Chronic AMPK activation via loss of FLCN induces functional beige adipose tissue through PGC-1α/ERRα
Bibliographic record
Abstract
The tumor suppressor folliculin (FLCN) forms a repressor complex with AMP-activated protein kinase (AMPK). Given that AMPK is a master regulator of cellular energy homeostasis, we generated an adipose-specific Flcn (Adipoq-FLCN) knockout mouse model to investigate the role of FLCN in energy metabolism. We show that loss of FLCN results in a complete metabolic reprogramming of adipose tissues, resulting in enhanced oxidative metabolism. Adipoq-FLCN knockout mice exhibit increased energy expenditure and are protected from high-fat diet (HFD)-induced obesity. Importantly, FLCN ablation leads to chronic hyperactivation of AMPK, which in turns induces and activates two key transcriptional regulators of cellular metabolism, proliferator-activated receptor γ (PPARγ) coactivator-1α (PGC-1α) and estrogen-related receptor α (ERRα). Together, the AMPK/PGC-1α/ERRα molecular axis positively modulates the expression of metabolic genes to promote mitochondrial biogenesis and activity. In addition, mitochondrial uncoupling proteins as well as other markers of brown fat are up-regulated in both white and brown FLCN-null adipose tissues, underlying the increased resistance of Adipoq-FLCN knockout mice to cold exposure. These findings identify a key role of FLCN as a negative regulator of mitochondrial function and identify a novel molecular pathway involved in the browning of white adipocytes and the activity of brown fat.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".