Metabolism regulation and control mechanism analysis method for treating WD hepatic fibrosis by using liver bean paste

Abstract

The invention discloses a metabolic regulation and control mechanism analysis method for treating WD hepatic fibrosis by using hepatolianling, and belongs to the technical field of medicines, and the method comprises the following steps: performing pharmacological research on components entering blood and a network; the method comprises the following steps: acquiring a metabolic marker of the remarkable callback of the liver-bean lingering on the basis of serum metabonomics, and performing enrichment analysis on the metabolic marker of the remarkable callback of the liver-bean lingering to obtain a remarkable metabolic pathway; a'small molecule metabolite-reaction-enzyme-target 'composite metabolic network is constructed; screening a liver bean drug effect material basis by a molecular docking technology; and a multi-dimensional network of the Ganguoling pharmacodynamic material basis, the core action target, the core metabolic marker, the core metabolic pathway and the WD hepatic fibrosis is visualized. According to the application, 25 drug effect material bases, 11 core action targets, 12 core metabolic markers and 9 core metabolic pathways are obtained.

Description

Technical Field

[0001] The present application relates to the field of medical technology, and in particular to a method for analyzing the metabolic regulation mechanism of heparin in treating WD liver fibrosis. Background Art

[0002] Wilson disease (WD), also known as hepato-lenticular degeneration (HLD), is an autosomal recessive copper metabolism disorder caused by mutations in the ATP7B gene. Its main clinical features are liver damage, neuropsychiatric symptoms, and corneal KF rings.

[0003] Western medicine often recommends penicillamine and succimer for the treatment of WD, but numerous adverse reactions limit their use. Previous studies have shown that Traditional Chinese Medicine (TCM) plays a beneficial role in the treatment of WD. For example, the TCM preparation Gandouling (GDL) has been shown to be effective.

[0004] Heparinol is composed of six traditional Chinese medicines: salvia miltiorrhiza, turmeric, zedoaria, millet vine, coptis chinensis, and raw rhubarb. Turmeric and coptis chinensis serve as the main ingredients, promoting blood circulation and qi, resolving phlegm and dampness. Salvia miltiorrhiza, zedoaria, and millet vine serve as the auxiliary ingredients, promoting blood circulation and removing blood stasis, softening and dispersing nodules, and activating meridians to expel copper turbidity and toxicity. Rhubarb is used as an adjuvant to promote bowel movement and dampness removal, as well as to expel copper and detoxify. Together, these herbs promote qi circulation, blood circulation, phlegm removal, blood stasis removal, and copper excretion. Previous studies have shown that heparinol can significantly improve liver function and the degree of liver fibrosis in patients with WD. Other studies have confirmed that heparinol can promote bile secretion, allowing copper to be excreted through the intestines, reducing copper damage to the liver and other systemic systems. It can significantly improve liver function markers and inhibit the progression of cirrhosis. However, the mechanism of action of heparinol in treating liver fibrosis in WD remains unclear.

[0005] Because Traditional Chinese Medicine (TCM) exhibits multiple components, pathways, and targets, and its efficacy depends on the absorption of its components into the bloodstream, research into the material basis and mechanisms of its efficacy remains a bottleneck in the modernization of TCM. In recent years, metabolomics, focusing on small-molecule metabolites in vivo, has rapidly developed. Its research strategy, based on monitoring the dynamic changes of global metabolites, aligns with the holistic perspective of Traditional Chinese Medicine (TCM), creating new opportunities for addressing bottlenecks in the development of TCM. It is currently widely used to uncover the material basis and mechanisms of TCM efficacy. Consequently, leveraging diverse approaches, including serum pharmacological chemistry, metabolomics, and bioinformatics, we have uncovered the metabolic regulatory mechanisms of GDL in the treatment of liver fibrosis in WD, providing experimental evidence for its clinical application. Summary of the Invention

[0006] In response to the above-mentioned shortcomings of the prior art, the present application provides a method for analyzing the metabolic regulation mechanism of heparin in the treatment of WD liver fibrosis.

