Multiple and Specific mRNA Processing Targets for the Major Human hnRNP Proteins
Bibliographic record
Abstract
Alternative splicing is a key mechanism regulating gene expression, and it is often used to produce antagonistic activities particularly in apoptotic genes. Heterogeneous nuclear ribonucleoparticle (hnRNP) proteins form a family of RNA-binding proteins that coat nascent pre-mRNAs. Many but not all major hnRNP proteins have been shown to participate in splicing control. The range and specificity of hnRNP protein action remain poorly documented, even for those affecting splice site selection. We used RNA interference and a reverse transcription-PCR screening platform to examine the implications of 14 of the major hnRNP proteins in the splicing of 56 alternative splicing events in apoptotic genes. Out of this total of 784 alternative splicing reactions tested in three human cell lines, 31 responded similarly to a knockdown in at least two different cell lines. On the other hand, the impact of other hnRNP knockdowns was cell line specific. The broadest effects were obtained with hnRNP K and C, two proteins whose role in alternative splicing had not previously been firmly established. Different hnRNP proteins affected distinct sets of targets with little overlap even between closely related hnRNP proteins. Overall, our study highlights the potential contribution of all of these major hnRNP proteins in alternative splicing control and shows that the targets for individual hnRNP proteins can vary in different cellular contexts.
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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".