Published online Dec 28, 2026. doi: 10.3748/wjg.123033
Revised: July 1, 2026
Accepted: September 4, 2026
Published online: December 28, 2026
Processing time: 181 Days and 14.7 Hours
As the most popular chronic liver disease in the world, one third of metabolic dysfunction-associated steatotic liver disease cases will progress to metabolic dysfunction-associated steatohepatitis (MASH), yet current therapeutic options remain strikingly limited. Jiangtang Qingre formula (JQF) has shown significant clinical efficacy, but the underlying mechanism of it is still not clear.
To elucidate the mechanism by which JQF ameliorates MASH through integrated multi-omics and experimental validation.
The MASH mouse model was constructed using the methionine-choline-deficient diet, and treated with JQF to evaluate its efficacy. Hepatic proteomics and me
JQF alleviated hepatic injury, steatosis, oxidative stress, inflammation, and fibrosis in MASH mice. Multi-omics analysis suggested that JQF may act by regulating arachidonic acid metabolism, and glutathione metabolism, involving multiple targets including Gsta2, Gsta5, Ifi27 L2b, and Lmod3, which were validated by qPCR/WB. Notably, knockdown of Lmod3 partially alleviated FFA-induced AML12 cell injury, an effect further augmented by JQF. Molecular docking confirmed that timosaponins exhibited the strongest binding affinity to these targets. Targeted metabolomics further revealed that timosaponin A III and timosaponin B II were significantly elevated in serum and liver, with timosaponin A III showing the most prominent enrichment in the liver, while sarsasapogenin, as the major secondary metabolite of timosaponins, was detected at high abundance in feces.
JQF ameliorates MASH through multi-target synergistic mechanisms, with Lmod3 identified as a key target and timosaponins as important pharmacologically active substances.
Core Tip: This study demonstrated that Jiangtang Qingre formula (JQF) ameliorates metabolic dysfunction-associated steatohepatitis through multi-target mechanisms. Multi-omics revealed arachidonic acid metabolism, and glutathione metabolism, with multiple targets implicated including Lmod3. Molecular docking confirmed timosaponins exhibited the strongest binding affinity to these targets. Knockdown of Lmod3 partially alleviated free fatty acids-induced AML12 cell injury, an effect further augmented by JQF. Additionally, timosaponin A III and timosaponin B II were effectively absorbed into the bloodstream and enriched in the liver, while their secondary metabolite sarsasapogenin was detected at high abundance in feces, further supporting the role of timosaponins as key pharmacologically active substances.
- Citation: Zhou LJ, Wu JL, Wang H, Han R, Tang C, Fang F, Zhu DP. Integrating proteomics, metabolomics, and molecular docking to elucidate the mechanism of Jiangtang Qingre formula against metabolic dysfunction-associated steatohepatitis. World J Gastroenterol 2026; 32(48): 123033
- URL: https://www.wjgnet.com/1007-9327/full/v32/i48/123033.htm
- DOI: https://dx.doi.org/10.3748/wjg.123033
Metabolic dysfunction-associated steatotic liver disease (MASLD), the most prevalent chronic liver disease globally, currently has an estimated prevalence of 30%-40% and projections exceeding 55% by 2040[1]. Nearly one-third of these patients progress to metabolic dysfunction-associated steatohepatitis (MASH), which substantially confers an increased risk of liver failure, liver transplantation, and all-cause mortality[2]. However, the underlying molecular mechanisms behind MASH remain unclear, and clinical treatment strategies are still very limited[3]. Current approaches consist mainly of lifestyle intervention, bariatric surgery, and pharmacotherapy (to date, only two drugs have been authorized for treating MASH patients[4,5], but these are generally hampered by poor adherence, significant side effects, high cost, or restricted indications, leaving a considerable unmet clinical need.
Traditional Chinese medicine (TCM), with its unique strengths of the synergistic multi-component, multi-target mechanism, holds substantial potential for the management and prevention of MASLD/MASH. Jiangtang Qingre formula (JQF) was developed by Professor Xiao-Lin Tong by adapting the classic formula Dahuang Huanglian Xiexin decoction based on his extensive clinical experience. It is an effective formula commonly used in clinical practice to treat obesity, MASLD/MASH, and type 2 diabetes (T2D). This has additionally been corroborated by a real-world clinical big-data study that included a total of 4561 patients with T2D or prediabetes treated by Professor Xiao-Lin Tong in the outpatient setting between 2000 and 2015, encompassing 19857 consultations[6]. Nevertheless, the specific mechanisms of action of JQF remain to be systematically elucidated, which has limited its further development and clinical dissemination.
JQF is composed of seven herbal medicines: Semen Oryzae Cum Monascom (Hongqu in Chinese), Rhei Radix et Rhizoma (Shengdahuang in Chinese), Paeoniae Radix Rubra (Chishao in Chinese), Coptidis Rhizoma (Huanglian in Chinese), Zingiberis Rhizoma Recens (Ganjiang in Chinese), Anemarrhenae Rhizoma (Zhimu in Chinese), and Citri Reticulatae Pericarpium (Chenpi in Chinese). It has been granted a Chinese national invention patent (Patent No. ZL202310576959.9). Our team’s previous compositional identification and quantification studies[7] have shown that the major active constituents of this formula include coptisine, emodin, berberine, aloe-emodin, paeoniflorin, timosaponin B II, palmatine, and albiflorin, among others. Modern pharmacological research has confirmed that these representative components (e.g., timosaponin B II, berberine, paeoniflorin, and emodin) possess prominent anti-fibrotic activities, antioxidant, and anti-inflammatory[8-11]. Our earlier work has also demonstrated that JQF significantly ameliorates insulin sensitivity and hepatic lipid accumulation in high-fat diet (HFD)-triggered MASLD mice, alleviates hepatic and systemic inflammation, mitigates liver injury, and improves liver fibrosis by targeting the hepatic monocyte-derived macrophage subpopulation[12].
