C.5 The generation of alternative transcripts as a method of regulating phosphorylation in Sonic Hedgehog (Shh) Subtype Medulloblastoma (MB)
Bibliographic record
Abstract
Background: Protein phosphorylation is critical in development and tumor progression, but kinase promiscuity limits selective regulation. Alternative transcription start sites (ATSS) and alternative splicing (AS) may generate isoforms with or without the phosphorylation sites, offering a mechanism of precise control. Sonic Hedgehog (Shh) medulloblastoma (MB), the most common pediatric cerebellar tumor, arises from granule neuron precursors (GNPs) via aberrant Shh signaling. Highly proliferative postnatal day 7 (P7) GNPs and MB share transcriptomic and molecular characteristics, acting as a model for further investigation. Here, we examined the regulation of phosphorylation sites in the mRNA level in P7 GNP and MB. Methods: Integrated phosphoproteomics, proteomics, and RNA-Seq datasets were analyzed to identify candidates producing alternative transcripts with phosphorylation changes in murine P7 GNPs and Ptch1 +/- MB. Differential isoform expression was validated via RT-qPCR and RNA-FISH. Results: Rnf220 and Septin9 were identified as candidates that utilize ATSS to produce differential isoforms with or without the phosphorylation sites. RT-qPCR and RNA-FISH showed significant upregulation of their long isoforms in Shh MB compared to GNPs. Conclusions: Alternative transcript generation may act as a novel mechanism for regulating phosphorylation in Shh MB. Differential expression of Rnf220 and Septin9 phosphorylated isoforms suggests their involvement in MB development, warranting further functional investigations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".