ATAD5 restricts R-loop formation through PCNA unloading and RNA helicase maintenance at the replication fork
Bibliographic record
Abstract
R-loops are formed when replicative forks collide with the transcriptional machinery and can cause genomic instability. However, it is unclear how R-loops are regulated at transcription-replication conflict (TRC) sites and how replisome proteins are regulated to prevent R-loop formation or mediate R-loop tolerance. Here, we report that ATAD5, a PCNA unloader, plays dual functions to reduce R-loops both under normal and replication stress conditions. ATAD5 interacts with RNA helicases such as DDX1, DDX5, DDX21 and DHX9 and increases the abundance of these helicases at replication forks to facilitate R-loop resolution. Depletion of ATAD5 or ATAD5-interacting RNA helicases consistently increases R-loops during the S phase and reduces the replication rate, both of which are enhanced by replication stress. In addition to R-loop resolution, ATAD5 prevents the generation of new R-loops behind the replication forks by unloading PCNA which, otherwise, accumulates and persists on DNA, causing a collision with the transcription machinery. Depletion of ATAD5 reduces transcription rates due to PCNA accumulation. Consistent with the role of ATAD5 and RNA helicases in maintaining genomic integrity by regulating R-loops, the corresponding genes were mutated or downregulated in several human tumors.
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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) | 0.001 | 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".