MyoD Synergizes with the E-Protein HEBβ To Induce Myogenic Differentiation
Bibliographic record
Abstract
The MyoD family of basic helix-loop-helix transcription factors function as heterodimers with members of the E-protein family to induce myogenic gene activation. The E-protein HEB is alternatively spliced to generate alpha and beta isoforms. While the function of these molecules has been studied in other cell types, questions persist regarding the molecular functions of HEB proteins in skeletal muscle. Our data demonstrate that HEB alpha expression remains unchanged in both myoblasts and myotubes, whereas HEB beta is upregulated during the early phases of terminal differentiation. Upon induction of differentiation, a MyoD-HEB beta complex bound the E1 E-box of the myogenin promoter leading to transcriptional activation. Importantly, forced expression of HEB beta with MyoD synergistically lead to precocious myogenin expression in proliferating myoblasts. However, after differentiation, HEB alpha and HEB beta synergized with myogenin, but not MyoD, to activate the myogenin promoter. Specific knockdown of HEB beta by small interfering RNA in myoblasts blocked differentiation and inhibited induction of myogenin transcription. Therefore, HEB alpha and HEB beta play novel and central roles in orchestrating the regulation of myogenic factor activity through myogenic differentiation.
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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".