Coupled degradation of a small regulatory RNA and its mRNA targets in <i>Escherichia coli</i>
Bibliographic record
Abstract
RyhB is a small antisense regulatory RNA that is repressed by the Fur repressor and negatively regulates at least six mRNAs encoding Fe-binding or Fe-storage proteins in Escherichia coli. When Fe is limiting, RyhB levels rise, and target mRNAs are rapidly degraded. RyhB is very stable when measured after treatment of cells with the transcription inhibitor rifampicin, but is unstable when overall mRNA transcription continues. We propose that RyhB turnover is coupled to and dependent on pairing with the target mRNAs. Degradation of both mRNA targets and RyhB is dependent on RNase E and is slowed in degradosome mutants. RyhB requires the RNA chaperone Hfq. In the absence of Hfq, RyhB is unstable, even when general transcription is inhibited; degradation is dependent upon RNase E. Hfq and RNase E bind similar sites on the RNA; pairing may allow loss of Hfq and access by RNase E. Two other Hfq-dependent small RNAs, DsrA and OxyS, are also stable when overall transcription is off, and unstable when it is not, suggesting that they, too, are degraded when their target mRNAs are available for pairing. Thus, this large class of regulatory RNAs share an unexpected intrinsic mechanism for shutting off their action.
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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) | 0.000 | 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".