Si-Ni-San improves the deposition of lipid droplets in MAFLD through modulating the FXR-GPAT4 axis

Si-Ni-San improves the deposition of lipid droplets in MAFLD through modulating the FXR-GPAT4 axis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Si-Ni-San improves the deposition of lipid droplets in MAFLD through modulating the FXR-GPAT4 axis Haibo Fan, Yalei Hou, Yue Li, Zhiwen Zheng, Yunfeng Li, Yongmin Li This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7457859/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 13 Jan, 2026 Read the published version in Chinese Medicine → Version 1 posted 11 You are reading this latest preprint version Abstract Background Metabolic-associated fatty liver disease (MAFLD) is a common metabolic disease with complex pathogenesis and a lack of effective treatment. Si-Ni-San (SNS), a traditional Chinese medicine, has emerged as a promising candidate for MAFLD treatment. However, its mechanism of action remains unclear. Methods C57BL/6 mice were fed a high-fat diet (HFD) for 12 weeks to establish a mouse model of MAFLD. Second, an MAFLD cell model was established by inducing HepG2 cells with oleic acid. The effects of SNS and the positive drug obeticholic acid on hepatic lipid droplet deposition in MAFLD mice and cell models were evaluated. The expression levels of farnesoid X receptor (FXR) and glycerol 3-phosphate acyltransferase 4 (GPAT4) were detected by Western Blot assay. siRNA assay and Dual-Luciferase reporter assay were used to detect the interaction between FXR and GPAT4. Active components in the aqueous decoction of SNS were screened by HPLC, and their binding to targets was further validated by molecular docking and molecular dynamics simulations. Results SNS ameliorates hepatic lipid droplet deposition in both the MAFLD mouse and cell models. It activates hepatic FXR, inhibits hepatic GPAT4, and regulates proteins related to hepatic lipolysis and lipophagy. FXR reduces lipid droplet accumulation by inhibiting GPAT4. The Dual-Luciferase reporter assay confirms that FXR transcriptionally regulates and inhibits GPAT4 expression. Seven active components in SNS were detected by HPLC, and their binding to FXR and GPAT4 was confirmed through molecular docking and molecular dynamics simulations. Conclusion This study provides a new mechanistic exploration for FXR in improving MAFLD and broadens the research direction on the mechanisms by which SNS reduces hepatic lipid droplet deposition. It also offers a molecular dynamics basis for subsequent studies on how active components in SNS exert their effects through binding to FXR. MAFLD SNS FXR GPAT4 Molecular dynamics simulations Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 1. Introduction Metabolic-associated fatty liver disease (MAFLD) is an excessive accumulation of fat in the liver that is closely associated with metabolic dysfunction and insulin resistance in the form of overweight or obesity [ 1 ]. In 2020, an international consensus changed non-alcoholic fatty liver disease (NAFLD) to MAFLD [ 2 ]. MAFLD can progress to metabolic dysfunction-associated steatohepatitis (MASH), liver cirrhosis, and hepatocellular carcinoma [ 3 ]. The global incidence rate of MAFLD is 25%, which has led to high costs for the global health system. Approximately 1.7 billion people have suffered from MAFLD [ 4 ]. By 2030, MAFLD will affect about 33.5% of the adult population, and MASH will affect about 27% of cases [ 5 ]. Currently, the "multiple hits" theory accepted by most researchers is the combined result of chronic inflammation, oxidative stress, epigenetics, genetic susceptibility factors such as obesity, high-fat diet (HFD) intake, and insulin resistance (IR) [ 6 ]. Due to the complex pathogenesis of MAFLD, there are currently no approved effective therapeutic drugs [ 7 ]. Therefore, this study explored the efficacy and mechanism of traditional Chinese medicine (TCM) in the prevention and treatment of MAFLD. Bile acids (BAs) are bioactive molecules synthesized in the liver and secreted into the intestine through the bile duct [ 8 ]. BAs may have great potential as a therapeutic breakthrough in the treatment of MAFLD. Farnesol X receptor (FXR) is a key endogenous receptor of BAs, and it mainly expresses in hepatocytes and intestinal epithelial cells [ 9 ]. In the liver, FXR reduces hepatic lipids through the small heterodimer mate (SHP)-sterol regulatory element-binding protein 1c (SREBP1c) pathway-mediated hepatic de novo lipogenesis [ 10 ]. Additionally, FXR activation enhances the transcriptional activity of peroxisome proliferator-activated receptor α (PPARα), promoting fatty acid β-oxidation [ 11 ]. In summary, bile acid homeostasis is a key point for improving MAFLD, and FXR is widely recognized as a landmark target in the MAFLD field. Lipid droplets (LDs) are important subcellular organelles that maintain lipid homeostasis by coordinating lipid synthesis, lipid storage, and lipid secretion, and they are surrounded by phospholipid monolayers around the lipid core. Two major pathways mediate LD protein targeting. Glycerol 3-phosphate acyltransferase 4 (GPAT4) is involved in one pathway, the endoplasmic reticulum (ER) -LD targeting, and GPAT4 is transported to LDs through the ER-LD membrane bridge [ 12 ]. GPAT4 is predominantly distributed in hepatocytes and is expressed only in the ER. At the subcellular level, GPAT4 can be transported from the ER to the lipid droplets and promote the growth of the lipid droplets [ 13 ]. Studies have shown that GPAT4 −/− mice lose 25% of their body weight and are resistant to diet-induced obesity and hereditary obesity, with a 45% reduction in total GPAT activity and TAG content in the liver of GPAT4 −/− mice [ 14 ]. However, whether hepatic FXR can affect GPAT4 and then improve lipid accumulation is not clear. Lipolysis and lipophagy play crucial roles in lipid metabolism and are closely associated with MAFLD. Lipolysis, mediated by enzymes such as adipose triglyceride lipase (ATGL), Monoacylglycerol Lipase (MAGL), and hormone-sensitive lipase (HSL), breaks down triglycerides stored in adipocytes, releasing fatty acids for energy utilization [ 15 ]. In MAFLD, impaired lipolysis can lead to excessive lipid accumulation in the liver. Lipophagy, a selective form of autophagy related to lipid metabolism, involves proteins like p62, Beclin1, and LC3. P62 acts as a linker molecule, recruiting lipid droplets to the autophagosome formation site. Beclin1 initiates the autophagy process, and LC3 is conjugated to the autophagosome membrane, facilitating lipid degradation [ 16 ]. Dysfunction in lipophagy can disrupt the balance of lipid turnover in the liver, contributing to the progression of MAFLD. Several studies have shown that in MAFLD models, decreased expression of ATGL and abnormal activation of p62-mediated lipophagy were observed, leading to aggravated hepatic steatosis [ 17 ]. TCM has certain advantages in the treatment of MAFLD [ 7 ]. Clinically, most patients present with symptoms such as distending pain in the hypochondrium, abdominal fullness, fatigue, light red tongue, and white greasy coating of the tongue. Clinical studies have shown that regulating the liver and spleen is an effective treatment for MAFLD patients [ 18 ]. Si-Ni-San (SNS) is one of the Classic prescriptions for Shaoyang disease, which is composed of equal proportions of Bupleurum falcatum L., Citrus aurantium L., Paeonia lactiflora Pall., and Glycyrrhiza aspera Pall. SNS has the effect of penetrating evil and relieving depression, relieving the liver and spleen. Our previous work demonstrated that SNS reduced LD deposition in MAFLD and confirmed that SNS could improve LD deposition by inhibiting YAP1 expression [ 19 , 20 ]. While this study investigated the relationship between FXR and GPAT4 and the mechanism by which SNS improves LD deposition in MAFLD. 2. Materials and Methods 2.1 Preparation of the SNS decoction for animals and drug serum for cells Following our previous study [ 20 ], equal masses of bupleurum, peony, bitter orange, and licorice (sourced from the National Medical Hall of Hebei University of Traditional Chinese Medicine) were mixed in a 1:1:1:1 ratio and decocted twice. The two resulting solutions were combined and stored at 4℃. Healthy male Sprague-Dawley (SD) rats (5–6 weeks old, weighing 180 ± 10 g) were obtained from Changsheng Biotechnology Co., Ltd. (Liaoning, China). After a one-week acclimation period, the rats were administered SNS decoction (3.6 g/kg) or normal saline (NS) in equal volumes daily for one week [ 21 ]. Blood was collected from the abdominal aorta after the rats were anesthetized with 1% pentobarbital (0.01 mL/g). The serum was subsequently isolated, inactivated at 56℃, and stored at − 80℃ for subsequent cell experiments. 2.2 Prepare obeticholic acid (OCA) for animals and cells Obeticholic acid (OCA), used in this study, was obtained from Shanghai Aladdin Biochemical Technology Co., Ltd. For the animal experiments, OCA was dissolved in corn oil to prepare a 10 mg/kg solution for intragastric administration to mice. For the cell experiments, OCA was dissolved in dimethyl sulfoxide (DMSO) to achieve a final concentration of 1 µmol/L. 2.3 High Performance Liquid Chromatography (HPLC) The SNS formulation contained equal masses (6 g each) of Paeoniae Radix Alba (Baishao), Bupleuri Radix (Chaihu), Aurantii Fructus Immaturus (Zhishi), and Glycyrrhizae Radix et Rhizoma Praeparata (Zhigancao). Botanical materials were hydrated in 10-fold distilled H₂O (v/w) for 60 min, followed by sequential extraction: primary decoction at vigorous boiling (100°C, 20 min), secondary extraction at gentle simmering (90°C, 40 min). The combined filtrates were gauze-filtered and concentrated to 70 mg/mL via rotary evaporation. The qualitative analysis of SNS was performed on an Agilent Series 1260 Infinity HPLC system (Agilent Technologies Inc., Santa Clara, CA, USA) equipped with a Zorbax StableBond-AQ C18 column (250×4.6 mm, 5 µm) (Agilent Technologies Inc) maintained at 30°C. The mobile phase consisted of acetonitrile (solvent A) and water with 0.1% phosphoric acid (solvent B), and the following linear gradient elution procedure was used: 0–5 min, 5–10% A; 5–14 min, 10–12% A; 14–25 min, 12–18% A; 25–50 min, 18-23.5% A; 50–55 min, 23.5–28.5% A; 55–70 min, 28.5–36% A; 70–75 min, 36–40% A; 75–85 min, 40–44% A; 85–90 min, 44–90% A. This analysis was performed at a wavelength of 240 nm with a flow rate of 1 mL/min and an injection volume of 10 µL. 2.4 Animal study The proposed experimental protocol was authorized by the Animal Ethics Committee of Hebei University of Chinese Medicine on March 5, 2024, with the ethical approval number DWLL202403092. Forty male C57BL/6 mice aged 6–7 weeks, certified as specific pathogen-free (SPF), were purchased from Changsheng Biotechnology Co., Ltd. (Liaoning, China). The mice were housed in a temperature-controlled environment with a 12-hour light/dark cycle. After a one-week acclimation period, the mice were randomly assigned to four groups with 10 mice in each group. NC group: Mice were maintained on a normal diet with free access to water throughout the experiment and received intragastric administration of distilled water at 11–12 weeks. The remaining three groups were fed a high-fat diet (HFD) (D12451, Huanyu Hekang Biotechnology, Henan, China) with free access to water throughout the experiment. At 11–12 weeks, they received different interventions: HFD group: Intragastric administration of distilled water; SNS group: Intragastric administration of SNS decoction (5.2 g/kg/d, 0.1 mL/mouse); OCA group: Intragastric administration of OCA (10 mg/kg/d). As shown in Fig. 1 B, after the experiment, the body weights and liver weights of the mice were measured, and the liver-to-body weight ratio (liver weight/body weight) was calculated. 2.5 The Serum Biochemical Assays Serum samples were collected to detect triglyceride (TG), total cholesterol (TC), alanine aminotransferase (ALT), aspartate aminotransferase (AST), high-density lipoprotein cholesterol (HDL-C), and low-density lipoprotein cholesterol (LDL-C) levels. This experiment was completed by Servicebio Biotechnology Co., Ltd. The Reado/Changchun Huili kit was used for the assay, which was automatically measured by a fully automatic biochemistry analyzer. 2.6 Cell lines The HepG2 cell line was purchased from the American Type Culture Collection (Manassas, VA, USA) and cultured in RPMI 1640 medium (Solarbio, Beijing, China) with 10% fetal bovine serum (Gibco, USA) in an atmosphere with 5% CO 2 and 100% humidity at 37 ℃. Oleic acid (OA) was purchased from Shanghai Macklin Biochemical Co., Ltd. (C11391320, Macklin). For incubation, the stock solutions of OA (100 µmol/L) were prepared in isopropanol at a concentration of 20 mmol/L and then temporarily diluted in RPMI 1640 medium with 10% fetal bovine serum, so that the final concentration of the isopropanol did not exceed 1% [ 22 ]. Cells were inoculated into 6-well plates and 96-well plates and treated with OA solution and SNS/NS serum for 24 h. Based on our previous study, we used an SNS concentration of 15% in our experiments, which did not affect cell viability [ 19 ]. 2.7 Histopathological analysis Liver tissue from each group of mice was fixed in 4% formalin, embedded in paraffin, and prepared as paraffin tissue sections with a thickness of 5 µm, and stained with a hematoxylin-eosin kit (DH0006, LEAGENE). In order to assess Lipid droplets (LD) deposition. The deposition of LD in each group was observed microscopically (Leica DM2500, Germany). 2.8 Oil red O staining Lipid accumulation in vivo was assessed by oil red O staining (G1261, Solarbio, China). For liver tissue, frozen tissue sections were prepared at a thickness of 10µm. After fixation in formalin, the sections were washed in 60% isopropanol and then stained with freshly prepared oil red O working solution for 15 min. Following rinsing with 60% isopropanol, the sections were examined under a microscope. According to the manufacturer’s instructions of the Oil red O kit (G1262, Solarbio), model cells were seeded in 6-well plates (3 × 10 5 cells/well). After the cells were washed with PBS, ORO fixative was added to each well for 20–30 min, and 60% isopropyl alcohol was added to each well for 5 min. Then, ORO staining solution was used to stain the cells for 10–20 min. After the cells were washed with water, Mayer hematoxylin staining solution was used to restain the nucleus for 1–2 min. Then, we added ORO buffer for 1 min. Finally, we used glycerin gelatin to seal the tablets. The changes in LD were measured with ImagePro-Plus v6.0 software (Media Cybemetic, USA). 2.9 Quantitative Real-time PCR (qRT-PCR) Total RNA was extracted from cells and reverse transcribed into complementary DNA (cDNA) for the assessment of gene expression levels. These reactions were amplified in a LightCyclerR 480 Quantitative PCR System (Roche, USA), and the resulting data were analyzed using the ΔΔCt method. This enabled the determination of transcript expression levels by normalizing to the expression levels of a housekeeping gene. The primer sequences used in this study are shown in Table 1 . Table 1 Sequences of qPCR primers Gene name Sequence (5’ to 3’) FXR F ACTTCCGTCTGGGCATTCTGAC R GCTGTAAGCAGAGCATACTCCTC GPAT4 F ACTGGCTTTCACAGGGATTAG R CGCAGATCCGGTAACACATTA GAPDH F ATCCCATCACCATCTTCC R CCATCACGCCACAGTTC 2.10 Western blot Total protein was extracted using a protein extraction kit (PC201Plus, Epizyme). Then, the protein concentration was measured using a BCA kit (PC0020, Solarbio). 50 µg of the protein samples were loaded onto an SDS-PAGE gel. Following electrophoresis, the protein bands were transferred to PVDF membranes. Whereafter, the membranes were first blocked with 5% nonfat dry milk + TBST. Western blot analysis was performed with primary antibodies against NR1H4 (FXR) (bs-12867R, Bioss), GPAT4 (bs-15587R, Bioss), P62 (380612-50, Zenbio), HSL (344379-50, Zenbio), MAGL (PAD223Hu01, Cloud-Clone Corp), Beclin1 (BS-1353R-50ul, Bioss), ATGL (R389129-50, Bioss), LC3Ⅰ/Ⅱ (ABC929, Merck), and β-actin (bs-0061R, Bioss). The secondary antibodies were goat anti-rabbit antibody (abs20002, Absin) and goat anti-mouse antibody (GB23301, Servicebio). The bands were detected using an ECL detection kit (sb-wb012, Shanghai Shenger Biotechnology Co., Ltd). The results were quantified using ImageJ software (National Institutes of Health, America). 