Tn5 transposition in Escherichia coli is repressed by Hfq and activated by over-expression of the small non-coding RNA SgrS
Bibliographic record
Abstract
Hfq functions in post-transcriptional gene regulation in a wide range of bacteria, usually by promoting base pairing of mRNAs with trans-encoded sRNAs. It was previously shown that Hfq down-regulates Tn10 transposition by inhibiting IS10 transposase expression at the post-transcriptional level. This provided the first example of Hfq playing a role in DNA transposition and led us to ask if a related transposon, Tn5, is similarly regulated. We show that Hfq strongly suppresses Tn5 transposition in Escherichia coli by inhibiting IS50 transposase expression. However, in contrast to the situation for Tn10, Hfq primarily inhibits IS50 transposase transcription. As Hfq does not typically function directly in transcription, we searched for a transcription factor that also down-regulated IS50 transposase transcription and is itself under Hfq control. We show that Crp (cyclic AMP receptor protein) fits these criteria as: (1) disruption of the crp gene led to an increase in IS50 transposase expression and the magnitude of this increase was comparable to that observed for an hfq disruption; and (2) Crp expression decreased in hfq − . We also demonstrate that IS50 transposase expression and Tn5 transposition are induced by over-expression of the sRNA SgrS and link this response to glucose limitation. Tn5 transposition is negatively regulated by Hfq primarily through inhibition of IS50 transposase transcription. Preliminary results support the possibility that this regulation is mediated through Crp. We also provide evidence that glucose limitation activates IS50 transposase transcription and transposition.
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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) | 0.001 | 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".