Baicalin alleviates lipid accumulation in adipocytes via inducing metabolic reprogramming and targeting Adenosine A1 receptor

Excessive lipid accumulation can lead to obesity, metabolic-associated fatty liver disease, and type 2 diabetes. However, there are currently few drugs that could effectively and safely inhibit the accumulation of intracellular lipid. In this study, we observed that baicalin significantly altered cellular respiration by reducing mitochondrial oxygen consumption while enhancing glycolytic flux, accompanied by increased phosphorylation of AMPK and ACC, suggesting an adaptation to altered energy availability. Baicalin effectively reduced lipid droplet formation and intracellular triglyceride levels in adipocytes, as marked by downregulating genes and proteins associated with lipid storage, including Cd36, Fabp4, and FASN. Transcriptomic analysis identified 2,150 differentially expressed genes in baicalin-treated adipocytes, with significant enrichment in metabolic pathways such as glycolysis, gluconeogenesis, and lipid metabolism. Further analysis revealed that baicalin upregulated glycolytic and fatty acid ß-oxidation (FAO) pathways while downregulating pyruvate dehydrogenase, inducing a shift toward glycolysis and FAO for energy production. Molecular docking analysis revealed that adenosine A1 receptor (ADORA1) was the target of baicalin, which inhibited the maturation of sterol regulatory element binding protein 1 (SREBP1) and finally alleviated lipid deposition. These results demonstrate that baicalin induces metabolic reprogramming of adipocytes by inhibiting glucose aerobic metabolism while enhancing anaerobic glycolysis and FAO. Meanwhile, baicalin targets ADORA1, which subsequently influences the processing of SREBP1 and downregulates lipid biosynthesis, positioning baicalin as a potential therapeutic agent against obesity and related metabolic disorders. Overall design: RNA sequencing data of adipocytes treated or not treated with 200µM baicalin. featureCounts v1.5.0-p3 was used to count the reads numbers mapped to each gene.And then FPKM of each gene was calculated based on the length of the gene and reads count mapped to this gene. FPKM, expected number of Fragments Per Kilobase of transcript sequence per Millions base pairs sequenced, considers the effect of sequencing depth and gene length for the reads count at the same time, and is currently the most commonly used method for estimating gene expression levels.