A neural alternative splicing program controls cellular function and growth in Pancreatic Neuroendocrine tumours
Bibliographic record
Abstract
Abstract Pancreatic neuroendocrine tumours (PanNETs) are a rare heterogeneous group of neoplasms that arise from pancreatic islet cells. The hormone secreting function of pancreatic neuroendocrine cells is altered in PanNETs, rendering these tumours functional or non— functional (secreting excessive or lower levels of hormones, respectively). Genome wide approaches have revealed the genomic landscape of PanNETs but have not shed light on this problematic hormone secretion. In the present work, we show that alternative splicing (AS) deregulation is responsible for changes in the secretory ability of PanNET cells. We reveal a group of alternative microexons that are regulated by the RNA binding protein SRRM3 and are preferentially included in mRNAs in PanNET cells, where SRRM3 is also upregulated. These microexons are part of a larger neural program regulated by SRRM3. We show that their inclusion gives rise to protein isoforms that change stimulus-induced secretory vesicles and their trafficking in PanNET cells. Moreover, the increased inclusion of these microexons results in an enhanced neuronal component in PanNET tumours. Using knock-down and splicing switching oligonucleotides in cellular and animal PanNET models, we show that decrease of the SRRM3 levels or even of the inclusion levels of the three most deregulated microexons can significantly alter the PanNET cell characteristics. Collectively, our study links secretory impairment and nerve dependency to alternative splicing deregulation in PanNETs, providing promising therapeutic targets for PanNET treatment.
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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".