AMP-Activated Protein Kinase-Regulated Activation of the PGC-1α Promoter in Skeletal Muscle Cells
Bibliographic record
Abstract
The mechanisms by which PGC-1alpha gene expression is controlled in skeletal muscle remains largely undefined. Thus, we sought to investigate the transcriptional regulation of PGC-1alpha using AICAR, an activator of AMPK, that is known to increase PGC-1alpha expression. A 2.2 kb fragment of the human PGC-1alpha promoter was cloned and sequence analysis revealed that this TATA-less sequence houses putative consensus sites including a GC-box, a CRE, several IRSs, a SRE, binding sites for GATA, MEF2, p 53, NF-kappaB, and EBox binding proteins. AMPK activation for 24 hours increased PGC-1alpha promoter activity with concomitant increases in mRNA expression. The effect of AICAR on transcriptional activation was mediated by an overlapping GATA/EBox binding site at -495 within the PGC-1alpha promoter based on gel shift analyses that revealed increases in GATA/EBox DNA binding. Mutation of the EBox within the GATA/EBox binding site in the promoter reduced basal promoter activity and completely abolished the AICAR effect. Supershift analyses identified USF-1 as a DNA binding transcription factor potentially involved in regulating PGC-1alpha promoter activity, which was confirmed in vivo by ChIP. Overexpression of either GATA-4 or USF-1 alone increased the p851 PGC-1alpha promoter activity by 1.7- and 2.0-fold respectively, while co-expression of GATA-4 and USF-1 led to an additive increase in PGC-1alpha promoter activity. The USF-1-mediated increase in PGC-1alpha promoter activation led to similar increases at the mRNA level. Our data identify a novel AMPK-mediated regulatory pathway that regulates PGC-1alpha gene expression. This could represent a potential therapeutic target to control PGC-1alpha expression in skeletal muscle.
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 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".