Coregulation of fast contractile protein transgene and glycolytic enzyme expression in mouse skeletal muscle
Bibliographic record
Abstract
Little is known of the gene regulatory mechanisms that coordinate the contractile and metabolic specializations of skeletal muscle fibers. Here we report a novel connection between fast isoform contractile protein transgene and glycolytic enzyme expression. In quantitative histochemical studies of transgenic mouse muscle fibers, we found extensive coregulation of the glycolytic enzyme glycerol-3-phosphate dehydrogenase (GPDH) and transgene constructs based on the fast skeletal muscle troponin I (TnIfast) gene. In addition to a common IIB > IIX > IIA fiber type pattern, TnIfast transgenes and GPDH showed correlated fiber-to-fiber variation within each fast fiber type, concerted emergence of high-level expression during early postnatal muscle maturation, and parallel responses to muscle under- or overloading. Regulatory information for GPDH-coregulated expression is carried by the TnIfast first-intron enhancer (IRE). These results identify an unexpected contractile/metabolic gene regulatory link that is amenable to further molecular characterization. They also raise the possibility that the equal expression in all fast fiber types observed for the endogenous TnIfast gene may be driven by different metabolically coordinated mechanisms in glycolytic (IIB) vs. oxidative (IIA) fast fibers.
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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 | 0.001 | 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 | 0.001 |
| 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".