Multi-omics analysis reveals gut microbiota remodeling and lipid metabolism regulation during the treatment of nonalcoholic fatty liver disease with Yindan Pinggan capsule
Jinli Hou, Sen Li, Fengrong Zhang, Honghe Xiao, Xianyu Li, Hongjun Yang
TL;DR
This study shows that Yindan Pinggan capsule improves nonalcoholic fatty liver disease by changing gut bacteria and regulating liver metabolism.
Contribution
The study reveals novel mechanisms of Yindan Pinggan capsule in treating NAFLD through gut microbiota remodeling and lipid metabolism regulation.
Findings
YDPG treatment reduced body weight, liver index, hepatic lipid accumulation, and inflammation in NAFLD mice.
YDPG reshaped gut microbiota by decreasing harmful genera and increasing beneficial ones.
YDPG modulated key metabolic pathways including PPAR signaling and bile acid biosynthesis.
Abstract
Nonalcoholic fatty liver disease (NAFLD) is a common chronic liver disorder with limited treatment options. Yindan Pinggan capsule (YDPG), a traditional Chinese medicine, has demonstrated potential in managing liver diseases, yet its efficacy and mechanisms in NAFLD remain unclear. A high-fat diet (HFD)-induced NAFLD mouse model was established. The major bioactive components of YDPG, including baicalin, geniposide, and glycyrrhizic acid, were quantified using UPLC-QQQ-MS/MS. Integrated 16S rRNA sequencing, serum metabolomics, and liver transcriptomics were employed to analyze gut microbiota and metabolic profiles, and gene expression. qPCR was used to evaluate the expression of key regulatory genes. YDPG treatment significantly reduced body weight, liver index, hepatic lipid accumulation, and inflammation, while improving serum lipid profiles and liver function (AST/ALT). Integrated…
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Figure 9- —Scientific and Technological Innovation Project of CACMS
- —National Natural Science Foundation of China
- —Fundamental Research Funds for the Central Public Welfare Research Institutes
- —Scientific and technological innovation project of China Academy of Chinese Medical Sciences
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TopicsGut microbiota and health · Liver Disease Diagnosis and Treatment · Metabolomics and Mass Spectrometry Studies
Introduction
Nonalcoholic fatty liver disease (NAFLD) has emerged as the most prevalent chronic liver condition globally, affecting over 29% of the world's population [1]. As NAFLD progresses, it can evolve into more severe forms, including steatohepatitis (NASH), cirrhosis, and hepatocellular carcinoma [2, 3]. Beyond its hepatic manifestations, NAFLD is intricately linked with major extrahepatic complications, including cardiovascular disease [4], type 2 diabetes [5], chronic kidney disease [6], and hypertension [7].
The etiology of NAFLD is complex and multifactorial, involving dysregulated lipid metabolism, insulin resistance, and chronic inflammation. Crucially, emerging evidence highlights the gut-liver axis as a central player in NAFLD pathogenesis. Intestinal dysbiosis disrupts gut barrier integrity, increases endotoxin translocation (e.g., lipopolysaccharide), and alters microbial metabolite production (e.g., short-chain fatty acids and bile acids). These changes directly promote hepatic lipid accumulation (steatosis), inflammation, and insulin resistance, driving NAFLD development and progression [8, 9]. Concurrently, impaired hepatic fatty acid oxidation, enhanced de novo lipogenesis, and defective very-low-density lipoprotein secretion are key metabolic disturbances underpinning hepatic steatosis and lipotoxicity [10, 11].
Current therapeutic options for NAFLD remain limited. While lifestyle modification is foundational, pharmacotherapy is often necessary. In 2024, Rezdiffra (resmetirom) became the first and only drug approved by the FDA specifically for the treatment of nonalcoholic steatohepatitis, though it is contraindicated in patients with decompensated cirrhosis. Commonly reported adverse effects include diarrhea, nausea, pruritus, abdominal pain, vomiting, constipation, and dizziness; notably, diarrhea and nausea often emerge early in treatment and are typically mild to moderate [12]. In contrast, Yindan Pinggan capsule (YDPG), a traditional Chinese formulation, offers a complementary approach. Given the pivotal role of the gut-liver axis in NAFLD pathogenesis as described above, YDPG's multi-herb composition may provide a holistic strategy by potentially modulating gut microbiota, restoring intestinal barrier integrity, and ameliorating metabolic disturbances. Its historical use in various liver diseases suggests favorable tolerability. However, it still lacks robust clinical trial validation and regulatory approval for NAFLD/NASH specifically in most countries. Its precise mechanism of action, particularly concerning lipid metabolism and gut microbiome interaction, remains less elucidated than Rezdiffra’s targeted THR-β agonism, and potential herb-drug interactions require careful consideration. Other agents such as vitamin E [13], pioglitazone [14], and metformin [15] also show variable efficacy and/or safety concerns in clinical trials [16], underscoring the ongoing need for more effective, well-tolerated, and mechanism-based treatments.
YDPG is a proprietary traditional Chinese medicine produced by Zhangzhou Pien Tze Huang Pharmaceutical Co., Ltd. Its composition includes a blend of herbal elements: Yin Chen Hao (YCH, Artemisia capillaris Thunb.), Long Dan (LD, Gentiana scabra Bunge), Huang Qin (HQ, Scutellaria baicalensis Georgi), Zhi Zi (ZZ, Gardenia jasminoides J.Ellis), Bai Shao (BS, Paeonia lactiflora Pall.), Dang Gui (DG, Angelica sinensis (Oliv.) Diels), Zhu Dan Fen (ZDF, Sus scrofa domestica Brisson), and Gan Cao (GC, Glycyrrhiza uralensis Fisch.). Previous research has focused on two main aspects. The determination of Chlorogenic acid, ferulic acid, geniposide, ammonium glycyrrhetinic acid, gentiopicroside, baicalin lays the foundation for establishing the YDPG quality standard [17]. Contrarily, YDPG is often utilized to deal with liver diseases, showing good efficacy and safety in treating alcoholic liver disease, chronic hepatitis, alcoholic liver fibrosis and other diseases [17]. However, its potential therapeutic effects on NAFLD, particularly concerning the modulation of gut microbiota and lipid metabolism, have not been investigated.
Given the established roles of gut dysbiosis and metabolic dysfunction in NAFLD, and the historical use of YDPG constituents in liver disorders, this study proposes the central hypothesis that YDPG may ameliorate NAFLD by targeting pathways related to the gut-liver axis. To systematically test this hypothesis and elucidate the underlying mechanisms, the following specific aims were defined: (1) To characterize the material basis of YDPG by identifying its major chemical components using UPLC-QQQ-MS/MS. (2) To evaluate the overall therapeutic effects of YDPG in a high-fat diet (HFD)-induced NAFLD mouse model, with comprehensive assessment of hepatic steatosis, inflammation, and metabolic parameters. (3) To investigate the potential mechanisms through a multi-omics approach by integrating 16S rRNA gene sequencing of the gut microbiota, serum untargeted metabolomics, and hepatic transcriptomics, thereby systematically exploring the network changes in host-microbiota co-metabolism and liver gene expression upon YDPG intervention. (4) To validate key regulatory targets by quantifying the expression of pivotal genes involved in lipid metabolism and inflammatory response in the liver using quantitative real-time PCR (qPCR), based on findings from the multi-omics analyses. Through this stepwise investigation from animal efficacy to multi-omics correlation and molecular validation, this study aims to progressively uncover the potential mechanisms by which YDPG improves NAFLD.
