An RRM–ZnF RNA recognition module targets RBM10 to exonic sequences to promote exon exclusion
Bibliographic record
Abstract
RBM10 is an RNA-binding protein that plays an essential role in development and is frequently mutated in the context of human disease. RBM10 recognizes a diverse set of RNA motifs in introns and exons and regulates alternative splicing. However, the molecular mechanisms underlying this seemingly relaxed sequence specificity are not understood and functional studies have focused on 3΄ intronic sites only. Here, we dissect the RNA code recognized by RBM10 and relate it to the splicing regulatory function of this protein. We show that a two-domain RRM1-ZnF unit recognizes a GGA-centered motif enriched in RBM10 exonic sites with high affinity and specificity and test that the interaction with these exonic sequences promotes exon skipping. Importantly, a second RRM domain (RRM2) of RBM10 recognizes a C-rich sequence, which explains its known interaction with the intronic 3΄ site of NUMB exon 9 contributing to regulation of the Notch pathway in cancer. Together, these findings explain RBM10's broad RNA specificity and suggest that RBM10 functions as a splicing regulator using two RNA-binding units with different specificities to promote exon skipping.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".