RAD18 opposes transcription-associated genome instability through FANCD2 recruitment
Bibliographic record
Abstract
Abstract DNA replication is a vulnerable time for genome stability maintenance. Intrinsic stressors, as well as oncogenic stress, can challenge replication by fostering conflicts with transcription and stabilizing DNA:RNA hybrids. RAD18 is an E3 ubiquitin ligase for PCNA that is involved in coordinating DNA damage tolerance pathways to preserve genome stability during replication. In this study, we show that RAD18 deficient cells have higher levels of transcription-replication conflicts and accumulate DNA:RNA hybrids that induce DNA double strand breaks and replication stress. We find that these effects are driven in part by failure to recruit the Fanconi Anemia protein FANCD2 at difficult to replicate and R-loop prone genomic sites. While RAD18 is important for FANCD2 localization after splicing inhibition or aphidicolin treatment, it is comparatively less important during interstrand crosslink repair. Thus, we highlight a RAD18 dependent pathway promoting FANCD2-mediated suppression of R-loops and transcription-replication conflicts. AUTHOR SUMMARY Genome instability, a state in which cells acquire mutations more quickly, drives cancer initiation and progression. DNA is normally protected from such mutations by a host of specialized factors that recognize and repair potentially damaging circumstances. Here we describe under-appreciated links between a protein called RAD18, which recognizes potentially damaging stress, and a phenomenon called a transcription-replication conflict. If not repaired such conflicts can lead to DNA breaks. We find that RAD18 plays an important role in recruiting another repair protein called FANCD2 to sites of transcription-replication conflicts. By doing so, RAD18 protects the human genome from damage when it is faced with stresses such as those seen in cancer cells. This work provides new insight to how the FANCD2 protein is brought to potentially damaging sites in our genomes.
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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) | 0.003 | 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".