Differential regulation of the fiber type-specific gene expression of the sarcoplasmic reticulum calcium-ATPase isoforms induced by exercise training
Bibliographic record
Abstract
The regulatory role of adenosine monophosphate-activated protein kinase (AMPK)-α2 on sarcoplasmic reticulum calcium-ATPase (SERCA) 1a and SERCA2a in different skeletal muscle fiber types has yet to be elucidated. Sedentary (Sed) or exercise-trained (Ex) wild-type (WT) and AMPKα2-kinase dead (KD) transgenic mice, which overexpress a mutated and inactivated AMPKα2 subunit, were utilized to characterize how genotype or exercise training influenced the regulation of SERCA isoforms in gastrocnemius. As expected, both Sed and Ex KD mice had >40% lower AMPK phosphorylation and 30% lower SERCA1a protein than WT mice (P < 0.05). In contrast, SERCA2a protein was not different among KD and WT mice. Exercise increased SERCA1a and SERCA2a protein content among WT and KD mice, compared with their Sed counterparts. Maximal SERCA activity was lower in KD mice, compared with WT. Total phospholamban protein was higher in KD mice than in WT and lower in Ex compared with Sed mice. Exercise training increased phospholamban Ser(16) phosphorylation in WT mice. Laser capture microdissection and quantitative PCR indicated that SERCA1a mRNA expression among type I fibers was not altered by genotype or exercise, but SERCA2a mRNA was increased 30-fold in WT+Ex, compared with WT+Sed. In contrast, the exercise-stimulated increase for SERCA2a mRNA was blunted in KD mice. Exercise upregulated SERCA1a and SERCA2a mRNA among type II fibers, but was not altered by genotype. Collectively, these data suggest that exercise differentially influences SERCA isoform expression in type I and type II fibers. Additionally, AMPKα2 influences the regulation of SERCA2a mRNA in type I skeletal muscle fibers following exercise training.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".