The Small Nuclear Ribonucleoprotein Polypeptide A (SNRPA) binds to the G-quadruplex of the BAG-1 5′UTR
Bibliographic record
Abstract
The protein "BCL-2-associated athanogene-1" (BAG-1), which exists in multiple isoforms, promotes cancer cell survival and is overexpressed in many different cancers. As a result, BAG-1-targeted therapy appears to be a promising strategy with which to treat cancer. It has previously been shown that the 5'UTR of the BAG-1 mRNA contains a guanine rich region that folds into a G-quadruplex structure which can modulate both its cap-dependent and its cap-independent translation. Accumulating data regarding G-quadruplex binding proteins suggest that these proteins can play a central role in gene expression. Consequently, the identification of the proteins that could potentially bind to the G-quadruplex of the BAG-1 mRNA was undertaken. Label-free RNA pulldown assays were performed using protein extracts from colorectal cancer cells and this leads to the detection of RNA G4 binding proteins by LC-MS/MS. The use of G-quadruplex containing RNA, as well as of a mutated version, ensured that the proteins identified were specific for the RNA G-quadruplex structure and not just general RNA binding proteins. Following confirmation of the interaction, the Small Nuclear Ribonucleoprotein Polypeptide A (SNRPA) was shown to bind directly to the BAG-1 mRNA through the G-quadruplex, and knock down experiments in colorectal cancer cells suggested that it can modulate the expression level of BAG-1.
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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 | 0.001 |
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".