboswitches have exploited the coupling of cellular levels of ligand and divalent metal ions to tightly control gene expression.
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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".