Polyoxometalates Ameliorate Metabolic Dysfunction-Associated Steatotic Liver Disease by Activating the AMPK Signaling Pathway

Introduction: Metabolic dysfunction-associated steatotic liver disease (MASLD), the most prevalent chronic liver disorder, has garnered increasing attention globally owing to its associated health complications. However, the lack of available therapeutic medications and inadequate management of complications in metabolic dysfunction-associated steatohepatitis (MASH) present significant challenges. There are little studies evaluating the effectiveness of POM in treating MASLD. In this study, we synthesized polyoxometalates (POM) for potential treatment of MASLD. Methods: We induced liver disease in mice using two approaches: feeding a high-fat diet (HFD) to establish MASLD or feeding a methionine-choline deficient (MCD) diet to induce hepatic lipotoxicity and MASH. Various metabolic parameters were detected, and biochemical and histological evaluations were conducted on MASLD. Western blotting, qRT-PCR and immunofluorescence assays were used to elucidate the molecular mechanism of POM in the treatment of MASLD. Results: POM therapy resulted in significant improvements in weight gain, dyslipidemia, liver injury, and hepatic steatosis in mice fed a HFD. Notably, in a more severe dietary-induced MASH model with MCD diet, POM significantly attenuated hepatic lipid accumulation, inflammation, and fibrosis. POM treatment effectively attenuated palmitic acid and oleic acid-induced lipid accumulation in HepG2 and Huh7 cells by targeting the AMPK pathway to regulate lipid metabolism, which was confirmed by AMPK inhibitor. Additionally, the activation of AMPK signaling by POM suppressed the expression of lipid synthesis genes, including sterol regulatory element-binding protein 1c (SREBP1c) and SREBP2, while concurrently upregulating the expression of sirtuin 1 (SIRT1) to promote fatty acid oxidation. Conclusion: These findings suggest that POM is a promising therapeutic strategy with high efficacy in multiple MASLD models.