Separation of the gluconeogenic and mitochondrial functions of PGC-1α through S6 kinase
Bibliographic record
Abstract
PGC-1α is a transcriptional coactivator that powerfully regulates many pathways linked to energy homeostasis. Specifically, PGC-1α controls mitochondrial biogenesis in most tissues but also initiates important tissue-specific functions, including fiber type switching in skeletal muscle and gluconeogenesis and fatty acid oxidation in the liver. We show here that S6 kinase, activated in the liver upon feeding, can phosphorylate PGC-1α directly on two sites within its arginine/serine-rich (RS) domain. This phosphorylation significantly attenuates the ability of PGC-1α to turn on genes of gluconeogenesis in cultured hepatocytes and in vivo, while leaving the functions of PGC-1α as an activator of mitochondrial and fatty acid oxidation genes completely intact. These phosphorylations interfere with the ability of PGC-1α to bind to HNF4α, a transcription factor required for gluconeogenesis, while leaving undisturbed the interactions of PGC-1α with ERRα and PPARα, factors important for mitochondrial biogenesis and fatty acid oxidation. These data illustrate that S6 kinase can modify PGC-1α and thus allow molecular dissection of its functions, providing metabolic flexibility needed for dietary adaptation.
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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".