[0007] To achieve the above objectives, this application is implemented through the following technical solutions: The method for analyzing the metabolic regulation mechanism of hepatol in treating WD liver fibrosis includes the following steps: Step 1: Based on the blood-entering components of hepatol and network pharmacology research, the target of hepatol's blood-entering components in intervening in WD liver fibrosis was obtained; Step 2: Obtain metabolic markers that are significantly reduced by heparin based on serum metabolomics, and perform enrichment analysis on the metabolic markers that are significantly reduced by heparin to obtain significant metabolic pathways; Step 3: Using the blood-entering components of heparin to intervene in WD liver fibrosis, and the metabolic markers that significantly reduce the effect, a complex metabolic network of "small molecule metabolites-reactions-enzymes-targets" was constructed to screen the core action targets, core metabolic markers, and core metabolic pathways. Step 4: Molecular docking technology was used to screen the pharmacological basis of Gandouling and visualize the multidimensional network of "Gandouling pharmacological basis - core action targets - core metabolic markers - core metabolic pathways - WD liver fibrosis"; 25 pharmacological basis, 11 core action targets, 12 core metabolic markers, and 9 core metabolic pathways were obtained; The material basis of the efficacy of hepatic lin is: gallic acid, magnolamine, camphor glycoside A, 2,6-edible astragaloside, phloridzin, leucine, fucoxanthin E, 1,3-dihydroxy-9,10-dioxanthene-2-carboxylic acid, kaempferol, (+)-1,5-epoxy-norketoneguaiac-11-ene, 8-isopentenyl daidzein, 2-hydroxy-5-[3,4,5-trihydroxy-6-(hydroxymethyl)oxa-2-yl]oxybenzoic acid, protocatechuic acid-3-glucoside, (2S, 3R, 4S, 5S, 6R)- 2-(4-Hydroxy-2,6-dimethoxyphenoxy)-6-(hydroxymethyl)oxane-3,4,5-triol, bamboo mushroom inoculant A, cornusin, codonopsis lactone, polygonal dialdehyde, 5-O-methylvisamido-4-O-β-D-furanosyl-(1→6)-β-D-glucopyranoside, methyl syringin, schizoneside B, juncuronone, 2-[(9Z,12Z)-heptadeca-9,12-dienyl]-6-hydroxybenzoic acid, 7,2'-dihydroxy-3',4'-methylenedioxyisoflavone and pseudopurpurin.

[0008] Furthermore, the core targets are: AGR1, GLUL, TRY, HPRT1, PDE11A, NOS3, MPO, ADHIC, CYP1A2, CYP3A4 and GSTM1.

[0009] Furthermore, the core metabolic markers were N-iminomethylglutamate, dihydrofolate, 5'-phosphoribosyl-N-formylglycinamide, 3',5'-cyclic adenosine monophosphate, pyruvate, fumarate, 2-oxoglutarate, N-acetyl-L-aspartate, 3,4-dihydroxymandelic acid, 4-trimethylammoniumbutyraldehyde, L-3-hydroxykynurenine, and (S)-malate.

[0010] Furthermore, the core metabolic pathways are: purine metabolism; histidine metabolism; vitamin B9 metabolism; tyrosine metabolism; lysine metabolism; glycolysis and gluconeogenesis metabolism; TCA cycle; tryptophan metabolism; urea cycle and metabolism of arginine, proline, glutamate, aspartate and asparagine.

[0011] Compared with the existing technology, the present application has the following beneficial effects: the present application studies the molecular mechanism of metabolic regulation of hepatol, and based on the blood-entering components and network pharmacology research of hepatol, obtains the potential targets of hepatol for intervening in WD liver fibrosis; based on serum metabolomics, the metabolic regulation mechanism of hepatol for treating WD liver fibrosis is studied, metabolic markers that are significantly reduced by hepatol are obtained, and the metabolic markers that are significantly reduced by hepatol are enriched and analyzed to obtain significant metabolic pathways; the potential targets of hepatol for intervening in WD liver fibrosis and the metabolic markers that are significantly reduced by hepatol are used to construct a "small molecule metabolite-reaction-enzyme-target" composite metabolic network; and a multidimensional network diagram of "hepatol pharmacological material basis-core action targets-core metabolic markers-core metabolic pathways-WD liver fibrosis" is constructed, and 25 pharmacological material bases, 11 core action targets, 12 core metabolic markers and 9 core metabolic pathways are obtained. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] To more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For those of ordinary skill in the art, other drawings can be derived from these drawings without inventive effort.

[0013] Figure 1 These are the TIC graphs of the in vitro chemical components of heparin in positive and negative ion modes; A: TIC graph of the in vitro chemical components of heparin in positive ion mode, B: TIC graph of the in vitro chemical components of heparin in negative ion mode.

[0014] Figure 2 This is the classification and quantitative distribution diagram of the in vitro chemical components of heparin.

[0015] Figure 3These are TIC graphs of the positive and negative ion modes of the components of heparin entering the blood, where A: TIC graph of the positive ion mode of the components of heparin entering the blood, and B: TIC graph of the negative ion mode of the components of heparin entering the blood.

[0016] Figure 4 This is the PPI network diagram of the intersection of the targets of the blood-entering components of hepatic fibrosis and the targets of WD liver fibrosis.

[0017] Figure 5 Volcano plots of serum metabolic markers in TX mice after heparin treatment. A: Volcano plot analysis of serum metabolic markers in TX mice in the model and control groups; B: Volcano plot analysis of serum metabolic markers in TX mice in the heparin treatment group and the model group.

[0018] Figure 6 The metabolic pathways affected by heparin treatment. A: Bubble diagram of metabolic pathway enrichment; B: Sankey diagram of metabolic pathway. Darker P values ​​indicate a higher degree of enrichment and a smaller corresponding P value.

