Identification of UHRF2 as a novel DNA interstrand crosslink sensor protein
Bibliographic record
Abstract
The Fanconi Anemia (FA) pathway is important for repairing interstrand crosslinks (ICLs) between the Watson-Crick strands of the DNA double helix.An initial and essential stage in the repair process is the detection of the ICL.Here, we report the identification of UHRF2, a paralogue of UHRF1, as an ICL sensor protein.UHRF2 is recruited to ICLs in the genome within seconds of their appearance.We show that UHRF2 cooperates with UHRF1, to ensure recruitment of FANCD2 to ICLs.A direct protein-protein interaction is formed between UHRF1 and UHRF2, and between either UHRF1 and UHRF2, and FANCD2.Importantly, we demonstrate that the essential monoubiquitination of FANCD2 is stimulated by UHRF1/UHRF2.The stimulation is mediating by a retention of FANCD2 on chromatin, allowing for its monoubiquitination by the FA core complex.Taken together, we uncover a mechanism of ICL sensing by UHRF2, leading to FANCD2 recruitment and retention at ICLs, in turn facilitating activation of FANCD2 by monoubiquitination. Author summaryFanconi Anemia is a genetic disease where patients typically have congenital abnormalities, develop bone marrow failure and suffer from cancer predisposition.The cells in patients have a reduced ability to repair a type of DNA damage where the two strands of the DNA double helix are physically linked together, and this failure in repair is believed to contribute to cause of the disease.Many proteins are involved in repairing this type of DNA damage in healthy individuals, via a complex DNA repair pathway called the Fanconi Anemia pathway.Here, we report the identification of a new player in this pathway.The protein, called UHRF2, is able to sense the DNA damage in the genome, and thereby help to initiate the healthy repair of the damage.In addition to improving our molecular
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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 | 0.001 | 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".