Effective mismatch repair depends on timely control of PCNA retention on DNA by the Elg1 complex
Bibliographic record
Abstract
Proliferating cell nuclear antigen (PCNA) is a sliding clamp that acts as a central co-ordinator for mismatch repair (MMR) as well as DNA replication. Loss of Elg1, the major subunit of the PCNA unloader complex, causes over-accumulation of PCNA on DNA and also increases mutation rate, but it has been unclear if the two effects are linked. Here we show that timely removal of PCNA from DNA by the Elg1 complex is important to prevent mutations. Although premature unloading of PCNA generally increases mutation rate, the mutator phenotype of elg1Δ is attenuated by PCNA mutants PCNA-R14E and PCNA-D150E that spontaneously fall off DNA. In contrast, the elg1Δ mutator phenotype is exacerbated by PCNA mutants that accumulate on DNA due to enhanced electrostatic PCNA-DNA interactions. Epistasis analysis suggests that PCNA over-accumulation on DNA interferes with both MMR and MMR-independent process(es). In elg1Δ, over-retained PCNA hyper-recruits the Msh2-Msh6 mismatch recognition complex through its PCNA-interacting peptide motif, causing accumulation of MMR intermediates. Our results suggest that PCNA retention controlled by the Elg1 complex is critical for efficient MMR: PCNA needs to be on DNA long enough to enable MMR, but if it is retained too long it interferes with downstream repair steps.
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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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".