Materials and methods
Materials and chemicals
This research employed YDPG, manufactured by Zhangzhou Pien Tze Huang Pharmaceutical Co., Ltd, based in Zhangzhou, Fujian, China. HPLC grade acetonitrile (CH_3_CN) and methanol were procured from Merck KGaA (Merck KGaA, Darmstad, Germany). Additionally, formic acid (FA) was obtained from Aladdin Reagent (Shanghai) Co., Ltd (Aladdin, Shanghai, China). Baicalin (purity ≥ 98%), geniposide (purity ≥ 98%), paeoniflorin (purity ≥ 98%), liquiritin (purity ≥ 98%), glycyrrhizic acid (purity ≥ 98%), and gentiopicroside (purity ≥ 98%) were provided by Chengdu Must Bio-Technology Co., Ltd (Chengdu, China). Hematoxylin & eosin (H&E) and Oil Red O stains were obtained from Solarbio Science & Technology Co., Ltd. (Solarbio, Beijing, China). Assay kits for ALT, AST, TG, TC, HDL-C, and LDL-C were purchased from Nanjing Jiancheng Bioengineering Institute (Nanjing, Jiangsu, China).
YDPG sample preparation
According to the 2020 version of The Pharmacopoeia of the People's Republic of China, baicalin is a key marker for YDPG quality control. Additionally, geniposide, paeoniflorin, liquiritin, glycyrrhizic acid, and gentiopicroside were also used as quality markers. The mother liquor concentration of the standard was 5 mg/mL, and the detected concentration on the machine was 0.5 μg/mL. Furthermore, an ultrasonic extraction was performed by adding 129 mg of YDPG to a 4 mL solution consisting of 75% methanol. This extraction lasted for 60 min at a temperature of 25 ℃. Following this, the mixture underwent centrifugation at a speed of 13,000 rpm for a duration of 10 min. The liquid fraction obtained was diluted 100 times to prepare the YDPG test solution.
Identification of YDPG compounds by UPLC-QQQ-MS/MS
For the quality control evaluation of YDPG, an advanced analytical method known as UPLC-QQQ-MS/MS (Waters, Milford, MA, USA) alongside the Waters UPLC HSS T3 column (2.1 × 100 mm, 1.8 µm) was utilized. The parameters set for the analysis included a flow rate of 3 µl/min, a column thermal condition of 45℃, and a sample injection amount 2 μl. The UPLC solvent system comprises 0.1% formic acid in water (A) and acetonitrile (B). The elution gradient profile was established in the following manner: from 0 to 1 min, maintaining 15% B; from 1 to 6 min, increasing from 15 to 70% B; from 6 to 7 min, increasing to 100% B; maintaining 100% B from 7 to 9 min; quickly reducing to 15% B from 9 to 9.1 min; and holding at 15% B from 9.1 to 11 min.
The mass spectrometry settings included the following parameters: positive and negative electrospray ionization (ESI^+^ and ESI^–^), with capillary voltage adjusted to 3.0 kV; desolvation gas temperature set to 500℃; nitrogen cone gas flow rate of 10 L/h; and a desolvation nitrogen gas flow of 1000 L/h. The content of geniposide, paeoniflorin, liquiritin, baicalin, glycyrrhizic acid, and gentiopicroside in YDPG were calculated by the external standard single-point method. Information management was executed utilizing the MassLynx 4.1 and Target Lynx software (Waters).
Animal experiment
Construction of NAFLD model in vivo
In this study, 75 male C57BL/6 mice (averaging 19.2 ± 1.2 g) were obtained from Sibeifu Biotechnology Co., Ltd. (Beijing, China) and were meticulously maintained in the specific pathogen-free (SPF) animal facility of the Institute of Basic Research in Traditional Chinese Medicine, Chinese Academy of Traditional Chinese Medicine. The procurement of these mice was in strict adherence to the guidelines provided under Animal License No. SCXK (Beijing) 2019–0010. Mice were randomly divided into two groups: the control group (Ctrl, n = 15) fed a standard chow diet and the model group (HFD, n = 46) fed a HFD containing 45% fat. Mice had free access to water. Body weights were recorded weekly. After eight weeks, six Ctrl mice and twelve HFD mice were selected for ophthalmic tissue retrieval and blood sampling (Fig. 2A). The objective of this procedure was to verify the successful creation of the NAFLD model by assessing four blood lipid parameters.
Grouping and administration
The clinical dose of YDPG for human is 3 g/day (6 capsules). The equivalent mouse dose (YDPG-M) was calculated as 3 g/70 kg × 0.0026/0.02 = 0.39 g/kg/day. Half and double this dose were defined as YDPG-L and YDPG-H, respectively (YDPG-H: 0.39 g/kg × 2 = 0.78 g/kg/day; YDPG-L: 0.302 g/kg × 1/2 = 0.195 g/kg/day).
After successful model establishment (confirmed by increased body weight and serum TG, TC, HDL-C, and LDL-C levels), the HFD mice were randomly assigned to four groups (n = 9 each): model group (HFD), low-dose YDPG (HFD + YDPG-L, 0.195 g/kg), medium-dose YDPG (HFD + YDPG-M, 0.39 g/kg), and high-dose YDPG (HFD + YDPG-H, 0.78 g/kg). The Ctrl group (n = 9) continued on a standard diet. During the four-week treatment period, the HFD and all YDPG groups received the 45% HFD, while the Ctrl group received the standard diet. YDPG or an equal volume of saline (for Ctrl and HFD groups) was administered daily by oral gavage at a volume of 0.15 mL/10 g body weight. All animal experiments were approved by the Ethical Committee of Experimental Animal Welfare of the Experimental Research Center, China Academy of Chinese Medicine Science (approval ID: ERCCACMS21-2307–02).
Harvesting experimental samples
Body weight was recorded twice weekly for four weeks. On day 28, after final weighing, mice were anesthetized with tribromoethanol (0.2 mL/10 g, Beijing Lab Animal Technology Develop Co.). Orbital blood samples collected, centrifuged at 3500 rpm and 4 ℃ for 10 min to obtain serum, and stored individually. Subsequently, cecal contents were collected aseptically and stored at − 80 ℃. The liver and epididymal white adipose tissue were weighed to calculate organ indices. A portion of each liver was fixed for histological staining (H&E and Oil Red O). The remaining liver tissue was snap-frozen and stored at – 80 ℃ for subsequent analysis.
HE staining
Liver tissues were fixed in 4% paraformaldehyde at room temperature for 48 h, embedded in paraffin, and sectioned at 4 μm. The sections were stained with hematoxylin and eosin (H&E staining kit, Solarbio, Beijing, China) and examined under a light microscope (× 20) for pathological assessment.
Oil red O Staining
Liver tissues were fixed in 4% paraformaldehyde at room temperature for 48 h and then cryosectioned. Frozen sections were stained with Oil Red O for 10 min, differentiated in 60% isopropanol, and rinsed with distilled water. Sections were then counterstained with hematoxylin for 2 min, washed, differentiated in 60% alcohol for 6 s, and rinsed again. Finally, sections were mounted with glycerol gelatin. Lipid deposition was observed under a microscope.
Biochemical analysis
A biochemical analyzer was employed automatically to measure four blood lipid parameters (TC, TG, LDL-C, HDL-C), along with AST and ALT levels in serum, for the assessment of abnormal blood lipids and liver damage.