2.11 Immunohistochemistry assay (IHC) The liver tissue samples were paraffin-embedded and cut into 5 µm-thick sections. IHC antibodies for NR1H4 (FXR) (bs-12867R, Bioss) and GPAT4 (bs-1924R, Bioss) were used. The sections were developed with a 3,3-diaminobenzidine (DAB) kit, counterstained with hematoxylin, differentiated, and rinsed with warm water to stain the nuclei blue; after this, the sections were dehydrated, made transparent, and sealed. Finally, the sections were microscopically observed with the magnification power of 20 and 40 for brown peroxidase in liver tissue. The optical density was measured using Image-Pro-Plus v6.0 software (Media Cybemetic, USA). 2.12 RNA interference Small interfering RNA (siRNA)-to knock down FXR (si-FXR), GPAT4 siRNA (si-GPAT4), and negative control NC (si-NC) were designed and synthesized by GENEPHARMA (Jiangsu, China). HepG2 cell lines were transfected with the siRNAs using sirna mate plus (G04026, GENEPHARMA, China). Cells were seeded in complete medium approximately 12 h before transfection. Then, siRNAs mixed with siRNA Mate Plus were added to the cells with fresh Opti-MEM medium (Gibco, USA). SiRNAs were transfected at a concentration of 50 nM. After 6 h, the medium containing siRNAs and sirna mate plus was replaced with complete medium. The sequences of siRNAs are shown in Table 2 . Table 2 The sequences of siRNAs Name Forward Reverse FXR-homo-651 CCACAGAUUUCCUCGUCAUTT AUGACGAGGAAAUCUGUGGTT FXR-homo-786 GCAGAGAUGCCUGUAACAATT UUGUUACAGGCAUCUCUGCTT FXR-homo-877 CCUCUGGAUACCACUAUAATT UUAUAGUGGUAUCCAGAGGTT GPAT4-Homo-1362 GCUGAGCAGAACCAAUUAUTT AUAAUUGGUUCUGCUCAGCTT GPAT4-Homo-1505 GCACAACUGUGGUGGGAUATT UAUCCCACCACAGUUGUGCTT GPAT4-Homo-1965 GCACAACUGUGGUGGGAUATT AUACUUGAUAGCAACAGGGTT 2.13 Database prediction and screening Analyze the correlation and binding sites between the GPAT4 promoter region and the transcription factor FXR using the JASPAR database (www.jaspar.elixir.no ). Predict and screen the transcription factors of GPAT4 using the UCSC Genome Browser (www.genome.ucsc.edu ) and obtain the binding score of FXR and GPAT4. 2.14 Dual-luciferase reporter assays Six plasmids (pcDNA3.1(+)-NR1H4-3xFLAG, pcDNA3.1(+)-MCS-3xFLAG, pGL4.10-GPAT4 promoter (WT), pGL4.10, pGL4.10-GPAT4 promoter (MUT), pRL-CMV) were purchased from Obio Technology Corp (Shanghai, China) for the interaction of FXR and GPAT4-promoter experiment. HepG2 cells were randomly divided into the following four groups: pcDNA3.1(+)-NR1H4-3Xflag + pGL4.10-GPAT4 promoter (WT); pcDNA3.1(+)-NR1H4-3xFLAG + pGL4.10-GPAT4 promoter (MUT); pcDNA3.1(+)-MCS-3xFLAG + pGL4.10-GPAT4 promoter (WT); pcDNA3.1(+)-MCS-3xFLAG + pGL4.10-GPAT4 promoter (MUT) Four groups of cells were transfected with the corresponding plasmids and the pRL-CMV plasmid for 24 h. The cells were collected to calculate the ratio of Firefly to Renilla luciferase activity using the Dual Luciferase Reporter Gene Assay Kit (Promega, Madison, WI, USA). 2.15 Molecular docking simulations The 3D structure models of core target proteins were downloaded from the Protein Data Bank (RCSB-PDB, https://www.rcsb.org/ ) and imported into PyMOL 2.5.2 for dehydration and ligand separation. The 2D structures of ligand small molecules were downloaded from the PubChem database ( https://pubchem.ncbi.nlm.nih.gov/ ), and key components were converted into mol2 format files using ChemBioOffice. AutoDockTools-1.5.7 was used to add non-polar hydrogens to the receptor protein and ligand small molecules, calculate charges for the protein structure, and identify rotatable bonds in the ligand molecules. Appropriate docking boxes and parameters were set according to the structural sizes of the receptor and ligand, and molecular docking was performed using AutoDockTools 1.5.7. Visualization analysis was conducted using PyMOL. 2.16 Molecular dynamics simulations (MD simulations) In this study, Gromacs 2022 was used for molecular dynamics simulations. Force field parameters were obtained using the pdb2gmx tool in Gromacs and the AutoFF web server. During the simulation, the CHARMM36 force field [ 23 ] was applied to the molecular parameters of the receptor protein, while the CGenff force field was used for the ligand molecular parameters. The system was solvated by adding a 1 nm TIP3P cubic water box around it. Ions were added to the system using the GROMACS genion tool to achieve electrical neutrality. Long-range electrostatic interactions were handled by the Particle Mesh Ewald (PME) method with a cutoff distance of 1 nm. All bond constraints were implemented via the SHAKE algorithm, and the Verlet leapfrog algorithm was adopted with an integration time step of 1 fs for the molecular dynamics simulation process. Prior to molecular dynamics simulation, the system underwent energy minimization, which included 3000 steps of steepest descent optimization followed by 2000 steps of conjugate gradient optimization. The optimization steps were as follows: first, the solute was constrained, and energy minimization was performed on water molecules; then, the counterions were constrained for energy minimization; finally, energy minimization was conducted on the entire system without constraints. The simulation was run under the conditions of 310 K and an NPT system at constant pressure, with a total simulation time of 100 ns. During the simulation, the g-rmsd, g-rmsf, g-hbond, g-Rg, and g-sasa tools were used to calculate the root mean square deviation (RMSD), root mean square fluctuation (RMSF), hydrogen bonds (HBonds), radius of gyration (Rg), and solvent-accessible surface area (SASA), respectively. 2.17 Statistical analysis All measurement data are expressed as mean ± standard deviation (mean ± SD). All data were analyzed using SPSS 23.0 software. Normality tests were performed on all data. For data satisfying normal distribution, one-way analysis of variance (ANOVA) was used for comparison. When variances were homogeneous, Turkey's method was applied for pairwise comparisons; when variances were heterogeneous, the Dunnett T3 method was used for pairwise comparisons. * p < 0.05 and ** p < 0.01 were considered statistically significant. All graphs were created by GraphPad Prism 8.0 software. 3 Results 3.1 SNS reduces the body weight of high-fat-fed mice To examine the effect of SNS on MAFLD, we developed a mouse model of MAFLD induced by HFD (Fig. 1 A). Body weight measurements were taken weekly over a 12-week duration. In the HFD group, there was a consistent increase in body weight, with a statistically significant difference observed in comparison to NC group. Commencing in week 10, treatments with SNS and OCA were initiated, leading to either stabilization or a gradual reduction in body weight (Fig. 1 C). By the 12th week, body weights were assessed, revealing that the HFD group had the highest body weight, significantly differing from the NC group. Both SNS and OCA adminstration treatments significantly decreased body weight relative to the HFD group, indicating that SNS and OCA are effective in attenuating body weight gain in MAFLD-afflicted mice (Fig. 1 D). In our study, we observed that the livers of mice subjected to HFD exhibited enlargement, a yellowish discoloration, and a hardened texture. In contrast, the livers of mice in the SNS and OCA treatment groups closely resembled those in the NC group (Fig. 1 B). Subsequent measurements of liver weights indicated that treatment with SNS and OCA resulted in a significant reduction in liver weights in mice with MAFLD induced by a high-fat diet (Fig. 1 E). Additionally, the liver coefficient, defined as the ratio of liver weight to body weight, was calculated (Fig. 1 F). The findings revealed that the liver coefficient in the HFD group was significantly lower than that in the NC group, whereas the coefficients in the SNS and OCA groups were restored to normal levels. Overall, these phenotypic observations demonstrate that both SNS and OCA effectively ameliorated MAFLD, exhibiting comparable therapeutic efficacy. 3.2 SNS improves liver lipid droplet deposition in MAFLD mice To investigate the mechanisms by which SNS and OCA alleviate MAFLD, we further examined liver lipid droplet deposition. H&E and Oil Red O (ORO) staining revealed an increase in lipid droplet vacuoles and ORO-positive areas in the HFD group compared to the NC group. In contrast, the SNS and OCA groups exhibited significantly reduced lipid droplet areas compared to the HFD group, indicating that both SNS and OCA effectively alleviated lipid droplet accumulation (Fig. 2 A-B). Serum analysis showed changes in the levels of TC, TG, HDL-C, LDL-C, ALT, and AST. Lipid parameters, including TG, TC, and LDL-C, were elevated in the HFD group but reduced following SNS and OCA treatment. However, SNS had a limited effect on HFD-induced increases in HDL-C levels, while its effects on other parameters were comparable to OCA (Fig. 2 C-D, G-H). Serum ALT and AST are markers of liver injury. Significantly elevated ALT and AST were observed in the HFD group, while SNS and OCA treatment effectively reduced liver injury induced by high-fat diet, suggesting that the drugs do not cause liver injury in mice (Fig. 2 E-F). Histopathological analysis and serum biochemical assays showed that SNS and OCA improved hepatic lipid droplet deposition and liver injury in MAFLD mice. 3.3 SNS activates FXR to regulate lipolysis- and lipophagy-related protein expression to improve MAFLD To investigate the molecular mechanism by which SNS improves MAFLD, the total bile acid (TBA) levels in the livers of MAFLD mice were measured. A high-fat diet significantly elevated hepatic TBA levels, while SNS and OCA treatments restored TBA levels to normal (Fig. 3 A). Since FXR is a bile acid receptor, hepatic FXR expression was also analyzed. A high-fat diet suppressed hepatic FXR expression, but SNS and OCA treatments activated its expression (Fig. 3 B, D). FXR in the colon also plays a critical role in MAFLD. In our study, FXR expression in the colons of HFD group mice was reduced, whereas SNS and OCA treatments elevated colonic FXR expression. Meanwhile, the results of liver immunohistochemistry (IHC) showed that SNS and OCA increased the positive localization of FXR in liver tissues, and the localization of FXR was mainly concentrated in hepatic cell nuclei and hepatic sinusoidal epithelial cells (Fig. 3 M). These results suggest that SNS alleviates MAFLD by activating FXR in both the liver and colon (Fig. 3 B, E). FXR activation by OCA prompted further investigation of lipolysis- and lipophagy-related proteins in the liver. SNS and OCA treatments increased ATGL expression and decreased HSL and MAGL expression, promoting lipolysis and improving MAFLD (Fig. 3 C, F, G, I). Regarding lipophagy, SNS and OCA treatments suppressed P62 expression, activated Beclin1, and inhibited LC3Ⅰ expression, with no significant effect on LC3Ⅱ levels. The ratio of LC3Ⅱ/LC3Ⅰ reflects the degree of lipophagy, and both SNS and OCA upregulate this ratio. (Fig. 3 H, J-L). These findings suggest that SNS and OCA improve MAFLD by activating hepatic FXR, which subsequently regulates lipolysis- and lipophagy-related protein expression. 3.4 SNS activates FXR to modulate lipid droplet transport Upon FXR activation, it may influence the targeting of proteins from the ER to LDs. Previous studies have identified multiple cargos involved in both early and late ER-to-LD targeting pathways. In this study, we focused on GPAT4, a late cargo responsible for ER-to-LD targeting [ 12 ]. A high-fat diet suppressed FXR expression while promoting GPAT4 expression. In contrast, SNS and OCA activated FXR and inhibited GPAT4 expression (Fig. 4 A and B). Meanwhile, the results of liver immunohistochemistry showed that compared with the HFD group, SNS and OCA reduced the positive localization of GPAT4 in liver tissues, and the localization of GPAT4 was mainly concentrated in hepatic cytoplasm and hepatic sinusoidal epithelial cells (Fig. 4 D). Although we did not systematically investigate the mechanism of endoplasmic reticulum-lipid droplet targeting early cargo, we preliminarily detected the protein level of LDAH in early cargo and found that both SNS and OCA also downregulated LDAH. This may suggest that the drugs also inhibit the early trafficking of lipid droplets (Fig. 4 A, C). This phenomenon was further validated in an MAFLD cell model. Oil Red O staining showed that compared with NS serum, SNS serum significantly alleviated intracellular lipid droplet accumulation, which was consistent with the results of OCA (Fig. 4 E-F). Subsequently, the expression levels of FXR and GPAT4 in the cells were analyzed. Consistent with tissue results, SNS and OCA increased FXR expression and inhibited GPAT4 expression in cells (Fig. 4 G-L). However, the precise regulatory mechanism linking FXR and GPAT4 requires further investigation. 3.5 Knockdown of GPAT4 reduced lipid droplet deposition, while knockdown of FXR increased GPAT4 expression To investigate the role of FXR and GPAT4 in cellular lipid droplet accumulation, siRNA targeting FXR (siRNA-FXR) was transfected into normal HepG2 cells, while siRNA-GPAT4 was transfected into oleic acid-induced HepG2 cells. First, WB and PCR were used to screen and verify the knockdown efficiency of GPAT4, and 1505 was selected as the effective GPAT4 knockdown for subsequent experiments (Fig. 5 A-C). Oil red O staining of cells showed that after effective knockdown of GPAT4 by transfection with 1505, the positive area of lipid droplet deposition was reduced compared with the OA group (Fig. 5 D-E). Meanwhile, WB and PCR were used to screen and verify the knockdown efficiency of FXR, and 786 was selected as the effective FXR knockdown for subsequent experiments (Fig. 5 F-H). Subsequently, Western blot (WB) and PCR experiments were performed to detect the expression level of GPAT4 in cells after FXR knockdown. The results showed that the expression level of GPAT4 was upregulated after the FXR knockdown. Combined with the finding that GPAT4 expression was inhibited after FXR activation, it was concluded that there is a close relationship between FXR and GPAT4, and transcriptional regulation may be involved (Fig. 5 I-K). Then, Oil Red O staining was used to observe the effect of FXR knockdown on lipid droplet deposition in cells. The results showed that FXR knockdown led to an increase in lipid droplet deposition in cells. Considering that the inhibition of FXR may induce the development of MAFLD (Fig. 5 L-M). These findings suggest a close relationship between FXR and GPAT4. As GPAT4 is involved in lipid droplet transport, its inhibition may represent a novel therapeutic approach for improving MAFLD. 