Despite this evidence, whether JQF engages additional pathways to ameliorate MASH remains unknown. Accordingly, this study utilized a well-established methionine-choline-deficient (MCD) diet-triggered MASH mouse model in order to systematically assess the therapeutic efficacy of JQF. We then integrated hepatic metabolomic and proteomic profiling to pinpoint key targets, employed molecular docking to assess the binding interactions between the major bioactive components of JQF and the candidate targets, and conducted targeted metabolomics to quantify the levels of these components in serum, liver, and feces. Subsequent molecular biology and cell experiments were conducted to validate these findings, aiming to define the potential targets and signaling pathways responsible for the therapeutic benefits of JQF in MASH.
JQF was obtained from Kangmei Pharmaceutical Co., Ltd (Puning, China). The formula consists of 7 herbal components in a fixed ratio of 3:10:20:20:6:4:2: Rhei Radix et Rhizoma (Shengdahuang in Chinese) 4.5 g (10 g/bag, lot No. 250200129), Coptidis Rhizoma (Huanglian in Chinese) 15 g (10 g/bag, lot No. 250600221), Anemarrhenae Rhizoma (Zhimu in Chinese) 30 g (10 g/bag, lot No. 250606941), Paeoniae Radix Rubra (Chishao in Chinese) 30 g (5 g/bag, lot No. 250502551), Citri Reticulatae Pericarpium (Chenpi in Chinese) 9 g (10 g/bag, lot No. 250501671), Zingiberis Rhizoma Recens (Ganjiang in Chinese) 6 g (10 g/bag, lot No. 250503591), and Semen Oryzae Cum Monascom (Hongqu in Chinese) 3 g (6 g/bag, lot No. 241219). Polyene phosphatidylcholine capsules (PPC) were obtained from Sanofi Pharmaceutical Co., Ltd. (Beijing, China; 228 mg/capsule, lot No. FBJD0089).
The seven herbal ingredients were weighed according to the specified ratio and subjected to decoction twice with distilled water. Water-to-material ratios of 1:8 and 1:6 (weight/volume) were applied for the first and second decoctions, respectively. Specifically, all herbal pieces were soaked in distilled water for 60 minutes, followed by decoction for
Forty 8-week-old male C57BL/6J mice [Experimental Animal Production License: SCXK (Chuan) 2020-034] were used in this study. All animals were randomly assigned to 5 groups using a random number table method (n = 8 each group): Control group [methionine-choline-sufficient (MCS)], model group (MCD), PPC group (MCD + PPC), JQF medium-dose group (MCD + JQF), and JQF high-dose group [MCD + JQF-high (JQFH)]. Following 1 week of acclimatization, the blank group was maintained on a MCS diet throughout the study, meanwhile the remaining groups were maintained on a MCD diet. Following 2 weeks of modeling, drug interventions were administered for 2 weeks. All animals were accom
Both the MCS control and MCD model groups were gavaged with an equivalent volume of vehicle at 10 mL/kg per day. The PPC group was administered PPC at 207.48 mg/kg/day. The JQF medium- and high-dose groups respectively received JQF at 14.7875 g/kg/day, 29.575 g/kg/day. Drug administration was performed once daily, beginning after one week of adaptive feeding, with two weeks of modeling followed by two weeks of drug intervention. Using the allometric dose translation approach, the equivalent dose for mice was estimated as approximately 9.1 times that of humans. The clinical human dose of JQF is 1.625 g/kg/day (97.5 g crude drug/day for a 60 kg adult). Accordingly, the medium dose in mice was calculated as 14.7875 (1.625 × 9.1) g/kg/day, and the high dose (29.575 g/kg/day) was twice the medium dose. Similarly, the clinical human dose of PPC is 22.8 mg/kg/day (1368 mg/day for a 60 kg adult). Using the same conversion method, the equivalent dose of PPC in mice was calculated as 207.48 (22.8 × 9.1) mg/kg/day. PPC was dissolved in distilled water prior to administration.
Following a 12-hour overnight fast, mice were anesthetized (2% sodium pentobarbital, intraperitoneal), and retro-orbital blood was collected. After clotting, samples were centrifuged (3000 rpm, 15 minutes, 4 °C), and the supernatant was harvested. The serum levels of free fatty acids (FFA), total cholesterol (TC), triglycerides (TG), alanine aminotransferase (ALT), aspartate aminotransferase (AST), high-density lipoprotein cholesterol, and low-density lipoprotein (LDL) cholesterol were assayed on a Toshiba automated clinical chemistry analyzer (Japan) in accordance with the supplier's instructions.
Hepatic TC, FFA, and TG were assayed with kits obtained from Elabscience (E-BC-K109-M, E-BC-K792-M, and E-BC-K261-M, respectively). Levels of glutathione peroxidase (GSH-Px), malondialdehyde (MDA), and total superoxide dismutase (T-SOD) in the liver were determined using Elabscience colorimetric kits (E-BC-K096-M, E-BC-K025-M, and E-BC-K020-M, respectively). All assays were conducted following the manufacturer's protocols.
