The muscle‐specific transcription cofactor Vgll2 promotes myogenic differentiation through a casein kinase II dependent mechanism
Bibliographic record
Abstract
In D. melanogaster , the transcription factor of Scalloped (Sd) and its cofactor, Vestigial (Vg), control the development of indirect flight muscles and wings. Vestigial‐like 2 (Vgll2), a vertebrates homolog of Vg, is specifically expressed in skeletal muscle. Over‐expression of Vgll2 enhances myotube formation whereas Vgll2 knockdown blocks myogenic differentiation, demonstrating the important role of Vgll2 in skeletal muscle differentiation. However, the mechanisms that regulate Vgll2 interaction with TEA domain (TEAD) transcription factors (homologs of Sd) and MEF2 remain poorly characterized. An evolutionarily conserved casein kinase II (CKII) phosphorylation site (Serine 96) in all vestigial‐like (Vgll) proteins. Bacterially‐expressed purified GST‐Vgll2 protein was selectively phosphorylated by CKII in vitro. Two mutant constructs generated by site‐directed mutagenesis, S96A and S96E, were tested in mammalian‐two hybrid assays. The alanine mutant completely abolished the Vgll2‐TEAD1 interaction whereas the glutamate mutant enhanced this interaction. Immunocytochemistry showed that the alanine mutant was inappropriately localized in the periphery of the nucleus while the glutamate mutant was associated with euchromatin. These results imply that phosphorylation of the serine 96 residue is necessary for the Vgll2‐based 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) | 0.001 | 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".