Cooperation between myogenic regulatory factors and SIX family transcription factors is important for myoblast differentiation
Bibliographic record
Abstract
Precise regulation of gene expression is crucial to myogenesis and is thought to require the cooperation of various transcription factors. On the basis of a bioinformatic analysis of gene regulatory sequences, we hypothesized that myogenic regulatory factors (MRFs), key regulators of skeletal myogenesis, cooperate with members of the SIX family of transcription factors, known to play important roles during embryonic skeletal myogenesis. To this day little is known regarding the exact molecular mechanism by which SIX factors regulate muscle development. We have conducted a functional genomic study of the role played by SIX1 and SIX4 during the differentiation of skeletal myoblasts, a model of adult muscle regeneration. We report that SIX factors cooperate with the members of the MRF family to activate gene expression during myogenic differentiation, and that their function is essential to this process. Our findings also support a model where SIX factors function not only 'upstream' of the MRFs during embryogenesis, but also 'in parallel' to them during myoblast differentiation. We have identified new essential nodes that depend on SIX factor function, in the myogenesis regulatory network, and have uncovered a novel way by which MRF function is modulated during differentiation.
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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 | 0.000 | 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) | 0.000 | 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".