<scp>DsrA</scp> regulatory <scp>RNA</scp> represses both <i>hns</i> and <i>rbs</i><scp><i>D</i></scp> m<scp>RNA</scp>s through distinct mechanisms in <scp><i>E</i></scp><i>scherichia coli</i>
Bibliographic record
Abstract
The 87 nucleotide long DsrA sRNA has been mostly studied for its translational activation of the transcriptional regulator RpoS. However, it also represses hns mRNA, which encodes H-NS, a major regulator that affects expression of nearly 5% of Escherichia coli genes. A speculative model previously suggested that DsrA would block hns mRNA translation by binding simultaneously to start and stop codon regions of hns mRNA (coaxial model). Here, we show that DsrA efficiently blocked translation of hns mRNA by base-pairing immediately downstream of the start codon. In addition, DsrA induced hns mRNA degradation by actively recruiting the RNA degradosome complex. Data presented here led to a model of DsrA action on hns mRNA, which supports a canonical mechanism of sRNA-induced mRNA degradation by binding to the translation initiation region. Furthermore, using MS2-affinity purification coupled with RNA sequencing technology (MAPS), we also demonstrated that DsrA targets rbsD mRNA, involved in ribose utilization. Surprisingly, DsrA base pairs far downstream of rbsD start codon and induces rapid degradation of the transcript. Thus, our study enables us to draw an extended DsrA targetome.
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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 | 0.001 |
| 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".