Growth inhibition mediated by excess negative supercoiling: the interplay between transcription elongation, R‐loop formation and DNA topology
Bibliographic record
Abstract
It has been known for a long time that supercoiling can affect gene expression at the level of promoter activity. Moreover, the results of a genome-wide analysis have recently led to the proposal that supercoiling could play a role in the regulation of gene expression at this level by acting as a second messenger, relaying environmental signals to regulatory networks. Although evidence is lacking for a regulatory role of supercoiling following transcription initiation, recent results from both yeast and bacteria suggest that the effect of supercoiling on gene expression can be considerably more dramatic after this initiation step. Transcription-induced supercoiling and its associated R-loops seem to be involved in this effect. In this context, one major function of topoisomerases would be to prevent the generation of excess negative supercoiling by transcription elongation, to inhibit R-loop formation and allow gene expression. This function would be especially evident when substantial and rapid gene expression is required for stress resistance, and it may explain, at least in part, why topoisomerase I synthesis is directed from stress-induced promoters in Escherichia coli. Growth inhibition mediated by excess negative supercoiling might be related to this interplay between transcription elongation and supercoiling.
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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) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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".