16S rRNA sequencing
Genomic DNA was isolated from the sample using the CTAB technique, with the purity of the DNA subsequently evaluated via agarose gel electrophoresis. A suitable quantity of sample DNA was transferred into a centrifuge tube (1 ng/μl). We performed PCR amplification by utilizing diluted genomic DNA as the template. We employed New England Biolabs' Phase^®^ PCR Master Mix having high fidelity and GC Buffer, along with an efficient high-fidelity enzyme. Additionally, we utilized V3/V4 primers with Barcode for the amplification process (341F: CCTAYGGGRBGCASCAG; 806R: GGACTACNNGGGTATCTAAT). The detection of PCR products involved electrophoresis, utilizing agarose gel at a concentration of 2%. Subsequently, magnetic bead purification was conducted on PCR products that successfully met the criteria set by the test. For library construction, they utilized the TruSeq® DNA PCR-Free Sample Preparation Kit. The resulting library was then evaluated for quantity using Qubit and qPCR techniques. Once the library passed the quality control, sequencing was carried out using the NovaSeq6000 machine. The Uparse algorithm (obtained from USARCH v7 software, available at http://www.drive5.com/Uparse/) must be employed. It clusters every Effective Tag from all samples with a 97% level of consistency regarding their identity. Mothur method and SILVA138.1 (http://www.arb-silva.de/) were used to annotate and analyze species (threshold range of 0.8–1). R package, GraphPad Prism 8.0 and other software were utilized to examine the diversity of microbial composition in the samples.
LC–MS analysis of the serum
Serum samples were thawed on ice. A 50 μl aliquot of each sample was mixed with 300 μl of an extraction solvent (20% acetonitrile in methanol, containing internal standards). The mixture was vortexed vigorously for 3 min and then centrifuged at 12,000 rpm and 4 °C for 15 min. A 200 μl aliquot of the supernatant was transferred to a new tube, placed at -20 °C for 30 min, and centrifuged again at 12,000 rpm and 4 °C for 3 min. Finally, 180 μl of the supernatant was transferred to a vial insert for LC–MS analysis.
The UPLC analysis was performed utilizing a Shimadzu LC-30A system (Japan) with a BEH C18 chromatography column (2.1 × 100 mm, 1.8 μm; Waters, MA, USA). The operating parameters included a flow rate of 4 μl/min, a column temperature of 40 °C, and a sample injection volume of 2 μl. The mobile phase for the UPLC system consisted of 0.1% formic acid in water (A) and acetonitrile (B). A gradient elution method was employed for the separation process: starting with 5% B at 0 min, increasing from 5 to 90% B over 11 min, maintaining at 90% B from 11 to 12 min, decreasing back to 5% B from 12 to 12.1 min, and then holding at 5% B from 12.1 to 14 min.
The electric spray ionization (ESI) source-equipped SCIEX TripleTOF 6600 + located in Foster City, CA, USA was utilized to acquire the mass spectrum data at ESI^+^ and ESI^−^ scanning modes. The parameter settings for the operation were: gas temperature 550℃ (ESI^+^) and 450℃ (ESI^−^); Ion Source Gas 50 psi; Capillary voltage 5500 V (ESI^+^) and -4500 V (ESI^−^); Declustering Potential 60 V. The mass interval was set from 50 to 1000 Da.
Data processing
The raw LC–MS data files were converted to mzXML format using ProteoWizard. Peak picking, alignment, and retention time correction were performed using XCMS software. Peak area was normalized using the "SVR" method. Peaks with a missing rate > 50% in each sample group or a relative standard deviation (RSD) > 30% in quality control samples were filtered out. Metabolites were identified by searching against in-house and public databases.
RNA-sequencing analysis
Total RNA was extracted from liver tissues (n = 3) of the respective experimental groups. Ribosomal RNA was then depleted from the total RNA to enrich messenger RNA. To prepare for downstream applications, the RNA was fragmented using a fragmentation buffer. First-strand cDNA synthesis was performed with random hexamer primers and the fragmented RNA as template. Second-strand cDNA was synthesized by adding buffer, dNTPs, and DNA polymerase I to generate double-stranded cDNA. The resulting double-stranded cDNA was purified, end-repaired, and adenylated at the 3′ ends to facilitate adapter ligation. AMPure XP beads were employed for size selection of the resulting fragments. The cDNA library was subsequently amplified via PCR. After library construction, quantification was performed using the Qubit 2.0 system, and insert size distribution was evaluated with the Agilent Bioanalyzer 2100. Libraries were pooled in equimolar amounts according to the required sequencing depth and subjected to sequencing on the Illumina platform. Differentially expressed genes (DEGs) were identified using thresholds of (|log2FC|) ≥ 1.24 and P < 0.05. Functional annotation of DEGs was carried out through Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses using the DAVID database.
Linkage analysis of gut microbiota, metabolites and genes
Spearman and Pearson correlation analyses were employed to examine the associations between differential gut microbiota and serum metabolites, and between serum metabolites and liver DEGs, respectively. Significantly altered metabolites, microbial data, and DEGs were imported into the Tutools platform (http://www.cloudtutu.com) for analysis. The results were visualized as heatmaps to intuitively display the correlation coefficients.
qPCR
Total RNA was extracted from liver tissue utilizing the Total RNA Isolation Kit V2 (Vazyme, China). The qPCR method system: 40 cycles of PCR, each cycle comprising denaturation at 95 °C for 10 s and annealing at 60 °C for 30 s. A dissolving curve analysis concluded the procedure, progressively thermal treatment from 60 to 95 °C at a rate of 0.05 °C/s. The 2^−ΔΔCt^ method was applied in this research. Please consult Table S7 for the detailed primer sequences employed in this research.
Statistical analysis
The statistical analyses and the creation of histograms were conducted utilizing GraphPad Prism (8.0, San Diego, USA). Results were presented as the average accompanied by the standard deviation. The examination of variance within groups for differences was executed utilizing ANOVA, supplemented by Tukey's post hoc and t-tests (P < 0.05). For PCoA (Principal Co-ordinates Analysis) and Rank abundance curve analyses, R software (Version 4.1.2) was used. For LEfSe analysis, the LEfSe software was used with a filtering value of 4 for the default LDA score. Additionally, the adoption of PCA was carried out using R (base package, version 3.5.1), while R (MetaboAnalystR, version 1.0.1) was utilized for OPLS-DA.
