FAM72A antagonizes UNG2 to promote mutagenic uracil repair during antibody maturation
Bibliographic record
Abstract
Activation-induced cytidine deaminase (AID) catalyzes the deamination of deoxycytidines within Immunoglobulin ( Ig ) genes to induce somatic hypermutation (SHM) and class switch recombination (CSR) 1,2 . AID-induced deoxyuracils within Ig loci are recognized and processed by subverted base excision and mismatch repair pathways that ensure a mutagenic outcome in B lymphocytes 3–8 . However, it is unclear why DNA repair pathways that remove deoxyuracil from DNA are not efficient at faithfully repairing AID-induced lesions. Here, we identified through a genome-wide CRISPR screen that FAM72A, a protein with no ascribed function, is a major determinant for the error-prone processing of deoxyuracil. Fam72a -deficient CH12F3-2 B cells and primary B cells from Fam72a −/− mice have reduced CSR and SHM frequencies. The SHM spectrum in B cells from Fam72a −/− mice is opposite to that observed in Ung2 −/− mice 9 , suggesting that UNG2 is hyperactive in FAM72A-deficient cells. Indeed, FAM72A binds to UNG2 resulting in reduced UNG2 activity, and significantly reduced protein levels in the G1 phase, coinciding with peak AID activity. This effect leads to increased genome-wide deoxyuracils in B cells. By antagonizing UNG2, FAM72A therefore increases U•G mispairs that engage mutagenic mismatch repair promoting error-prone processing of AID-induced deoxyuracils. This work shows that FAM72A bridges base-excision repair and mismatch repair to modulate antibody maturation.
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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 | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".