MyoD and E-protein heterodimers switch rhabdomyosarcoma cells from an arrested myoblast phase to a differentiated state
Bibliographic record
Abstract
Rhabdomyosarcomas are characterized by expression of myogenic specification genes, such as MyoD and/or Myf5, and some muscle structural genes in a population of cells that continues to replicate. Because MyoD is sufficient to induce terminal differentiation in a variety of cell types, we have sought to determine the molecular mechanisms that prevent MyoD activity in human embryonal rhabdomyosarcoma cells. In this study, we show that a combination of inhibitory Musculin:E-protein complexes and a novel splice form of E2A compete with MyoD for the generation of active full-length E-protein:MyoD heterodimers. A forced heterodimer between MyoD and the full-length E12 robustly restores differentiation in rhabdomyosarcoma cells and broadly suppresses multiple inhibitory pathways. Our studies indicate that rhabdomyosarcomas represent an arrested progress through a normal transitional state that is regulated by the relative abundance of heterodimers between MyoD and the full-length E2A proteins. The demonstration that multiple inhibitory mechanisms can be suppressed and myogenic differentiation can be induced in the RD rhabdomyosarcomas by increasing the abundance of MyoD:E-protein heterodimers suggests a central integrating function that can be targeted to force differentiation in muscle cancer cells.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".