Results
UPLC-QQQ-MS/MS examination of the primary bioactive compounds in YDPG
Baicalin, geniposide, paeoniflorin, liquiritin, glycyrrhizic acid, and gentiopicroside were used as reference standards for quality control. The reference standards and typical total ion chromatograms [18] of YDPG were shown in Fig. 1. Table 1 indicated the analysis results of UPLC-QQQ-MS/MS for YDPG. The quality control compounds in YDPG were identified as gentiopicroside in G. scabra, geniposide in G. jasminoides, liquiritin and glycyrrhizic acid in G. uralensis, baicalin in S. baicalensis, paeoniflori in P. lactiflora. The levels of specific compounds in YDPG were analyzed and found to be 10.056 mg/g of gentiopicroside, 13.113 mg/g of geniposide, 0.798 mg/g of liquiritin, 3.009 mg/g of glycyrrhizic acid, 6.576 mg/g of baicalin, and 8.771 mg/g of paeoniflori.Fig. 1. The chemical compositions of YDPG were characterized using UPLC-QQQ-MS/MS. In negative A and positive C ion modes, the profiles of YDPG are presented. The standards in negative ion mode B include 1: geniposide; 2: paeoniflorin; 3: liquiritin; 4: baicalin; 5: glycyrrhizic acid. Additionally, the standard for gentiopicroside (6) is shown in positive ion mode DTable 1. Results of UPLC-QQQ-MS/MS analysis of YDPGNOCompoundFormulaAdductsMRMRTPeak area (reference standards)Peak area (sample)Content (mg/g)1GeniposideC_17_H_24_O_10_M-H387.10 > 225.10 1.658562.03156135.74613.1122PaeoniflorinC_23_H_28_O_11_M + FA^−^525.45 > 449.51 2.31718055.8753149042.2508.7713LiquiritinC_21_H_22_O_9_M-H417.36 > 255.392.772879280.2501149247.3750.7984BaicalinC_21_H_18_O_11_M-H445.03 > 269.123.472706863.7508899785.0006.5765Glycyrrhizic acidC_42_H_62_O_16_M-H821.79 > 351.494.71143790.766216338.9843.0096GentiopicrosideC_16_H_20_O_9_M + H_2_O374.10 > 195.202.5611831.76159492.00010.056
YDPG reduced body weight, liver index, and white fat index in HFD-induced NAFLD
The body weight of HFD-fed mice increased over time, as shown in Figure S1A. After eight weeks, the body weight of the HFD group was significantly higher than that of the Ctrl group (Figure S1B). Blood samples from the Ctrl and model groups were centrifuged (3,000 rpm, 4℃, 10 min), and the supernatant was analyzed for four blood lipid parameters. The HFD group exhibited significantly higher levels of these lipid parameters compared with the Ctrl group (Figure S1C-F). These results confirm that the HFD successfully induced dyslipidemia.
To evaluate the therapeutic effect of YDPG, the Ctrl and HFD groups were received physiological saline, while the treatment groups was received different doses of YDPG via gavage daily for 28 days: HFD + YDPG-L (0.151 g/kg), HFD + YDPG-M (0.302 g/kg), and HFD + YDPG-H (0.604 g/kg). Body weight was monitored throughout the treatment period. The Ctrl group mice maintained a stable weight, whereas the HFD group mice showed gain and exhibited signs such as hair loss on their back and tail. In contrast, YDPG-treated mice showed an initial reduction in weight gain, followed by weight stabilization, and did not display hair loss (Fig. 2B-C). Prolonged HFD feeding can lead to increased liver and white adipose tissue indices. In this study, the liver index was substantially higher in the HFD group compared to the Ctrl group. YDPG treatment significantly reduced the liver index, with the medium dose exerting the most pronounced effect (Fig. 2D). Similarly, the epididymal white fat index was lower in the Ctrl group than in the HFD group. YDPG administration also reduced the white fat index, though not in a strictly diminished mice's liver index without a dose-dependent manner (Fig. 2E). In summary, YDPG treatment significantly attenuated the HFD-induced increases in body weight, liver index, and epididymal white fat index.Fig. 2YDPG reduced body weight, liver index, and white fat index in HFD-induced NAFLD. A Schematic diagram of animal experimental design. B The curve of mice body weight change within 16 weeks. C The mice body weight on day 29. D Liver index. E White adipose index. *** P< 0.001
YDPG decreased liver tissue damage and inflammation in HFD-induced NAFLD
HE staining effectively revealed the histological structure and pathological alterations in liver tissues. In the HFD group, liver tissue architecture was disordered, hepatocyte arrangement was irregular, hepatocyte nuclei were enlarged, and marked hepatic steatosis (vacuolization) was observed. In the YDPG treatment group, the overall tissue disorganization was alleviated, and the degree of hepatic steatosis was notably reduced. Nuclei enlargement was also ameliorated, particularly in the HFD + YDPG-M group (Fig. 3A). Serum levels of AST and ALT-key indicators of liver injury-were significantly elevated in the HFD group compared with the Ctrl group. YDPG administration significantly reduced these levels. Specifically, HFD + YDPG-L and HFD + YDPG-M could significantly reduce the AST, while HFD + YDPG-M could significantly reduce the ALT (Fig. 3D). These results suggest that HFD + YDPG-M had a better therapeutic effect on HFD-induced liver injury in NAFLD.Fig. 3YDPG mitigated HFD-induced NAFLD, as evidenced by (A) HE staining outcomes of liver tissue (magnification 20 ×), (B) Oil Red O staining results (magnification 20 ×), (C) levels of four blood lipid parameters in serum, (D) serum AST and ALT levels, and (E) qPCR analysis of TNF-α, IL-6, and IL-1β in liver tissue. Statistical significance is denoted as *P < 0.05, **P < 0.01, ***P < 0.001, and “ns” indicates no significant difference
To further the inflammatory response associated with liver damage, the mRNA expression of TNF-α, IL-6, and IL-1β in liver tissue was quantified by qPCR. Their expression levels were significantly higher in the HFD group than in the Ctrl group. YDPG treatment markedly downregulated the expression of these pro-inflammatory genes. Specifically, YDPG-M treatment significantly reduced the IL-1β mRNA levels, while YDPG-L effectively reduced TNF-α and IL-6 mRNA levels (Fig. 3E).
YDPG ameliorates hepatic steatosis and dyslipidemia in HFD-induced NAFLD
Oil Red O staining results (Fig. 3B) demonstrated minimal lipid droplet deposition in the livers of the control group. In contrast, the HFD group exhibited prominent and extensive hepatic lipid accumulation, indicating that a HFD disrupts lipid metabolism and induces significant steatosis. Following YDPG treatment, hepatic lipid droplet deposition was markedly reduced, and steatosis was significantly alleviated. A substantial decrease in lipid accumulation was particularly evident in the HFD + YDPG-L group.
Serum levels of the four lipid parameters (TC, TG, LDL-C, HDL-C) were significantly elevated in the HFD group compared to the Ctrl group. YDPG treatment significantly attenuated these increases. Among the treatment groups, the HFD + YDPG-M group showed a more pronounced effect in reducing these lipid levels (Fig. 3C). For the LDL-C specifically, YDPG exhibited a dose-dependent effect, with stronger inhibition observed at higher doses (e.g., HFD + YDPG-H). These findings collectively suggest that YDPG protects against hepatic steatosis in mice by modulating lipid metabolism and improving the serum lipid profile.
YDPG regulated configuration of gut microbiota in HFD-prompted NAFLD
Given the close association between gut microbiota composition and NAFLD, we performed 16S rRNA sequencing to analyze and compare the gut microbial profiles across the HFD and YDPG-treated groups. The Rank Abundance curve, which reflects species abundance and evenness, showed that the curves for the Ctrl, HFD, HFD + YDPG-L, and HFD + YDPG-M groups spanned a wider range and were relatively flat, indicating higher species richness and a more uniform species distribution. In contrast, the curve for the HFD + YDPG-H group suggested a comparatively lower richness (Fig. 4A). This study analyzed 8864 fecal microbiota sequencing readings, among which the average readings for the Ctrl, HFD, HFD + YDPG-L, HFD + YDPG-M, and HFD + YDPG-H groups were 1884, 1834, 1787, 1905, and 1454, respectively. The distribution of operational taxonomic units (OTUs) among groups was visualized using an UpSet plot. A core set of 673 OTUs was shared across all five groups. Notably, 64 OTUs were found to be common specifically to the Ctrl group and all YDPG treatment groups (Fig. 4B).Fig. 4YDPG controlled the gut microbiota in HFD-induced NAFLD. A Rank abundance curve (with OTU rank as the x-axis and the number of sequences contained in each OTU as the y-axis). B Upset plot analysis between OTU number and Ctrl, HFD, HFD + YDPG-L, HFD + YDPG-M, HFD + YDPG-H categories. C PCoA examination of each group (this plot based on Bray–Curtis distance, visualizes the overall structural differences, with ellipses representing the 95% confidence interval (95% CI)). D The occurrence at the phylum level in the bacterial community. E Differential microbiota at the taxonomic level of phylum. F The occurrence at the genus level in the bacterial community. G Differential microbiota at genus level in HFD-induced NAFLD. *P < 0.05, **P < 0.01, ***P < 0.001, and “ns” indicates no significant difference
Based on the PCoA diagram (Fig. 4C), a clear separation between the Ctrl group and both the HFD and YDPG-treated groups, indicating distinct gut microbial community structures. At the phylum level, the HFD group showed a marked increase in the relative abundance of Firmicutes and decreases in Fusobacteria and Bacteroidetes compared to the Ctrl group. YDPG intervention partially reversed these HFD-induced changes, tending to restore the abundances of these phyla toward normal levels (Fig. 4D). The Firmicutes to Bacteroidetes ratio, associated with metabolic diseases, was substantially increased in the HFD group. In contrast, this ratio was reduced in the YDPG-L and YDPG-M treatment groups, with a particularly significant decrease observed in the HFD + YDPG-M group (Fig. 4E).
