Hfq restructures RNA-IN and RNA-OUT and facilitates antisense pairing in the Tn<i>10</i>/IS<i>10</i> system
Bibliographic record
Abstract
Hfq functions in post-transcriptional gene regulation in a wide range of bacteria, usually by promoting base-pairing of mRNAs and trans-encoded sRNAs that share partial sequence complementarity. It is less clear if Hfq is required for pairing of cis-encoded RNAs (i.e., antisense RNAs) with their target mRNAs. In the current work, we have characterized the interactions between Escherichia coli Hfq and the components of the Tn10/IS10 antisense system, RNA-IN and RNA-OUT. We show that Hfq interacts with RNA-OUT through its proximal RNA-binding surface, as is typical for Hfq and trans-encoded sRNAs. In contrast, RNA-IN binds both proximal and distal RNA-binding surfaces in Hfq with a higher affinity for the latter, as is typical for mRNA interactions in canonical sRNA-mRNA pairs. Importantly, an amino acid substitution in Hfq that interferes with RNA binding to the proximal site negatively impacts RNA-IN:OUT pairing in vitro and suppresses the ability of Hfq to negatively regulate IS10 transposition in vivo. We also show that Hfq binding to RNA-IN and RNA-OUT alters secondary structure elements in both of these RNAs and speculate that this could be important in how Hfq facilitates RNA-IN:OUT pairing. Based on the results presented here, we suggest that Hfq could be involved in regulating RNA pairing in other antisense systems, including systems encoded by other transposable elements.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
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".