[0019] Figure 7 This is a multidimensional network of "Bisoprolol's pharmacological basis - core action targets - core metabolic markers - core metabolic pathways - WD liver fibrosis." The red ellipse represents WD liver fibrosis, the red polygon represents Bisoprolol, the blue represents the core action targets, the purple represents the core metabolites, the orange represents the core metabolic pathways, and the green represents Bisoprolol's pharmacological basis. DETAILED DESCRIPTION

[0020] To make the purpose, technical solutions, and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0021] Example 1: Study on the in vitro chemical composition of heparin based on UPLC-Q Exactive / MS technology 1. Experimental methods 1.1. Preparation of the test solution: Remove the heparin tablets and grind them evenly with liquid nitrogen. Weigh approximately 100 mg of the sample into a 1.5 mL centrifuge tube. Add 1 mL of water, vortex for 1 min, and add steel balls. Precool in a -40°C refrigerator for 2 min, then grind in a grinder (60 Hz, 2 min). Ultrasonic extraction in an ice-water bath for 60 min is then performed, followed by centrifugation for 10 min (12,000 rpm, 4°C). Dilute 2-fold with water, and transfer 200 μL of the supernatant to an LC-MS vial with an inner tube for analysis.

[0022] 1.2 Chromatographic conditions: The experimental analysis instrument was ACQUITY UPLC I-Class HF ultra-high performance liquid phase connected with QExactive TM Liquid chromatography-mass spectrometry system consisting of a high-resolution mass spectrometer. Column: ACQUITY UPLC HSS T3 (100 mm × 2.1 mm, 1.8 µm); column temperature: 45°C; mobile phase: A: water (containing 0.1% formic acid), B: acetonitrile; flow rate: 0.35 mL / min; injection volume: 5 µL. PDA scanning range: 210–400 nm.

[0023] Table 1 Gradient elution program

[0024] 1.3 Mass Spectrometry Conditions: Ion source: HESI; Sample mass spectrometry signals were acquired in both positive and negative ion scan modes. Data acquisition mode: DDA; Scan type: Full MS / dd-MS2 (TOP 8).

[0025] Table 2 Mass spectrometry parameter settings

[0026] 2. Experimental results (in vitro identification of the chemical components of heparin and identification of the prescribed medicinal materials) The in vitro chemical components of heparin were characterized by UPLC-Q Exactive / MS technology. The total ion chromatograms (TIC) in positive and negative ion modes are shown in Figure 1 A total of 1299 chemical components were identified in heparin, 596 of which were detected in positive ion mode and 703 in negative ion mode. In order to more intuitively describe the distribution of component types, a component classification quantity distribution diagram was drawn based on the data matrix ( Figure 2 ), contains a total of 20 types of ingredients, the top five of which are phenylpropanoids, terpenes, sugars and glycosides, organic heterocyclic compounds and flavonoids.

[0027] Combined with the prescription of hepatol, the sources of the chemical components of hepatol were attributed and analyzed according to the HerbDB database. 217 chemical components were attributed to rhubarb, 169 chemical components were attributed to salvia miltiorrhiza, 179 chemical components were attributed to coptis chinensis, 8 chemical components were attributed to zedoaria, 16 chemical components were attributed to turmeric, and 14 chemical components were attributed to millettia reticulata, covering the entire prescription of hepatol. The 439 chemical components that can be attributed to the prescription of hepatol were classified according to the compound structure, including 74 phenylpropanoids, 43 terpenes, 39 flavonoids, 14 alkaloids, 7 anthraquinones and 262 other compounds.

[0028] Example 2: Exploring the pharmacological basis and target of ganduling in the treatment of WD liver fibrosis based on serum medicinal chemistry and network pharmacology Based on the identification results of the in vitro chemical components of heparin in Example 1, the classic WD animal model - TX (Toxic milk, TX) mice were used as research subjects to track the components of heparin entering the blood, providing a basis for subsequent research on the pharmacological substance basis of heparin.

[0029] 3. Experimental methods 3.1 Animal Experiments and Sample Collection 3.1.1 Experimental Animals and Grouping TX mice, male, 5 months old, SPF-grade, weighing (30 ± 5) g, were provided by the Animal Experimentation Center of Sun Yat-sen University in Guangzhou, license number SCXK (Yue) 2021-0029. DL mice were obtained from the Animal Experimentation Center of the First Affiliated Hospital of Anhui University of Chinese Medicine. Mice were housed in the SPF-grade animal room of the Animal Experimentation Center of the First Affiliated Hospital of Anhui University of Chinese Medicine (room temperature 24–26°C, relative humidity 40–60%). They were placed in a light-dark cycle with 12-h alternations, randomly divided into groups, housed in separate cages, and provided with free access to food and water. The experiment began after one week of acclimatization. Twenty TX mice were randomly divided into a model group (n=10), a heparin-treated group (GDL), and a control group (n=10). The experimental protocol was approved by the Animal Ethics Committee of the First Affiliated Hospital of Anhui University of Chinese Medicine (Animal Ethics Number: AZYFY-2024-2003).

[0030] 3.1.2 Experimental Dosing Regimen Preparation of intragastric administration solution: Grind the heparin tablets into powder in a mortar, weigh an appropriate dose, and add distilled water to prepare a heparin suspension.

[0031] Oral gavage dose: TX mice in the drug-treated group were given 0.2 mL / 10 g / d of the drug, while the control and model groups were given an equal volume of distilled water (0.2 mL / 10 g / d) for 8 consecutive weeks.

[0032] 3.1.3. Sample Acquisition At the end of the eighth week of dosing, mice in each group fasted for 12 hours before sampling. Thirty minutes after gavage, eyeballs were removed and blood was collected. The blood was then allowed to settle for 60 minutes and centrifuged at 3000 rpm for 15 minutes. The supernatant was stored at -80°C for subsequent studies. Following blood collection, the liver was immediately removed, and surface blood stains were rinsed with saline, dried with filter paper, and fixed in 10% neutral formalin.