Figure 4F illustrates the relative abundance of the gut bacterial community at the genus level. Compared with the Ctrl group, the HFD group showed a significant increase in the abundance of Clostridioides, Ileibacterium, Allobaculum, and Enterococcus (P < 0.05), whereas the abundance of Dubosiella was decreased. Treatment with YDPG-L and YDPG-M significantly reversed the HFD-induced increases in Clostridioides, Ileibacterium, and Enterococcus. Furthermore, the abundance of Allobaculum was notably decreased in both the YDPG-L and YDPG-H groups. In contrast, the abundance of Dubosella saw a considerably increased in the YDPG-M and YDPG-H groups (Fig. 4G). Linear discriminant analysis Effect Size (LEfSe) further identified specific bacterial taxa associated with each group. At the genus level, the pervasiveness of Aerococcus, Colidextribacter, and Lysinibacillus was increased by YDPG-L treatment, whereas YDPG-H treatment raised the levels of Akkermansia, Ileibacterium, and Staphylococcus (Fig. 5).Fig. 5. Gut microbiota analysis using LEfSe in the control group (Ctrl), high-fat diet group (HFD), low dose YDPG group (HFD + YDPG-L), medium dose YDPG group (HFD + YDPG-M), and high dose YDPG group (HFD + YDPG-H). A Phylogenetic tree showcasing the variations in gut microbiota from the phylum to genus level. B Bar graph displaying discriminatory power measured by LDA score. The threshold for the LDA score exceeded 4
YDPG affected the distribution of serum metabolites in HFD-induced NAFLD
The composition of the gut microbiota influences the host's metabolic profile, thereby affecting disease pathogenesis. In this study, serum metabolic alterations were investigated using untargeted metabolomics. PCA and OPLS-DA were performed on samples from the Ctrl, HFD, and YDPG-treated groups. PCA revealed clear separation among the Ctrl, HFD, and YDPG-treated groups (Fig. 6A). OPLS-DA results indicated that Ctrl, HFD, HFD + YDPG-M, and HFD + YDPG-H can be distinguished. However, the HFD + YDPG-L, HFD + YDPG-M, and HFD + YDPG-H groups were not completely separable from each other (Fig. 6B). The reliability of the OPLS-DA model was assessed using R^2^X, R^2^Y and Q^2^ values. The model showed good predictive ability with Q^2^ = 0.801, R^2^Y = 0.94, and P < 0.05 (Fig. 6C). A screening method was utilized to pinpoint differential metabolites distinguishing the Ctrl from the HFD groups (FC ≥ 2 or ≤ 0.5, VIP ≥ 1 and P < 0.05) (Figure S2, Table S1-4). This analysis identified 125 upregulated and 228 downregulated metabolites (Fig. 6D). Furthermore, 109, 166, and 132 differential metabolites were identified when comparing the HFD group to the HFD + YDPG-L, HFD + YDPG-M, and HFD + YDPG-H groups, respectively (Fig. 6E).Fig. 6YDPG affected the distribution of serum metabolites in HFD-prompted NAFLD. A PCA analysis result (95% CI). B OPLS-DA analysis result (95% CI). C Conducting 200 random permutation to gauge the accuracy of the OPLS-DA model. D Volcano chart illustrating differential metabolites between the Ctrl group and the HFD group. EVenn plot showing the overlap of metabolites in different groups. F Differences in short-chain fatty acids, bile acids, and neurotransmitters observed between the HFD and Ctrl groups. G Identifying enriched pathways related to differential short-chain fatty acids, bile acids, and neurotransmitters
YDPG alleviated HFD-induced NAFLD by regulating short-chain fatty acids, bile acids, and neurotransmitters
Analysis of cecal contents revealed that YDPG modulated the abundance of specific genera, including Clostridioides, Ileibacterium, Allobaculum, Enterococcus, and Dubosella. Research has found that these bacteria can affect the production of bile acids, neurotransmitters, and short-chain fatty acids. The interaction among these compounds plays a crucial role in the onset and advancement of NAFLD. Therefore, we conducted an in-depth analysis of the differential metabolites, like short-chain fatty acids, bile acids, and neurotransmitters. Compared to the Ctrl group, the HFD group exhibited significant alterations in 19 short-chain fatty acids, 22 bile acids, and 2 neurotransmitters (Fig. 6F, Table S5).
The pathways enriched in these differential metabolites include linoleic acid metabolism, ovarian steroidogenesis, cortisol synthesis and secretion, primary bile acid biosynthesis, steroid hormone biosynthesis, bile secret, cAMP signaling pathway, and PPAR signaling pathway (Fig. 6G). These top enriched pathways are closely associated with lipid metabolism, suggesting that the regulation of lipid metabolism is a key mechanism through which YDPG treats NAFLD.
