Loss of hepatic Flcn protects against fibrosis and inflammation by activating autophagy pathways
Bibliographic record
Abstract
Non-alcoholic fatty liver disease (NAFLD) is the most frequent liver disease worldwide and can progress to non-alcoholic steatohepatitis (NASH), which is characterized by triglyceride accumulation, inflammation, and fibrosis. No pharmacological agents are currently approved to treat these conditions, but it is clear now that modulation of lipid synthesis and autophagy are key biological mechanisms that could help reduce or prevent these liver diseases. The folliculin (FLCN) protein has been recently identified as a central regulatory node governing whole body energy homeostasis, and we hypothesized that FLCN regulates highly metabolic tissues like the liver. We thus generated a liver specific Flcn knockout mouse model to study its role in liver disease progression. Using the methionine- and choline-deficient diet to mimic liver fibrosis, we demonstrate that loss of Flcn reduced triglyceride accumulation, fibrosis, and inflammation in mice. In this aggressive liver disease setting, loss of Flcn led to activation of transcription factors TFEB and TFE3 to promote autophagy, promoting the degradation of intracellular lipid stores, ultimately resulting in reduced hepatocellular damage and inflammation. Hence, the activity of FLCN could be a promising target for small molecule drugs to treat liver fibrosis by specifically activating autophagy. Collectively, these results show an unexpected role for Flcn in fatty liver disease progression and highlight new potential treatment strategies.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".