[0033] 3.2 Analysis of blood components of Hepatotoxicity 3.2.1. Serum sample preparation The sample stored at -80°C was removed and slowly thawed on ice. 120 μL of the sample was transferred to a 1.5 mL EP tube. 480 μL of protein precipitant methanol-acetonitrile (V:V = 2:1) was added and vortexed for 1 min. The sample was extracted by ultrasonication in an ice-water bath for 10 min and allowed to stand at -40°C for 30 min. After centrifugation for 10 min (12,000 rpm, 4°C), 450 μL of the supernatant was transferred to an LC-MS injection vial and evaporated to dryness. The sample was reconstituted with 200 μL of water-methanol-acetonitrile (V:V:V = 1:2:1), vortexed for 1 min, and ultrasonicated for 3 min. The sample was allowed to stand at -40°C overnight. After centrifugation for 10 min (12,000 rpm, 4°C), 120 μL of the supernatant was transferred to an LC-MS injection vial with a foot-lined tube for analysis.

[0034] 3.2.2 Chromatographic and mass spectrometry conditions: the same as in step 1.2 and step 1.3 of Example 1.

[0035] 3.2.3 Data processing and identification of blood components Raw data were processed using the metabolomics processing software XCMS v4.5.1 for baseline filtering, peak identification, integration, retention time correction, peak alignment, and normalization. Exogenous compounds in serum were identified based on accurate mass, secondary fragmentation, and isotopic distribution, and compared with the in vitro chemical component identification results of heparin from Example 1. An automated judgment program combined with manual review was used to optimize the accuracy of the identification results. Automated judgment criteria included the presence of a peak for the in vitro chemical component of heparin (TCM group), a peak in the heparin-treated group, and a peak area ratio of ≥ FC (fold change) between the heparin-treated group and the model group. Manual review criteria included: ① The peaks in the heparin-treated and TCM groups must be detected simultaneously with consistent retention times (RTs); ② The peak area ratio between the heparin-treated and model groups must be ≥ FC, or the model group must not be detected. For substances identified in the database search, substances with FC values ​​≥ 10 between the heparin-treated and model groups were considered as blood-entry components. Substances in positive and negative ion modes were merged and deduplicated.

[0036] 3.3 Network Pharmacology Research 3.3.1. Obtaining the target sites of the blood-entering components of hepatic steroids Based on the blood-entering components of heparin, a target library for heparin's blood-entering components was constructed using the Swiss Target Prediction website, the BATMAN-TCM database, the TCMSP database, and the Superpred database. The resulting target protein names were entered into the UniprotKB database within the Uniprot database, with the species set to Homo sapiens. The target protein names were standardized, and the corresponding gene names were collated, merged, and duplicates removed to identify the targets of the heparin's blood-entering components.

[0037] 3.3.2 Disease Target Acquisition Gene targets related to WD liver fibrosis were predicted using the GeneCards, OMIM, and pharmGKB databases. The obtained disease targets were deduplicated and deleted to obtain disease targets related to WD liver fibrosis.

[0038] 3.3.3. Intersection target acquisition and PPI network construction The identified blood-entering targets of heparin and the disease targets for WD liver fibrosis were imported into the Microbiology Online website for Venn diagram construction. This generated intersection targets between heparin and WD liver fibrosis. The resulting intersection targets were then imported into the Multiple Proteins analysis box of the STRING database, with the species set to Homosapiens and the confidence level set to ≥0.4, to construct a protein-protein interaction network (PPI).

[0039] 4. Results 4.1 Characterization, Identification, and Medicinal Material Classification of Hepatotoxicity The blood components of heparin were characterized by UPLC-Q Exactive / MS technology. Total ion chromatogram (TIC) in positive and negative ion modes, see Figure 3 In the positive and negative ion modes, a total of 41 blood-entering components of Heparin were detected (Table 3), of which 13 were detected in the positive ion mode and 28 were detected in the negative ion mode.

[0040] According to the HerbDB database, the sources of chemical components were analyzed for the medicinal materials of the prescription of Hepatol. Six blood-entering components can be attributed to Coptis chinensis, six blood-entering components can be attributed to Salvia miltiorrhiza, ten blood-entering components can be attributed to Rhubarb, one blood-entering component can be attributed to Curcuma longa, one blood-entering component can be attributed to Millettia reticulata, covering the full range of Hepatol prescription medicinal flavors.

[0041] Table 3 Identification results of heparin-containing components in blood

[0042]

[0043] 4.2 Classification and Identification of Blood Components of Heparin 4.2.1 Identification of Phenypropanoids Two phenylpropanoid components were identified in the original form of hepatol. Compound 7, as shown in the low energy mass spectrum, has a quasi-molecular ion of m / z 235.0602[M+H]+, detected in high energy mass spectrometry m / z 189.0527, m / z 217.0495, m / z 235.0600. According to the phenylpropanoid fragmentation rule, the compound is identified as armillarin A. Compound 10, in the low energy mass spectrum, shows that the quasi-molecular ion is m / z 197.0448[MH]-, detected in high energy mass spectrometry m / z 179.03438, m / z 135.04468, m / z 123.04475, m / z 72.99288, judging that the compound is 2,3,4-trihydroxyphenylpropionic acid.

