An emerging picture of FANCJ’s role in G4 resolution to facilitate DNA replication
Bibliographic record
Abstract
Abstract A well-accepted hallmark of cancer is genomic instability, which drives tumorigenesis. Therefore, understanding the molecular and cellular defects that destabilize chromosomal integrity is paramount to cancer diagnosis, treatment and cure. DNA repair and the replication stress response are overarching paradigms for maintenance of genomic stability, but the devil is in the details. ATP-dependent helicases serve to unwind DNA so it is replicated, transcribed, recombined and repaired efficiently through coordination with other nucleic acid binding and metabolizing proteins. Alternatively folded DNA structures deviating from the conventional anti-parallel double helix pose serious challenges to normal genomic transactions. Accumulating evidence suggests that G-quadruplex (G4) DNA is problematic for replication. Although there are multiple human DNA helicases that can resolve G4 in vitro, it is debated which helicases are truly important to resolve such structures in vivo. Recent advances have begun to elucidate the principal helicase actors, particularly in cellular DNA replication. FANCJ, a DNA helicase implicated in cancer and the chromosomal instability disorder Fanconi Anemia, takes center stage in G4 resolution to allow smooth DNA replication. We will discuss FANCJ’s role with its protein partner RPA to remove G4 obstacles during DNA synthesis, highlighting very recent advances and implications for cancer therapy.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".