The metabolites highlighted in the red box in Fig. 6F exhibited significant variations among the Ctrl, HFD, and YDPG treated groups. Among these, indole-3-propionic acid, 3-(methylthio)propionic acid, and valeric acid are distinct short-chain fatty acids. Levels of 3-(methylthio)propionic acid and valeric acid were significantly elevated in the HFD group but substantially reduced in the HFD + YDPG-L group. Conversely, indole-3-propionic acid was significantly decreased in the HFD group but markedly increased in the HFD + YDPG-H group (Fig. 7A). Significantly altered bile acids included nordeoxycholic acid, glycolic acid, tauoursodeoxycholic acid, and taurochenodeoxycholic acid. The HFD group demonstrated a significant elevation in glycolic acid, taurosodeoxycholic acid, taurocholic acid, and taurochenodeoxycholic acid, while their content decreased significantly in the HFD + YDPG-L group. Conversely, nordeoxycholic acid experienced a significant decrease in the HFD group, but its content increased remarkably in the HFD + YDPG-H group (Fig. 7B). Serotonin, a neurotransmitter with differential expression, showed a remarkable reduction in the HFD group, while its content markedly increased in the HFD + YDPG-M group (Fig. 7C).Fig. 7. Statistical analysis of key differential metabolites in Ctrl, HFD, and YDPG groups. *P < 0.05, **P < 0.01, ***P < 0.001, and “ns” indicates no significant difference
Liver transcriptome analysis unveils the mechanism of YDPG in ameliorating NAFLD through PPAR signaling and primary bile acid biosynthesis pathways
To investigate the molecular mechanisms underlying the therapeutic effects of YDPG on NAFLD, transcriptomic sequencing was performed on liver tissues. PCA score plots revealed clear separation among the Ctrl, HFD, and YDPG-L, M, H groups (Figs. 8A), indicating substantial changes in the hepatic transcriptome of NAFLD mice and the modulatory influence of YDPG on hepatic gene expression. DEGs were screened using thresholds of |log₂FC|≥ 1.24 and P < 0.05 (Figure S3). A total of 985 DEGs were identified between the Ctrl and HFD groups, among which 766 were up-regulated and 219 were down-regulated (Fig. 8B).Fig. 8. Liver Transcriptome Analysis. A PCA analysis result (95% CI). B Volcano plot of DEGs between Ctrl and HFD groups. C GO enrichment analysis of DEGs. D KEGG pathway enrichment analysis. E Venn diagram of shared pathways from transcriptomic and metabolomic analyses. F Heatmap of 17 key DEGs expression across treatment groups
Functional enrichment analysis of the DEGs was performed using Gene Ontology (GO) annotation across three categories: biological process (BP), molecular function (MF), and cellular component (CC). The top 10 enriched terms in each category were selected for visualization. In the BP category, DEGs were primarily involved in processes such as the oxidation–reduction process, alpha-amino acid metabolic process, and catabolic process. In terms of MF, these genes were mainly associated with oxidoreductase activity, cofactor binding, and heme binding. Regarding CC, the DEGs were predominantly localized to the proteasome core complex, cytoplasmic part, and peptidase complex (Fig. 8C). KEGG pathway enrichment analysis was further performed on the identified DEGs, and all pathways with P < 0.05 were visualized. The results indicated that the DEGs were mainly enriched in pathways including tryptophan metabolism, fatty acid metabolism, PPAR signaling pathway, primary bile acid biosynthesis, and other related signaling pathways (Fig. 8D).
To focus on NAFLD-related genes, we intersected the KEGG pathways enriched in both the transcriptomic and metabolomic analyses, identifying two common pathways: primary bile acid biosynthesis and the PPAR signaling pathway (Fig. 8E). A total of 17 DEGs were involved in these two pathways (Table S9). Their expression trends across different groups were visualized in a heatmap (Fig. 8F). The heatmap revealed that the expression patterns of these genes could be broadly categorized into two types: one group, including Aqp7, Scd3, and Cyp8b1, showed high expression in the Ctrl group, down-regulation after modeling, and no significant reversal following YDPG treatment; the other group, including Cyp7a1, Scd1, and Pparg, exhibited low expression in the Ctrl group, up-regulation after modeling, and a dose-dependent down-regulatory trend in response to YDPG intervention.
Results of the correlation analysis between differential microbial, metabolite, and gene
Spearman correlation analysis was conducted to examine the associations between differential gut microbes in cecal contents and differential serum metabolites between the Ctrl and HFD groups. As illustrated in the correlation heatmap, Taurocholic acid, Taurochenodeoxycholic acid, and Tauroursodeoxycholic acid exhibited significant positive correlations with Clostridioides, Ileibacterium, and Enterococcus (marked by red blocks and statistical significance, P < 0.05, P < 0.01 or P < 0.001). For Allobaculum, it showed significant positive correlations with taurochenodeoxycholic acid, valeric acid, and 3-(Methylthio)propionic acid, while serotonin and nordeoxycholic acid displayed significant negative correlations with Allobaculum. Notably, serotonin also presented significant negative correlations with Clostridioides, Ileibacterium, and Enterococcus. In addition, indole-3-propionic acid was significantly negatively correlated with Ileibacterium and Enterococcus. For Dubosiella, it showed scattered significant correlations with individual metabolites (e.g., positive correlation with serotonin, negative correlation with valeric acid, and positive correlation with nordeoxycholic acid). These findings collectively indicate that there are significant associations between differential gut microbes and the metabolites modulated in this study, suggesting that gut microbiota may participate in metabolic regulation by interacting with these serum metabolites (Fig. 9A, Table S6).Fig. 9. Correlation analysis. A Spearman correlation assessment between differential metabolites and gut microbiota. B Pearson correlation analysis of differential metabolites and genes. Significance levels: *P < 0.05, **P < 0.01, ***P < 0.001. C Ecological network of microbes, metabolites, and genes
We further performed Pearson correlation analysis between the 9 identified differential metabolites and the 17 DEGs. The results indicated that only 12 of the genes showed significant correlations with the metabolites. Among them, Cyp39a1, Cyp8b1, Scd1, and Pparg exhibited significant correlations with multiple metabolites, such as glycocholic acid, nordeoxycholic acid, serotonin, and 3-(Methylthio)propionic acid (Fig. 9B, Table S8). These findings suggest that while different metabolites display distinct regulatory or associative tendencies-either positive or negative correlation-with genes, they may collectively participate in relevant physiological or pathological processes by modulating the expression of specific genes. Furthermore, an interaction network linking microbes, metabolites, and genes was constructed through correlation analysis. This network revealed that gut microbiota may influence host gene expression by modulating metabolite levels-particularly bile acids-thereby providing visual evidence for the underlying "microbiota-metabolite-host" interaction mechanism (Fig. 9C).
YDPG regulates the PPAR signaling pathway and primary bile acid biosynthesis pathway to improve HFD-induced NAFLD
Based on the aforementioned correlation analysis, we selected the top five differentially expressed genes (Cyp39a1, Cyp7a1, Pparg, Scd1, and Cyp8b1) that showed the strongest correlations with the differential metabolites. Their mRNA expression levels were further validated by qPCR to confirm the regulatory role of YDPG in the PPAR signaling pathway (involving Cyp7a1, Pparg, Scd1, and Cyp8b1) and the primary bile acid biosynthesis pathway (involving Cyp39a1, Cyp7a1, and Cyp8b1), thereby providing deeper insight into the molecular mechanism by which YDPG ameliorates NAFLD.
The qPCR results revealed that, compared with the Ctrl group, the mRNA expression levels of Cyp39a1, Pparg, Scd1, and Cyp7a1 were significantly increased in the HFD group, while YDPG treatment effectively reversed their expression (Fig. 10A–D). In contrast, the expression of Cyp8b1 was markedly downregulated in the HFD group and showed no significant recovery after YDPG administration (Fig. 10E).Fig. 10. The results of mRNA of the five key genes. *P < 0.05, **P < 0.01, ***P < 0.001, and “ns” indicates no significant difference
In summary, YDPG likely alleviates HFD-induced NAFLD by modulating the expression of key genes involved in the PPAR signaling pathway and the primary bile acid biosynthesis pathway.
Discussion
Fatty liver disease includes alcoholic fatty liver disease and NAFLD. Excessive and prolonged alcohol intake leads to the progression of alcoholic fatty liver disease. Moreover, this condition possesses the potential to evolve into alcoholic hepatitis, liver fibrosis, and cirrhosis. When there is no alcohol consumption, fat accumulates abnormally in the liver to form NAFLD. It is related to metabolic syndrome, obesity, hypertension, hyperlipidemia, diabetes and other metabolic diseases. Previous studies have shown that YDPG improves acute alcoholic hepatitis by inhibiting inflammatory responses and oxidative stress levels [17]. However, the effect of YDPG on NAFLD has not been reported. The results of this study indicated that YDPG exerts protective effects against HFD-induced NAFLD in mice, ameliorating key pathological features such as body weight gain, hepatic steatosis, liver injury, inflammation, and dyslipidemia. More importantly, our integrated approach combining pharmacochemical analysis, gut microbiota profiling, and serum metabolomics reveals that the therapeutic mechanism of YDPG is multi-targeting and holistic, primarily mediated through the remodeling of gut microbiota structure and the subsequent regulation of microbial-host co-metabolites, including short-chain fatty acids, bile acids, and neurotransmitters, which ultimately modulates hepatic lipid metabolic pathways [19, 20].
