RNA sequence and structure control assembly and function of RNA condensates
Bibliographic record
Abstract
Intracellular condensates formed through liquid–liquid phase separation (LLPS) primarily contain proteins and RNA. Recent evidence points to major contributions of RNA self-assembly in the formation of intracellular condensates. As the majority of previous studies on LLPS have focused on protein biochemistry, effects of biological RNAs on LLPS remain largely unexplored. In this study, we investigate the effects of crowding, metal ions, and RNA structure on formation of RNA condensates lacking proteins. Using bacterial riboswitches as a model system, we first demonstrate that LLPS of RNA is promoted by molecular crowding, as evidenced by formation of RNA droplets in the presence of polyethylene glycol (PEG 8K). Crowders are not essential for LLPS, however. Elevated Mg2+concentrations promote LLPS of specific riboswitches without PEG. Calculations identify key RNA structural and sequence elements that potentiate the formation of PEG-free condensates; these calculations are corroborated by key wet-bench experiments. Based on this, we implement structure-guided design to generate condensates with novel functions including ligand binding. Finally, we show that RNA condensates help protect their RNA components from degradation by nucleases, suggesting potential biological roles for such higher-order RNA assemblies in controlling gene expression through RNA stability. By utilizing both natural and artificial RNAs, our study provides mechanistic insight into the contributions of intrinsic RNA properties and extrinsic environmental conditions to the formation and regulation of condensates comprised of RNAs.
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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".