Pbx homeodomain proteins direct Myod activity to promote fast-muscle differentiation
Bibliographic record
Abstract
The basic helix-loop-helix (bHLH) transcription factor Myod directly regulates gene expression throughout the program of skeletal muscle differentiation. It is not known how a Myod-driven myogenic program is modulated to achieve muscle fiber-type-specific gene expression. Pbx homeodomain proteins mark promoters of a subset of Myod target genes, including myogenin (Myog); thus, Pbx proteins might modulate the program of myogenesis driven by Myod. By inhibiting Pbx function in zebrafish embryos, we show that Pbx proteins are required in order for Myod to induce the expression of a subset of muscle genes in the somites. In the absence of Pbx function, expression of myog and of fast-muscle genes is inhibited, whereas slow-muscle gene expression appears normal. By knocking down Pbx or Myod function in combination with another bHLH myogenic factor, Myf5, we show that Pbx is required for Myod to regulate fast-muscle, but not slow-muscle, development. Furthermore, we show that Sonic hedgehog requires Myod in order to induce both fast- and slow-muscle markers but requires Pbx only to induce fast-muscle markers. Our results reveal that Pbx proteins modulate Myod activity to drive fast-muscle gene expression, thus showing that homeodomain proteins can direct bHLH proteins to establish a specific cell-type identity.
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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 | 0.001 |
| 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".