3.6 Database predictions of FXR binding to GPAT4, validated by the dual-luciferase assay To investigate the direct regulatory role of FXR on GPAT4, the GPAT4 promoter region sequence was retrieved from the NCBI database. The JASPAR database predicted a correlation and binding site between the GPAT4 promoter and the transcription factor FXR, the binding site located at positions 2116–2126,1614–1624,681–693 in the promoter region (Fig. 6 D). Similarly, predictions using the UCSC database showed that FXR was the transcription factor most strongly correlated with the GPAT4 promoter, with a binding score of 419 and a p-value of less than 0.01 (Fig. 6 A-B). These results from the two databases suggest a potential binding interaction between FXR and the promoter region of GPAT4. Schematic diagram of the sequence in the FXR binding element (Fig. 6 C) Then, the FXR overexpression plasmid and wild-type/mutant plasmids of the GPAT4 promoter region (with binding site mutations) were constructed and transfected into cells (Fig. 6 E, H). Western blot analysis showed that FXR expression in cells transfected with the overexpression plasmid was higher than that in the empty vector group (Fig. 6 F-G). Finally, a dual-luciferase reporter gene assay was performed to detect the luciferase activity values of the binding between the FXR overexpression plasmid/empty vector and the wild-type/mutant GPAT4 promoter regions, respectively. The results showed that in the wild-type group, the luciferase activity of FXR overexpression was lower than that of the empty vector group. However, after mutation of the GPAT4 promoter binding site, there was no significant difference in luciferase activity between the FXR overexpression and empty vector groups. These findings suggest that FXR binds to GPAT4 and inhibits its expression, while mutation of the binding site abolishes this interaction, leading to no significant difference in expression (Fig. 6 I). This also suggests that SNS may ameliorate lipid droplet deposition in MAFLD hepatocytes through the FXR-GPAT4 axis. 3.7 Seven active components in the aqueous decoction of SNS were screened by HPLC HPLC was used to detect and screen the major active components in the aqueous decoction of SNS (Fig. 7 A-B). Based on literature reports on the regulation of MAFLD by SNS active components, seven compounds were selected: Neohesperidin, Peoniflorin, Hesperidin, Glycyrrhizin, Liquiritin, Isorhamnetin, and Nobiletin (Table 3 ). Among them, Neohesperidin, Hesperidin, and Nobiletin are derived from Aurantii Fructus Immaturus (bitter orange), Glycyrrhizin, Liquiritin, and Isorhamnetin from Glycyrrhizae Radix et Rhizoma (licorice), Isorhamnetin also from Bupleuri Radix (bupleurum), and Peoniflorin from Paeoniae Radix Alba (white peony root). Table 3 Seven major active components in the aqueous decoction of SNS Active ingredient Peak area Concentration (mg/mL) Neohesperidin 12578.7 2.316 Peoniflorin 777.3 0.327 Hesperidin 1107.4 0.202 Glycyrrhizin 1191.5 0.093 Liquiritin 727.6 0.067 Isorhamnetin 96.4 0.005 Nobiletin 122.7 0.002 3.8 The active components of SNS can bind to FXR and GPAT4 To further investigate the binding of active components in SNS to FXR and GPAT4, these components were then subjected to molecular docking with FXR and GPAT4, respectively. Notably, the largest cavity identified on GPAT4 overlaps with the binding pocket of GPAT1, and three major cavities and multiple minor cavities on the GPAT1 structure (PDB ID: 8E50) were experimentally confirmed as intact binding sites. The binding energies of SNS active components with FXR and GPAT4 are presented in Table 4 . First, the binding energies of all active components in SNS with FXR and GPAT4 were less than − 7 kcal/mol, indicating a favorable binding affinity. Among them, Glycyrrhizin showed the lowest binding energies with FXR (-14.7 kcal/mol) and GPAT4 (-13.1 kcal/mol), indicating the strongest binding affinity. Second, Liquiritin showed a binding energy of -9.2 kcal/mol with FXR, while Neohesperidin and Hesperidin displayed binding energies of -9.2 kcal/mol and − 9.0 kcal/mol with GPAT4, respectively. These values, all less than − 9 kcal/mol, suggest relatively stronger binding of Liquiritin to FXR and of Neohesperidin/Hesperidin to GPAT4. Subsequently, molecular docking visualization was performed for each of these seven active components with FXR and GPAT4, respectively (Fig. 8 A-N). Table 4 Results of molecular docking Key targets PDB ID Active ingredient Binding energy (kcal/mol) FXR 3FXV Neohesperidin -8.8 Peoniflorin -8.1 Hesperidin -8.9 Glycyrrhizin -14.7 Liquiritin -9.2 Isorhamnetin -8.2 Nobiletin -7.6 GPAT4 8E50 Neohesperidin -9.2 Peoniflorin -8.9 Hesperidin -9.0 Glycyrrhizin -13.1 Liquiritin -8.6 Isorhamnetin -7.8 Nobiletin -7.4 Subsequently, molecular dynamics simulations were conducted to further observe the binding stability between small-molecule active components and target proteins. Among them, Glycyrrhizin showed the highest binding affinity to FXR and GPAT4 (with binding energies of -14.7 kcal/mol and − 13.1 kcal/mol). However, since the 3D structure of Glycyrrhizin is unavailable, Liquiritin (ranked second) was selected for molecular dynamics simulation of its binding to FXR, and Neohesperidin (ranked second) was chosen for simulation of its binding to GPAT4. Additionally, both Liquiritin-FXR and Neohesperidin-GPAT4 bindings involve approximately 4–5 hydrogen bonds, indicating good stability. RMSD is a reliable indicator for evaluating the conformational stability of proteins and ligands, as well as the degree of deviation of atomic positions from their initial positions. A smaller deviation indicates better conformational stability. Both the FXR system and the FXR-Liquiritin complex system reached equilibrium after 5 ns, with final fluctuations around 0.3 nm and 0.22 nm, respectively. The Liquiritin small molecule reached equilibrium after 70 ns, with final fluctuations around 0.39 nm. Thus, the Liquiritin small molecule exhibits high stability when binding to the target protein FXR. Both the GPAT4 system and the GPAT4-Neohesperidin complex system reached equilibrium after 10 ns, with final fluctuations around 0.4 nm and 0.33 nm, respectively. The Neohesperidin small molecule reached equilibrium after 10 ns, with final fluctuations around 0.55 nm. Therefore, the Neohesperidin small molecule also shows high stability when binding to the target protein GPAT4(Fig. 8 O, R). The Rg can describe changes in the overall structure and characterize the compactness of protein structures; larger Rg changes indicate a more expanded system. The FXR-Liquiritin complex showed relatively stable fluctuations during movement, suggesting no significant expansion or contraction of the small molecule-target protein complex. The GPAT4-Neohesperidin complex system exhibited slight fluctuations during movement, indicating conformational changes in the small molecule-target protein complex (Fig. 8 P, S). RMSF can reflect the flexibility of amino acid residues in proteins. The RMSF values of the FXR-Liquiritin complex were relatively low (mostly below 0.3 nm), and those of the GPAT4-Neohesperidin complex were also relatively low (mostly below 0.4 nm), indicating low flexibility and high stability (Fig. 8 Q, T). In summary, the complex systems exhibit stable binding with favorable hydrogen bond interactions. Therefore, the small molecules bind well to the target proteins. Discussion MAFLD is now one of the most common chronic liver diseases worldwide, with its incidence rising significantly due to links to obesity, diabetes, and metabolic syndrome—affecting about 25%-30% of the general population and over 50% in high-risk groups. It can progress to severe liver conditions like fibrosis, cirrhosis, and hepatocellular carcinoma, and is linked to extrahepatic issues such as cardiovascular and metabolic disorders. Its complex pathogenesis, explained by the "multiple hit" hypothesis, involves hepatic fat accumulation from lifestyle factors, followed by inflammation and fibrosis, with insulin resistance playing a key role by disrupting lipid metabolism and worsening inflammation[ 24 ]. Additional contributing factors to MAFLD include gut microbiota dysbiosis and genetic predisposition. Management primarily focuses on lifestyle changes—such as a low-sugar, low-fat, high-fiber diet and at least 150 minutes of weekly moderate aerobic exercise—along with pharmacological interventions like metformin for insulin resistance, vitamin E for antioxidant effects, statins for lipid regulation, and potential antifibrotic agents like pentoxifylline[ 25 ]. In conclusion, the management of MAFLD necessitates a comprehensive and individualized approach aimed at enhancing prognosis and reducing the risk of complications. BAs regulate lipid metabolism in MAFLD via the FXR pathway, which suppresses hepatic lipogenesis genes to reduce triglyceride synthesis. They may interact with TGR5 to activate anti-inflammatory pathways and modulate cytokines, while FXR also inhibits hepatic stellate cell activation to mitigate fibrosis[ 26 ]. Additionally, BAs and gut microbiota interact bidirectionally—BAs alter microbial composition, and microbial metabolites modify BA metabolism—jointly influencing MAFLD pathogenesis and progression[ 27 ]. In our study, we examined the expression of FXR in both the liver and colon and observed that SNS influenced both. However, this study primarily focused on the hepatic FXR mechanism of action. The limited exploration of other mechanisms related to bile acid metabolism and MAFLD constitutes a limitation of this research. FXR is a key target in MAFLD research, with its activation alleviating the disease through multiple mechanisms. It enhances bile acid transport genes like BSEP to promote hepatic bile acid excretion, reducing toxic accumulation and liver injury, while downregulating lipogenic enzymes (ACC, FAS) to lower triglyceride synthesis and hepatic fat[ 28 , 29 ]. Additionally, FXR modulates inflammation by suppressing pro-inflammatory cytokines (TNF-α, IL-6) and inhibits hepatic stellate cell activation to exert anti-fibrotic effects, slowing liver fibrosis progression[ 30 ]. Despite these insights, the precise mechanisms by which hepatic FXR ameliorates MAFLD continue to be the subject of ongoing research. Activating hepatic FXR effectively improves MAFLD, and this research will further explore its mechanisms. The TCM SNS shows promise in treating MAFLD by regulating lipid metabolism, improving insulin sensitivity, and reducing liver lipid deposition—its effects involve decreasing YAP1, activating AMPK to inhibit p300, reducing SREBP-1c stability, and suppressing Fasn to lower hepatocyte lipid accumulation[ 31 , 32 ]. Additionally, SNS may enhance lipid metabolism via the AKT/AMPK/HSL axis and counteract stress-related MAFLD through the AMPK/SIRT1 pathway[ 33 ]. ATGL, HSL, and MAGL are key lipid-metabolizing enzymes—ATGL initiates triglyceride hydrolysis, HSL further degrades products, and MAGL acts on monoacylglycerols—collectively regulating lipid storage and mobilization in adipose tissue and liver[ 34 ].Autophagy-related proteins P62 (selective degradation), Beclin1 (autophagy initiation), and LC3Ⅱ (autophagosome marker) are vital for cellular homeostasis and impact lipid metabolism and liver health[ 35 ].FXR activation modulates the expression/activity of ATGL, HSL, and MAGL to affect lipid catabolism/synthesis, and influences autophagic flux by regulating P62-substrate interactions, Beclin1 activation, and LC3Ⅰ-to-LC3Ⅱ conversion[ 36 ]. FXR activation modulates the expression/activity of ATGL, HSL, and MAGL to affect lipid catabolism/synthesis, and influences autophagic flux by regulating P62-substrate interactions, Beclin1 activation, and LC3Ⅰ-to-LC3Ⅱ conversion. GPAT4, a key triglyceride synthesis enzyme, contributes to MAFLD through abnormal overexpression that causes excessive hepatocellular triglyceride accumulation and disrupts lipid droplet dynamics, while LDHA affects MAFLD progression by altering glycolysis, energy metabolism, and redox state to impact lipid droplet transport and hepatic steatosis. Additionally, GPAT4 and LDHA act as late and early cargos for lipid transport from the endoplasmic reticulum to nascent lipid droplets.In our research, it has been indicated that FXR can regulate GPAT4 and LDHA to improve MAFLD[ 12 ].This study focused on GPAT4 based on these results. LDAH and GPAT4 are two proteins that target the ER to LD, influencing the transport of proteins from the ER to LDs, thereby reducing lipid droplet synthesis. This research discussed the regulation of GPAT4 by FXR, yet the exploration of the mechanism regarding LDAH was not in depth. Our research investigated SNS and FXR agonist OCA in improving MAFLD, finding they had similar effects: activating hepatic FXR altered expressions of lipolysis- and lipophagy-related proteins (HSL, ATGL, MAGL, p62, LC3Ⅱ, Beclin1), inhibited lipid droplet transporters GPAT4 and LDHA, with FXR acting as an upstream transcriptional inhibitor of GPAT4 to reduce lipid accumulation. Additionally, SNS serum and OCA were effective in HepG2 cells, and inhibiting FXR in normal cells promoted lipid production, offering a potential clinical prevention idea for MAFLD. This study explored SNS’s multi-component mechanism via HPLC and molecular docking, identifying seven active components (including Neohesperidin, Peoniflorin, and Glycyrrhizin) with known roles in alleviating MAFLD-related processes like lipid accumulation and inflammation[ 37 ]. All components showed strong binding affinity to FXR and GPAT4 (binding energies < -7 kcal/mol), with Glycyrrhizin exhibiting the lowest, Liquiritin (to FXR) and Neohesperidin/Hesperidin (to GPAT4) showing high selectivity, supported by stable binding in molecular dynamics simulations. These findings indicate SNS acts through multi-component synergistic targeting of FXR and GPAT4, laying a foundation for its material basis research pending further in vitro and in vivo validation. Liver IHC showed FXR and GPAT4 were prominently localized in hepatic sinusoids and vascular epithelial cells, with slight but insignificant co-localization in cells, though co-IP confirmed their important protein interaction—this localization provides new insights into hepatic sinusoids’ role in lipid droplet metabolism while highlighting the study’s limitations. Additionally, questions about how FXR localization affects lipid droplet regulation, sinusoidal substance exchange’s relationship with lipid metabolism, and FXR’s role in these processes merit further exploration. The dual-luciferase reporter assay studies gene expression regulation by fusing a gene’s promoter to a luciferase reporter and co-transfecting with a transcription factor vector; luciferase activity changes indicate the transcription factor’s binding ability and regulatory effect on the promoter. For example, it demonstrated that p53 binds to the p21 gene promoter and activates its transcription, crucial for cell cycle regulation[ 38 ]. This study used dual-luciferase assays to establish that FXR transcriptionally inhibits GPAT4, confirming their direct upstream-downstream regulatory relationship, though cell immunofluorescence showed no significant co-localization specificity. With mutations at three predicted sites, FXR may act as GPAT4’s direct upstream regulator, but their specific binding sites require further exploration. However, this study has some limitations. The dosage of traditional Chinese medicine used in this study was calculated based on the conversion of body surface area between adults and mice, and since this study aimed to further explore the mechanism following the previous articles of our research group, no gradient of Chinese medicine dosage was designed. Meanwhile, this study screened the active components in the aqueous decoction of SNS; in subsequent studies, we will continue to detect the active components in SNS-containing serum and conduct in vivo and in vitro experimental studies on these active components. Conclusion This study verified that SNS can effectively ameliorate lipid droplet deposition in both the MAFLD mouse and cell models. It explored the mechanism by which SNS improves MAFLD through the FXR-GPAT4 axis and preliminarily investigated that the active components in SNS can bind to FXR and GPAT4, thus providing a mechanism innovation and experimental data support for the improvement of MAFLD by SNS (Fig. 9 ). Abbreviations ATGL, Adipose Triglyceride Lipase. ER, endoplasmic reticulum. FXR, Farnesol X receptor. GPAT4, glycerol 3-phosphate acyltransferase 4. H&E, hematoxylin and eosin staining. HDL-C, high-density lipoprotein cholesterol. HFD, high-fat diet. IHC, Immunohistochemistry. LC3II /Ⅰ, Microtubule-Associated Protein 1 Light Chain 3- II/Ⅰ. LD, lipid droplet. LDL-C, low-density lipoprotein cholesterol. MAFLD, metabolic dysfunction-associated fatty liver disease. NC, normal control. NS, normal saline. OA, oleic acid. OCA, obeticholic acid. ORO, oil red O. SNS, Si-Ni-San. SPF, specific pathogen free. SQSTM1, sequestosome – 1. TC, total cholesterol. TCM, traditional Chinese medicine. TG, triglycerides. Declarations Acknowledgements Not applicable. Author contributions Haibo Fan: Conceptualization, Investigation, Data curation, Visualization, Writing -Original Draft. Yalei Hou: Conceptualization, Methodology, Writing -Original Draft. Yue Li: Investigation. Zhiwen Zheng: Investigation. Yunfeng Li: Conceptualization, Project administration. Yongmin Li: Conceptualization, Project administration, Funding acquisition. Funding We are grateful for the funding support of the Natural Science Foundation of Hebei Province (No. H2022423346) Data availability The datasets supporting this study’s conclusions are accessible through the corresponding author upon a reasonable request. Ethics approval and consent to participate The animal study was approved by the Animal Ethics Committee of Hebei University of Chinese Medicine on March 5, 2024, with the ethical approval number DWLL202403092. The study was conducted in accordance with the local legislation and institutional requirements. Consent for publication Not applicable. Competing interests The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Author details a Hebei University of Chinese Medicine, Shijiazhuang, 050200, China. b Hebei Key Laboratory of Health Care with Traditional Chinese Medicine, Shijiazhuang, 050200, China. c The seventh People’s Hospital of Hebei Province, Dingzhou, 073000, China. d Hebei Key Laboratory of Chinese Medicine Research on Cardio-cerebrovascular Disease, Shijiazhuang, 050091, China References Badmus OO, Hillhouse SA, Anderson CD, Hinds TD, Stec DE. Molecular mechanisms of metabolic associated fatty liver disease (MAFLD): functional analysis of lipid metabolism pathways. Clin Sci Lond Engl 1979. 2022;136:1347–66. Eslam M, Sarin SK, Wong VW-S, Fan J-G, Kawaguchi T, Ahn SH, et al. 