Helicases in R-loop Formation and Resolution
Bibliographic record
Abstract
With the development and wide usage of CRISPR technology, the presence of R-loop structures, which consist of an RNA-DNA hybrid and a displaced single-strand (ss) DNA, has become well accepted. R-loop structures have been implicated in a variety of circumstances and play critical roles in the metabolism of nucleic acid and relevant biological processes, including transcription, DNA repair, and telomere maintenance. Helicases are enzymes that use an ATP-driven motor force to unwind double-strand (ds) DNA, dsRNA, or RNA-DNA hybrids. Additionally, certain helicases have strand-annealing activity. Thus, helicases possess unique positions for R-loop biogenesis: they utilize their strand-annealing activity to promote the hybridization of RNA to DNA, leading to the formation of R-loops; conversely, they utilize their unwinding activity to separate RNA-DNA hybrids and resolve R-loops. Indeed, numerous helicases such as senataxin (SETX), Aquarius (AQR), WRN, BLM, RTEL1, PIF1, FANCM, ATRX (alpha-thalassemia/mental retardation, X-linked), CasDinG, and several DEAD/H-box proteins are reported to resolve R-loops; while other helicases, such as Cas3 and UPF1, are reported to stimulate R-loop formation. Moreover, helicases like DDX1, DDX17, and DHX9 have been identified in both R-loop formation and resolution. In this review, we will summarize the latest understandings regarding the roles of helicases in R-loop metabolism. Additionally, we will highlight challenges associated with drug discovery in the context of targeting these R-loop helicases.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".