Hepatic tissue specimens were preserved with 4% paraformaldehyde, paraffin-embedded, and sectioned. After hematoxylin and eosin (H&E) staining, histological alterations and inflammatory cell infiltration were examined by light microscopy (Nikon Eclipse E100, Japan). The non-alcoholic fatty liver disease activity score was employed to grade MASH severity on a scale of 0-8. In brief, steatosis and lobular inflammation were each scored from 0 to 3, while hepatocellular ballooning was graded from 0 to 2[13].
Oil red O staining was performed on liver cryosections, and examined by light microscopy to assess lipid accumulation. Automatic quantification of the lipid-positive areas was performed using Aipathwell, with results expressed as the ratio of positive area to tissue area.
After deparaffinization and rehydration of 4% paraformaldehyde-fixed and paraffin-embedded liver sections, they were stained with Sirius red. Images were taken with a Nikon upright light microscope (Nikon Eclipse E100, Japan). Automatic quantification of Sirius red-positive areas was carried out using Aipathwell software. The results are presented as the ratio of positive area to total tissue area.
After deparaffinization with an environmentally friendly solution and rehydration, liver paraffin sections underwent antigen retrieval. Following inhibition of endogenous peroxidase activity and incubating with 3% bovine serum albumin, the sections were probed with an anti- α-smooth muscle actin (α-SMA) primary antibody (1:1000, GB111364, Servicebio, Wuhan, China) overnight at 4 °C, followed by incubation with a horseradish peroxidase-conjugated secondary antibody. Following 3,3'-Diaminobenzidine chromogenic detection, nuclei were lightly stained with hematoxylin. The liver sections underwent dehydration, clearing, and coverslipping, followed by examination under a light microscope. Semi-qua
Proteins were extracted from samples using pre-cooled lysis buffer by wet grinding, incubated at 95 °C, and centrifuged to collect the supernatant. Protein concentration and quality were assessed by the bicinchoninic acid (BCA) assay and sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE). Based on protein concentration, 5-20 μg of protein was brought to 20 μL with sodium deoxycholate buffer and sequentially subjected to reduction, alkylation, trypsin digestion, acid precipitation, and centrifugation. The supernatant was desalted using StageTip C18 columns, dried, and quantified for peptide content using a NanoDrop One spectrophotometer (Thermo, United States). Equal amounts of peptides were analyzed by data-independent acquisition using a Vanquish Neo liquid chromatography system (Thermo, United States) coupled to an Astral mass spectrometer (Thermo, United States). Separation was performed on a homemade column (15 cm × 100 μm, 1.7 μm) with a 14.4 minutes gradient elution, in positive ion mode with an ion source voltage of 2.2 kV. The MS1 scan covered the range of m/z from 380 to 980, while the MS2 scan covered the range of m/z from 150 to 2000. Data were acquired using Thermo Xcalibur 4.7 software, raw files were processed with Spectronaut 19. We applied a false discovery rate threshold of ≤ 0.01 at both the peptide and protein levels and employed label-free quantification via MaxLFQ. Analysis was conducted on the Majorbio bioinformatics tool (http://cloud.majorbio.com). Differentially expressed proteins (DEPs) were determined using a t-test, applying a significance criterion of P < 0.05 and a fold change greater than 2. Subsequently, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) over-representation analyses were performed employing Goatools within the Python environment.
Liver samples (50 ± 5 mg) were mixed with 400 μL of extraction solvent (methanol:water = 4:1, containing internal standards), homogenized, and centrifuged, and the supernatant was collected for analysis. Equal aliquots of supernatant from each sample were pooled as quality control samples. Liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis was carried out via an AB SCIEX TripleTOF 6600 mass spectrometer coupled to an ACQUITY UPLC system. An HSS T3 column (100 mm × 2.1 mm, 1.8 μm) was employed for separation. Mass spectra were recorded in both ionization polarities (positive and negative) across m/z 50-1200. Raw data were imported into Progenesis QI v3.0 software for peak detection, alignment, and database identification [human metabolome database (HMDB), Metlin]. After database searching, analysis was conducted on the Majorbio bioinformatics tool. Orthogonal partial least squares discriminant analysis together with t-test was run via ropls (version 1.6.2) in R. The criteria of variable importance in projection (VIP) > 1 combined with P < 0.05 were applied to define significantly altered metabolites. Pathway enrichment analysis and Spearman correlation analysis were conducted using the Python package scipy.stats.
Serum samples (100 μL), along with 15 mg of liver tissue and fecal samples, were homogenized and subjected to ultrasonication. After homogenization and centrifugation, the supernatant was taken for detection. A BEH C18 column (1.7 μm, 2.1 mm × 100 mm) mounted on an ACQUITY UPLC I-Class system (Waters, United States) was employed for the chromatographic step. Mass spectrometric detection was conducted on an ACQUITY TQ-S triple quadrupole tandem mass spectrometer (Waters, United States) operating in both ESI+ and ESI- modes under multiple reaction monitoring (MRM). A mixed standard solution was prepared at 6 concentration levels (1000 ng/mL, 100 ng/mL, 10 ng/mL, 1 ng/mL, 0.5 ng/mL, and 0.1 ng/mL) using an internal standard at 1 μg/mL to generate calibration curves, with R² values greater than 0.99.
