Hepatitis C virus nonstructural protein 5A perturbs lipid metabolism by modulating AMPK/SREBP-1c signaling
Bibliographic record
Abstract
BACKGROUND: Steatosis is an important clinical manifestation associated with chronic hepatitis C virus (HCV) infection. AMP-activated protein kinase (AMPK), a major mediator of lipid metabolism, regulates HCV-associated hepatic steatosis, but the underlying mechanisms remain obscure. Here we investigated the mechanism of HCV nonstructural protein 5A (NS5A)-induced lipid accumulation by the AMPK/SREBP-1c pathway. METHODS: We generated model mice by injecting recombinant lentiviral particles expressing the NS5A protein (genotype 3a) via the tail vein. The serum levels of alanine aminotransferase (ALT), free fatty acids (FFAs) and triglycerides (TG) were examined. H&E and Oil Red O staining were used to examine lipid droplets. Immunohistochemistry staining, quantitative real-time PCR and Western blotting were used to determine the expression of lipogenic genes. RESULTS: Our results showed that the serum levels of ALT, FFAs and TG, as well as the accumulation of hepatic lipid droplets, were increased significantly in mice infected with NS5A-expressing lentiviral particles. NS5A inhibited AMPK phosphorylation and increased the expression levels of sterol regulatory element binding protein-1c (SREBP-1c), acetyl-coenzyme A carboxylase 1 (ACC1) and fatty acid synthase (FASN) in vivo and in vitro. Further investigation revealed that pharmacological activation or ectopic expression of AMPK neutralized the upregulation of SREBP-1c, ACC1 and FASN, and ameliorated hepatic lipid accumulation induced by NS5A. Ectopic expression of SREBP-1c enhanced NS5A-induced hepatic lipid accumulation, which was dramatically reversed by pharmacological activation of AMPK. CONCLUSIONS: Collectively, we demonstrate that NS5A induces hepatic lipid accumulation via the AMPK/SREBP-1c pathway.
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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".