The most salient therapeutic advantage of YDPG, particularly when contrasted with current single-target pharmacotherapies like the THR-β agonist Rezdiffra (resmetirom), lies in its multi-component, multi-pathway holistic regulatory strategy. Our UPLC-QQQ-MS/MS analysis confirmed that YDPG contains a spectrum of bioactive compounds, such as baicalin, geniposide, glycyrrhizic acid, and paeoniflorin, which are known to possess anti-inflammatory, antioxidant, and lipid-modulating properties [21–24]. This phytochemical complexity allows YDPG to synchronously target the core axes of NAFLD pathogenesis-gut dysbiosis, metabolic disturbance, and inflammation-a feat difficult to achieve with a single synthetic molecule. While Rezdiffra offers targeted, potent THR-β agonism, its use is restricted to non-cirrhotic patients and is associated with a notable incidence of gastrointestinal adverse events [12]. In contrast, YDPG exhibited remarkable tolerability in our model, aligning with its historical use in various liver conditions. Critically, during the 28-day treatment period, no signs of toxicity (e.g., severe diarrhea, lethargy, or mortality) were observed in any YDPG-treated group, including the high-dose (HFD + YDPG-H) group. This safety profile, coupled with the non-linear dose–response (where the medium dose was often most effective), supports the interpretation that the observed "dose–effect reversal" is more likely a reflection of the complex, systems-level pharmacology characteristic of many botanical formulations-where optimal efficacy arises from the balanced modulation of multiple targets-rather than a consequence of high-dose toxicity [25, 26]. It produced therapeutic effects across a wide range of metabolic and inflammatory parameters without observed adverse events. However, since Rezdiffra is currently unavailable through official channels in China, we did not conduct a direct experimental comparison in our study.
A key mechanistic insight from this study is the pivotal role of gut-liver axis modulation in YDPG's efficacy. The HFD-induced dysbiosis, characterized by an elevated Firmicutes/Bacteroidetes ratio and a bloom of pro-inflammatory genera like Clostridioides, Ileibacterium, and Enterococcus, was significantly reversed by YDPG treatment. Concomitantly, YDPG promoted the abundance of beneficial genera like Dubosiella and Akkermansia, which are associated with improved gut barrier integrity and metabolic health [27, 28]. This microbiota remodeling directly translated into a normalization of critical microbial-derived metabolites. YDPG administration significantly rectified the imbalances in serum levels of bile acids (e.g., taurocholic acid, taurochenodeoxycholic acid), short-chain fatty acids (e.g., valeric acid, indole-3-propionic acid), and the neurotransmitter serotonin. The strong correlations between these differential metabolites and the altered gut microbes, as revealed by Spearman analysis, solidify the premise that YDPG acts first on the gut ecosystem, with systemic metabolic consequences.
Short-chain fatty acid (SCFA), including acetic acid (AA), propionic acid (PA), and butyric acid (BA) [29], is a key bridge connecting intestinal microbiota and the body, which can enrich beneficial bacteria, inhibit harmful bacteria, and affect intestinal health and systemic metabolism. PA can attenuate steatohepatitis by inhibiting endotoxin leakage [30]. BA alleviated lipid formation and inflammation [31]. In untargeted metabolomics data analysis, two propionic acids differed significantly between YDPG and HFD. The content of Indole-3-propionic acid displayed a significant reduction within the HFD group. Conversely, the YDPG group exhibited a notable ability to increase its concentration, enhance the abundance of advantageous microorganisms, and suppress the proliferation of detrimental bacteria. Furthermore, it was found that the HFD group remarkably increased valeric acid content. However, after YDPG administration, the valeric acid content was significantly inhibited. Among them, 3- (Methylthio) propionic acid and valeric acid content were positively linked to the concentration of Clostridioides, Ileibacterium, Allobaculum, and Enterococcus. Nonetheless, there's a significant negative linkage between indole-3-propionic acid and the abundance of Ileibacterium and Enterococcus.
Bile acids are an important bile component produced by cholesterol metabolism. Bile acids play diverse roles in the human body, encompassing synthesis and absorption of cholesterol [32], antibacterial effects [33], liver metabolism [34] and impact on the occurrence and progression of NAFLD [18]. Five distinct bile acids were pinpointed in this study between the YDPG and HFD groups. Among them, the concentrations of Taurocholic acid, Taurosodeoxycholic acid, Glycolic acid, and Taurochenodeoxycholic acid remarkably elevated in the HFD category and significantly diminished after administration of YDPG. Subsequently, the differences between the HFD and TDPG groups of nordeoxycholic acid were opposite to the results of the other four bile acids. Taurocholic acid, taurosodeoxycholic acid, glycocholic acid, taurochenodeoxycholic acid, and nordeoxycholic acid were remarkably favorably linked to the concentration of Clostridioides, Illeiberium, Allobaculum, and Enterocus. The reduction in bile acids content was consistent with the decrease in the abundance and diversity of gut microbiota. Results suggested that YDPG elicited favorable outcomes for the host through the regulation of the structure and metabolites of intestinal microorganisms.
Based on the integrated multi-omics and experimental validation, we further elucidated the downstream hepatic mechanisms through which YDPG exerts its therapeutic effects. Transcriptomic analysis revealed that YDPG significantly modulated the expression of key genes involved in the PPAR signaling pathway and primary bile acid biosynthesis pathway. Specifically, the major bioactive components of YDPG interact significantly with these core pathways: Baicalin, a flavonoid component of YDPG, directly binds to PPARγ ligand-binding domain to inhibit its transcriptional activity, thereby downregulating the expression of lipogenesis-related target genes Scd1 and Pparg [35, 36]. Geniposide enhances the nuclear translocation of PPARα to promote fatty acid oxidation [37, 38]. Glycyrrhizic acid regulates cholesterol metabolism by suppressing the expression of Cyp7a1 [39]. These component-specific regulatory effects collectively contribute to the observed amelioration of hepatic steatosis and dyslipidemia. Importantly, these mechanisms are closely linked to the core pathophysiology of NAFLD: PPAR signaling pathway dysfunction is a key driver of hepatic lipid accumulation (a hallmark of NAFLD), while dysregulation of primary bile acid biosynthesis disrupts the enterohepatic circulation, further exacerbating gut dysbiosis and hepatic inflammation-two critical pathological processes in NAFLD progression [40, 41]. By targeting these two pathways simultaneously, YDPG effectively interrupts the vicious cycle of NAFLD pathogenesis, which explains its comprehensive therapeutic effects on multiple pathological features of NAFLD (e.g., steatosis, inflammation, liver injury).