From PubChem (https://pubchem.ncbi.nlm.nih.gov/) and ChemSpider (https://www.chemspider.com/search/), the three-dimensional conformations of key JQF constituents were retrieved and then converted to PDB files with OpenBabel 3.1.1. Protein receptor structures of the targets were collected from UniProt (https://www.uniprot.org/) and the PDB (http://www.rcsb.org/). Receptor protein structures were preprocessed by removing water molecules and ligands using PyMOL. Both preprocessed receptor proteins and converted ligand molecules were then subjected to hydrogen addition and charge balancing using Autodock Tools, and both were converted to pdbqt format. AutoDock Vina 1.2.7 was applied for molecular docking. Ligand-receptor pairs with binding energies equal to or lower than -5.0 kcal/mol were classified as strongly interacting. PyMOL 2.6.0 and Discovery Studio 2016 were employed for structure visualization.
Twelve C57BL/6J mice [6-week-old, male; Experimental Animal Production License: SCXK (Chuan) 2020-034] were used to prepare JQF-containing serum, liver and fecal samples. Upon completion of a 5-day acclimatization, the animals were randomly divided by the random number table method into a JQF group and a control group (n = 6 each group). Mice in the JQF group received JQF intragastrically at a clinically equivalent dose of 14.7875 g/kg/day, whereas control mice received an equal volume of distilled water by the same route, both twice daily for 3 consecutive days. On the morning of day 4, 2 hours after the last administration, the mice were anesthetized with 2% sodium pentobarbital, blood was collected from the orbital vein, centrifuged, and packaged. The serum used for cell experiments was inactivated, filtered, and stored at -20 °C until use. The remaining fraction was used for targeted metabolomics analysis. Livers were rapidly removed, and feces were collected and stored over liquid nitrogen at -80 °C until metabolomics analysis.
AML12 mouse hepatocytes were obtained from Wuhan Procell Biotechnology Co., Ltd. (CL-0602, Wuhan, China). Cells were incubated at 37 °C under 5% CO2 in AML12-specific medium (CM-0602, Procell). Cells were detached at 80%-90% confluence with 0.25% trypsin-EDTA. Sodium oleate (OA, O7501, Sigma-Aldrich, United States) and sodium palmitate (PA, P9767, Sigma-Aldrich, United States) were solubilized and adjusted to final working concentrations of 400 μmol/L and 200 μM, respectively. The solution was incubated on a shaker for 2 hours and filter-sterilised through a 0.22 μm filter. Steatosis and lipotoxicity were induced in AML12 cells by 24-hour incubation with the FFA mixture at an OA:PA ratio of 2:1.
Small interfering RNA (siRNA) targeting mouse Lmod3 (si-Lmod3) and scrambled negative control siRNA (si-NC) were synthesised by Hanbio Biotechnology Co., Ltd. (JX260608, Shanghai, China). Exponentially growing AML12 cells were plated in 6-well plates at 3 × 105 cells/well to 5 × 105 cells/well and cultured overnight to reach 50%-70% confluence. Transfection was accomplished with the RNAFit-specific agent in line with the manufacturer's guidelines, with a final siRNA concentration of 30-50 nM. At 4-6-hour post-transfection, the transfection mixture was exchanged for fresh growth medium. Cells were collected 48 hours later, and Lmod3 protein levels were determined by immunoblotting to evaluate silencing efficiency.
Eight groups were established: AML12, AML12 + FFA, AML12 + FFA + 20% blank serum, AML12 + FFA + 20% JQF serum, AML12 + FFA + si-NC, AML12 + FFA + si-Lmod3, AML12 + FFA + si-Lmod3 + 20% blank serum, AML12 + FFA + si-Lmod3 + 20% JQF serum. At 48 hours post-transfection, the culture medium was replaced with fresh medium supplemented with FFA (OA 400 μmol/L + PA 200 μmol/L), in every group apart from the blank control, with the serum
Cell viability was measured by cell counting kit-8 (CCK-8) assay. The cells were seeded with 1 × 104 cells/well in 96-well plate. After corresponding treatment, 10 μL CCK-8 solution was added to each well, and the cells were incubated at 37 °C for 2 hours. The absorbance was measured at 450 nm. Cell viability was expressed as a percentage relative to the blank control to exclude cytotoxic interference of siRNA transfection with FFA treatment.
Intracellular TG content was quantified using a fluorometric TG assay kit (E-BC-F033, Elabscience, Wuhan, China). Cells were harvested and lysed, and the measurement was carried out following the manufacturer's instructions. Total protein content in each lysate was determined by the BCA assay for normalization.
MDA levels in cell lysates were measured using an enhanced cellular MDA colorimetric assay kit (E-BC-K814-M, Elabscience, Wuhan, China), operating strictly according to the kit instructions.
