RECQL5 at the Intersection of Replication and Transcription
Bibliographic record
Abstract
Maintenance of genome stability is essential to prevent the accumulation of DNA mutations that can initiate oncogenesis and facilitate tumor progression. Studies of DNA repair genes have revealed a highly dynamic and redundant network of genes and proteins responsible for maintaining genome stability. Cancer cells are often deficient in DNA repair and the resulting genome instability decreases their fitness but also allows for more rapid evolution under selective pressure. Of particular interest for genome stability are the RecQ class of helicases. Five genes in this class, RECQL1, BLM, WRN, RECQL4 and RECQL5, are unique to mammals as simpler eukaryotes and bacteria appear to have only one homolog, RecQ. The precise role of each of the five mammalian RecQ helicases remains to be determined. Whereas loss of function mutations of BLM, WRN and RECQL4 in humans are associated with specific diseases, RECQL1 and RECQL5 have not yet been associated with specific disorders. Mice deficient in Recql5 are more likely to develop cancer and human cells deficient in RECQL5 display chromosomal instability and elevated sister chromatid exchange events similar to cells deficient in any of the other RecQ helicases. Recent studies support that RECQL5 can resolve intermediate DNA repair structures resulting from the collision of DNA transcription and replication machinery. In this review, we aim to summarize current knowledge regarding RECQL5 in the context of DNA repair, replication and transcription to help uncover the role of RECQL5 in maintenance of genome stability.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".