Smad proteins function as co-modulators for MEF2 transcriptional regulatory proteins
Bibliographic record
Abstract
An emerging theme in transforming growth factor-ss (TGF-ss) signalling is the association of the Smad proteins with diverse groups of transcriptional regulatory proteins. Several Smad cofactors have been identified to date but the diversity of TGF-ss effects on gene transcription suggests that interactions with other co-regulators must occur. In these studies we addressed the possible interaction of Smad proteins with the myocyte enhancer-binding factor 2 (MEF2) transcriptional regulators. Our studies indicate that Smad2 and 4 (Smad2/4) complexes cooperate with MEF2 regulatory proteins in a GAL4-based one-hybrid reporter gene assay. We have also observed in vivo interactions between Smad2 and MEF2A using co-immunoprecipitation assays. This interaction is confirmed by glutathione S:-transferase pull-down analysis. Immunofluorescence studies in C2C12 myotubes show that Smad2 and MEF2A co-localise in the nucleus of multinuclear myotubes during differentiation. Interestingly, phospho-acceptor site mutations of MEF2 that render it unresponsive to p38 MAP kinase signalling abrogate the cooperativity with the Smads suggesting that p38 MAP Kinase-catalysed phosphorylation of MEF2 is a prerequisite for the Smad-MEF2 interaction. Thus, the association between Smad2 and MEF2A may subserve a physical link between TGF-ss signalling and a diverse array of genes controlled by the MEF2 cis element.
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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) | 0.001 | 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) | 0.004 | 0.002 |
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".