Regulation of Egr-1, SRF, and Sp1 mRNA expression in contracting skeletal muscle cells
Bibliographic record
Abstract
The early cellular signals associated with contractile activity initiate the activation and induction of transcription factors that regulate changes in skeletal muscle phenotype. The transcription factors Egr-1, Sp1, and serum response factor (SRF) are potentially important mediators of mitochondrial biogenesis based on the prevalence of binding sites for them in the promoter regions of genes encoding mitochondrial proteins, including PGC-1 alpha, the important regulator of mitochondrial biogenesis. Thus, to further define a role for transcription factors at the onset of contractile activity, we examined the time-dependent alterations in Egr-1, Sp1, and SRF mRNA and the levels in electrically stimulated mouse C(2)C(12) skeletal muscle cells. Early transient increases in Egr-1 mRNA levels within 30 min (P < 0.05) of contractile activity led to threefold increases (P < 0.05) in Egr-1 protein by 60 min. The increase in Egr-1 mRNA was not because of increased stability, as Egr-1 mRNA half-life after 30 min of stimulation showed only a 58% decline. Stimulation of muscle cells had no effect on Sp1 mRNA but led to progressive increases (P < 0.05) in SRF mRNA by 30 and 60 min. This was not matched by increases in SRF protein but occurred coincident with increases (P < 0.05) in SRF-serum response element DNA binding at 30 and 60 min as a result of SRF phosphorylation on serine-103. To assess the importance of the recovery period, 12 h of continuous contractile activity was compared with four successive 3-h bouts, with an intervening 21-h recovery period after each bout. Continuous contractile activity led to a twofold increase (P < 0.05) in Egr-1 mRNA, no change in SRF mRNA, and a 43% decrease in Sp1 mRNA expression. The recovery period prevented the decline in Sp1 mRNA, produced a decrease in Egr-1 mRNA, and had no effect on SRF mRNA. Thus continuous and intermittent contractile activity evoked different specific transcription factor expression patterns, which may ultimately contribute to divergent qualitative, or temporal patterns of, phenotypic adaptation in muscle.
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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".