Hypernegative Supercoiling Inhibits Growth by Causing RNA Degradation
Bibliographic record
Abstract
Transcription-induced hypernegative supercoiling is a hallmark of Escherichia coli topoisomerase I (topA) mutants. However, its physiological significance has remained unclear. Temperature downshift of a mutant yielded transient growth arrest and a parallel increase in hypernegative supercoiling that was more severe with lower temperature. Both properties were alleviated by overexpression of RNase HI. While ribosomes in extracts showed normal activity when obtained during growth arrest, mRNA on ribosomes was reduced for fis and shorter for crp, polysomes were much less abundant relative to monosomes, and protein synthesis rate dropped, as did the ratio of large to small proteins. Altered processing and degradation of lacA and fis mRNA was also observed. These data are consistent with truncation of mRNA during growth arrest. These effects were not affected by a mutation in the gene encoding RNase E, indicating that this endonuclease is not involved in the abnormal mRNA processing. They were also unaffected by spectinomycin, an inhibitor of protein synthesis, which argued against induction of RNase activity. In vitro transcription revealed that R-loop formation is more extensive on hypernegatively supercoiled templates. These results allow us, for the first time, to present a model by which hypernegative supercoiling inhibits growth. In this model, the introduction of hypernegative supercoiling by gyrase facilitates degradation of nascent RNA; overproduction of RNase HI limits the accumulation of hypernegative supercoiling, thereby preventing extensive RNA degradation.
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 | 0.000 |
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".