[0044] 4.2.2 Identification of terpenoid components A total of 6 terpenoid components were identified in the original form of hepatol. Taking compound 36 as an example, the quasi-molecular ion peak was detected in the low-energy mass spectrum. m / z 413.2167[M+FA-H]-, detected in high energy mass spectrometry m / z 305.1853, m / z 251.1648, m / z 101.0243, m / z 85.0293, based on the fragmentation pattern of terpenoid compounds, the compound was identified as gallol B. Compounds 11, 16, 26, 30, and 25 were identified as fucoidan E, serrenin, codonopsis lactone, methyl syringin, and cornusin, respectively, based on the fragmentation pattern of terpenoid compounds and combined with database comparison.

[0045] 4.2.3 Identification of flavonoid components A total of 6 flavonoid components were identified in the original form of hepatic lecithin. Taking compound 38 as an example, the low energy mass spectrum shows that the quasi-molecular ion is m / z 515.2405[M+Na]+, three ion fragments were detected in the high energy mass spectrum, namely m / z 310.1414, m / z 287.1247, m / z 269.1148. Based on the fragmentation patterns of flavonoids, this compound was identified as sophorol B. Compounds 6, 14, 39, 41, and 20 were identified as phlorizin, kaempferol, 7,2'-dihydroxy-3',4'-methylenedioxyisoflavone, kaempferol-3-O-glucoside-7-O-rhamnoside, and 8-isopentenyl daidzein, respectively, based on the fragmentation patterns of flavonoids and combined with database comparison.

[0046] 4.2.4 Identification of alkaloid compounds One alkaloid component was detected in the original form of heparin. Compound 3, as shown in the low energy mass spectrum, has a quasi-molecular ion of m / z 342.1699[M+H]+, four fragment ions were detected in the high energy mass spectrum, namely m / z 297.1120, m / z 282.0861, m / z 265.0846. Based on the fragmentation patterns of alkaloid compounds and database comparison, the compound was identified as magnolamine.

[0047] 4.3. Collection of blood-entering target sites of hepatic steroids Based on 41 heparin-entering blood components, the targets of heparin-entering blood components were predicted using the Swiss Target Prediction, BATMAN-TCM, TCMSP, Superpred, Uniprot, and Pubchem databases. After removing duplicate values, a total of 964 targets were obtained.

[0048] 4.4. Disease Target Acquisition for WD Liver Fibrosis Using "Wilson's disease and Liver Fibrosis, Wilson disease and Liver Fibrosis" as keywords, relevant targets were downloaded from GeneCards, OMIM, and pharmGKB online databases, and duplicate values ​​were removed, resulting in a total of 778 WD liver fibrosis disease-related targets.

[0049] 4.5. Target Screening and PPI Network Construction of Hepatol for the Intervention of Liver Fibrosis in WD After the intersection analysis of the blood-entering components of hepatic steroids and disease targets, 128 intersection targets were obtained. The intersection targets were imported into the STRING website to construct the PPI network. The results showed that there were 128 points and 2338 edges ( Figure 4 ).

[0050] Example 3: Study on the metabolic regulation mechanism of lentinan in the treatment of WD liver fibrosis based on serum metabolomics Metabolomics was used to investigate the metabolic regulatory mechanisms of heparin in the treatment of liver fibrosis in WD. Using the TX mouse model, UPLC-Q Exactive / MS was used to characterize the serum metabolic profiles of the control, model, and heparin-treated mice. Metabolic markers of WD liver fibrosis were screened, as well as the effect of heparin on the metabolic markers, in order to reveal the metabolic regulatory mechanisms of heparin in the treatment of WD liver fibrosis.

[0051] 5. Experimental methods 5.1. Animal experiment and sample collection: Same as step 3.1 in Example 2.

[0052] 5.2 Pretreatment of biological samples Samples stored at -80°C were removed and thawed in an ice-water mixture. 50 μL of the sample was transferred to a 1.5 mL EP tube. 200 μL of a protein precipitant (methanol-acetonitrile, V:V = 2:1, containing 4 μg / mL of a mixed internal standard 1, 2, 3, 4, and 5) was added and vortexed for 1 min. Ultrasonic extraction was performed in an ice-water bath for 10 min, followed by overnight storage at -40°C. Centrifugation was performed for 20 min (12,000 rpm, 4°C). 150 μL of the supernatant was transferred to an LC-MS injection vial with a foot-lined tube for analysis. Quality control (QC) samples were prepared by mixing equal volumes of extracts from all samples. Note: All extraction reagents were pre-chilled at -20°C prior to use.

[0053] 5.3 Liquid chromatography-mass spectrometry analysis conditions 5.3.1 Chromatographic conditions Chromatographic column: ACQUITY UPLC HSS T3 (100 mm×2.1 mm, 1.8 μm); column temperature: 45 °C; mobile phase: A-water (containing 0.1% formic acid), B-acetonitrile; flow rate: 0.35 mL / min; injection volume: 4 μL.

