Contrasting silencing mechanisms of the same target mRNA by two regulatory RNAs in Escherichia coli
Bibliographic record
Abstract
Small RNAs are key components of complex regulatory networks. These molecules can integrate multiple cellular signals to control specific target mRNAs. The recent development of high-throughput methods tremendously helped to characterize the full targetome of sRNAs. Using MS2-affinity purification coupled with RNA sequencing (MAPS) technology, we reveal the targetomes of two sRNAs, CyaR and RprA. Interestingly, both CyaR and RprA interact with the 5'-UTR of hdeD mRNA, which encodes an acid-resistance membrane protein. We demonstrate that CyaR classically binds to the RBS of hdeD, interfering with translational initiation. We identified an A/U-rich motif on hdeD, which is bound by the RNA chaperone Hfq. Our results indicate that binding of this motif by Hfq is required for CyaR-induced degradation of hdeD mRNA. Additional data suggest that two molecules of RprA must bind the 5'-UTR of hdeD to block translation initiation. Surprisingly, while both CyaR and RprA sRNAs bind to the same motif on hdeD mRNA, RprA solely acts at the translational level, leaving the target RNA intact. By interchanging the seed region of CyaR and RprA sRNAs, we also swap their regulatory behavior. These results suggest that slight changes in the seed region could modulate the regulation of target mRNAs.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".