Total RNA was purified from hepatic tissue by TRIzol extraction (Invitrogen), followed by reverse transcription with the HiFiScript cDNA Synthesis Kit (Cwbiotech, CW2569, Taizhou, China). Real-time quantitative PCR was performed using FastSYBR Mixture (High ROX; Cwbiotech, CW2622, Taizhou, China). Relative gene expression was determined by 2-ΔΔCt, with mRNA abundance normalized against the control group. The primer sequences used in this study are listed in Table 1.
| Gene name | Primer sequences (5’-3’) |
| Gsta2 | F: ACAAATTGAAGAAGCAAGGAAGGT |
| R: TTATTATTGCACAATAGCCAGAATC | |
| Gsta5 | F: GCCTTTGAAAAGGTGTTGAAGG |
| R: CCTTCAGCAGAGGGAAAGGA | |
| Ifi27 L2b | F: AATATCATCCTTGGAGCTGTTGG |
| R: TCCTTTGGCTCTTGCTGAAGAT | |
| Lmod3 | F: GCTCTTGAATGACATCCGACAC |
| R: TCCAGCTCTGAGCTTGCTGTT |
Total protein was isolated from liver tissues and cultured cells with radioimmunoprecipitation assay lysis buffer. Protein concentrations were measured using a BCA protein assay kit (Cat. No. 02912E, Cwbiotech). Proteins were separated by SDS-PAGE and electrotransferred onto polyvinylidene difluoride membranes. After sealing with 5% fat-free milk, the primary antibodies of α-SMA (1:1000; 19245, CST, Wuhan, China), GSTA1/A2/A3/A4/A5 (1:2000; PB0671, Boster, Wuhan, China), IFI27 (1:2000; IFI27-112AP, Thermo, United States), and LMOD3 (1:4000 for tissue samples, 1:1000 for cell samples; 14948-1-AP, Proteintech, Wuhan, China) were used for detection. Then, the secondary antibodies matching the species were incubated. Protein bands were detected with an enhanced chemiluminescence detection kit (Millipore, WBKLS0500, Germany). Band densities were quantified using a Tanon Gel Image System (ver. 4.00, Shanghai, China), with GAPDH (cells) or β-actin (tissue) as the internal loading control.
SPSS 26.0 and GraphPad Prism 9.0 were used for data processing and mapping. All data are showed as mean ± SE. When homogeneity and normality of variance were satisfied, between-group differences were assessed by independent sample t-test, and differences across multi-group were evaluated by one-way ANOVA with Tukey's post-hoc test. Or else, two-group contrasts were assessed by the Mann-Whitney U test, multi-group differences were evaluated by the Kruskal-Wallis H test, and Dunn's test with Bonferroni correction served for pairwise comparisons. The threshold for statistical significance was set at P < 0.05.
JQF was phytochemically profiled by UHPLC-LTQ-Orbitrap HRMS, which tentatively identified 63 compounds, primarily alkaloids, organic acids, anthraquinones, flavonoids, and saponins. Key constituents included emodin, aloe emodin, berberine, coptisine, palmatine, sarsasapogenin, paeoniflorin, paeonidin E, malic acid, citric acid, nobiletin, and 6-gingerol. Among these, 10 compounds—such as emodin, aloe emodin, berberine, palmatine, coptisine, jatrorrhizine, and epiberberine—were found to be absorbed into the bloodstream as prototype forms. Additionally, a validated UHPLC-ESI-QqQ method in MRM mode was applied to quantify 24 representative constituents in 11 batches of JQF. The constituents with the highest contents, ranked in descending order, were malic acid, citric acid, timosaponin B, berberine, albiflorin, jatrorrhizine, coptisine, paeoniflorin, and timosaponin A III. Full details are available in our previous study[7].
MASH was induced in mice by 2 weeks of MCD diet feeding, after which drug treatments were administered for an additional 2 weeks (Figure 1A). Although the body weight have decreased, the liver mass and liver organ coefficient of the MCD group still showed a significantly higher level compared to the MCS group (Figure 1B-D). Serum ALT, AST, sensitive markers of hepatic function, were markedly elevated, and lipid profiles, including serum TC, TG, and LDL, liver TC, and liver TG, were substantially increased (Figure 1E-G), collectively indicative of hepatic injury and steatosis in the MASH model. Crucially, these indices were significantly improved by both PPC and JQF treatment, with the JQF high-dose group consistently showing the most pronounced beneficial effects.
To assess hepatic pathological alterations, hepatic sections were evaluated by H&E, oil red O, Sirius red, and α-SMA immunohistochemistry (IHC) staining with semi-quantitative analysis. In the MCD group, H&E staining revealed extensive hepatocellular edema and swelling with pale, loosened cytoplasm; many hepatocytes displayed marked ballooning degeneration, and scattered focal infiltrates of lymphocytes and granulocytes were evident in the hepatic lobules, reflecting substantial steatosis and inflammatory infiltration in MCD-induced MASH mice. Oil red O staining corroborated the presence of steatosis and lipid deposition, while Sirius red staining and α-SMA IHC indicated mild hepatic fibrosis (Figure 2). Notably, these pathological changes were markedly ameliorated by both JQF and PPC treatment, indicating that JQF effectively mitigates MCD diet-induced hepatic pathological injury.
Oxidative stress is a well-established risk factor driving MASLD progression. The pathogenesis of MASLD is fueled by lipotoxicity: Excessive FFA promote the generation of reactive oxygen species, thereby causing lipid peroxidation, and forming aldehyde substances with cytotoxicity and pro-fibrotic effects, such as MDA[14]. We therefore assessed oxidative stress markers. MCD-fed mice displayed significantly increased serum and hepatic FFA (Figure 3A) and elevated hepatic MDA (Figure 3B), along with notably decreased activities of the antioxidant enzymes T-SOD and GSH-Px (Figure 3C and D), suggesting that lipotoxicity had triggered substantial oxidative stress, propelling MASLD toward the inflammatory-fibrotic transition of MASH. JQF and PPC treatment both significantly alleviated MCD-triggered hepatic lipotoxicity and oxidative stress.
