Expression of utrophin A mRNA correlates with the oxidative capacity of skeletal muscle fiber types and is regulated by calcineurin/NFAT signaling
Bibliographic record
Abstract
Utrophin levels have recently been shown to be more abundant in slow vs. fast muscles, but the nature of the molecular events underlying this difference remains to be fully elucidated. Here, we determined whether this difference is due to the expression of utrophin A or B, and examined whether transcriptional regulatory mechanisms are also involved. Immunofluorescence experiments revealed that slower fibers contain significantly more utrophin A in extrasynaptic regions as compared with fast fibers. Single-fiber RT-PCR analysis demonstrated that expression of utrophin A transcripts correlates with the oxidative capacity of muscle fibers, with cells expressing myosin heavy chain I and IIa demonstrating the highest levels. Functional muscle overload, which stimulates expression of a slower, more oxidative phenotype, induced a significant increase in utrophin A mRNA levels. Because calcineurin has been implicated in controlling this slower, high oxidative myofiber program, we examined expression of utrophin A transcripts in muscles having altered calcineurin activity. Calcineurin inhibition resulted in an 80% decrease in utrophin A mRNA levels. Conversely, muscles from transgenic mice expressing an active form of calcineurin displayed higher levels of utrophin A transcripts. Electrophoretic mobility shift and supershift assays revealed the presence of a nuclear factor of activated T cells (NFAT) binding site in the utrophin A promoter. Transfection and direct gene transfer studies showed that active forms of calcineurin or nuclear NFATc1 transactivate the utrophin A promoter. Together, these results indicate that expression of utrophin A is related to the oxidative capacity of muscle fibers, and implicate calcineurin and its effector NFAT in this mechanism.
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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".