Interaction with single‐stranded DNA‐binding protein localizes ribonuclease HI to DNA replication forks and facilitates R‐loop removal
Bibliographic record
Abstract
DNA replication complexes (replisomes) routinely encounter proteins and unusual nucleic acid structures that can impede their progress. Barriers can include transcription complexes and R-loops that form when RNA hybridizes with complementary DNA templates behind RNA polymerases. Cells encode several RNA polymerase and R-loop clearance mechanisms to limit replisome exposure to these potential obstructions. One such mechanism is hydrolysis of R-loops by ribonuclease HI (RNase HI). Here, we examine the cellular role of the interaction between Escherichia coli RNase HI and the single-stranded DNA-binding protein (SSB) in this process. Interaction with SSB localizes RNase HI foci to DNA replication sites. Mutation of rnhA to encode an RNase HI variant that cannot interact with SSB but that maintains enzymatic activity (rnhAK60E) eliminates RNase HI foci. The mutation also produces a media-dependent slow-growth phenotype and an activated DNA damage response in cells lacking Rep helicase, which is an enzyme that disrupts stalled transcription complexes. RNA polymerase variants that are thought to increase or decrease R-loop accumulation enhance or suppress, respectively, the growth phenotype of rnhAK60E rep::kan strains. These results identify a cellular role for the RNase HI/SSB interaction in helping to clear R-loops that block DNA replication.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".