Genomic Uracil Homeostasis during Normal B Cell Maturation and Loss of This Balance during B Cell Cancer Development
Bibliographic record
Abstract
Activation-induced deaminase (AID) converts DNA cytosines to uracils in immunoglobulin genes, creating antibody diversification. It also causes mutations and translocations that promote cancer. We examined the interplay between uracil creation by AID and its removal by UNG2 glycosylase in splenocytes undergoing maturation and in B cell cancers. The genomic uracil levels remain unchanged in normal stimulated B cells, demonstrating a balance between uracil generation and removal. In stimulated UNG(-/-) cells, uracil levels increase by 11- to 60-fold during the first 3 days. In wild-type B cells, UNG2 gene expression and enzymatic activity rise and fall with AID levels, suggesting that UNG2 expression is coordinated with uracil creation by AID. Remarkably, a murine lymphoma cell line, several human B cell cancer lines, and human B cell tumors expressing AID at high levels have genomic uracils comparable to those seen with stimulated UNG(-/-)splenocytes. However, cancer cells express UNG2 gene at levels similar to or higher than those seen with peripheral B cells and have nuclear uracil excision activity comparable to that seen with stimulated wild-type B cells. We propose that more uracils are created during B cell cancer development than are removed from the genome but that the uracil creation/excision balance is restored during establishment of cell lines, fixing the genomic uracil load at high levels.
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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) | 0.000 | 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".