RNA secondary structure ensemble mapping in a living cell identifies conserved RNA regulatory switches and thermometers
Bibliographic record
Abstract
Abstract RNA molecules can populate ensembles of alternative structural conformations, but comprehensively mapping RNA conformational landscapes within living cells presents significant challenges and has, as such, so far remained elusive. Here, we generated the first transcriptome-scale maps of RNA secondary structure ensembles in a living cell, using Escherichia coli as a model. Our analysis uncovered features of structurally-dynamic regions, as well as the existence of hundreds of highly-conserved bacterial RNA structural elements. Conditional structure mapping revealed extensive restructuring of RNA ensembles during cold shock, leading to the discovery of several novel RNA thermometers in the 5′ UTRs of the cspG, cspI, cpxP and lpxP mRNAs. We mechanistically characterized how these thermometers switch structure in response to cold shock and revealed the cspE chaperone-mediated regulation of lpxP . Collectively, this work reveals a previously unappreciated complexity of RNA structural dynamics in living cells, and it provides a key resource to significantly accelerate the discovery of regulatory RNA switches.
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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".