Expression and comparative genomics of two serum response factor genes in zebrafish
Bibliographic record
Abstract
Serum response factor (SRF) is a single copy, highly conserved transcription factor that governs the expression of hundreds of genes involved with actin cytoskeletal organization, cellular growth and signaling, neuronal circuitry and muscle differentiation. Zebrafish have emerged as a facile and inexpensive vertebrate model to delineate gene expression, regulation, and function, and yet the study of SRF in this animal has been virtually unexplored. Here, we report the existence of two srf genes in zebrafish, with partially overlapping patterns of expression in 3 and 7 day old developing animals. The mammalian ortholog (srf1) encodes for a 520 amino acid protein expressed in adult vascular and visceral smooth muscle cells, cardiac and skeletal muscle, as well as neuronal cells. The second zebrafish srf gene (srf2), encoding for a presumptive protein of only 314 amino acids, is transcribed at lower levels and appears to be less widely expressed across adult tissues. Both srf genes are induced by the SRF coactivator myocardin and attenuated with a short hairpin RNA to mammalian SRF. Promoter studies with srf1 reveal conserved CArG boxes that are the targets of SRF-myocardin in embryonic zebrafish cells. These results reveal that SRF was duplicated in the zebrafish genome and that its protein expression in all three muscle cell types is highly conserved across vertebrate animals suggesting an ancient code for transcriptional regulation of genes unique to muscle cell lineages.
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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 | 0.000 |
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 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".