The correlation network analysis provided crucial insights into the interconnected nature of these regulatory effects. The significant correlations observed between specific gut microbes, serum metabolites, and hepatic genes, particularly the strong associations of Cyp39a1, Cyp8b1, Scd1, and Pparg with multiple bile acids and neurotransmitters, suggest the existence of a gut-liver axis-mediated regulatory network. While these robust correlations establish a critical associative framework, they do not, by themselves, definitively establish causality-i.e., whether the YDPG-induced shifts in specific microbial taxa are drivers of the observed metabolic improvements or secondary consequences of improved liver function. Elucidating such causal relationships would require more targeted experimental designs, such as fecal microbiota transplantation (FMT) from YDPG-treated donors to NAFLD recipients, or the use of germ-free animals colonized with defined microbial consortia. Nevertheless, the strong and specific correlations identified in this study pinpoint key microbial and metabolic nodes (e.g., Akkermansia and propionic acid; Clostridioides and taurocholic acid) that form a compelling causal hypothesis for future mechanistic investigations. Notably, beyond the correlational evidence, we further propose potential causal relationships between YDPG-induced microbial shifts and metabolic changes in NAFLD: the YDPG-enriched beneficial genus Akkermansia directly degrades intestinal mucus glycoproteins to produce SCFAs (e.g., propionic acid), which in turn activate PPARα signaling in the liver to promote fatty acid oxidation [42–45]; conversely, the suppressed pro-inflammatory genus Clostridioides reduces the deconjugation of primary bile acids (e.g., taurocholic acid), thereby decreasing the toxic accumulation of secondary bile acids in the liver and alleviating hepatic inflammation [46, 47]. These findings suggest that YDPG-induced microbial remodeling is not merely associated with metabolic improvements but also likely drives the restoration of metabolic homeostasis in NAFLD. To further validate these causal relationships and predict functional outcomes from microbiota compositions, future studies could employ advanced statistical approaches such as co-occurrence network analysis (to identify keystone taxa in YDPG-modulated microbiota) and machine learning models (e.g., random forest or support vector machines) to construct microbiota-based predictive models for NAFLD severity and therapeutic response to YDPG.
Notably, the therapeutic effects of YDPG demonstrated a non-strict dose-dependency, with the medium dose (HFD + YDPG-M) frequently achieving optimal efficacy. This pattern is characteristic of multi-target botanical drugs, where a balanced modulation of multiple pathways often produces superior therapeutic outcomes compared to maximal inhibition or activation of individual targets [25, 26]. The ability of YDPG to restore metabolic balance without triggering compensatory mechanisms underscores its potential as a holistic therapeutic approach for NAFLD.
It is important to acknowledge the limitations of the present study. First, this study only employed a HFD-induced NAFLD mouse model. Although the HFD model is widely used to simulate the metabolic abnormalities underlying NAFLD, it fails to capture the full spectrum of NAFLD progression in humans [48]. Second, the current study is limited to preclinical animal experiments, and the translational value of YDPG needs to be further verified in clinical trials. Future directions should therefore focus on: (1) Validating the therapeutic efficacy of YDPG in more clinically relevant NAFLD models, such as diet-induced NASH models (e.g., HFD combined with high-fructose/sucrose diet) or genetic NAFLD models; (2) Conducting phase I/II clinical trials to evaluate the safety and efficacy of YDPG in NAFLD patients, with special attention to herb-drug interactions (e.g., potential interactions with lipid-lowering drugs or antidiabetic agents commonly used in NAFLD patients) and patient population variability (e.g., differences in therapeutic response across different stages of NAFLD or different genetic backgrounds); (3) Employing germ-free mice or fecal microbiota transplantation experiments to directly validate the causal role of gut microbiota in YDPG's therapeutic effects. (4) Applying advanced statistical and computational approaches to the multi-omics data. Such as, co-occurrence network analysis could help identify keystone species within the YDPG-modulated microbial community, while machine learning models (e.g., random forest or regression models) could be trained to predict NAFLD phenotypic outcomes or YDPG treatment response based on baseline microbiota composition or dynamic changes in specific metabolites, thereby enhancing the functional interpretation and translational potential of our findings.
The clinical relevance of our findings lies in the potential of YDPG as a novel holistic therapeutic agent for NAFLD, which addresses the unmet clinical need for safe and effective treatments. Currently, the clinical management of NAFLD is dominated by lifestyle interventions (e.g., diet control, exercise), while pharmacological treatments are limited: Rezdiffra (resmetirom) is only approved for non-cirrhotic NASH, and other agents (e.g., pioglitazone) have significant side effects (e.g., weight gain, edema) that restrict long-term use [12, 39]. In contrast, YDPG, as a traditional botanical formula with a long history of clinical use in liver diseases, exhibited excellent tolerability in our preclinical model without observed adverse events, which provides a favorable foundation for its clinical translation. However, several clinical challenges need to be addressed before YDPG can be widely applied in human NAFLD treatment: (1) Definition of optimal clinical dosage: The non-strict dose-dependency observed in preclinical studies suggests that the optimal dosage for humans may not be the highest dose, and clinical trials need to determine the dose–response relationship in NAFLD patients. (2) Identification of predictive biomarkers: Given the variability in patient responses, future clinical studies should explore microbiota or metabolite biomarkers (e.g., the differential bile acids or SCFAs identified in this study) to predict which NAFLD patients are most likely to benefit from YDPG treatment. By addressing these challenges, YDPG has the potential to become a valuable addition to the current armamentarium for NAFLD treatment, especially for patients who cannot tolerate or are ineligible for existing pharmacological therapies.
In conclusion, our findings demonstrate that YDPG ameliorates HFD-induced NAFLD through coordinated regulation of the PPAR signaling pathway and primary bile acid biosynthesis pathway, mediated via gut microbiota-metabolite-gene interactions. This multi-level, multi-target mechanism provides a comprehensive foundation for understanding the therapeutic potential of YDPG in metabolic liver diseases.
Conclusion
In conclusion, our findings provide robust preclinical evidence that YDPG is a promising multi-target therapeutic agent for NAFLD. Its unique strength stems from a systems-level approach that concurrently ameliorates gut microbiota dysbiosis, corrects deranged microbial metabolism, and mitigates hepatic inflammatory and metabolic stress, thereby addressing the intricate pathophysiology of NAFLD more comprehensively than current single-target therapies. This study not only validates the traditional use of YDPG but also propels it into the modern therapeutic landscape as a gut-microbiota-focused regulator of metabolic health. Future clinical trials are warranted to translate these promising findings into a validated treatment strategy for NAFLD patients.
Supplementary Information
Additional file 1: Figure S1. Assessment outcomes of comprehensive metrics in mice subjected to a high-fat diet for 56 days. *P<0.05, **P<0.01, ***P<0.001. Assessment outcomes of comprehensive metrics in mice subjected to a high-fat diet for 56 daysAdditional file 2: Figure S2. Volcano plot analysis results of metabolomics in five groups.Additional file 3: Figure S3. Volcano plot analysis results of transcriptomics in five groups.Additional file 4: Table S1. Analysis findings of varying metabolic products between the Ctrl group and the HFD group.Additional file 5: Table S2. Identification results of distinct metabolic products between the HFD group and the HFD+YDPG-L group.Additional file 6: Table S3. Identification results of distinct metabolic products between the HFD group and the HFD+YDPG-M groupAdditional file 7: Table S4. Identification results of distinct metabolic products between the HFD group and the HFD+YDPG-H group.Additional file 8: Table S5. Within the pool of diverse metabolites, 43 compounds were found to be linked to short-chain fatty acids, bile acids, and neurotransmitters.Additional file 9: Table S6. Spearman correlation analysis data of different intestinal bacteria and metabolites.Additional file 10: Table S7. Primers in this study.Additional file 11: Table S8. Pearson correlation analysis data of different metabolites and genes.Additional file 12: Table S9. 17 DEGs associated with both primary bile acid biosynthesis and PPAR signaling pathway.