The expression of <i>Myf5</i> in the developing mouse embryo is controlled by discrete and dispersed enhancers specific for particular populations of skeletal muscle precursors
Bibliographic record
Abstract
The development of skeletal muscle in vertebrate embryos is controlled by a transcriptional cascade that includes the four myogenic regulatory factors Myf5, Myogenin, MRF4 and MyoD. In the mouse embryo, Myf5 is the first of these factors to be expressed and mutational analyses suggest that this protein acts early in the process of commitment to the skeletal muscle fate. We have therefore analysed the regulation of Myf5 gene expression using transgenic technology and find that its control is markedly different from that of the other two myogenic regulatory factor genes previously analysed, Myogenin and MyoD. We show that Myf5 is regulated through a number of distinct and discrete enhancers, dispersed throughout 14 kb spanning the MRF4/Myf5 locus, each of which drives reporter gene expression in a particular subset of skeletal muscle precursors. This region includes four separate enhancers controlling expression in the epaxial muscle precursors of the body, some hypaxial precursors of the body, some facial muscles and the central nervous system. These elements separately or together are unable to drive expression in the cells that migrate to the limb buds and in some other muscle subsets and to correctly maintain expression at late times. We suggest that this complex mechanism of control has evolved because different inductive signals operate in each population of muscle precursors and thus distinct enhancers, and cognate transcription factors, are required to interpret them.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".