[0054] 5.3.2 Mass spectrometry conditions Table 4 Mass spectrometry parameter settings

[0055] 5.4 Data Quality Control Accurately measure 50 μL of each sample extract, mix evenly, and use as a QC sample. Follow the same procedure as in 5.2. During the analysis, insert a QC sample every 10 samples to verify the stability of the LC-MS system throughout the analysis.

[0056] 5.5 Metabolite Identification During the identification and characterization of metabolites, the raw data were first subjected to baseline filtering, peak identification, integration, and retention time correction using the metabolomics processing software XCMS v4.5.1. Metabolites were then identified by comprehensively comparing the retention time, accurate mass, secondary fragmentation, and isotope distribution of the compounds using The Human Metabolome Database, Lipidmaps (v2.3), and METLIN databases, as well as the PubChem local database. Data matrices for the quality control group, heparin-treated group, model group, and control group were obtained.

[0057] 5.6 Multivariate Statistical Analysis Before screening for differentially expressed metabolites, the data matrix was imported into SIMCA 14.1 software for unsupervised principal component analysis (PCA), followed by supervised partial least squares analysis (PLS-DA), and finally supervised orthogonal partial least squares-discriminant analysis (OPLS-DA). The PLS-DA and OPLS-DA models were validated using 200 permutation tests.

[0058] 5.7 Metabolic pathway enrichment analysis Combining the results of multivariate statistical analysis and T-test, the screening criteria for differential metabolites were set as P < 0.05 and |FC| ≥ 2. The screened differential metabolites were imported into the online software MetaboAnalyst 6.0, and metabolic pathway enrichment analysis was performed using the Enrichment Analysis and Pathway Analysis modules.

[0059] 6. Results 6.1 Metabolomics Data Quality Control The chromatograms were generated by continuously plotting the strongest ion intensities in the chromatograms of the quality control (QC) samples at each time point. In both positive and negative ion acquisition modes, the response intensities and retention times of the QC sample peaks were highly reproducible, indicating minimal variability due to instrumental error throughout the experiment.

[0060] To further evaluate the stability of the instrument, principal component analysis (PCA) was performed on the overall data of the collected QC samples. The PCA model graph was obtained through 7-fold cross-validation; the QC samples were closely clustered together with good repeatability, indicating that the liquid chromatography-mass spectrometry system was verified to have good stability throughout the experiment. To more intuitively display the correlation between QC samples, correlation analysis and plotting were performed on the QC samples (if the number of QCs is greater than 20, the first 10 and the last 10 will be shown). The results showed that the metabolite contents of the QC samples tested in the positive and negative ion modes were basically linear, and the correlation coefficients were all > 0.99, indicating that the entire testing system had good repeatability.

[0061] 6.2. Identification and Global Analysis of Metabolites A total of 4,583 metabolites were detected from the samples of the three groups. The statistical classification results of the metabolites showed that the top five were: 1,649 lipid and lipid-like molecular compounds, 849 organic acids and their derivatives, 812 organic heterocyclic compounds, 456 benzene compounds, and 358 organic oxygen compounds.

[0062] 6.3. Verification of the Consistency within the Mouse Serum Groups and the Differences in Metabolic Levels between Groups Based on Serum Metabolic Profiles 6.3.1. Principal Component Analysis (PCA) To observe the overall distribution among the samples and the stability of the entire analysis process, PCA analysis was performed on the serum samples of the three groups of mice. The results showed an obvious separation trend among the control group, the model group, and the group administered with hepatolentil, indicating that the serum metabolite levels of TX mice changed significantly and there were differences in the metabolic profiles among the three groups.

[0063] 6.3.2. Partial Least Squares Discriminant Analysis (PLS-DA) To distinguish the overall differences in the metabolic profiles among the groups, PLS-DA analysis was performed. The results of the 200 permutation tests of the PLS-DA model showed that the R 2 X, R 2 Y, and Q 2 values were 0.633, 0.994, and 0.967 respectively; and Q 2 < R (Table 5), indicating that the model did not show overfitting and had reliable interpretive and predictive capabilities. In the PLS-DA multi-group analysis model, the differentiation among the control group, the model group, and the group administered with hepatolentil was good, and the within-group differences were relatively small.

[0064] 6.3.3. Orthogonal Projection to Latent Structures Discriminant Analysis (OPLS-DA) In order to filter out noise irrelevant to classification information and maximize the differences between different groups within the model, OPLS-DA analysis was performed. The results showed that the explanation rates of the OPLS-DA model for the X and Y matrices in the model group and the control group were R and R, respectively. 2 X=0.501, R 2 Y=0.999, Q predicted by the model 2 The value was 0.962; R 2 X, R 2 Y and Q 2 The values ​​were 0.553, 0.994, and 0.951, respectively (Table 5). This shows that the OPLS-DA model constructed in this study has good explanatory and predictive abilities and is effective.

