FAM72A degrades UNG2 through the GID/CTLH complex to promote mutagenic repair during antibody maturation
Bibliographic record
Abstract
A diverse antibody repertoire is essential for humoral immunity. Antibody diversification requires the introduction of deoxyuridine (dU) mutations within immunoglobulin genes to initiate somatic hypermutation (SHM) and class switch recombination (CSR). dUs are normally recognized and excised by the base excision repair (BER) protein uracil-DNA glycosylase 2 (UNG2). However, FAM72A downregulates UNG2 permitting dUs to persist and trigger SHM and CSR. How FAM72A promotes UNG2 degradation is unknown. Here, we show that FAM72A recruits a C-terminal to LisH (CTLH) E3 ligase complex to target UNG2 for proteasomal degradation. Deficiency in CTLH complex components result in elevated UNG2 and reduced SHM and CSR. Cryo-EM structural analysis reveals FAM72A directly binds to MKLN1 within the CTLH complex to recruit and ubiquitinate UNG2. Our study further suggests that FAM72A hijacks the CTLH complex to promote mutagenesis in cancer. These findings show that FAM72A is an E3 ligase substrate adaptor critical for humoral immunity and cancer development. Antibody diversification relies on the intentional mutagenesis of immunoglobulin genes for adaptive immune responses. Here, the authors identified a CTLH E3 ubiquitin ligase complex that co-opts FAM72A to recruit and degrade the UNG2 base excision repair factor to permit mutagenesis.
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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 | 0.001 | 0.000 |
| 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".