Activation of Liver mTORC1 Protects Against NASH via Dual Regulation of VLDL-TAG Secretion and De Novo Lipogenesis
Bibliographic record
Abstract
BACKGROUND & AIMS: Dysregulation of liver lipid metabolism is associated with the development and progression of nonalcoholic fatty liver disease, a spectrum of liver diseases including nonalcoholic steatohepatitis (NASH). In the liver, insulin controls lipid homeostasis by increasing triglyceride (TAG) synthesis, suppressing fatty acid oxidation, and enhancing TAG export via very low-density lipoproteins. Downstream of insulin signaling, the mechanistic target of rapamycin complex 1 (mTORC1), is a key regulator of lipid metabolism. Here, we define the role of hepatic mTORC1 activity in mouse models of NASH and investigate the mTORC1-dependent mechanisms responsible for protection against liver damage in NASH. METHODS: Utilizing 2 rodent NASH-promoting diets, we demonstrate that hepatic mTORC1 activity was reduced in mice with NASH, whereas under conditions of insulin resistance and benign fatty liver, mTORC1 activity was elevated. To test the beneficial effects of hepatic mTORC1 activation in mouse models of NASH, we employed an acute, liver-specific knockout model of TSC1 (L-TSC-KO), a negative regulator of mTORC1. RESULTS: L-TSC-KO mice are protected from and have improved markers of NASH including reduced steatosis, decreased circulating transaminases, and reduced expression of inflammation and fibrosis genes. Mechanistically, protection from hepatic inflammation and fibrosis by constitutive mTORC1 activity occurred via promotion of the phosphatidylcholine synthesizing enzyme, CCTα, and enhanced very low-density lipoprotein-triglyceride export. Additionally, activation of mTORC1 protected from hepatic steatosis via negative feedback of the mTORC2-AKT-FOXO-SREBP1c lipogenesis axis. CONCLUSIONS: Collectively, this study identifies a protective role for liver mTORC1 signaling in the initiation and progression of NASH in mice via dual control of lipid export and synthesis.
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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) | 0.001 | 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".