RBT1, a novel transcriptional co-activator, binds the second subunit of Replication Protein A
Bibliographic record
Abstract
Replication Protein A (RPA) is required for DNA recombination, repair and replication in all eukaryotes. RPA participation in these pathways is mediated by single-stranded DNA binding and protein interactions. We herein identify a novel protein, Replication Protein Binding Trans-Activator (RBT1), in a yeast two-hybrid assay employing the second subunit of human RPA (RPA32) as bait. RBT1-RPA32 binding was confirmed by glutathione S:-transferase pull-down and co-immunoprecipitation. Fluorescence microscopy indicates that green fluorescence protein-tagged RBT1 is localized to the nucleus in vivo. RBT1 mRNA expression, determined by semi-quantitative RT-PCR, is significantly higher in cancer cell lines MCF-7, ZR-75, SaOS-2 and H661, compared to the cell lines normal non-immortalized human mammary epithelial cells and normal non-immortalized human bronchial epithelial cells. Further, yeast and mammalian one-hybrid analysis shows that RBT1 is a strong transcriptional co-activator. Interestingly, mammalian transactivation data is indicative of significant variance between cell lines; the GAL4-RBT1 fusion protein has significantly higher transcriptional activity in human cancer cells compared to human normal primary non-immortalized epithelial cells. We propose that RBT1 is a novel transcriptional co-activator that interacts with RPA, and has significantly higher activity in transformed cells.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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".