Defective Repair of Uracil Causes Telomere Defects in Mouse Hematopoietic Cells
Bibliographic record
Abstract
Uracil in the genome can result from misincorporation of dUTP instead of dTTP during DNA synthesis, and is primarily removed by uracil DNA glycosylase (UNG) during base excision repair. Telomeres contain long arrays of TTAGGG repeats and may be susceptible to uracil misincorporation. Using model telomeric DNA substrates, we showed that the position and number of uracil substitutions of thymine in telomeric DNA decreased recognition by the telomere single-strand binding protein, POT1. In primary mouse hematopoietic cells, uracil was detectable at telomeres, and UNG deficiency further increased uracil loads and led to abnormal telomere lengthening. In UNG-deficient cells, the frequencies of sister chromatid exchange and fragility in telomeres also significantly increased in the absence of telomerase. Thus, accumulation of uracil and/or UNG deficiency interferes with telomere maintenance, thereby underscoring the necessity of UNG-initiated base excision repair for the preservation of telomere integrity. Background Telomeres may be susceptible to uracil misincorporation, which is removed by uracil DNA glycosylase (UNG). Results UNG deficiency increases uracil in telomeres and causes alterations in multiple aspects of telomere maintenance in proliferating mouse hematopoietic cells. Conclusion Accumulation of uracil interferes with telomere maintenance. Significance UNG-initiated base excision repair is necessary for the preservation of telomere integrity.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".