To investigate the underlying mechanisms by which JQF improves MCD-induced MASH, we conducted liver proteomic profiling. The MCD diet caused a marked shift in the hepatic proteome compared with MCS controls, with 326 upregulated and 187 downregulated DEPs. JQFH treatment resulted in a more dramatic proteomic response, with 2295 upregulated and 714 downregulated DEPs (Figure 4A). Venn analysis identified 247 DEPs that were markedly altered in both the MCD and JQFH groups (Figure 4B). Under the context of GO and KEGG enrichment analysis, these DEPs were primarily enriched in fatty acid metabolism-related processes and pathways, including "unsaturated fatty acid metabolic process", "long-chain fatty acid metabolic process", and "icosanoid metabolic process" (GO), and "biosynthesis of unsaturated fatty acids", "fatty acid elongation", and "arachidonic acid metabolism" (KEGG). Redox and detoxification pathways, "glutathione metabolism", "drug metabolism-cytochrome P450", and "retinol metabolism", as well as tumor necrosis factor pathway exhibited similarly significant enrichment (Figure 4C and D). Among these 247 DEPs, we further identified 26 proteins that were fully normalized to control levels by JQFH treatment (Figure 4B), a result substantiated by hierarchical clustering analysis (Figure 4E). These proteins are likely central to the therapeutic action of JQFH.
To explore the effect of JQF on the hepatic metabolic landscape in MCD-induced MASH, we analyzed liver metabolites using LC-MS. We detected 3826 positive-ion and 5437 negative-ion peaks. Partial least squares discrimination analysis score plots showed clear separation among the MCS, MCD, and MCD + JQFH groups, indicating distinct metabolic profiles (Figure 5A). Metabolites with VIP values exceeding 1.0 and P values below 0.05 were deemed statistically differential. Relative to MCS, the MCD group had 214 upregulated and 203 downregulated metabolites, portraying the MCD-driven metabolic background. Compared with MCD, the MCD + JQFH group exhibited 219 upregulated and 212 down
A total of 245 overlapping differential metabolites between the two comparisons indicated those altered by MCD and subsequently reversed by JQFH. The HMDB annotations mainly classify these metabolites into four major categories, including lipid and lipid-like molecules, alkaloids and their derivatives, organic acids and their derivatives, and organic oxygen-containing compounds (Figure 5C). KEGG topology analysis indicated that JQFH might function through cysteine and methionine metabolism, galactose metabolism, and biosynthesis of leucine, valine and isoleucine (Figure 5D). Integrated KEGG enrichment analysis of DEPs and differential metabolites highlighted arachidonic acid metabolism, PPAR pathway, and glutathione metabolism as significantly enriched overlapping pathways (Figure 5E), implying that they are crucial for JQF's therapeutic action. According to the criteria of a P value less than 0.05 and a VIP greater than 1, and |log2(FC)| ≥ 1, 85 differential metabolites were filtrated in the MCD + JQFH group vs MCD group, and 115 were filtrated in the MCD group vs MCS group. After intersecting the two sets, 31 significantly differential metabolites were obtained (Figure 6 and Supplementary Figure 1). Correlation analysis linking these metabolites with JQFH-improved phenotypes identified the top 20 phenotype-associated metabolites. Metabolites significantly reduced by JQFH, such as carbocysteine-lysine, maltoheptaose, and 4-nitrophenyl maltopentaoside, were positively correlated with liver weight, ALT, AST, serum and liver TG, and liver MDA, and inversely associated with hepatic T-SOD, and GSH-Px. In contrast, metabolites elevated by JQFH, including afzelin, hexadecenoylcarnitine, panomifene, berberine, N-palmitoyl valine, and gamma-glutamylglutamic acid, showed negative correlations with the above injury markers and positive correlations with antioxidant enzyme activities (Figure 5F). Network correlation analysis between these 20 metabolites and the 26 core DEPs identified 7 key proteins. Among them, Lmod3 and Gsta2 proteins showed strong negative correlations with berberine and afzelin, while Ifi27 L2b protein exhibited strong negative correlations with afzelin, hexadecenoylcarnitine, and gamma-glutamylglutamic acid (Figure 5G).
Drawing on our previous mass spectrometric characterization, quantification, and blood-component profiling of JQF, we selected 15 principal bioactive constituents for molecular docking against the seven key proteomic targets to assess their binding capacities. Virtual docking was conducted with AutoDock Vina. All 15 compounds, emodin, aloe-emodin, berberine, palmatine, epiberberine, coptisine, jatrorrhizine, timosaponin B II, timosaponin A III, sarsasapogenin, paeoniflorin, albiflorin, paeonidin E, 6-gingerol, and nobiletin, bound to the targets, and over 90% of the combined ability was less than -5 kcal/mol (Figure 7A). The representative stable binding complexes between each key target and its respective compound(s) are illustrated in Figure 7B. Notably, timosaponin A III, timosaponin B II, sarsasapogenin, paeoniflorin, berberine, and coptisine dominated the docking interactions, displaying strong binding affinities (Figure 7B).
Given that molecular docking results indicated that timosaponins exhibited the strongest binding affinity to the key targets, targeted metabolomics was further employed to quantify the contents of timosaponin A III, timosaponin B II and sarsasapogenin in the serum, liver and feces of mice following JQF administration. Compared to the control, JQF group markedly increased the levels of timosaponin A III and timosaponin B II in both serum and liver, with timosaponin A III exhibiting the most prominent enrichment in the liver (Figure 8A and B). Meanwhile, markedly increased levels of timosaponin A III and sarsasapogenin were detected in feces. sarsasapogenin, as the secondary aglycone derived from intestinal hydrolysis of timosaponins by gut microbiota, was detected at high abundance in feces, corroborating the intestinal metabolic transformation of these components (Figure 8C). Collectively, these findings demonstrate that simosaponin A III and simosaponin B II are effectively entered the circulation and accumulated in the liver, supporting their role as key pharmacologically active substances underlying the therapeutic effects of JQF.
