Rad51 catalytic mutants differentially affect the Rad51 nucleoprotein filament <i>in vivo</i>
Bibliographic record
Abstract
ABSTRACT In this study, we utilized mouse hybridoma cell lines stably expressing ectopic wild-type Rad51, or the Rad51-K133A and Rad51-K133R catalytic mutants deficient in ATP binding and ATP hydrolysis, respectively, to investigate effects on the Rad51 nucleoprotein filament in vivo . Immunoprecipitation studies reveal interactions between ectopic wild-type Rad51, Rad51-K133A and Rad51-K133R and endogenous Rad51, Brca2 and p53 proteins. Importantly, the expression of Rad51-K133A and Rad51-K133R catalytic mutants (but not wild-type Rad51) targets endogenous Rad51, Brca2 and p53 proteins for proteasome-mediated degradation. Expression of Rad51-K133R significantly reduces nascent DNA synthesis (3’ polymerization) during homologous recombination (HR), but the effects of Rad51-K133A on 3’ polymerization are considerably more severe. Provision of additional wild-type Rad51 in cell lines expressing Rad51-K133A or Rad51-K133R does not restore diminished levels of endogenous Brca2, Rad51 or p53, nor restore the deficiency in 3’ polymerization. Cells expressing Rad51-K133A are also significantly reduced in their capacity to drive strand exchange through regions of heterology. Our results reveal an interesting mechanistic dichotomy in the way mutant Rad51-K133A and Rad51-K133R proteins influence 3’ polymerization and provide novel insight into the mechanism of their dominant-negative phenotypes.
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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) | 0.001 | 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".