Intronic Binding Sites for hnRNP A/B and hnRNP F/H Proteins Stimulate Pre-mRNA Splicing
Bibliographic record
Abstract
hnRNP A/B proteins modulate the alternative splicing of several mammalian and viral pre-mRNAs, and are typically viewed as proteins that enforce the activity of splicing silencers. Here we show that intronic hnRNP A/B-binding sites (ABS) can stimulate the in vitro splicing of pre-mRNAs containing artificially enlarged introns. Stimulation of in vitro splicing could also be obtained by providing intronic ABS in trans through the use of antisense oligonucleotides containing a non-hybridizing ABS-carrying tail. ABS-tailed oligonucleotides also improved the in vivo inclusion of an alternative exon flanked by an enlarged intron. Notably, binding sites for hnRNP F/H proteins (FBS) replicate the activity of ABS by improving the splicing of an enlarged intron and by modulating 5' splice-site selection. One hypothesis formulated to explain these effects is that bound hnRNP proteins self-interact to bring in closer proximity the external pair of splice sites. Consistent with this model, positioning FBS or ABS at both ends of an intron was required to stimulate splicing of some pre-mRNAs. In addition, a computational analysis of the configuration of putative FBS and ABS located at the ends of introns supports the view that these motifs have evolved to support cooperative interactions. Our results document a positive role for the hnRNP A/B and hnRNP F/H proteins in generic splicing, and suggest that these proteins may modulate the conformation of mammalian pre-mRNAs.
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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) | 0.002 | 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".