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Cite Share Download PDF Status: Published Journal Publication published 13 Jan, 2026 Read the published version in Chinese Medicine → Version 1 posted Editorial decision: Revision requested 24 Sep, 2025 Reviews received at journal 23 Sep, 2025 Reviews received at journal 18 Sep, 2025 Reviews received at journal 14 Sep, 2025 Reviewers agreed at journal 02 Sep, 2025 Reviewers agreed at journal 02 Sep, 2025 Reviewers agreed at journal 02 Sep, 2025 Reviewers invited by journal 02 Sep, 2025 Editor assigned by journal 26 Aug, 2025 Submission checks completed at journal 26 Aug, 2025 First submitted to journal 25 Aug, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7457859","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":510269892,"identity":"a1b5a651-3de9-43e3-afe0-3ae4a1af598d","order_by":0,"name":"Haibo Fan","email":"","orcid":"","institution":"Hebei University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Haibo","middleName":"","lastName":"Fan","suffix":""},{"id":510269893,"identity":"2a90df2a-1be5-4e87-91af-a17f955c33dc","order_by":1,"name":"Yalei Hou","email":"","orcid":"","institution":"Hebei University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Yalei","middleName":"","lastName":"Hou","suffix":""},{"id":510269894,"identity":"ef287c4e-b279-4820-9711-e20a537e2dd9","order_by":2,"name":"Yue Li","email":"","orcid":"","institution":"Hebei University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Yue","middleName":"","lastName":"Li","suffix":""},{"id":510269895,"identity":"c78b7263-8065-490f-97dc-dca5ae5be0ca","order_by":3,"name":"Zhiwen Zheng","email":"","orcid":"","institution":"Hebei University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Zhiwen","middleName":"","lastName":"Zheng","suffix":""},{"id":510269896,"identity":"350ed36c-26bc-4262-97fa-71bf1f28d9a3","order_by":4,"name":"Yunfeng Li","email":"","orcid":"","institution":"The seventh People’s Hospital of Hebei Province","correspondingAuthor":false,"prefix":"","firstName":"Yunfeng","middleName":"","lastName":"Li","suffix":""},{"id":510269897,"identity":"a1567921-abad-4da8-a299-a87a06e8c924","order_by":5,"name":"Yongmin Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4klEQVRIiWNgGAWjYBACPmYIzcPA3sMAZSfg18IG18JzhlgtcJZEDrFa2HkMHxf82iZjLvn24OOCmm0M/Ow5Bgw/d+BzGI+x8cy+2zyWs/OSjWccu80g2fPGgLH3DF4tZtK8Pbd5DG7nABkNtxkMbuQYMDO2EaPl5hnz3yAt9kRp4fkB1HKDx4wZbIsEQS1sxcZAlTwGZ3KMpYF+4ZE486zgYC8eLfz8hzc+5vlz297g+BnDzwU1t+X425M3PviJRwsDA4cBA7IzeEDEAXwaGBjYHzAw/MGvZBSMglEwCkY4AADzzUirUC3fEAAAAABJRU5ErkJggg==","orcid":"","institution":"Hebei University of Chinese Medicine","correspondingAuthor":true,"prefix":"","firstName":"Yongmin","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2025-08-26 02:23:30","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7457859/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7457859/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s13020-025-01309-5","type":"published","date":"2026-01-13T16:29:56+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":90815454,"identity":"09031543-3629-4a48-9b3c-0e38f10a0985","added_by":"auto","created_at":"2025-09-08 13:07:26","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":4803630,"visible":true,"origin":"","legend":"\u003cp\u003eSNS reduces the body weight of high-fat-fed mice. \u003cstrong\u003eA\u003c/strong\u003e Representative macroscopic image of liver tissues from each group. \u003cstrong\u003eB\u003c/strong\u003e Schematic illustration of the animal model construction and drug administration protocol.\u003cstrong\u003eC\u003c/strong\u003e Body weight changes were recorded over a 12-week period. \u003cstrong\u003eD\u003c/strong\u003e Body weights at week 12. \u003cstrong\u003eE\u003c/strong\u003e Liver weights at the end of the study. \u003cstrong\u003eF\u003c/strong\u003eLiver-to-body weight ratio. NC, normal control; HFD, high-fat diet; SNS, Si-Ni-San; OCA, obeticholic acid. Data are presented as mean ± SD (n = 5). *\u003cem\u003ep\u003c/em\u003e\u0026lt; 0.05, **\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01.\u003c/p\u003e","description":"","filename":"figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-7457859/v1/da164d6873477265db93c1b0.png"},{"id":90816201,"identity":"2fc4348e-caaa-4500-a447-86a575225e9c","added_by":"auto","created_at":"2025-09-08 13:15:27","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":22279856,"visible":true,"origin":"","legend":"\u003cp\u003eSNS Reduces Liver Lipid Droplet Deposition in MAFLD Mice. \u003cstrong\u003eA, B\u003c/strong\u003eRepresentative H\u0026amp;E and Oil Red O (ORO) staining images of liver sections from each group, accompanied by the quantification and statistical analysis. Lipid droplet vacuoles are indicated by black arrows, and hepatocyte ballooning degeneration is indicated by yellow arrows. (n = 5).\u003cstrong\u003e C, D, G, H\u003c/strong\u003e Serum levels of four blood lipid markers are shown. \u003cstrong\u003eE, F\u003c/strong\u003e Serum ALT and AST levels indicate the extent of liver injury. Data are expressed as mean ± SD (n = 5), with statistical significance indicated as *\u003cem\u003ep \u003c/em\u003e\u0026lt; 0.05 and **\u003cem\u003ep\u003c/em\u003e\u0026lt; 0.01.\u003c/p\u003e","description":"","filename":"figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-7457859/v1/4d7c0ec303d84afe8a352fea.png"},{"id":90816204,"identity":"1fa0b967-a646-4e23-a651-6535042a8367","added_by":"auto","created_at":"2025-09-08 13:15:27","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":28337388,"visible":true,"origin":"","legend":"\u003cp\u003eSNS activates FXR to regulate lipolysis and lipophagy and thus improve MAFLD. \u003cstrong\u003eA\u003c/strong\u003eThe level of TBA in the liver. \u003cstrong\u003eB, D, E\u003c/strong\u003e The expression levels of FXR protein in the liver and colon.\u003cstrong\u003e C, F-L\u003c/strong\u003e The expression level of proteins related to lipolysis and lipophagy in the liver. \u003cstrong\u003eM\u003c/strong\u003e IHC shows the expression level and localization of FXR in liver tissues. FXR is positively localized in hepatic stellate cells and hepatic sinusoids. Low magnification (×200); scale bar, 200 μm. High magnification (×400); scale bar, 100 μm. Data are presented as mean ± SD (n = 3), *\u003cem\u003ep \u003c/em\u003e\u0026lt; 0.05. **\u003cem\u003ep \u003c/em\u003e\u0026lt; 0.01.\u003c/p\u003e","description":"","filename":"figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-7457859/v1/b2e153fd906aa59526d94a07.png"},{"id":90815472,"identity":"f91b67a2-4bea-4277-8123-e0e12bd02a3a","added_by":"auto","created_at":"2025-09-08 13:07:27","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":33005658,"visible":true,"origin":"","legend":"\u003cp\u003eSNS activates FXR to modulate lipid droplet transport. \u003cstrong\u003eA-C\u003c/strong\u003e Protein expression levels of GPAT4 and LDAH in liver tissues. \u003cstrong\u003eD\u003c/strong\u003e IHC staining demonstrates GPAT4 expression levels and localization in liver tissues. \u003cstrong\u003eE, F\u003c/strong\u003e ORO staining demonstrated successful model establishment, and SNS and OCA reduced lipid droplet deposition in cells. \u003cstrong\u003eG-L\u003c/strong\u003e Western blot analysis was performed to confirm that SNS and OCA activated FXR and inhibited GPAT4 expression in vitro. Data are presented as mean ± SD (n = 3). *\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05. **\u003cem\u003ep \u003c/em\u003e\u0026lt; 0.01.\u003c/p\u003e","description":"","filename":"figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-7457859/v1/5865033ba17c596a7f516e29.png"},{"id":90815461,"identity":"f182b4d3-1252-428e-85e9-9374b3d0302d","added_by":"auto","created_at":"2025-09-08 13:07:27","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":13499631,"visible":true,"origin":"","legend":"\u003cp\u003eThe effect of inhibiting FXR/GPAT4 on cellular lipid droplet accumulation. \u003cstrong\u003eA, B\u003c/strong\u003e The knockdown efficiency of GPAT4 was detected by WB assay \u003cstrong\u003eC\u003c/strong\u003e The knockdown efficiency of GPAT4 was verified by PCR experiments, and 1505 was selected as the effective GPAT4 knockdown for subsequent experiments. \u003cstrong\u003eD, E\u003c/strong\u003e Oil red O staining was used to analyze the effect of GPAT4 knockdown on lipid droplet deposition in cells. \u003cstrong\u003eF, G, H\u003c/strong\u003e WB and PCR experiments were used to screen and verify the knockdown efficiency of FXR, and 786 was selected for subsequent experiments. \u003cstrong\u003eI, J, K\u003c/strong\u003e After knocking down FXR, WB and PCR experiments were performed to detect the expression level of GPAT4. \u003cstrong\u003eL, M\u003c/strong\u003e Oil red O staining was used to analyze the effect of FXR knockdown on lipid droplet deposition in cells. Data are expressed as mean ± SD (n = 3). *\u003cem\u003ep \u003c/em\u003e\u0026lt; 0.05. **\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01.\u003c/p\u003e","description":"","filename":"figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-7457859/v1/1af02ef3928336e903932347.png"},{"id":90815459,"identity":"0e323dec-e2fb-47c9-a121-de38505604df","added_by":"auto","created_at":"2025-09-08 13:07:27","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":5247315,"visible":true,"origin":"","legend":"\u003cp\u003eDatabase predictions of FXR binding to GPAT4, validated by the dual-luciferase assay. \u003cstrong\u003eA, B\u003c/strong\u003e The UCSC database was used to predict correlation scores and their corresponding p-values. \u003cstrong\u003eC\u003c/strong\u003e Predicted binding sequence of FXR. \u003cstrong\u003eD\u003c/strong\u003eThe JASPAR database analyzed the binding correlation and potential binding sites between FXR and GPAT4. \u003cstrong\u003eE\u003c/strong\u003e Schematic diagram of constructing the plasmid for overexpressing NR1H4. \u003cstrong\u003eF, G\u003c/strong\u003e Western blot was used to detect the expression level of FXR in cells transfected with the plasmid overexpressing NR1H4.\u003cstrong\u003e H\u003c/strong\u003e Schematic diagram of FXR binding sites in the three predicted GPAT4 promoter regions. \u003cstrong\u003eI\u003c/strong\u003eDual - luciferase reporter gene assay was used to detect the binding of transcription factor FXR to the GPAT4 promoter region. Data are presented as mean ± SD (n = 3). *\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05. **\u003cem\u003ep \u003c/em\u003e\u0026lt; 0.01.\u003c/p\u003e","description":"","filename":"figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-7457859/v1/740484a54a37bf9ca1bc805d.png"},{"id":90815455,"identity":"2e150562-5611-4016-b13b-a4f4a6a06bef","added_by":"auto","created_at":"2025-09-08 13:07:26","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":1303704,"visible":true,"origin":"","legend":"\u003cp\u003eSeven active components in the aqueous decoction of SNS were screened by HPLC. \u003cstrong\u003eA\u003c/strong\u003e HPLC chromatogram of the concentrated solution of SNS. \u003cstrong\u003eB\u003c/strong\u003e HPLC chromatogram of the mixed standard solution. Peaks 1, 2, 3, 4, 5, 6, and 7 correspond to the seven active components, respectively.\u003c/p\u003e","description":"","filename":"figure7.png","url":"https://assets-eu.researchsquare.com/files/rs-7457859/v1/d81fb932024d308f5d29acaa.png"},{"id":90815469,"identity":"bf13f5d4-86a0-48f0-a092-35864357f702","added_by":"auto","created_at":"2025-09-08 13:07:27","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":26990647,"visible":true,"origin":"","legend":"\u003cp\u003eThe active components of SNS can bind to FXR and GPAT4. \u003cstrong\u003eA\u003c/strong\u003e Visualization of the binding of Glycyrrhizin to FXR. \u003cstrong\u003eB\u003c/strong\u003e Visualization of the binding of Hesperidin to FXR. \u003cstrong\u003eC\u003c/strong\u003e Visualization of the binding of Liquiritin to FXR. \u003cstrong\u003eD\u003c/strong\u003e Visualization of the binding of Neohesperidin to FXR. \u003cstrong\u003eE\u003c/strong\u003e Visualization of the binding of Isorhamnetin to FXR. \u003cstrong\u003eF\u003c/strong\u003e Visualization of the binding of Peoniflorin to FXR. \u003cstrong\u003eG \u003c/strong\u003eVisualization of the binding of Nobiletin to FXR. \u003cstrong\u003eH \u003c/strong\u003eVisualization of the binding of Glycyrrhizin to GPAT4. \u003cstrong\u003eI \u003c/strong\u003eVisualization of the binding of Hesperidin to GPAT4.\u003cstrong\u003e J \u003c/strong\u003eVisualization of the binding of Neohesperidin to GPAT4.\u003cstrong\u003e K \u003c/strong\u003eVisualization of the binding of Peoniflorin to GPAT4.\u003cstrong\u003e L \u003c/strong\u003eVisualization of the binding of Liquiritin to GPAT4.\u003cstrong\u003e M \u003c/strong\u003eVisualization of the binding of Isorhamnetin to GPAT4.\u003cstrong\u003e N \u003c/strong\u003eVisualization of the binding of Nobiletin to GPAT4. \u003cstrong\u003eO\u003c/strong\u003e The RMSD values of the FXR-Liquiritin complex over time. \u003cstrong\u003eP\u003c/strong\u003eThe Rg values of the FXR-Liquiritin complex over time. \u003cstrong\u003eQ\u003c/strong\u003e The RMSF values of the FXR-Liquiritin complex over time. \u003cstrong\u003eR \u003c/strong\u003eThe RMSD values of the GPAT4-Neohesperidin complex over time. \u003cstrong\u003eS\u003c/strong\u003e The Rg values of the GPAT4-Neohesperidin complex over time. \u003cstrong\u003eT\u003c/strong\u003e The RMSF values of the GPAT4-Neohesperidin complex over time.\u003c/p\u003e","description":"","filename":"figure8.png","url":"https://assets-eu.researchsquare.com/files/rs-7457859/v1/2afd85c52e2667bd6213dcc7.png"},{"id":90816640,"identity":"13fe4406-617c-4d3a-93cb-d5c79a964330","added_by":"auto","created_at":"2025-09-08 13:23:27","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":4856613,"visible":true,"origin":"","legend":"\u003cp\u003eAs illustrated in the figure, SNS activates hepatic FXR, inhibits the lipid droplet transport protein GPAT4, and modulates the expression of proteins involved in lipolysis and lipophagy, including P62, Beclin1, LC3Ⅰ/Ⅱ, HSL, ATGL, and MAGL. Activation of hepatic FXR inhibits GPAT4 expression. Conversely, when FXR is inhibited, lipid droplet deposition in liver cells worsens, leading to feedback upregulation of GPAT4 expression. The FXR-GPAT4 axis was validated using a dual-luciferase assay.\u003c/p\u003e","description":"","filename":"figure9.png","url":"https://assets-eu.researchsquare.com/files/rs-7457859/v1/9b58efdffc42086eedacbd1b.png"},{"id":100614766,"identity":"5bad8594-b480-4367-9dc5-8d618b30120c","added_by":"auto","created_at":"2026-01-19 17:24:29","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":129150438,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7457859/v1/76cd4987-0d20-40e4-bc9e-566e3d16a4ef.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Si-Ni-San improves the deposition of lipid droplets in MAFLD through modulating the FXR-GPAT4 axis","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eMetabolic-associated fatty liver disease (MAFLD) is an excessive accumulation of fat in the liver that is closely associated with metabolic dysfunction and insulin resistance in the form of overweight or obesity [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. In 2020, an international consensus changed non-alcoholic fatty liver disease (NAFLD) to MAFLD [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. MAFLD can progress to metabolic dysfunction-associated steatohepatitis (MASH), liver cirrhosis, and hepatocellular carcinoma [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. The global incidence rate of MAFLD is 25%, which has led to high costs for the global health system. Approximately 1.7\u0026nbsp;billion people have suffered from MAFLD [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. By 2030, MAFLD will affect about 33.5% of the adult population, and MASH will affect about 27% of cases [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Currently, the \"multiple hits\" theory accepted by most researchers is the combined result of chronic inflammation, oxidative stress, epigenetics, genetic susceptibility factors such as obesity, high-fat diet (HFD) intake, and insulin resistance (IR) [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Due to the complex pathogenesis of MAFLD, there are currently no approved effective therapeutic drugs [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Therefore, this study explored the efficacy and mechanism of traditional Chinese medicine (TCM) in the prevention and treatment of MAFLD.