Based on the molecular docking analysis, four targets with the highest binding affinities—Gsta2, Gsta5, Ifi27 L2b, and Lmod3—were selected for expression validation in liver tissues by quantitative PCR (qPCR)/Western blotting (WB). Our results indicated that treatment with JQF and JQFH significantly reduced the mRNA levels of Gsta2, Gsta5, Ifi27 L2b, and Lmod3 (Figure 9A). WB showed that the expression level of these proteins was generally consistent with the mRNA expression level (Figure 9B), with the high-dose JQF group exhibiting the most pronounced overall effect. These results were generally consistent with the proteomic data, further supporting that these four proteins may serve as core targets mediating the therapeutic effects of JQF.
To further investigate whether Lmod3 serves as a key functional target of JQF in ameliorating MASH, siRNA-mediated knockdown experiments were carried out in an FFA-induced AML12 hepatocyte lipotoxicity model. Transfection with si-Lmod3 resulted in a marked reduction of Lmod3 protein expression (Figure 10A). Knockdown of Lmod3 reproducibly ameliorated FFA-induced lipid accumulation, oxidative stress, and fibrotic marker expression, suggesting a detrimental role of Lmod3 in FFA-induced hepatocellular injury. Furthermore, in Lmod3-knockdown cells, JQF-containing serum treatment was associated with additional reductions in intracellular TG content, MDA levels, and α-SMA protein expression (Figure 10B-D), suggesting a potential additive protective effect when Lmod3 is suppressed. CCK-8 assay showed no significant differences in cell viability across all groups, excluding potential cytotoxicity of the experimental treatments (Figure 10E). Collectively, these preliminary findings suggest that Lmod3 may represent one of the potential targets through which JQF exerts its beneficial effects against MASH.
MASLD and its progressive form MASH are multi-systemic, heterogeneous metabolic disorders. MASH constitutes an increasingly serious global public health concern, given its potential to evolve into irreversible liver conditions, notably hepatic fibrosis, cirrhosis, and primary hepatic malignancy. However, current treatment strategies remain very limited. Although the 2024 approval of resmetirom as the first MASH therapeutic is groundbreaking, its efficacy is modest, as histological resolution is achieved in only 25%-30% of subjects, and challenges related to durability, accessibility, and adverse effects such as gastrointestinal reactions persist[15]. TCM has distinctive strengths in this field. JQF, a clinically effective formula for MASLD/MASH, was previously shown by our group to ameliorate liver steatosis, inflammation, and fibrosis in HFD-fed MASLD mice via modulating hepatic macrophages[12]. Whether JQF engages additional pathways to ameliorate more severe MASH remains unclear. MCD is known to promote chronic liver disease, and various MCD-based diets have been used to model MASH in mice[16]. We therefore employed the classical MCD-induced MASH model to systematically evaluate the therapeutic impact and multi-target mechanisms of JQF.
Our study first demonstrated that JQF significantly reduced liver weight, liver organ coefficient, restored liver function, improved liver injury, and lowered blood lipids in MCD-induced MASH mice. Histological analyses confirmed that JQF markedly improved steatosis, lipid accumulation, inflammation, and fibrosis. JQF also significantly decreased serum and hepatic FFA content and the lipid peroxidation product MDA, while increasing the antioxidant enzymes GSH-Px and T-SOD, thus substantially attenuating hepatic lipotoxicity and oxidative stress. A dose-dependent trend was evident, with the high-dose group achieving the greatest efficacy. These results complement our earlier HFD findings, underscoring JQF's protective effects across distinct metabolic liver injury models.
In order to elucidate the molecular machinery involved JQF in the treatment of MASH, we further performed an integrative analysis combining hepatic proteomics and metabolomics. Proteomic profiling elucidated the critical DEPs and the pathways implicated in the therapeutic efficacy of JQF, including arachidonic acid metabolism, glutathione metabolism, and the tumor necrosis factor signaling cascade. Metabolomics revealed the significantly altered differential metabolites and their related pathways following JQF treatment, especially those associated with branched-chain amino acid metabolic disturbances closely linked to MASH, such as the valine, leucine, and isoleucine biosynthesis pathways[17]. Notably, berberine and palmatine, the core pharmacologically active components of JQF, were entered the cir
In the pathological progression of MASH, lipotoxic injury, oxidative damage, and low-grade chronic inflammation collectively contribute to hepatic injury and fibrosis[19]. Our omics data revealed that MASH mice exhibited metabolic disorders characterized by overactivation of arachidonic acid metabolism and glutathione metabolism. Arachidonic acid is a critical lipid mediator in MASH, participating in disease progression through lipid oxidative damage and inflammatory reaction[20]. In this study, the expression level of cytochrome P450 family members CYP2b9 and CYP4a31, which are involved in arachidonic acid metabolism, was significantly downregulated after JQF treatment. This coordinated downregulation may effectively interrupt the lipotoxicity-oxidative stress vicious cycle, thereby restoring hepatic fatty acid metabolic homeostasis. Additionally, the abundance of 11,12-epoxyeicosatrienoic acid, a differential metabolite involved in the arachidonic acid metabolic pathway, was significantly elevated after JQF treatment, which can effectively alleviate lipid metabolic disorders and protect against MASLD[21].
