Splicing arrays reveal novel RBM10 targets, including SMN2 pre-mRNA
Bibliographic record
Abstract
BACKGROUND: RBM10 is an RNA binding protein involved in message stabilization and alternative splicing regulation. The objective of the research described herein was to identify novel targets of RBM10-regulated splicing. To accomplish this, we downregulated RBM10 in human cell lines, using small interfering RNAs, then monitored alternative splicing, using a reverse transcription-PCR screening platform. RESULTS: RBM10 knockdown (KD) provoked alterations in splicing events in 10-20% of the pre-mRNAs, most of which had not been previously identified as RBM10 targets. Hierarchical clustering of the genes affected by RBM10 KD revealed good conservation of alternative exon inclusion or exclusion across cell lines. Pathway annotation showed RAS signaling to be most affected by RBM10 KD. Of particular interest was the finding that splicing of SMN pre-mRNA, encoding the survival of motor neuron (SMN) protein, was influenced by RBM10 KD. Inhibition of RBM10 resulted in preferential expression of the full-length, exon 7 retaining, SMN transcript in four cancer cell lines and one normal skin fibroblast cell line. SMN protein is expressed from two genes, SMN1 and SMN2, but the SMN1 gene is homozygously disrupted in people with spinal muscular atrophy; as a consequence, all of the SMN that is expressed in people with this disease is from the SMN2 gene. Expression analyses using primary fibroblasts from control, carrier and spinal muscle atrophy donors demonstrated that RBM10 KD resulted in preferential expression of the full-length, exon 7 retaining, SMN2 transcript. At the protein level, upregulation of the full-length SMN2 was also observed. Re-expression of RBM10, in a stable RBM10 KD cancer cell line, correlated with a reversion of the KD effect, demonstrating specificity. CONCLUSION: Our work has not only expanded the number of pre-mRNA targets for RBM10, but identified RBM10 as a novel regulator of SMN2 alternative inclusion.
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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 | 0.001 |
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".