\u003c/p\u003e\u003cp\u003eBile acids (BAs) are bioactive molecules synthesized in the liver and secreted into the intestine through the bile duct [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. BAs may have great potential as a therapeutic breakthrough in the treatment of MAFLD. Farnesol X receptor (FXR) is a key endogenous receptor of BAs, and it mainly expresses in hepatocytes and intestinal epithelial cells [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. In the liver, FXR reduces hepatic lipids through the small heterodimer mate (SHP)-sterol regulatory element-binding protein 1c (SREBP1c) pathway-mediated hepatic de novo lipogenesis [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Additionally, FXR activation enhances the transcriptional activity of peroxisome proliferator-activated receptor α (PPARα), promoting fatty acid β-oxidation [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. In summary, bile acid homeostasis is a key point for improving MAFLD, and FXR is widely recognized as a landmark target in the MAFLD field.\u003c/p\u003e\u003cp\u003eLipid droplets (LDs) are important subcellular organelles that maintain lipid homeostasis by coordinating lipid synthesis, lipid storage, and lipid secretion, and they are surrounded by phospholipid monolayers around the lipid core. Two major pathways mediate LD protein targeting. Glycerol 3-phosphate acyltransferase 4 (GPAT4) is involved in one pathway, the endoplasmic reticulum (ER) -LD targeting, and GPAT4 is transported to LDs through the ER-LD membrane bridge [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. GPAT4 is predominantly distributed in hepatocytes and is expressed only in the ER. At the subcellular level, GPAT4 can be transported from the ER to the lipid droplets and promote the growth of the lipid droplets [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Studies have shown that GPAT4\u003csup\u003e\u0026minus;/\u0026minus;\u003c/sup\u003e mice lose 25% of their body weight and are resistant to diet-induced obesity and hereditary obesity, with a 45% reduction in total GPAT activity and TAG content in the liver of GPAT4\u003csup\u003e\u0026minus;/\u0026minus;\u003c/sup\u003e mice [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. However, whether hepatic FXR can affect GPAT4 and then improve lipid accumulation is not clear.\u003c/p\u003e\u003cp\u003eLipolysis and lipophagy play crucial roles in lipid metabolism and are closely associated with MAFLD. Lipolysis, mediated by enzymes such as adipose triglyceride lipase (ATGL), Monoacylglycerol Lipase (MAGL), and hormone-sensitive lipase (HSL), breaks down triglycerides stored in adipocytes, releasing fatty acids for energy utilization [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. In MAFLD, impaired lipolysis can lead to excessive lipid accumulation in the liver. Lipophagy, a selective form of autophagy related to lipid metabolism, involves proteins like p62, Beclin1, and LC3. P62 acts as a linker molecule, recruiting lipid droplets to the autophagosome formation site. Beclin1 initiates the autophagy process, and LC3 is conjugated to the autophagosome membrane, facilitating lipid degradation [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Dysfunction in lipophagy can disrupt the balance of lipid turnover in the liver, contributing to the progression of MAFLD. Several studies have shown that in MAFLD models, decreased expression of ATGL and abnormal activation of p62-mediated lipophagy were observed, leading to aggravated hepatic steatosis [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eTCM has certain advantages in the treatment of MAFLD [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Clinically, most patients present with symptoms such as distending pain in the hypochondrium, abdominal fullness, fatigue, light red tongue, and white greasy coating of the tongue. Clinical studies have shown that regulating the liver and spleen is an effective treatment for MAFLD patients [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Si-Ni-San (SNS) is one of the Classic prescriptions for Shaoyang disease, which is composed of equal proportions of Bupleurum falcatum L., Citrus aurantium L., Paeonia lactiflora Pall., and Glycyrrhiza aspera Pall. SNS has the effect of penetrating evil and relieving depression, relieving the liver and spleen. Our previous work demonstrated that SNS reduced LD deposition in MAFLD and confirmed that SNS could improve LD deposition by inhibiting YAP1 expression [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. While this study investigated the relationship between FXR and GPAT4 and the mechanism by which SNS improves LD deposition in MAFLD.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Preparation of the SNS decoction for animals and drug serum for cells\u003c/h2\u003e\u003cp\u003eFollowing our previous study [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], equal masses of bupleurum, peony, bitter orange, and licorice (sourced from the National Medical Hall of Hebei University of Traditional Chinese Medicine) were mixed in a 1:1:1:1 ratio and decocted twice. The two resulting solutions were combined and stored at 4℃.\u003c/p\u003e\u003cp\u003eHealthy male Sprague-Dawley (SD) rats (5\u0026ndash;6 weeks old, weighing 180\u0026thinsp;\u0026plusmn;\u0026thinsp;10 g) were obtained from Changsheng Biotechnology Co., Ltd. (Liaoning, China). After a one-week acclimation period, the rats were administered SNS decoction (3.6 g/kg) or normal saline (NS) in equal volumes daily for one week [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Blood was collected from the abdominal aorta after the rats were anesthetized with 1% pentobarbital (0.01 mL/g). The serum was subsequently isolated, inactivated at 56℃, and stored at \u0026minus;\u0026thinsp;80℃ for subsequent cell experiments.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Prepare obeticholic acid (OCA) for animals and cells\u003c/h2\u003e\u003cp\u003eObeticholic acid (OCA), used in this study, was obtained from Shanghai Aladdin Biochemical Technology Co., Ltd. For the animal experiments, OCA was dissolved in corn oil to prepare a 10 mg/kg solution for intragastric administration to mice. For the cell experiments, OCA was dissolved in dimethyl sulfoxide (DMSO) to achieve a final concentration of 1 \u0026micro;mol/L.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 High Performance Liquid Chromatography (HPLC)\u003c/h2\u003e\u003cp\u003eThe SNS formulation contained equal masses (6 g each) of Paeoniae Radix Alba (Baishao), Bupleuri Radix (Chaihu), Aurantii Fructus Immaturus (Zhishi), and Glycyrrhizae Radix et Rhizoma Praeparata (Zhigancao). Botanical materials were hydrated in 10-fold distilled H₂O (v/w) for 60 min, followed by sequential extraction: primary decoction at vigorous boiling (100\u0026deg;C, 20 min), secondary extraction at gentle simmering (90\u0026deg;C, 40 min). The combined filtrates were gauze-filtered and concentrated to 70 mg/mL via rotary evaporation.\u003c/p\u003e\u003cp\u003eThe qualitative analysis of SNS was performed on an Agilent Series 1260 Infinity HPLC system (Agilent Technologies Inc., Santa Clara, CA, USA) equipped with a Zorbax StableBond-AQ C18 column (250\u0026times;4.6 mm, 5 \u0026micro;m) (Agilent Technologies Inc) maintained at 30\u0026deg;C. The mobile phase consisted of acetonitrile (solvent A) and water with 0.1% phosphoric acid (solvent B), and the following linear gradient elution procedure was used: 0\u0026ndash;5 min, 5\u0026ndash;10% A; 5\u0026ndash;14 min, 10\u0026ndash;12% A; 14\u0026ndash;25 min, 12\u0026ndash;18% A; 25\u0026ndash;50 min, 18-23.5% A; 50\u0026ndash;55 min, 23.5\u0026ndash;28.5% A; 55\u0026ndash;70 min, 28.5\u0026ndash;36% A; 70\u0026ndash;75 min, 36\u0026ndash;40% A; 75\u0026ndash;85 min, 40\u0026ndash;44% A; 85\u0026ndash;90 min, 44\u0026ndash;90% A. This analysis was performed at a wavelength of 240 nm with a flow rate of 1 mL/min and an injection volume of 10 \u0026micro;L.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Animal study\u003c/h2\u003e\u003cp\u003e The proposed experimental protocol was authorized by the Animal Ethics Committee of Hebei University of Chinese Medicine on March 5, 2024, with the ethical approval number DWLL202403092. Forty male C57BL/6 mice aged 6\u0026ndash;7 weeks, certified as specific pathogen-free (SPF), were purchased from Changsheng Biotechnology Co., Ltd. (Liaoning, China). The mice were housed in a temperature-controlled environment with a 12-hour light/dark cycle. After a one-week acclimation period, the mice were randomly assigned to four groups with 10 mice in each group.\u003c/p\u003e\u003cp\u003eNC group: Mice were maintained on a normal diet with free access to water throughout the experiment and received intragastric administration of distilled water at 11\u0026ndash;12 weeks. The remaining three groups were fed a high-fat diet (HFD) (D12451, Huanyu Hekang Biotechnology, Henan, China) with free access to water throughout the experiment. At 11\u0026ndash;12 weeks, they received different interventions: HFD group: Intragastric administration of distilled water; SNS group: Intragastric administration of SNS decoction (5.2 g/kg/d, 0.1 mL/mouse); OCA group: Intragastric administration of OCA (10 mg/kg/d). As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB, after the experiment, the body weights and liver weights of the mice were measured, and the liver-to-body weight ratio (liver weight/body weight) was calculated.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.5 The Serum Biochemical Assays\u003c/h2\u003e\u003cp\u003eSerum samples were collected to detect triglyceride (TG), total cholesterol (TC), alanine aminotransferase (ALT), aspartate aminotransferase (AST), high-density lipoprotein cholesterol (HDL-C), and low-density lipoprotein cholesterol (LDL-C) levels. This experiment was completed by Servicebio Biotechnology Co., Ltd. The Reado/Changchun Huili kit was used for the assay, which was automatically measured by a fully automatic biochemistry analyzer.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e2.6 Cell lines\u003c/h2\u003e\u003cp\u003eThe HepG2 cell line was purchased from the American Type Culture Collection (Manassas, VA, USA) and cultured in RPMI 1640 medium (Solarbio, Beijing, China) with 10% fetal bovine serum (Gibco, USA) in an atmosphere with 5% CO\u003csub\u003e2\u003c/sub\u003e and 100% humidity at 37 ℃. Oleic acid (OA) was purchased from Shanghai Macklin Biochemical Co., Ltd. (C11391320, Macklin). For incubation, the stock solutions of OA (100 \u0026micro;mol/L) were prepared in isopropanol at a concentration of 20 mmol/L and then temporarily diluted in RPMI 1640 medium with 10% fetal bovine serum, so that the final concentration of the isopropanol did not exceed 1% [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Cells were inoculated into 6-well plates and 96-well plates and treated with OA solution and SNS/NS serum for 24 h. Based on our previous study, we used an SNS concentration of 15% in our experiments, which did not affect cell viability [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e2.7 Histopathological analysis\u003c/h2\u003e\u003cp\u003eLiver tissue from each group of mice was fixed in 4% formalin, embedded in paraffin, and prepared as paraffin tissue sections with a thickness of 5 \u0026micro;m, and stained with a hematoxylin-eosin kit (DH0006, LEAGENE). In order to assess Lipid droplets (LD) deposition. The deposition of LD in each group was observed microscopically (Leica DM2500, Germany).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e2.8 Oil red O staining\u003c/h2\u003e\u003cp\u003eLipid accumulation in vivo was assessed by oil red O staining (G1261, Solarbio, China). For liver tissue, frozen tissue sections were prepared at a thickness of 10\u0026micro;m. After fixation in formalin, the sections were washed in 60% isopropanol and then stained with freshly prepared oil red O working solution for 15 min. Following rinsing with 60% isopropanol, the sections were examined under a microscope.\u003c/p\u003e\u003cp\u003eAccording to the manufacturer\u0026rsquo;s instructions of the Oil red O kit (G1262, Solarbio), model cells were seeded in 6-well plates (3 \u0026times; 10\u003csup\u003e5\u003c/sup\u003e cells/well). After the cells were washed with PBS, ORO fixative was added to each well for 20\u0026ndash;30 min, and 60% isopropyl alcohol was added to each well for 5 min. Then, ORO staining solution was used to stain the cells for 10\u0026ndash;20 min. After the cells were washed with water, Mayer hematoxylin staining solution was used to restain the nucleus for 1\u0026ndash;2 min. Then, we added ORO buffer for 1 min. Finally, we used glycerin gelatin to seal the tablets. The changes in LD were measured with ImagePro-Plus v6.0 software (Media Cybemetic, USA).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e2.9 Quantitative Real-time PCR (qRT-PCR)\u003c/h2\u003e\u003cp\u003eTotal RNA was extracted from cells and reverse transcribed into complementary DNA (cDNA) for the assessment of gene expression levels. These reactions were amplified in a LightCyclerR 480 Quantitative PCR System (Roche, USA), and the resulting data were analyzed using the ΔΔCt method. This enabled the determination of transcript expression levels by normalizing to the expression levels of a housekeeping gene. The primer sequences used in this study are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSequences of qPCR primers\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGene name\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSequence (5\u0026rsquo; to 3\u0026rsquo;)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eFXR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eACTTCCGTCTGGGCATTCTGAC\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eGCTGTAAGCAGAGCATACTCCTC\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eGPAT4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eACTGGCTTTCACAGGGATTAG\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCGCAGATCCGGTAACACATTA\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eGAPDH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eATCCCATCACCATCTTCC\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCCATCACGCCACAGTTC\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e2.10 Western blot\u003c/h2\u003e\u003cp\u003eTotal protein was extracted using a protein extraction kit (PC201Plus, Epizyme). Then, the protein concentration was measured using a BCA kit (PC0020, Solarbio). 50 \u0026micro;g of the protein samples were loaded onto an SDS-PAGE gel. Following electrophoresis, the protein bands were transferred to PVDF membranes. Whereafter, the membranes were first blocked with 5% nonfat dry milk\u0026thinsp;+\u0026thinsp;TBST. Western blot analysis was performed with primary antibodies against NR1H4 (FXR) (bs-12867R, Bioss), GPAT4 (bs-15587R, Bioss), P62 (380612-50, Zenbio), HSL (344379-50, Zenbio), MAGL (PAD223Hu01, Cloud-Clone Corp), Beclin1 (BS-1353R-50ul, Bioss), ATGL (R389129-50, Bioss), LC3Ⅰ/Ⅱ (ABC929, Merck), and β-actin (bs-0061R, Bioss). The secondary antibodies were goat anti-rabbit antibody (abs20002, Absin) and goat anti-mouse antibody (GB23301, Servicebio). The bands were detected using an ECL detection kit (sb-wb012, Shanghai Shenger Biotechnology Co., Ltd). The results were quantified using ImageJ software (National Institutes of Health, America).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e2.11 Immunohistochemistry assay (IHC)\u003c/h2\u003e\u003cp\u003eThe liver tissue samples were paraffin-embedded and cut into 5 \u0026micro;m-thick sections. IHC antibodies for NR1H4 (FXR) (bs-12867R, Bioss) and GPAT4 (bs-1924R, Bioss) were used. The sections were developed with a 3,3-diaminobenzidine (DAB) kit, counterstained with hematoxylin, differentiated, and rinsed with warm water to stain the nuclei blue; after this, the sections were dehydrated, made transparent, and sealed. Finally, the sections were microscopically observed with the magnification power of 20 and 40 for brown peroxidase in liver tissue. The optical density was measured using Image-Pro-Plus v6.0 software (Media Cybemetic, USA).