Dynamic interactions between the RNA chaperone Hfq, small regulatory RNAs and mRNAs in live bacterial cells
Bibliographic record
Abstract
Abstract RNA binding proteins play myriad roles in controlling and regulating RNAs and RNA-mediated functions, often through simultaneous binding to other cellular factors. In bacteria, the RNA chaperone Hfq modulates post-transcriptional gene regulation. Absence of Hfq leads to the loss of fitness and compromises the virulence of bacterial pathogens. Using live-cell super-resolution imaging, we are able to distinguish Hfq binding to different sizes of cellular RNAs. We demonstrate that under normal growth conditions, Hfq exhibits widespread mRNA binding activity. Particularly, the distal face of Hfq contributes mostly to the mRNA binding in vivo . In addition, binding of Hfq to these mRNAs can recruit RNase E to promote turnover of these mRNAs in an sRNA-independent manner, providing one mechanism to release Hfq from the pre-bound mRNAs. Finally, our data indicate that sRNAs, once expressed, can either co-occupy Hfq with the mRNA or displace the mRNA from Hfq, suggesting mechanisms through which sRNAs rapidly access Hfq to induce sRNA-mediated gene regulation. Our data collectively demonstrate that Hfq dynamically changes its interactions with different RNAs in response to changes in cellular conditions.
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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 | 0.001 |
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".