Determining the functional role of Sept9 phosphorylated isoforms in Sonic Hedgehog Medulloblastoma
Bibliographic record
Abstract
Proteins are the molecular machines of our cells. They can be controlled by adding tag groups called phosphates. In healthy cells, this phosphorylating system keeps cellular processes running smoothly. In cancer, however, this system is broken, where proteins are mistagged, leading to cancer cells growing uncontrollably. Previous our lab found that in medulloblastoma, the most common malignant pediatric brain cancer, there were more harmful phosphorylated versions of some proteins compared to normal precursor cells. One of these proteins was Septin-9 (SEPT9). The role SEPT9 plays in the cancer cells remains unclear. To address this, we are assessing the role that the phosphorylated SEPT9 plays in Sonic Hedgehog medulloblastoma (SHH MB). To investigate the difference of harmful phosphorylated protein versions in medulloblastoma (MB) cells compared to normal granule neuron precursors (GNP), we used a mouse model of medulloblastoma. MB cells which either lack harmful versions of these proteins or overexpress them are being developed. We have shown that there are greater amounts of harmful versions of Sept9 in SHH MB cells compared to GNPs. To assess the role of these harmful versions, we are creating cell models that either reduce the amount of mistagged proteins or have increased amounts. These models are called knockdown (KD) and overexpression (OE) models respectively and will be used to determine the functional role that Sept9 phosphorylated isoforms play in SHH MB. This work may unveil a way cancer cells grow by altering which versions of proteins are present. Ongoing work will help us understand exactly how these proteins support cancer cells and may point towards new therapies targeting rogue versions of proteins in cancers.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".