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e2.12 RNA interference\u003c/h2\u003e\u003cp\u003eSmall interfering RNA (siRNA)-to knock down FXR (si-FXR), GPAT4 siRNA (si-GPAT4), and negative control NC (si-NC) were designed and synthesized by GENEPHARMA (Jiangsu, China). HepG2 cell lines were transfected with the siRNAs using sirna mate plus (G04026, GENEPHARMA, China). Cells were seeded in complete medium approximately 12 h before transfection. Then, siRNAs mixed with siRNA Mate Plus were added to the cells with fresh Opti-MEM medium (Gibco, USA). SiRNAs were transfected at a concentration of 50 nM. After 6 h, the medium containing siRNAs and sirna mate plus was replaced with complete medium. The sequences of siRNAs are shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eThe sequences of siRNAs\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eName\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eForward\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eReverse\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFXR-homo-651\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCCACAGAUUUCCUCGUCAUTT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAUGACGAGGAAAUCUGUGGTT\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFXR-homo-786\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGCAGAGAUGCCUGUAACAATT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eUUGUUACAGGCAUCUCUGCTT\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFXR-homo-877\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCCUCUGGAUACCACUAUAATT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eUUAUAGUGGUAUCCAGAGGTT\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGPAT4-Homo-1362\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGCUGAGCAGAACCAAUUAUTT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAUAAUUGGUUCUGCUCAGCTT\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGPAT4-Homo-1505\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGCACAACUGUGGUGGGAUATT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eUAUCCCACCACAGUUGUGCTT\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGPAT4-Homo-1965\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGCACAACUGUGGUGGGAUATT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAUACUUGAUAGCAACAGGGTT\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e2.13 Database prediction and screening\u003c/h2\u003e\u003cp\u003eAnalyze the correlation and binding sites between the GPAT4 promoter region and the transcription factor FXR using the JASPAR database (www.jaspar.elixir.no\u003c/a\u003e\u003c/span\u003e\u003cspan address=\"http://www.jaspar.elixir.no\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Predict and screen the transcription factors of GPAT4 using the UCSC Genome Browser (www.genome.ucsc.edu\u003c/a\u003e\u003c/span\u003e\u003cspan address=\"http://www.genome.ucsc.edu\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and obtain the binding score of FXR and GPAT4.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003e2.14 Dual-luciferase reporter assays\u003c/h2\u003e\u003cp\u003eSix plasmids (pcDNA3.1(+)-NR1H4-3xFLAG, pcDNA3.1(+)-MCS-3xFLAG, pGL4.10-GPAT4 promoter (WT), pGL4.10, pGL4.10-GPAT4 promoter (MUT), pRL-CMV) were purchased from Obio Technology Corp (Shanghai, China) for the interaction of FXR and GPAT4-promoter experiment. HepG2 cells were randomly divided into the following four groups: pcDNA3.1(+)-NR1H4-3Xflag\u0026thinsp;+\u0026thinsp;pGL4.10-GPAT4 promoter (WT); pcDNA3.1(+)-NR1H4-3xFLAG\u0026thinsp;+\u0026thinsp;pGL4.10-GPAT4 promoter (MUT); pcDNA3.1(+)-MCS-3xFLAG\u0026thinsp;+\u0026thinsp;pGL4.10-GPAT4 promoter (WT); pcDNA3.1(+)-MCS-3xFLAG\u0026thinsp;+\u0026thinsp;pGL4.10-GPAT4 promoter (MUT) Four groups of cells were transfected with the corresponding plasmids and the pRL-CMV plasmid for 24 h. The cells were collected to calculate the ratio of Firefly to Renilla luciferase activity using the Dual Luciferase Reporter Gene Assay Kit (Promega, Madison, WI, USA).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003e2.15 Molecular docking simulations\u003c/h2\u003e\u003cp\u003eThe 3D structure models of core target proteins were downloaded from the Protein Data Bank (RCSB-PDB, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.rcsb.org/\u003c/span\u003e\u003cspan address=\"https://www.rcsb.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and imported into PyMOL 2.5.2 for dehydration and ligand separation. The 2D structures of ligand small molecules were downloaded from the PubChem database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pubchem.ncbi.nlm.nih.gov/\u003c/span\u003e\u003cspan address=\"https://pubchem.ncbi.nlm.nih.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), and key components were converted into mol2 format files using ChemBioOffice. AutoDockTools-1.5.7 was used to add non-polar hydrogens to the receptor protein and ligand small molecules, calculate charges for the protein structure, and identify rotatable bonds in the ligand molecules. Appropriate docking boxes and parameters were set according to the structural sizes of the receptor and ligand, and molecular docking was performed using AutoDockTools 1.5.7. Visualization analysis was conducted using PyMOL.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003e2.16 Molecular dynamics simulations (MD simulations)\u003c/h2\u003e\u003cp\u003eIn this study, Gromacs 2022 was used for molecular dynamics simulations. Force field parameters were obtained using the pdb2gmx tool in Gromacs and the AutoFF web server. During the simulation, the CHARMM36 force field [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] was applied to the molecular parameters of the receptor protein, while the CGenff force field was used for the ligand molecular parameters. The system was solvated by adding a 1 nm TIP3P cubic water box around it. Ions were added to the system using the GROMACS genion tool to achieve electrical neutrality. Long-range electrostatic interactions were handled by the Particle Mesh Ewald (PME) method with a cutoff distance of 1 nm. All bond constraints were implemented via the SHAKE algorithm, and the Verlet leapfrog algorithm was adopted with an integration time step of 1 fs for the molecular dynamics simulation process.\u003c/p\u003e\u003cp\u003ePrior to molecular dynamics simulation, the system underwent energy minimization, which included 3000 steps of steepest descent optimization followed by 2000 steps of conjugate gradient optimization. The optimization steps were as follows: first, the solute was constrained, and energy minimization was performed on water molecules; then, the counterions were constrained for energy minimization; finally, energy minimization was conducted on the entire system without constraints.\u003c/p\u003e\u003cp\u003eThe simulation was run under the conditions of 310 K and an NPT system at constant pressure, with a total simulation time of 100 ns. During the simulation, the g-rmsd, g-rmsf, g-hbond, g-Rg, and g-sasa tools were used to calculate the root mean square deviation (RMSD), root mean square fluctuation (RMSF), hydrogen bonds (HBonds), radius of gyration (Rg), and solvent-accessible surface area (SASA), respectively.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003e2.17 Statistical analysis\u003c/h2\u003e\u003cp\u003eAll measurement data are expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD). All data were analyzed using SPSS 23.0 software. Normality tests were performed on all data. For data satisfying normal distribution, one-way analysis of variance (ANOVA) was used for comparison. When variances were homogeneous, Turkey's method was applied for pairwise comparisons; when variances were heterogeneous, the Dunnett T3 method was used for pairwise comparisons. *\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and **\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01 were considered statistically significant. All graphs were created by GraphPad Prism 8.0 software.\u003c/p\u003e\u003c/div\u003e"},{"header":"3 Results","content":"\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\u003ch2\u003e3.1 SNS reduces the body weight of high-fat-fed mice\u003c/h2\u003e\u003cp\u003eTo examine the effect of SNS on MAFLD, we developed a mouse model of MAFLD induced by HFD (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). Body weight measurements were taken weekly over a 12-week duration. In the HFD group, there was a consistent increase in body weight, with a statistically significant difference observed in comparison to NC group. Commencing in week 10, treatments with SNS and OCA were initiated, leading to either stabilization or a gradual reduction in body weight (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). By the 12th week, body weights were assessed, revealing that the HFD group had the highest body weight, significantly differing from the NC group. Both SNS and OCA adminstration treatments significantly decreased body weight relative to the HFD group, indicating that SNS and OCA are effective in attenuating body weight gain in MAFLD-afflicted mice (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD).\u003c/p\u003e\u003cp\u003eIn our study, we observed that the livers of mice subjected to HFD exhibited enlargement, a yellowish discoloration, and a hardened texture. In contrast, the livers of mice in the SNS and OCA treatment groups closely resembled those in the NC group (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). Subsequent measurements of liver weights indicated that treatment with SNS and OCA resulted in a significant reduction in liver weights in mice with MAFLD induced by a high-fat diet (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE). Additionally, the liver coefficient, defined as the ratio of liver weight to body weight, was calculated (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eF). The findings revealed that the liver coefficient in the HFD group was significantly lower than that in the NC group, whereas the coefficients in the SNS and OCA groups were restored to normal levels. Overall, these phenotypic observations demonstrate that both SNS and OCA effectively ameliorated MAFLD, exhibiting comparable therapeutic efficacy.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\u003ch2\u003e3.2 SNS improves liver lipid droplet deposition in MAFLD mice\u003c/h2\u003e\u003cp\u003eTo investigate the mechanisms by which SNS and OCA alleviate MAFLD, we further examined liver lipid droplet deposition. H\u0026amp;E and Oil Red O (ORO) staining revealed an increase in lipid droplet vacuoles and ORO-positive areas in the HFD group compared to the NC group. In contrast, the SNS and OCA groups exhibited significantly reduced lipid droplet areas compared to the HFD group, indicating that both SNS and OCA effectively alleviated lipid droplet accumulation (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA-B). Serum analysis showed changes in the levels of TC, TG, HDL-C, LDL-C, ALT, and AST. Lipid parameters, including TG, TC, and LDL-C, were elevated in the HFD group but reduced following SNS and OCA treatment. However, SNS had a limited effect on HFD-induced increases in HDL-C levels, while its effects on other parameters were comparable to OCA (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC-D, G-H). Serum ALT and AST are markers of liver injury. Significantly elevated ALT and AST were observed in the HFD group, while SNS and OCA treatment effectively reduced liver injury induced by high-fat diet, suggesting that the drugs do not cause liver injury in mice (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE-F). Histopathological analysis and serum biochemical assays showed that SNS and OCA improved hepatic lipid droplet deposition and liver injury in MAFLD mice.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec23\" class=\"Section2\"\u003e\u003ch2\u003e3.3 SNS activates FXR to regulate lipolysis- and lipophagy-related protein expression to improve MAFLD\u003c/h2\u003e\u003cp\u003eTo investigate the molecular mechanism by which SNS improves MAFLD, the total bile acid (TBA) levels in the livers of MAFLD mice were measured. A high-fat diet significantly elevated hepatic TBA levels, while SNS and OCA treatments restored TBA levels to normal (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). Since FXR is a bile acid receptor, hepatic FXR expression was also analyzed. A high-fat diet suppressed hepatic FXR expression, but SNS and OCA treatments activated its expression (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB, D). FXR in the colon also plays a critical role in MAFLD. In our study, FXR expression in the colons of HFD group mice was reduced, whereas SNS and OCA treatments elevated colonic FXR expression. Meanwhile, the results of liver immunohistochemistry (IHC) showed that SNS and OCA increased the positive localization of FXR in liver tissues, and the localization of FXR was mainly concentrated in hepatic cell nuclei and hepatic sinusoidal epithelial cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eM). These results suggest that SNS alleviates MAFLD by activating FXR in both the liver and colon (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB, E).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFXR activation by OCA prompted further investigation of lipolysis- and lipophagy-related proteins in the liver. SNS and OCA treatments increased ATGL expression and decreased HSL and MAGL expression, promoting lipolysis and improving MAFLD (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC, F, G, I). Regarding lipophagy, SNS and OCA treatments suppressed P62 expression, activated Beclin1, and inhibited LC3Ⅰ expression, with no significant effect on LC3Ⅱ levels. The ratio of LC3Ⅱ/LC3Ⅰ reflects the degree of lipophagy, and both SNS and OCA upregulate this ratio. (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eH, J-L). These findings suggest that SNS and OCA improve MAFLD by activating hepatic FXR, which subsequently regulates lipolysis- and lipophagy-related protein expression.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec24\" class=\"Section2\"\u003e\u003ch2\u003e3.4 SNS activates FXR to modulate lipid droplet transport\u003c/h2\u003e\u003cp\u003eUpon FXR activation, it may influence the targeting of proteins from the ER to LDs. Previous studies have identified multiple cargos involved in both early and late ER-to-LD targeting pathways. In this study, we focused on GPAT4, a late cargo responsible for ER-to-LD targeting [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. A high-fat diet suppressed FXR expression while promoting GPAT4 expression. In contrast, SNS and OCA activated FXR and inhibited GPAT4 expression (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA and B). Meanwhile, the results of liver immunohistochemistry showed that compared with the HFD group, SNS and OCA reduced the positive localization of GPAT4 in liver tissues, and the localization of GPAT4 was mainly concentrated in hepatic cytoplasm and hepatic sinusoidal epithelial cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD). Although we did not systematically investigate the mechanism of endoplasmic reticulum-lipid droplet targeting early cargo, we preliminarily detected the protein level of LDAH in early cargo and found that both SNS and OCA also downregulated LDAH. This may suggest that the drugs also inhibit the early trafficking of lipid droplets (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA, C). This phenomenon was further validated in an MAFLD cell model. Oil Red O staining showed that compared with NS serum, SNS serum significantly alleviated intracellular lipid droplet accumulation, which was consistent with the results of OCA (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE-F). Subsequently, the expression levels of FXR and GPAT4 in the cells were analyzed. Consistent with tissue results, SNS and OCA increased FXR expression and inhibited GPAT4 expression in cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eG-L). However, the precise regulatory mechanism linking FXR and GPAT4 requires further investigation.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec25\" class=\"Section2\"\u003e\u003ch2\u003e\u003cb\u003e3.5 Knockdown of GPAT4 reduced lipid droplet deposition, while knockdown of FXR increased GPAT4 expression\u003c/b\u003e\u003c/h2\u003e\u003cp\u003eTo investigate the role of FXR and GPAT4 in cellular lipid droplet accumulation, siRNA targeting FXR (siRNA-FXR) was transfected into normal HepG2 cells, while siRNA-GPAT4 was transfected into oleic acid-induced HepG2 cells. First, WB and PCR were used to screen and verify the knockdown efficiency of GPAT4, and 1505 was selected as the effective GPAT4 knockdown for subsequent experiments (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA-C). Oil red O staining of cells showed that after effective knockdown of GPAT4 by transfection with 1505, the positive area of lipid droplet deposition was reduced compared with the OA group (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD-E). Meanwhile, WB and PCR were used to screen and verify the knockdown efficiency of FXR, and 786 was selected as the effective FXR knockdown for subsequent experiments (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eF-H). Subsequently, Western blot (WB) and PCR experiments were performed to detect the expression level of GPAT4 in cells after FXR knockdown. The results showed that the expression level of GPAT4 was upregulated after the FXR knockdown. Combined with the finding that GPAT4 expression was inhibited after FXR activation, it was concluded that there is a close relationship between FXR and GPAT4, and transcriptional regulation may be involved (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eI-K). Then, Oil Red O staining was used to observe the effect of FXR knockdown on lipid droplet deposition in cells. The results showed that FXR knockdown led to an increase in lipid droplet deposition in cells. Considering that the inhibition of FXR may induce the development of MAFLD (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eL-M). These findings suggest a close relationship between FXR and GPAT4. As GPAT4 is involved in lipid droplet transport, its inhibition may represent a novel therapeutic approach for improving MAFLD.