[0065] Table 5 Model evaluation parameters

[0066] 6.4 Screening and pathway analysis of serum metabolic markers in TX mice 6.4.1 Screening of Serum Metabolic Markers in TX Mice Between the model group and the control group, with P < 0.05 and |FC| ≥ 2 as the screening criteria, a total of 1142 differential metabolites were screened out, of which 991 marker levels were significantly increased and 151 marker levels were significantly decreased ( Figure 5 ). This showed that there were significant differences in serum metabolite levels between the model group and the control group, and the serum metabolic status of TX mice was abnormal.

[0067] 6.4.2 Metabolic pathway enrichment analysis of serum metabolic markers in TX mice 1142 TX mouse metabolic markers were imported into metaboanalyst 6.0 software for enrichment analysis. The results showed that 32 metabolic pathways had changed, mainly involving steroid hormone biosynthesis, citric acid cycle (TCA cycle), arginine biosynthesis, alanine, aspartate and glutamate metabolic pathways, pyruvate metabolic pathway, tryptophan metabolic pathway, and tyrosine metabolic pathway.

[0068] 6.5 Screening of Serum Metabolic Markers in TX Mice with Significant Reduction of Heparin In order to explore the intervention effect of heparin on serum metabolic abnormalities in TX mice, based on the serum metabolic markers of TX mice screened above, with Log2(FC_MvsC) × Log2(FC_GvsM) < 0 and P < 0.05 as the standard, after treatment with heparin, 266 serum metabolic markers were significantly adjusted back, of which 253 metabolite levels were significantly increased and 13 metabolite levels were significantly decreased, indicating that heparin has a significant improvement effect on serum metabolic disorders in TX mice.

[0069] 6.6 Metabolic Pathway Analysis of Heparin Intervention The 266 metabolic markers that were significantly downregulated by heparin were imported into metaboanalyst 6.0 software for enrichment analysis. 21 metabolic pathways were obtained, which were mainly enriched in the citric acid cycle (TCA cycle) pathway, alanine, aspartate and glutamate metabolic pathways, pyruvate metabolic pathways, glyoxylate and dicarboxylic acid metabolic pathways, cysteine ​​and methionine metabolic pathways, tyrosine metabolic pathways, arginine biosynthesis pathways, lipoic acid metabolic pathways, and glycine, serine, and threonine metabolic pathways ( Figure 6 ).

[0070] Example 4: Joint analysis to explore the pharmacological basis and metabolic regulation mechanism of Gandouling in the treatment of WD liver fibrosis 7. Experimental methods 7.1. Joint Analysis of Network Pharmacology and Metabolomics to Construct a Complex Metabolic Network of “Small Molecule Metabolites-Reactions-Enzymes-Targets” The targets of heparin-containing components that enter the blood to intervene in WD liver fibrosis, as well as the metabolic markers that were significantly reduced by heparin-containing components, were imported into the MetScape plug-in in Cytoscape v3.9.1 software to construct a "small molecule metabolite-reaction-enzyme-target" complex metabolic network, and the core targets, core metabolic markers and core metabolic pathways were screened.

[0071] 7.2 Screening of the Substance Basis for the Effectiveness of Gandouling The obtained core action target was imported into the GeneCard database, and the protein structure of the corresponding target was downloaded by clicking on Protein. The blood-entering component of heparin was imported into the Pubchem database, and the three-dimensional structure of the corresponding component was downloaded and saved in SDF format. Finally, the 3D structure of the blood-entering component of heparin and the protein structure of the core action target were imported into the CB-Dock2 website for molecular docking, and the blood-entering component of heparin that can bind to the core action target was used as the material basis of its pharmacological activity.

[0072] 7.3. Construction of a multidimensional network of “Material basis of Gandouling’s efficacy - core action targets - core metabolic markers - core metabolic pathways - WD liver fibrosis” The core action targets, core metabolic markers and core metabolic pathways obtained from the "small molecule metabolite-reaction-enzyme-target" complex metabolic network and the data on the effective substance basis of levofloxacin obtained by molecular docking were organized into two tables, "Network" and "Type", and then imported into Cytoscape v3.9.1 software to visualize the multidimensional network of "levofloxacin effective substance basis-core action targets-core metabolic markers-core metabolic pathways-WD liver fibrosis".

[0073] 8. Results 8.1 Core Targets, Core Metabolic Markers, and Core Metabolic Pathways of Gandouling for the Treatment of Liver Fibrosis in WD In the constructed "small molecule metabolite-reaction-enzyme-target" complex metabolic network, a total of 11 core targets of heparin for the treatment of WD liver fibrosis (AGR1, GLUL, TRY, HPRT1, PDE11A, NOS3, MPO, ADHIC, CYP1A2, CYP3A4 and GSTM1) were screened out, and 12 core metabolic markers significantly reduced by heparin (N-iminomethylglutamate, dihydrofolate, 5'-phosphoribosyl-N-formylglycinamide, 3',5'-cyclohexane) were found. The results showed that heparin significantly affected 9 core metabolic pathways (purine metabolism; histidine metabolism; vitamin B9 metabolism; tyrosine metabolism; lysine metabolism; glycolysis and gluconeogenesis metabolism; TCA cycle; tryptophan metabolism; urea cycle and metabolism of arginine, proline, glutamate, aspartate and asparagine).

