RNase HI overproduction is required for efficient full‐length RNA synthesis in the absence of topoisomerase I in <i>Escherichia coli</i>
Bibliographic record
Abstract
It has long been known that Escherichia coli cells deprived of topoisomerase I (topA null mutants) do not grow. Because mutations reducing DNA gyrase activity and, as a consequence, negative supercoiling, occur to compensate for the loss of topA function, it has been assumed that excessive negative supercoiling is somehow involved in the growth inhibition of topA null mutants. However, how excess negative supercoiling inhibits growth is still unknown. We have previously shown that the overproduction of RNase HI, an enzyme that degrades the RNA portion of an R-loop, can partially compensate for the growth defects because of the absence of topoisomerase I. In this article, we have studied the effects of gyrase reactivation on the physiology of actively growing topA null cells. We found that growth immediately and almost completely ceases upon gyrase reactivation, unless RNase HI is overproduced. Northern blot analysis shows that the cells have a significantly reduced ability to accumulate full-length mRNAs when RNase HI is not overproduced. Interestingly, similar phenotypes, although less severe, are also seen when bacterial cells lacking RNase HI activity are grown and treated in the same way. All together, our results suggest that excess negative supercoiling promotes the formation of R-loops, which, in turn, inhibit RNA synthesis.
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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 | 0.001 | 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".