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec26\" class=\"Section2\"\u003e\u003ch2\u003e3.6 Database predictions of FXR binding to GPAT4, validated by the dual-luciferase assay\u003c/h2\u003e\u003cp\u003eTo investigate the direct regulatory role of FXR on GPAT4, the GPAT4 promoter region sequence was retrieved from the NCBI database. The JASPAR database predicted a correlation and binding site between the GPAT4 promoter and the transcription factor FXR, the binding site located at positions 2116\u0026ndash;2126,1614\u0026ndash;1624,681\u0026ndash;693 in the promoter region (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eD). Similarly, predictions using the UCSC database showed that FXR was the transcription factor most strongly correlated with the GPAT4 promoter, with a binding score of 419 and a p-value of less than 0.01 (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA-B). These results from the two databases suggest a potential binding interaction between FXR and the promoter region of GPAT4. Schematic diagram of the sequence in the FXR binding element (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC)\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThen, the FXR overexpression plasmid and wild-type/mutant plasmids of the GPAT4 promoter region (with binding site mutations) were constructed and transfected into cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eE, H). Western blot analysis showed that FXR expression in cells transfected with the overexpression plasmid was higher than that in the empty vector group (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eF-G). Finally, a dual-luciferase reporter gene assay was performed to detect the luciferase activity values of the binding between the FXR overexpression plasmid/empty vector and the wild-type/mutant GPAT4 promoter regions, respectively. The results showed that in the wild-type group, the luciferase activity of FXR overexpression was lower than that of the empty vector group. However, after mutation of the GPAT4 promoter binding site, there was no significant difference in luciferase activity between the FXR overexpression and empty vector groups. These findings suggest that FXR binds to GPAT4 and inhibits its expression, while mutation of the binding site abolishes this interaction, leading to no significant difference in expression (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eI). This also suggests that SNS may ameliorate lipid droplet deposition in MAFLD hepatocytes through the FXR-GPAT4 axis.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec27\" class=\"Section2\"\u003e\u003ch2\u003e3.7 Seven active components in the aqueous decoction of SNS were screened by HPLC\u003c/h2\u003e\u003cp\u003eHPLC was used to detect and screen the major active components in the aqueous decoction of SNS (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA-B). Based on literature reports on the regulation of MAFLD by SNS active components, seven compounds were selected: Neohesperidin, Peoniflorin, Hesperidin, Glycyrrhizin, Liquiritin, Isorhamnetin, and Nobiletin (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Among them, Neohesperidin, Hesperidin, and Nobiletin are derived from Aurantii Fructus Immaturus (bitter orange), Glycyrrhizin, Liquiritin, and Isorhamnetin from Glycyrrhizae Radix et Rhizoma (licorice), Isorhamnetin also from Bupleuri Radix (bupleurum), and Peoniflorin from Paeoniae Radix Alba (white peony root).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSeven major active components in the aqueous decoction of SNS\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eActive ingredient\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePeak area\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eConcentration (mg/mL)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNeohesperidin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e12578.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.316\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePeoniflorin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e777.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.327\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHesperidin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1107.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.202\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGlycyrrhizin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1191.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.093\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLiquiritin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e727.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.067\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIsorhamnetin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e96.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.005\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNobiletin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e122.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec28\" class=\"Section2\"\u003e\u003ch2\u003e3.8 The active components of SNS can bind to FXR and GPAT4\u003c/h2\u003e\u003cp\u003eTo further investigate the binding of active components in SNS to FXR and GPAT4, these components were then subjected to molecular docking with FXR and GPAT4, respectively. Notably, the largest cavity identified on GPAT4 overlaps with the binding pocket of GPAT1, and three major cavities and multiple minor cavities on the GPAT1 structure (PDB ID: 8E50) were experimentally confirmed as intact binding sites. The binding energies of SNS active components with FXR and GPAT4 are presented in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. First, the binding energies of all active components in SNS with FXR and GPAT4 were less than \u0026minus;\u0026thinsp;7 kcal/mol, indicating a favorable binding affinity. Among them, Glycyrrhizin showed the lowest binding energies with FXR (-14.7 kcal/mol) and GPAT4 (-13.1 kcal/mol), indicating the strongest binding affinity. Second, Liquiritin showed a binding energy of -9.2 kcal/mol with FXR, while Neohesperidin and Hesperidin displayed binding energies of -9.2 kcal/mol and \u0026minus;\u0026thinsp;9.0 kcal/mol with GPAT4, respectively. These values, all less than \u0026minus;\u0026thinsp;9 kcal/mol, suggest relatively stronger binding of Liquiritin to FXR and of Neohesperidin/Hesperidin to GPAT4. Subsequently, molecular docking visualization was performed for each of these seven active components with FXR and GPAT4, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eA-N).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eResults of molecular docking\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKey targets\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePDB ID\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eActive ingredient\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eBinding energy (kcal/mol)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFXR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3FXV\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNeohesperidin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-8.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePeoniflorin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-8.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHesperidin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-8.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eGlycyrrhizin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-14.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLiquiritin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-9.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eIsorhamnetin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-8.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNobiletin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-7.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGPAT4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8E50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNeohesperidin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-9.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePeoniflorin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-8.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHesperidin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-9.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eGlycyrrhizin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-13.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLiquiritin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-8.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eIsorhamnetin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-7.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNobiletin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-7.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eSubsequently, molecular dynamics simulations were conducted to further observe the binding stability between small-molecule active components and target proteins. Among them, Glycyrrhizin showed the highest binding affinity to FXR and GPAT4 (with binding energies of -14.7 kcal/mol and \u0026minus;\u0026thinsp;13.1 kcal/mol). However, since the 3D structure of Glycyrrhizin is unavailable, Liquiritin (ranked second) was selected for molecular dynamics simulation of its binding to FXR, and Neohesperidin (ranked second) was chosen for simulation of its binding to GPAT4. Additionally, both Liquiritin-FXR and Neohesperidin-GPAT4 bindings involve approximately 4\u0026ndash;5 hydrogen bonds, indicating good stability. RMSD is a reliable indicator for evaluating the conformational stability of proteins and ligands, as well as the degree of deviation of atomic positions from their initial positions. A smaller deviation indicates better conformational stability. Both the FXR system and the FXR-Liquiritin complex system reached equilibrium after 5 ns, with final fluctuations around 0.3 nm and 0.22 nm, respectively. The Liquiritin small molecule reached equilibrium after 70 ns, with final fluctuations around 0.39 nm. Thus, the Liquiritin small molecule exhibits high stability when binding to the target protein FXR. Both the GPAT4 system and the GPAT4-Neohesperidin complex system reached equilibrium after 10 ns, with final fluctuations around 0.4 nm and 0.33 nm, respectively. The Neohesperidin small molecule reached equilibrium after 10 ns, with final fluctuations around 0.55 nm. Therefore, the Neohesperidin small molecule also shows high stability when binding to the target protein GPAT4(Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eO, R). The Rg can describe changes in the overall structure and characterize the compactness of protein structures; larger Rg changes indicate a more expanded system. The FXR-Liquiritin complex showed relatively stable fluctuations during movement, suggesting no significant expansion or contraction of the small molecule-target protein complex. The GPAT4-Neohesperidin complex system exhibited slight fluctuations during movement, indicating conformational changes in the small molecule-target protein complex (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eP, S). RMSF can reflect the flexibility of amino acid residues in proteins. The RMSF values of the FXR-Liquiritin complex were relatively low (mostly below 0.3 nm), and those of the GPAT4-Neohesperidin complex were also relatively low (mostly below 0.4 nm), indicating low flexibility and high stability (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eQ, T).\u003c/p\u003e\u003cp\u003eIn summary, the complex systems exhibit stable binding with favorable hydrogen bond interactions. Therefore, the small molecules bind well to the target proteins.\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eMAFLD is now one of the most common chronic liver diseases worldwide, with its incidence rising significantly due to links to obesity, diabetes, and metabolic syndrome\u0026mdash;affecting about 25%-30% of the general population and over 50% in high-risk groups. It can progress to severe liver conditions like fibrosis, cirrhosis, and hepatocellular carcinoma, and is linked to extrahepatic issues such as cardiovascular and metabolic disorders. Its complex pathogenesis, explained by the \"multiple hit\" hypothesis, involves hepatic fat accumulation from lifestyle factors, followed by inflammation and fibrosis, with insulin resistance playing a key role by disrupting lipid metabolism and worsening inflammation[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Additional contributing factors to MAFLD include gut microbiota dysbiosis and genetic predisposition. Management primarily focuses on lifestyle changes\u0026mdash;such as a low-sugar, low-fat, high-fiber diet and at least 150 minutes of weekly moderate aerobic exercise\u0026mdash;along with pharmacological interventions like metformin for insulin resistance, vitamin E for antioxidant effects, statins for lipid regulation, and potential antifibrotic agents like pentoxifylline[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. In conclusion, the management of MAFLD necessitates a comprehensive and individualized approach aimed at enhancing prognosis and reducing the risk of complications.\u003c/p\u003e\u003cp\u003eBAs regulate lipid metabolism in MAFLD via the FXR pathway, which suppresses hepatic lipogenesis genes to reduce triglyceride synthesis. They may interact with TGR5 to activate anti-inflammatory pathways and modulate cytokines, while FXR also inhibits hepatic stellate cell activation to mitigate fibrosis[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Additionally, BAs and gut microbiota interact bidirectionally\u0026mdash;BAs alter microbial composition, and microbial metabolites modify BA metabolism\u0026mdash;jointly influencing MAFLD pathogenesis and progression[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. In our study, we examined the expression of FXR in both the liver and colon and observed that SNS influenced both. However, this study primarily focused on the hepatic FXR mechanism of action. The limited exploration of other mechanisms related to bile acid metabolism and MAFLD constitutes a limitation of this research.\u003c/p\u003e\u003cp\u003eFXR is a key target in MAFLD research, with its activation alleviating the disease through multiple mechanisms. It enhances bile acid transport genes like BSEP to promote hepatic bile acid excretion, reducing toxic accumulation and liver injury, while downregulating lipogenic enzymes (ACC, FAS) to lower triglyceride synthesis and hepatic fat[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Additionally, FXR modulates inflammation by suppressing pro-inflammatory cytokines (TNF-α, IL-6) and inhibits hepatic stellate cell activation to exert anti-fibrotic effects, slowing liver fibrosis progression[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Despite these insights, the precise mechanisms by which hepatic FXR ameliorates MAFLD continue to be the subject of ongoing research.