hnRNP A1 and hnRNP H can collaborate to modulate 5′ splice site selection
Bibliographic record
Abstract
The mammalian proteins hnRNP A1 and hnRNP H control many splicing decisions in viral and cellular primary transcripts. To explain some of these activities, we have proposed that self-interactions between bound proteins create an RNA loop that represses internal splice sites while simultaneously activating the external sites that are brought in closer proximity. Here we show that a variety of hnRNP H binding sites can affect 5' splice site selection. The addition of two sets of hnRNP H sites in a model pre-mRNA modulates 5' splice site selection cooperatively, consistent with the looping model. Notably, binding sites for hnRNP A1 and H on the same pre-mRNA can similarly collaborate to modulate 5' splice site selection. The C-terminal portion of hnRNP H that contains the glycine-rich domains (GRD) is essential for splicing activity, and it can be functionally replaced by the GRD of hnRNP A1. Finally, we used the bioluminescence resonance energy transfer (BRET) technology to document the existence of homotypic and heterotypic interactions between hnRNP H and hnRNP A1 in live cells. Overall, our study suggests that interactions between different hnRNP proteins bound to distinct locations on a pre-mRNA can change its conformation to affect splicing decisions.
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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".