[0074] 8.2 Molecular docking screening of the pharmacological basis of ganduling in the treatment of WD liver fibrosis To identify the pharmacological basis pairs for the treatment of liver fibrosis in WD patients with hepatic fibrosis, molecular docking was performed using the CB-Dock2 platform. Molecular docking results showed that 25 blood-entering components of hepatic fibrosis could bind to the core target, with binding energies of less than -6 kcal / mol (Table 6). Based on the negative correlation between binding energy and binding strength, and the fact that a binding energy of less than -5 kcal / mol indicates strong receptor affinity, these components demonstrated strong binding capacity and good binding activity, suggesting that they could serve as the pharmacological basis pairs for the treatment of liver fibrosis in WD patients with hepatic fibrosis.

[0075] Table 6 Molecular docking information of the active substance basis of Gandouling and its core targets

[0076]

[0077] 8.3. Multidimensional Network of "Material Basis of Gandouling's Efficacy - Core Targets - Core Metabolic Markers - Core Metabolic Pathways - WD Liver Fibrosis" By constructing a multidimensional network of "Material basis of Gandouling's efficacy-core action targets-core metabolic markers-core metabolic pathways-WD liver fibrosis" ( Figure 7) It can be seen that 25 effective substances of hepatol such as gallic acid, magnolamine and camphor glycoside A can act on 11 core targets such as AGR1, GLUL and TRY, regulate 12 core metabolites such as N-imidomethylglutamate, dihydrofolate and 5'-phosphoribosyl-N-formylglycinamide, affect 9 core metabolic pathways such as TCA cycle, purine metabolism and histidine metabolism, and thus play a therapeutic effect on WD liver fibrosis.

[0078] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements will not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for analyzing the metabolic regulation mechanism of hepatic fibrosis treated with hepatol, characterized in that: The following steps are involved: Step 1: Based on the blood-entering components of hepatol and network pharmacology research, the target of hepatol's blood-entering components in intervening in WD liver fibrosis was obtained; Step 2: Obtain metabolic markers that are significantly reduced by heparin based on serum metabolomics, and perform enrichment analysis on the metabolic markers that are significantly reduced by heparin to obtain significant metabolic pathways; Step 3: Using the blood-entering components of heparin to intervene in WD liver fibrosis, and the metabolic markers that significantly reverse the effect, a complex metabolic network of "small molecule metabolites-reactions-enzymes-targets" was constructed to screen the core targets, core metabolic markers, and core metabolic pathways. Step 4: Use molecular docking technology to screen the pharmacological basis of Gandouling and visualize the multidimensional network of "pharmacological basis of Gandouling - core action targets - core metabolic markers - core metabolic pathways - WD liver fibrosis"; Obtained 25 pharmacodynamic substance bases, 11 core action targets, 12 core metabolic markers and 9 core metabolic pathways; The material basis of the efficacy of hepatic lin is: gallic acid, magnolamine, camphor glycoside A, 2,6-edible astragaloside, phloridzin, leucine, fucoxanthin E, 1,3-dihydroxy-9,10-dioxanthene-2-carboxylic acid, kaempferol, (+)-1,5-epoxy-norketoneguaiac-11-ene, 8-isopentenyl daidzein, 2-hydroxy-5-[3,4,5-trihydroxy-6-(hydroxymethyl)oxa-2-yl]oxybenzoic acid, protocatechuic acid-3-glucoside, (2S, 3R, 4S, 5S, 6R)- 2-(4-Hydroxy-2,6-dimethoxyphenoxy)-6-(hydroxymethyl)oxane-3,4,5-triol, bamboo mushroom inoculant A, cornusin, codonopsis lactone, polygonal dialdehyde, 5-O-methylvisamido-4-O-β-D-furanosyl-(1→6)-β-D-glucopyranoside, methyl syringin, schizoneside B, juncuronone, 2-[(9Z,12Z)-heptadeca-9,12-dienyl]-6-hydroxybenzoic acid, 7,2'-dihydroxy-3',4'-methylenedioxyisoflavone and pseudopurpurin.

2. The method for analyzing the metabolic regulation mechanism of dapoxetine in treating WD liver fibrosis according to claim 1, characterized in that: The core targets are: AGR1, GLUL, TRY, HPRT1, PDE11A, NOS3, MPO, ADHIC, CYP1A2, CYP3A4 and GSTM1.

3. The method for analyzing the metabolic regulation mechanism of dapoxetine in treating WD liver fibrosis according to claim 1, characterized in that: Core metabolic markers were N-iminomethylglutamate, dihydrofolate, 5'-phosphoribosyl-N-formylglycinamide, 3',5'-cyclic adenosine monophosphate, pyruvate, fumarate, 2-oxoglutarate, N-acetyl-L-aspartate, 3,4-dihydroxymandelic acid, 4-trimethylammoniumbutyraldehyde, L-3-hydroxykynurenine, and (S)-malate.

4. The method for analyzing the metabolic regulation mechanism of dapoxetine in treating WD liver fibrosis according to claim 1, characterized in that: The core metabolic pathways are: purine metabolism; histidine metabolism; vitamin B9 metabolism; tyrosine metabolism; lysine metabolism; glycolysis and gluconeogenesis metabolism; TCA cycle; tryptophan metabolism; urea cycle and metabolism of arginine, proline, glutamate, aspartate and asparagine.

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