Glutathione acts as a free radical scavenger that reduces harmful hydrogen peroxide and lipid peroxides into non-toxic substances. In this study, hepatic glutathione S-transferases Gsta5 and Gsta2 were significantly upregulated in MASH model mice, suggesting exacerbated oxidative stress injury in the liver under disease conditions, with compensatory activation of the glutathione metabolic pathway. Following JQF treatment, Gsta5 and Gsta2 expression was significantly reversed to near-normal levels, synergizing with enhanced GSH-Px and T-SOD activities and reduced MDA levels, collectively constituting the antioxidant defense network of JQF. This was corroborated by the increased abundance of antioxidant metabolites such as afzelin and gamma-glutamylglutamic acid in metabolomics. Afzelin can act as an NRF2 activator, exerting antioxidant, anti-inflammatory, and cytoprotective effects[22]; gamma-glutamylglutamic acid possesses antioxidant capacity[23]. Additionally, the abundance of the pro-inflammatory metabolite maltoheptaose, which has been reported to stimulate the release of pro-inflammatory mediators, including interleukin (IL)-6 and IL-1β[24], was significantly decreased after JQF treatment.
PPARs are key regulators of metabolic homeostasis. In this study, the expression of the interferon-inducible protein Ifi27 L2b was significantly downregulated after JQF treatment. Studies have shown that Ifi27 L2b is a novel PPARγ target in fatty liver[25], and its alteration following JQF intervention corroborates the regulatory effect of JQF on this signaling pathway. In addition, Kdm4c, a negative regulator of hepatic fibrosis[26], was significantly upregulated after JQF treat
Among the key targets screened and validated in this study, Lmod3 warrants particular attention, as it was sig
In terms of pharmacologically active substance basis, we screened out six components—timosaponin A III, timo
Furthermore, we found that these components may exert synergistic effects. For instance, timosaponin B II possesses anti-inflammatory, antioxidant, anti-diabetic, and mitochondria-improving properties; timosaponin A III significantly reduces lipid accumulation and hepatic injury in MASH mice; and both, together with sarsasapogenin, exhibit prominent effects in anti-inflammatory and neuroprotective aspects[10,27], suggesting that their actions may be complementary with distinct emphases. Paeoniflorin can upregulate PPARγ expression by directly binding to enhancer of zeste homolog 2, thereby inhibiting hepatic stellate cell activation[11], while Ifi27 L2b, a novel PPARγ target identified in this study, was also significantly reversed by JQF, suggesting that components of the formula may synergize at different nodes of the PPAR signaling pathway. Similarly, both berberine and coptisine exhibit dual activities in regulating glucolipid homeostasis and exerting antioxidant effects, and the hepatoprotective mechanism of berberine also involves activation of PPARγ and PPARα[8,28], suggesting that they may exert synergistic effects through additive or complementary actions in vivo. Nonetheless, the potential synergy or antagonism between these various components merits additional investigation in future work.
In summary, this study, through the integration of multi-omics, molecular docking, and experimental validation, constructed a complete chain of evidence from holistic efficacy to molecular targets and further to active components, revealing the multi-target mechanisms of JQF in ameliorating MASH and identifying Lmod3 as a novel potential target, thereby laying a solid foundation for subsequent in-depth research. Nevertheless, this work is subject to certain limitations. Although the MCD-diet mice model effectively demonstrates the inflammation and fibrosis of MASH, it fails to reproduce the metabolic syndrome phenotypes such as obesity and insulin resistance. Future validation in compound models (e.g., HFD combined with MCD or Gubra amylin nash diet) is warranted. Moreover, although molecular docking was utilized to model compound-target interactions, employed to predict component-target interactions, direct experimental validation of the physical binding between the compounds and the target proteins is currently lacking. Future studies will incorporate surface plasmon resonance or cellular thermal shift assay to further corroborate these inte
In this study, we conducted a systematic evaluation of JQF's effects and mechanisms in MASH mice established by MCD diet feeding. we systematically evaluated the therapeutic effects and underlying mechanisms of JQF in a MCD diet-induced MASH mouse model. Our results demonstrated that JQF dose-dependently ameliorate liver steatosis, lipid accumulation, inflammatory injury and liver fibrosis, while also ameliorating lipotoxicity and oxidative stress injury. Integrated hepatic proteomic and metabolomic analyses revealed that the core pathways regulated by JQF were predominantly clustered in arachidonic acid metabolism, glutathione metabolism, from which four potential targets—Gsta2, Gsta5, Ifi27 L2b, and Lmod3—were identified. The expression changes of these targets were validated by qPCR and WB, showing consistent trends with the proteomic data. Among them, Lmod3 was preliminarily verified as a potential key target through siRNA knockdown experiments. Molecular docking predicted that timosaponin-class compounds exhibited relatively high binding affinities to the targets; targeted metabolomics further confirmed that timosaponin A III and timosaponin B II could be effectively absorbed into the bloodstream and enriched in the liver, while their primary secondary metabolite, sarsasapogenin, was detected at high abundance in feces. Collectively, these findings suggest that JQF ameliorates MASH through multi-target synergistic mechanisms, with Lmod3 serving as one of its key targets and timosaponin-class compounds representing an important pharmacological material basis (Figure 11).
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