Antagonistic roles of canonical and alternative RPA in tandem CAG repeat diseases
Bibliographic record
Abstract
ABSTRACT Tandem CAG repeat expansion mutations cause >15 neurodegenerative diseases, where ongoing expansions in patients’ brains are thought to drive disease onset and progression. Repeat length mutations will involve single-stranded DNAs prone to form mutagenic DNA structures. However, the involvement of single-stranded DNA binding proteins (SSBs) in the prevention or formation of repeat instability is poorly understood. Here, we assessed the role of two SSBs, canonical RPA (RPA1-RPA2-RPA3) and the related Alternative-RPA (Alt-RPA, RPA1-RPA4-RPA3), where the primate-specific RPA4 replaces RPA2. RPA is essential for all forms of DNA metabolism, while Alt-RPA has undefined functions. RPA and Alt-RPA are upregulated 2- and 10-fold, respectively, in brains of Huntington disease (HD) and spinocerebellar ataxia type 1 (SCA1) patients. Correct repair of slipped-CAG DNA structures, intermediates of expansion mutations, is enhanced by RPA, but blocked by Alt-RPA. Slipped-DNAs are bound and melted more efficiently by RPA than by Alt-RPA. Removal of excess slipped-DNAs by FAN1 nuclease is enhanced by RPA, but blocked by Alt-RPA. Protein-protein interactomes (BioID) reveal unique and shared partners of RPA and Alt-RPA, including proteins involved in CAG instability and known modifiers of HD and SCA1 disease. RPA overexpression inhibits rampant CAG expansions in SCA1 mouse brains, coinciding with improved neuron morphology and rescued motor phenotypes. Thus, SSBs are involved in repeat length mutations, where Alt-RPA antagonistically blocks RPA from suppressing CAG expansions and hence pathogenesis. The processing of repeat length mutations is one example by which an Alt-RPA↔RPA antagonistic interaction can affect outcomes, illuminating questions as to which of the many processes mediated by canonical RPA may also be modulated by Alt-RPA.
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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".