KAT2-mediated acetylation switches the mode of PALB2 chromatin association to safeguard genome integrity
Bibliographic record
Abstract
The tumour suppressor PALB2 stimulates RAD51-mediated homologous recombination (HR) repair of DNA damage, whilst its steady-state association with active genes protects these loci from replication stress. Here, we report that the lysine acetyltransferases 2A and 2B (KAT2A/2B, also called GCN5/PCAF), two well-known transcriptional regulators, acetylate a cluster of seven lysine residues (7K-patch) within the PALB2 chromatin association motif (ChAM) and, in this way, regulate context-dependent PALB2 binding to chromatin. In unperturbed cells, the 7K-patch is targeted for KAT2A/2B-mediated acetylation, which in turn enhances the direct association of PALB2 with nucleosomes. Importantly, DNA damage triggers a rapid deacetylation of ChAM and increases the overall mobility of PALB2. Distinct missense mutations of the 7K-patch render the mode of PALB2 chromatin binding, making it either unstably chromatin-bound (7Q) or randomly bound with a reduced capacity for mobilisation (7R). Significantly, both of these mutations confer a deficiency in RAD51 foci formation and increase DNA damage in S phase, leading to the reduction of overall cell survival. Thus, our study reveals that acetylation of the ChAM 7K-patch acts as a molecular switch to enable dynamic PALB2 shuttling for HR repair while protecting active genes during DNA replication.
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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) | 0.002 | 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".