\u003c/p\u003e\u003cp\u003eActivating hepatic FXR effectively improves MAFLD, and this research will further explore its mechanisms. The TCM SNS shows promise in treating MAFLD by regulating lipid metabolism, improving insulin sensitivity, and reducing liver lipid deposition\u0026mdash;its effects involve decreasing YAP1, activating AMPK to inhibit p300, reducing SREBP-1c stability, and suppressing Fasn to lower hepatocyte lipid accumulation[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Additionally, SNS may enhance lipid metabolism via the AKT/AMPK/HSL axis and counteract stress-related MAFLD through the AMPK/SIRT1 pathway[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eATGL, HSL, and MAGL are key lipid-metabolizing enzymes\u0026mdash;ATGL initiates triglyceride hydrolysis, HSL further degrades products, and MAGL acts on monoacylglycerols\u0026mdash;collectively regulating lipid storage and mobilization in adipose tissue and liver[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e].Autophagy-related proteins P62 (selective degradation), Beclin1 (autophagy initiation), and LC3Ⅱ (autophagosome marker) are vital for cellular homeostasis and impact lipid metabolism and liver health[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e].FXR activation modulates the expression/activity of ATGL, HSL, and MAGL to affect lipid catabolism/synthesis, and influences autophagic flux by regulating P62-substrate interactions, Beclin1 activation, and LC3Ⅰ-to-LC3Ⅱ conversion[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. FXR activation modulates the expression/activity of ATGL, HSL, and MAGL to affect lipid catabolism/synthesis, and influences autophagic flux by regulating P62-substrate interactions, Beclin1 activation, and LC3Ⅰ-to-LC3Ⅱ conversion.\u003c/p\u003e\u003cp\u003eGPAT4, a key triglyceride synthesis enzyme, contributes to MAFLD through abnormal overexpression that causes excessive hepatocellular triglyceride accumulation and disrupts lipid droplet dynamics, while LDHA affects MAFLD progression by altering glycolysis, energy metabolism, and redox state to impact lipid droplet transport and hepatic steatosis. Additionally, GPAT4 and LDHA act as late and early cargos for lipid transport from the endoplasmic reticulum to nascent lipid droplets.In our research, it has been indicated that FXR can regulate GPAT4 and LDHA to improve MAFLD[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].This study focused on GPAT4 based on these results. LDAH and GPAT4 are two proteins that target the ER to LD, influencing the transport of proteins from the ER to LDs, thereby reducing lipid droplet synthesis. This research discussed the regulation of GPAT4 by FXR, yet the exploration of the mechanism regarding LDAH was not in depth.\u003c/p\u003e\u003cp\u003eOur research investigated SNS and FXR agonist OCA in improving MAFLD, finding they had similar effects: activating hepatic FXR altered expressions of lipolysis- and lipophagy-related proteins (HSL, ATGL, MAGL, p62, LC3Ⅱ, Beclin1), inhibited lipid droplet transporters GPAT4 and LDHA, with FXR acting as an upstream transcriptional inhibitor of GPAT4 to reduce lipid accumulation. Additionally, SNS serum and OCA were effective in HepG2 cells, and inhibiting FXR in normal cells promoted lipid production, offering a potential clinical prevention idea for MAFLD.\u003c/p\u003e\u003cp\u003eThis study explored SNS\u0026rsquo;s multi-component mechanism via HPLC and molecular docking, identifying seven active components (including Neohesperidin, Peoniflorin, and Glycyrrhizin) with known roles in alleviating MAFLD-related processes like lipid accumulation and inflammation[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. All components showed strong binding affinity to FXR and GPAT4 (binding energies \u0026lt; -7 kcal/mol), with Glycyrrhizin exhibiting the lowest, Liquiritin (to FXR) and Neohesperidin/Hesperidin (to GPAT4) showing high selectivity, supported by stable binding in molecular dynamics simulations. These findings indicate SNS acts through multi-component synergistic targeting of FXR and GPAT4, laying a foundation for its material basis research pending further in vitro and in vivo validation.\u003c/p\u003e\u003cp\u003eLiver IHC showed FXR and GPAT4 were prominently localized in hepatic sinusoids and vascular epithelial cells, with slight but insignificant co-localization in cells, though co-IP confirmed their important protein interaction\u0026mdash;this localization provides new insights into hepatic sinusoids\u0026rsquo; role in lipid droplet metabolism while highlighting the study\u0026rsquo;s limitations. Additionally, questions about how FXR localization affects lipid droplet regulation, sinusoidal substance exchange\u0026rsquo;s relationship with lipid metabolism, and FXR\u0026rsquo;s role in these processes merit further exploration.\u003c/p\u003e\u003cp\u003eThe dual-luciferase reporter assay studies gene expression regulation by fusing a gene\u0026rsquo;s promoter to a luciferase reporter and co-transfecting with a transcription factor vector; luciferase activity changes indicate the transcription factor\u0026rsquo;s binding ability and regulatory effect on the promoter. For example, it demonstrated that p53 binds to the p21 gene promoter and activates its transcription, crucial for cell cycle regulation[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. This study used dual-luciferase assays to establish that FXR transcriptionally inhibits GPAT4, confirming their direct upstream-downstream regulatory relationship, though cell immunofluorescence showed no significant co-localization specificity. With mutations at three predicted sites, FXR may act as GPAT4\u0026rsquo;s direct upstream regulator, but their specific binding sites require further exploration.\u003c/p\u003e\u003cp\u003eHowever, this study has some limitations. The dosage of traditional Chinese medicine used in this study was calculated based on the conversion of body surface area between adults and mice, and since this study aimed to further explore the mechanism following the previous articles of our research group, no gradient of Chinese medicine dosage was designed. Meanwhile, this study screened the active components in the aqueous decoction of SNS; in subsequent studies, we will continue to detect the active components in SNS-containing serum and conduct in vivo and in vitro experimental studies on these active components.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study verified that SNS can effectively ameliorate lipid droplet deposition in both the MAFLD mouse and cell models. It explored the mechanism by which SNS improves MAFLD through the FXR-GPAT4 axis and preliminarily investigated that the active components in SNS can bind to FXR and GPAT4, thus providing a mechanism innovation and experimental data support for the improvement of MAFLD by SNS (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eATGL, Adipose Triglyceride Lipase. ER, endoplasmic reticulum. FXR, Farnesol X receptor. GPAT4, glycerol 3-phosphate acyltransferase 4. H\u0026amp;E, hematoxylin and eosin staining. HDL-C, high-density lipoprotein cholesterol. HFD, high-fat diet. IHC, Immunohistochemistry. LC3II /Ⅰ, Microtubule-Associated Protein 1 Light Chain 3- II/Ⅰ. LD, lipid droplet. LDL-C, low-density lipoprotein cholesterol. MAFLD, metabolic dysfunction-associated fatty liver disease. NC, normal control. NS, normal saline. OA, oleic acid. OCA, obeticholic acid. ORO, oil red O. SNS, Si-Ni-San. SPF, specific pathogen free. SQSTM1, sequestosome \u0026ndash; 1. TC, total cholesterol. TCM, traditional Chinese medicine. TG, triglycerides.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHaibo Fan: Conceptualization, Investigation, Data curation, Visualization, Writing -Original Draft. Yalei Hou: Conceptualization, Methodology, Writing -Original Draft. Yue Li: Investigation. Zhiwen Zheng: Investigation. Yunfeng Li: Conceptualization, Project administration. Yongmin Li: Conceptualization, Project administration, Funding acquisition.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe are grateful for the funding support of the Natural Science Foundation of Hebei Province (No. H2022423346)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets supporting this study\u0026rsquo;s conclusions are accessible through the corresponding author upon a reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe animal study was approved by the Animal Ethics Committee of Hebei University of Chinese Medicine on March 5, 2024, with the ethical approval number DWLL202403092. The study was conducted in accordance with the local legislation and institutional requirements.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor details\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003csup\u003ea\u003c/sup\u003e Hebei University of Chinese Medicine, Shijiazhuang, 050200, China. \u003csup\u003eb\u003c/sup\u003e Hebei Key Laboratory of Health Care with Traditional Chinese Medicine, Shijiazhuang, 050200, China. \u003csup\u003ec\u003c/sup\u003e The seventh People\u0026rsquo;s Hospital of Hebei Province, Dingzhou, 073000, China. \u003csup\u003ed\u003c/sup\u003e Hebei Key Laboratory of Chinese Medicine Research on Cardio-cerebrovascular Disease, Shijiazhuang, 050091, China\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBadmus OO, Hillhouse SA, Anderson CD, Hinds TD, Stec DE. 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Front Endocrinol. 2022;13:1107071.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZheng K, Zhou W, Ji J, Xue Y, Liu Y, Li C, et al. Si-Ni-San reduces lipid droplet deposition associated with decreased YAP1 in metabolic dysfunction\u0026ndash;associated fatty liver disease. J Ethnopharmacol. 2023;305:116081.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhu F, Li Y-M, Feng T-T, Wu Y, Zhang H-X, Jin G-Y, et al. Freeze-dried Si-Ni-San powder can ameliorate high fat diet-induced non-alcoholic fatty liver disease. World J Gastroenterol. 2019;25:3056\u0026ndash;68.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCheng F, Ma C, Wang X, Zhai C, Wang G, Xu X, et al. Effect of traditional Chinese medicine formula Sinisan on chronic restraint stress-induced nonalcoholic fatty liver disease: a rat study. 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Front Endocrinol. 2021;12:799648.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVerbeke L, Mannaerts I, Schierwagen R, Govaere O, Klein S, Vander Elst I, et al. FXR agonist obeticholic acid reduces hepatic inflammation and fibrosis in a rat model of toxic cirrhosis. Sci Rep. 2016;6:33453.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLan T, Geng X, Zhang S, Zeng X, Ying J, Xu Y, et al. Si-Ni-San inhibits hepatic Fasn expression and lipid accumulation in MAFLD mice through AMPK/p300/SREBP-1c axis. Phytomedicine. 2024;123:155209.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLi J, Wu K, Zhong Y, Kuang J, Huang N, Guo X, et al. Si-Ni-SAN ameliorates obesity through AKT/AMPK/HSL pathway-mediated lipolysis: Network pharmacology and experimental validation. J Ethnopharmacol. 2023;302:115892.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhang N, Liu T, Wang J, Xiao Y, Zhang Y, Dai J, et al. Si-Ni-San Reduces Hepatic Lipid Deposition in Rats with Metabolic Associated Fatty Liver Disease by AMPK/SIRT1 Pathway. Drug Des Devel Ther. 2023;17:3047\u0026ndash;60.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYang A, Mottillo EP. Adipocyte lipolysis: from molecular mechanisms of regulation to disease and therapeutics. Biochem J. 2020;477:985\u0026ndash;1008.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFilali-Mouncef Y, Hunter C, Roccio F, Zagkou S, Dupont N, Primard C, et al. The m\u0026eacute;nage \u0026agrave; trois of autophagy, lipid droplets and liver disease. Autophagy. 2022;18:50\u0026ndash;72.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRausch M, Samodelov SL, Visentin M, Kullak-Ublick GA. The Farnesoid X Receptor as a Master Regulator of Hepatotoxicity. Int J Mol Sci. 2022;23:13967.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTalari NK, Mattam U, Rahman AP, Hemmelgarn BK, Wyder MA, Sylvestre PB, et al. Functional compartmentalization of hepatic mitochondrial subpopulations during MASH progression. Commun Biol. 2025;8:258.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eItahana Y, Zhang J, G\u0026ouml;ke J, Vardy LA, Han R, Iwamoto K, et al. Histone modifications and p53 binding poise the p21 promoter for activation in human embryonic stem cells. Sci Rep. 2016;6:28112.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"chinese-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"cmed","sideBox":"Learn more about [Chinese Medicine](http://cmjournal.biomedcentral.com)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/cmed/default.aspx","title":"Chinese Medicine","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"MAFLD, SNS, FXR, GPAT4, Molecular dynamics simulations","lastPublishedDoi":"10.21203/rs.3.rs-7457859/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7457859/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eMetabolic-associated fatty liver disease (MAFLD) is a common metabolic disease with complex pathogenesis and a lack of effective treatment. Si-Ni-San (SNS), a traditional Chinese medicine, has emerged as a promising candidate for MAFLD treatment. However, its mechanism of action remains unclear.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eC57BL/6 mice were fed a high-fat diet (HFD) for 12 weeks to establish a mouse model of MAFLD. Second, an MAFLD cell model was established by inducing HepG2 cells with oleic acid. The effects of SNS and the positive drug obeticholic acid on hepatic lipid droplet deposition in MAFLD mice and cell models were evaluated. The expression levels of farnesoid X receptor (FXR) and glycerol 3-phosphate acyltransferase 4 (GPAT4) were detected by Western Blot assay. siRNA assay and Dual-Luciferase reporter assay were used to detect the interaction between FXR and GPAT4. Active components in the aqueous decoction of SNS were screened by HPLC, and their binding to targets was further validated by molecular docking and molecular dynamics simulations.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eSNS ameliorates hepatic lipid droplet deposition in both the MAFLD mouse and cell models. It activates hepatic FXR, inhibits hepatic GPAT4, and regulates proteins related to hepatic lipolysis and lipophagy. FXR reduces lipid droplet accumulation by inhibiting GPAT4. The Dual-Luciferase reporter assay confirms that FXR transcriptionally regulates and inhibits GPAT4 expression. Seven active components in SNS were detected by HPLC, and their binding to FXR and GPAT4 was confirmed through molecular docking and molecular dynamics simulations.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eThis study provides a new mechanistic exploration for FXR in improving MAFLD and broadens the research direction on the mechanisms by which SNS reduces hepatic lipid droplet deposition. It also offers a molecular dynamics basis for subsequent studies on how active components in SNS exert their effects through binding to FXR.\u003c/p\u003e","manuscriptTitle":"Si-Ni-San improves the deposition of lipid droplets in MAFLD through modulating the FXR-GPAT4 axis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-08 13:07:22","doi":"10.21203/rs.3.rs-7457859/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-09-25T02:05:29+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-23T10:20:46+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-18T11:00:00+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-14T16:48:32+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"67465964805351138920383198013509413113","date":"2025-09-03T01:48:15+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"67519825760411484038232361046106427132","date":"2025-09-02T16:06:30+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"75948767658179597296278542548988579010","date":"2025-09-02T12:44:06+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-02T12:06:46+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-08-26T09:44:41+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-08-26T09:43:24+00:00","index":"","fulltext":""},{"type":"submitted","content":"Chinese Medicine","date":"2025-08-26T02:20:19+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"chinese-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"cmed","sideBox":"Learn more about [Chinese Medicine](http://cmjournal.biomedcentral.com)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/cmed/default.aspx","title":"Chinese Medicine","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"4cc0bf2b-1ceb-4a47-a347-d0b753ca57f3","owner":[],"postedDate":"September 8th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-01-19T16:46:56+00:00","versionOfRecord":{"articleIdentity":"rs-7457859","link":"https://doi.org/10.1186/s13020-025-01309-5","journal":{"identity":"chinese-medicine","isVorOnly":false,"title":"Chinese Medicine"},"publishedOn":"2026-01-13 16:29:56","publishedOnDateReadable":"January 13th, 2026"},"versionCreatedAt":"2025-09-08 13:07:22","video":"","vorDoi":"10.1186/s13020-025-01309-5","vorDoiUrl":"https://doi.org/10.1186/s13020-025-01309-5","workflowStages":[]},"version":"v1","identity":"rs-7457859","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7457859","identity":"rs-7457859","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}